{"metadata":{"kernelspec":{"name":"python3","display_name":"Python 3","language":"python"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"nvidiaTeslaT4","dataSources":[{"sourceId":8616273,"sourceType":"datasetVersion","datasetId":5157078}],"dockerImageVersionId":30559,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"!pip install -q sentencepiece\n!pip install transformers -q\n!pip install wandb -q\n!pip install -q rouge-score rouge\n!pip install -q beautifulsoup4\n!pip install -q matplotlib","metadata":{"execution":{"iopub.status.busy":"2024-06-10T04:59:16.091449Z","iopub.execute_input":"2024-06-10T04:59:16.092226Z","iopub.status.idle":"2024-06-10T05:00:28.032762Z","shell.execute_reply.started":"2024-06-10T04:59:16.092196Z","shell.execute_reply":"2024-06-10T05:00:28.031487Z"},"trusted":true},"execution_count":3,"outputs":[]},{"cell_type":"code","source":"import tensorflow as tf\nfrom tensorflow.python.keras import backend as K\n\nlogger = tf.get_logger()\n\nclass AttentionLayer(tf.keras.layers.Layer):\n    \"\"\"\n    This class implements Bahdanau attention (https://arxiv.org/pdf/1409.0473.pdf).\n    There are three sets of weights introduced W_a, U_a, and V_a\n     \"\"\"\n\n    def __init__(self, **kwargs):\n        super(AttentionLayer, self).__init__(**kwargs)\n\n    def build(self, input_shape):\n        assert isinstance(input_shape, list)\n        # Create a trainable weight variable for this layer.\n\n        self.W_a = self.add_weight(name='W_a',\n                                   shape=tf.TensorShape((input_shape[0][2], input_shape[0][2])),\n                                   initializer='uniform',\n                                   trainable=True)\n        self.U_a = self.add_weight(name='U_a',\n                                   shape=tf.TensorShape((input_shape[1][2], input_shape[0][2])),\n                                   initializer='uniform',\n                                   trainable=True)\n        self.V_a = self.add_weight(name='V_a',\n                                   shape=tf.TensorShape((input_shape[0][2], 1)),\n                                   initializer='uniform',\n                                   trainable=True)\n\n        super(AttentionLayer, self).build(input_shape)  # Be sure to call this at the end\n\n    def call(self, inputs):\n        \"\"\"\n        inputs: [encoder_output_sequence, decoder_output_sequence]\n        \"\"\"\n        assert type(inputs) == list\n        encoder_out_seq, decoder_out_seq = inputs\n\n        logger.debug(f\"encoder_out_seq.shape = {encoder_out_seq.shape}\")\n        logger.debug(f\"decoder_out_seq.shape = {decoder_out_seq.shape}\")\n\n        def energy_step(inputs, states):\n            \"\"\" Step function for computing energy for a single decoder state\n            inputs: (batchsize * 1 * de_in_dim)\n            states: (batchsize * 1 * de_latent_dim)\n            \"\"\"\n\n            logger.debug(\"Running energy computation step\")\n\n            if not isinstance(states, (list, tuple)):\n                raise TypeError(f\"States must be an iterable. Got {states} of type {type(states)}\")\n\n            encoder_full_seq = states[-1]\n\n            \"\"\" Computing S.Wa where S=[s0, s1, ..., si]\"\"\"\n            # <= batch size * en_seq_len * latent_dim\n            W_a_dot_s = K.dot(encoder_full_seq, self.W_a)\n\n            \"\"\" Computing hj.Ua \"\"\"\n            U_a_dot_h = K.expand_dims(K.dot(inputs, self.U_a), 1)  # <= batch_size, 1, latent_dim\n\n            logger.debug(f\"U_a_dot_h.shape = {U_a_dot_h.shape}\")\n\n            \"\"\" tanh(S.Wa + hj.Ua) \"\"\"\n            # <= batch_size*en_seq_len, latent_dim\n            Ws_plus_Uh = K.tanh(W_a_dot_s + U_a_dot_h)\n\n            logger.debug(f\"Ws_plus_Uh.shape = {Ws_plus_Uh.shape}\")\n\n            \"\"\" softmax(va.tanh(S.Wa + hj.Ua)) \"\"\"\n            # <= batch_size, en_seq_len\n            e_i = K.squeeze(K.dot(Ws_plus_Uh, self.V_a), axis=-1)\n            # <= batch_size, en_seq_len\n            e_i = K.softmax(e_i)\n\n            logger.debug(f\"ei.shape = {e_i.shape}\")\n\n            return e_i, [e_i]\n\n        def context_step(inputs, states):\n            \"\"\" Step function for computing ci using ei \"\"\"\n\n            logger.debug(\"Running attention vector computation step\")\n\n            if not isinstance(states, (list, tuple)):\n                raise TypeError(f\"States must be an iterable. Got {states} of type {type(states)}\")\n\n            encoder_full_seq = states[-1]\n\n            # <= batch_size, hidden_size\n            c_i = K.sum(encoder_full_seq * K.expand_dims(inputs, -1), axis=1)\n\n            logger.debug(f\"ci.shape = {c_i.shape}\")\n\n            return c_i, [c_i]\n\n        # we don't maintain states between steps when computing attention\n        # attention is stateless, so we're passing a fake state for RNN step function\n        fake_state_c = K.sum(encoder_out_seq, axis=1)\n        fake_state_e = K.sum(encoder_out_seq, axis=2)  # <= (batch_size, enc_seq_len, latent_dim\n\n        \"\"\" Computing energy outputs \"\"\"\n        # e_outputs => (batch_size, de_seq_len, en_seq_len)\n        last_out, e_outputs, _ = K.rnn(\n            energy_step, decoder_out_seq, [fake_state_e], constants=[encoder_out_seq]\n        )\n\n        \"\"\" Computing context vectors \"\"\"\n        last_out, c_outputs, _ = K.rnn(\n            context_step, e_outputs, [fake_state_c], constants=[encoder_out_seq]\n        )\n\n        return c_outputs, e_outputs\n\n    def compute_output_shape(self, input_shape):\n        \"\"\" Outputs produced by the layer \"\"\"\n        return [\n            tf.TensorShape((input_shape[1][0], input_shape[1][1], input_shape[1][2])),\n            tf.TensorShape((input_shape[1][0], input_shape[1][1], input_shape[0][1]))\n        ]","metadata":{"execution":{"iopub.status.busy":"2024-06-10T05:00:28.035572Z","iopub.execute_input":"2024-06-10T05:00:28.035940Z","iopub.status.idle":"2024-06-10T05:00:36.303141Z","shell.execute_reply.started":"2024-06-10T05:00:28.035905Z","shell.execute_reply":"2024-06-10T05:00:36.302288Z"},"trusted":true},"execution_count":4,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.5\n  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n","output_type":"stream"}]},{"cell_type":"code","source":"import re\nimport wandb\nimport numpy as np\nimport pandas as pd\nfrom bs4 import BeautifulSoup\nimport matplotlib.pyplot as plt\nfrom sklearn.model_selection import train_test_split\n\nimport nltk\nfrom nltk.corpus import stopwords\nnltk.download('stopwords')","metadata":{"execution":{"iopub.status.busy":"2024-06-10T05:00:36.304171Z","iopub.execute_input":"2024-06-10T05:00:36.304716Z","iopub.status.idle":"2024-06-10T05:00:38.067529Z","shell.execute_reply.started":"2024-06-10T05:00:36.304689Z","shell.execute_reply":"2024-06-10T05:00:38.066647Z"},"trusted":true},"execution_count":5,"outputs":[{"name":"stdout","text":"[nltk_data] Downloading package stopwords to /usr/share/nltk_data...\n[nltk_data]   Package stopwords is already up-to-date!\n","output_type":"stream"},{"execution_count":5,"output_type":"execute_result","data":{"text/plain":"True"},"metadata":{}}]},{"cell_type":"code","source":"from transformers import T5Tokenizer,TFT5EncoderModel\nimport tensorflow as tf\nfrom tensorflow.keras.layers import Input, LSTM, Dense\nfrom tensorflow.keras.models import Model","metadata":{"execution":{"iopub.status.busy":"2024-06-10T05:00:38.069626Z","iopub.execute_input":"2024-06-10T05:00:38.070180Z","iopub.status.idle":"2024-06-10T05:00:41.614464Z","shell.execute_reply.started":"2024-06-10T05:00:38.070153Z","shell.execute_reply":"2024-06-10T05:00:41.613704Z"},"trusted":true},"execution_count":6,"outputs":[]},{"cell_type":"code","source":"wandb.init(project=\"Text Summarization Finetuning with T5-encoder & LSTM Decoder\")\nconfig = wandb.config          # Initialize config\nconfig.TRAIN_BATCH_SIZE = 4    # input batch size for training (default: 64)\nconfig.VALID_BATCH_SIZE = 4    # input batch size for testing (default: 1000)\nconfig.TRAIN_EPOCHS = 10       # number of epochs to train (default: 10)\nconfig.VAL_EPOCHS = 3\nconfig.LEARNING_RATE = 1e-4    # learning rate (default: 0.01)\nconfig.SEED = 42               # random seed (default: 42)\n\nnp.random.seed(config.SEED) # numpy random seed","metadata":{"execution":{"iopub.status.busy":"2024-06-10T05:00:41.615610Z","iopub.execute_input":"2024-06-10T05:00:41.616158Z","iopub.status.idle":"2024-06-10T05:12:42.665029Z","shell.execute_reply.started":"2024-06-10T05:00:41.616131Z","shell.execute_reply":"2024-06-10T05:12:42.663910Z"},"trusted":true},"execution_count":7,"outputs":[{"name":"stderr","text":"\u001b[34m\u001b[1mwandb\u001b[0m: Logging into wandb.ai. (Learn how to deploy a W&B server locally: https://wandb.me/wandb-server)\n\u001b[34m\u001b[1mwandb\u001b[0m: You can find your API key in your browser here: https://wandb.ai/authorize\n\u001b[34m\u001b[1mwandb\u001b[0m: Paste an API key from your profile and hit enter, or press ctrl+c to quit:","output_type":"stream"},{"output_type":"stream","name":"stdin","text":"  ········································\n"},{"name":"stderr","text":"\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":"wandb version 0.17.1 is available!  To upgrade, please run:\n $ pip install wandb --upgrade"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":"Tracking run with wandb version 0.15.9"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":"Run data is saved locally in <code>/kaggle/working/wandb/run-20240610_051211-2h3sm40m</code>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":"Syncing run <strong><a href='https://wandb.ai/mhaseeb1604/Text%20Summarization%20Finetuning%20with%20T5-encoder%20%26%20LSTM%20Decoder/runs/2h3sm40m' target=\"_blank\">vague-silence-8</a></strong> to <a href='https://wandb.ai/mhaseeb1604/Text%20Summarization%20Finetuning%20with%20T5-encoder%20%26%20LSTM%20Decoder' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":" View project at <a href='https://wandb.ai/mhaseeb1604/Text%20Summarization%20Finetuning%20with%20T5-encoder%20%26%20LSTM%20Decoder' target=\"_blank\">https://wandb.ai/mhaseeb1604/Text%20Summarization%20Finetuning%20with%20T5-encoder%20%26%20LSTM%20Decoder</a>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":" View run at <a href='https://wandb.ai/mhaseeb1604/Text%20Summarization%20Finetuning%20with%20T5-encoder%20%26%20LSTM%20Decoder/runs/2h3sm40m' target=\"_blank\">https://wandb.ai/mhaseeb1604/Text%20Summarization%20Finetuning%20with%20T5-encoder%20%26%20LSTM%20Decoder/runs/2h3sm40m</a>"},"metadata":{}}]},{"cell_type":"markdown","source":"## Data Loading","metadata":{}},{"cell_type":"code","source":"data = pd.read_csv(\"/kaggle/input/text-summarization-dataset/PsychologicalTS.csv\", encoding='latin1')\n\ndata.head()","metadata":{"execution":{"iopub.status.busy":"2024-06-10T05:12:42.666460Z","iopub.execute_input":"2024-06-10T05:12:42.667169Z","iopub.status.idle":"2024-06-10T05:12:42.785479Z","shell.execute_reply.started":"2024-06-10T05:12:42.667135Z","shell.execute_reply":"2024-06-10T05:12:42.784361Z"},"trusted":true},"execution_count":8,"outputs":[{"execution_count":8,"output_type":"execute_result","data":{"text/plain":"                                                Text  \\\n0  I have to say that I do miss talking to a good...   \n1  “Actually- no-” I reply- admiring my new colou...   \n2  “Yes. You think you are real and sit there and...   \n3  “Which manual do you refer to? We have quite a...   \n4  “Well- since you mentioned it-” he says. “And ...   \n\n                                           Summariez  \n0  The author misses talking to a good psychiatri...  \n1  The speaker admits to having a semi-permanent ...  \n2  The speaker expresses frustration with their f...  \n3  The speaker mentions having a manual and enjoy...  \n4  The speaker expresses frustration with the psy...  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Text</th>\n      <th>Summariez</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>I have to say that I do miss talking to a good...</td>\n      <td>The author misses talking to a good psychiatri...</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>“Actually- no-” I reply- admiring my new colou...</td>\n      <td>The speaker admits to having a semi-permanent ...</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>“Yes. You think you are real and sit there and...</td>\n      <td>The speaker expresses frustration with their f...</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>“Which manual do you refer to? We have quite a...</td>\n      <td>The speaker mentions having a manual and enjoy...</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>“Well- since you mentioned it-” he says. “And ...</td>\n      <td>The speaker expresses frustration with the psy...</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"data.drop_duplicates(subset=['Text'],inplace=True)\ndata.dropna(axis=0,inplace=True)","metadata":{"execution":{"iopub.status.busy":"2024-06-10T05:12:42.786730Z","iopub.execute_input":"2024-06-10T05:12:42.787164Z","iopub.status.idle":"2024-06-10T05:12:42.801877Z","shell.execute_reply.started":"2024-06-10T05:12:42.787131Z","shell.execute_reply":"2024-06-10T05:12:42.800907Z"},"trusted":true},"execution_count":9,"outputs":[]},{"cell_type":"markdown","source":"","metadata":{}},{"cell_type":"code","source":"data.info()","metadata":{"execution":{"iopub.status.busy":"2024-06-10T05:12:42.803196Z","iopub.execute_input":"2024-06-10T05:12:42.803578Z","iopub.status.idle":"2024-06-10T05:12:42.823707Z","shell.execute_reply.started":"2024-06-10T05:12:42.803549Z","shell.execute_reply":"2024-06-10T05:12:42.822768Z"},"trusted":true},"execution_count":10,"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nIndex: 2670 entries, 0 to 2739\nData columns (total 2 columns):\n #   Column     Non-Null Count  Dtype \n---  ------     --------------  ----- \n 0   Text       2670 non-null   object\n 1   Summariez  2670 non-null   object\ndtypes: object(2)\nmemory usage: 62.6+ KB\n","output_type":"stream"}]},{"cell_type":"markdown","source":"## Preprocessing","metadata":{}},{"cell_type":"code","source":"contraction_mapping = {\"ain't\": \"is not\", \"aren't\": \"are not\",\"can't\": \"cannot\", \"'cause\": \"because\", \"could've\": \"could have\", \"couldn't\": \"could not\",\n                           \"didn't\": \"did not\",  \"doesn't\": \"does not\", \"don't\": \"do not\", \"hadn't\": \"had not\", \"hasn't\": \"has not\", \"haven't\": \"have not\",\n                           \"he'd\": \"he would\",\"he'll\": \"he will\", \"he's\": \"he is\", \"how'd\": \"how did\", \"how'd'y\": \"how do you\", \"how'll\": \"how will\", \"how's\": \"how is\",\n                           \"I'd\": \"I would\", \"I'd've\": \"I would have\", \"I'll\": \"I will\", \"I'll've\": \"I will have\",\"I'm\": \"I am\", \"I've\": \"I have\", \"i'd\": \"i would\",\n                           \"i'd've\": \"i would have\", \"i'll\": \"i will\",  \"i'll've\": \"i will have\",\"i'm\": \"i am\", \"i've\": \"i have\", \"isn't\": \"is not\", \"it'd\": \"it would\",\n                           \"it'd've\": \"it would have\", \"it'll\": \"it will\", \"it'll've\": \"it will have\",\"it's\": \"it is\", \"let's\": \"let us\", \"ma'am\": \"madam\",\n                           \"mayn't\": \"may not\", \"might've\": \"might have\",\"mightn't\": \"might not\",\"mightn't've\": \"might not have\", \"must've\": \"must have\",\n                           \"mustn't\": \"must not\", \"mustn't've\": \"must not have\", \"needn't\": \"need not\", \"needn't've\": \"need not have\",\"o'clock\": \"of the clock\",\n                           \"oughtn't\": \"ought not\", \"oughtn't've\": \"ought not have\", \"shan't\": \"shall not\", \"sha'n't\": \"shall not\", \"shan't've\": \"shall not have\",\n                           \"she'd\": \"she would\", \"she'd've\": \"she would have\", \"she'll\": \"she will\", \"she'll've\": \"she will have\", \"she's\": \"she is\",\n                           \"should've\": \"should have\", \"shouldn't\": \"should not\", \"shouldn't've\": \"should not have\", \"so've\": \"so have\",\"so's\": \"so as\",\n                           \"this's\": \"this is\",\"that'd\": \"that would\", \"that'd've\": \"that would have\", \"that's\": \"that is\", \"there'd\": \"there would\",\n                           \"there'd've\": \"there would have\", \"there's\": \"there is\", \"here's\": \"here is\",\"they'd\": \"they would\", \"they'd've\": \"they would have\",\n                           \"they'll\": \"they will\", \"they'll've\": \"they will have\", \"they're\": \"they are\", \"they've\": \"they have\", \"to've\": \"to have\",\n                           \"wasn't\": \"was not\", \"we'd\": \"we would\", \"we'd've\": \"we would have\", \"we'll\": \"we will\", \"we'll've\": \"we will have\", \"we're\": \"we are\",\n                           \"we've\": \"we have\", \"weren't\": \"were not\", \"what'll\": \"what will\", \"what'll've\": \"what will have\", \"what're\": \"what are\",\n                           \"what's\": \"what is\", \"what've\": \"what have\", \"when's\": \"when is\", \"when've\": \"when have\", \"where'd\": \"where did\", \"where's\": \"where is\",\n                           \"where've\": \"where have\", \"who'll\": \"who will\", \"who'll've\": \"who will have\", \"who's\": \"who is\", \"who've\": \"who have\",\n                           \"why's\": \"why is\", \"why've\": \"why have\", \"will've\": \"will have\", \"won't\": \"will not\", \"won't've\": \"will not have\",\n                           \"would've\": \"would have\", \"wouldn't\": \"would not\", \"wouldn't've\": \"would not have\", \"y'all\": \"you all\",\n                           \"y'all'd\": \"you all would\",\"y'all'd've\": \"you all would have\",\"y'all're\": \"you all are\",\"y'all've\": \"you all have\",\n                           \"you'd\": \"you would\", \"you'd've\": \"you would have\", \"you'll\": \"you will\", \"you'll've\": \"you will have\",\n                           \"you're\": \"you are\", \"you've\": \"you have\"}","metadata":{"execution":{"iopub.status.busy":"2024-06-10T05:12:42.825507Z","iopub.execute_input":"2024-06-10T05:12:42.826114Z","iopub.status.idle":"2024-06-10T05:12:42.841495Z","shell.execute_reply.started":"2024-06-10T05:12:42.826083Z","shell.execute_reply":"2024-06-10T05:12:42.840482Z"},"trusted":true},"execution_count":11,"outputs":[]},{"cell_type":"code","source":"stop_words = set(stopwords.words('english')) \n\ndef text_cleaner(text,num):\n    newString = text.lower()\n    newString = BeautifulSoup(newString, \"lxml\").text\n    newString = re.sub(r'\\([^)]*\\)', '', newString)\n    newString = re.sub('\"','', newString)\n    newString = ' '.join([contraction_mapping[t] if t in contraction_mapping else t for t in newString.split(\" \")])    \n    newString = re.sub(r\"'s\\b\",\"\",newString)\n    newString = re.sub(\"[^a-zA-Z]\", \" \", newString) \n    newString = re.sub('[m]{2,}', 'mm', newString)\n    if(num==0):\n        tokens = [w for w in newString.split() if not w in stop_words]\n    else:\n        tokens=newString.split()\n    long_words=[]\n    for i in tokens:\n        if len(i)>1:                                                 #removing short word\n            long_words.append(i)   \n    return (\" \".join(long_words)).strip()","metadata":{"execution":{"iopub.status.busy":"2024-06-10T05:12:42.846306Z","iopub.execute_input":"2024-06-10T05:12:42.846585Z","iopub.status.idle":"2024-06-10T05:12:42.859341Z","shell.execute_reply.started":"2024-06-10T05:12:42.846562Z","shell.execute_reply":"2024-06-10T05:12:42.858410Z"},"trusted":true},"execution_count":12,"outputs":[]},{"cell_type":"code","source":"cleaned_text = []\nfor t in data['Text']:\n    cleaned_text.append(text_cleaner(t,0)) ","metadata":{"execution":{"iopub.status.busy":"2024-06-10T05:12:42.860980Z","iopub.execute_input":"2024-06-10T05:12:42.861302Z","iopub.status.idle":"2024-06-10T05:12:43.866586Z","shell.execute_reply.started":"2024-06-10T05:12:42.861278Z","shell.execute_reply":"2024-06-10T05:12:43.865609Z"},"trusted":true},"execution_count":13,"outputs":[]},{"cell_type":"code","source":"#call the function\ncleaned_summary = []\nfor t in data['Summariez']:\n    cleaned_summary.append(text_cleaner(t,1))","metadata":{"execution":{"iopub.status.busy":"2024-06-10T05:12:43.867747Z","iopub.execute_input":"2024-06-10T05:12:43.868015Z","iopub.status.idle":"2024-06-10T05:12:44.570568Z","shell.execute_reply.started":"2024-06-10T05:12:43.867992Z","shell.execute_reply":"2024-06-10T05:12:44.569494Z"},"trusted":true},"execution_count":14,"outputs":[{"name":"stderr","text":"/tmp/ipykernel_32/3463428437.py:5: MarkupResemblesLocatorWarning: The input looks more like a filename than markup. You may want to open this file and pass the filehandle into Beautiful Soup.\n  newString = BeautifulSoup(newString, \"lxml\").text\n","output_type":"stream"}]},{"cell_type":"code","source":"cleaned_summary[:10]","metadata":{"execution":{"iopub.status.busy":"2024-06-10T05:12:44.571833Z","iopub.execute_input":"2024-06-10T05:12:44.572176Z","iopub.status.idle":"2024-06-10T05:12:44.578910Z","shell.execute_reply.started":"2024-06-10T05:12:44.572150Z","shell.execute_reply":"2024-06-10T05:12:44.578050Z"},"trusted":true},"execution_count":15,"outputs":[{"execution_count":15,"output_type":"execute_result","data":{"text/plain":"['the author misses talking to good psychiatrist especially with phd in philosophy and extensive knowledge on madness however psychiatrists often dismiss them quickly and the author finds them tiresome and uninteresting',\n 'the speaker admits to having semi permanent psychosis but still leads productive life experiencing magic and faeries they maintain routine but their friends are disinterested as they tire them out',\n 'the speaker expresses frustration with their friends who believe they are real and preach their life path while they enjoy their day coffee hair and belief in fairies causing them to tire them out',\n 'the speaker mentions having manual and enjoying cappuccino at salon they mention confusion with the staff but are used to their eccentricities they mention bringing bible to read during hairdressing and need to live up to their bipolar diagnosis',\n 'the speaker expresses frustration with the psychiatry manual stating that it is useless and sciencey they argue that psychiatry strategy is to diagnose and treat individuals based on syllabus which they find tiresome and dislike',\n 'the speaker is bewildered by the response of psychiatrist who they miss and invented mental health professional for themselves they argue that they could help the psychiatrists manage their workload if they received an official invitation to help in their profession however the psychiatrists are unwilling to deal with them',\n 'the speaker discusses their experience with good psychiatrists and the importance of the profession as an institution they acknowledge their imaginary friend reminding them of more real psychiatrist who helped them they express interest in helping the psychiatrists manage their workload but they are hesitant to deal with them due to their exhaustion',\n 'the author describes their experience with alesha psychiatrist in amsterdam who agreed to see them despite having many clients they shared their belief in magic and the possibility of helping them manage their workload however the author clients are not interested in dealing with them due to their exhaustion',\n 'the imaginary psychiatrist in the mirror acknowledges the difficulty in finding mental people who appreciate them pointing out that the state failure to implement necessary reforms and societal stigma contribute to this issue',\n 'the speaker dislikes person who smokes in small office ignores no smoking sign and makes coffee while clearing space they believe in equality and oppose those who believe in authority give too fast diagnoses and overdose people']"},"metadata":{}}]},{"cell_type":"code","source":"data['cleaned_text']=cleaned_text\ndata['cleaned_summary']=cleaned_summary","metadata":{"execution":{"iopub.status.busy":"2024-06-10T05:12:44.580157Z","iopub.execute_input":"2024-06-10T05:12:44.580423Z","iopub.status.idle":"2024-06-10T05:12:44.589294Z","shell.execute_reply.started":"2024-06-10T05:12:44.580402Z","shell.execute_reply":"2024-06-10T05:12:44.588475Z"},"trusted":true},"execution_count":16,"outputs":[]},{"cell_type":"code","source":"data.replace('', np.nan, inplace=True)\ndata.dropna(axis=0,inplace=True)","metadata":{"execution":{"iopub.status.busy":"2024-06-10T05:12:44.590614Z","iopub.execute_input":"2024-06-10T05:12:44.590874Z","iopub.status.idle":"2024-06-10T05:12:44.604915Z","shell.execute_reply.started":"2024-06-10T05:12:44.590852Z","shell.execute_reply":"2024-06-10T05:12:44.603924Z"},"trusted":true},"execution_count":17,"outputs":[]},{"cell_type":"code","source":"text_word_count = []\nsummary_word_count = []\n\n# populate the lists with sentence lengths\nfor i in data['cleaned_text']:\n      text_word_count.append(len(i.split()))\n\nfor i in data['cleaned_summary']:\n      summary_word_count.append(len(i.split()))\n\nlength_df = pd.DataFrame({'text':text_word_count, 'summary':summary_word_count})\n\nlength_df.hist(bins = 30)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-06-10T05:12:44.606454Z","iopub.execute_input":"2024-06-10T05:12:44.606787Z","iopub.status.idle":"2024-06-10T05:12:45.209453Z","shell.execute_reply.started":"2024-06-10T05:12:44.606747Z","shell.execute_reply":"2024-06-10T05:12:45.208460Z"},"trusted":true},"execution_count":18,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 2 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"max(summary_word_count)","metadata":{"execution":{"iopub.status.busy":"2024-06-10T05:12:45.210868Z","iopub.execute_input":"2024-06-10T05:12:45.211227Z","iopub.status.idle":"2024-06-10T05:12:45.218804Z","shell.execute_reply.started":"2024-06-10T05:12:45.211194Z","shell.execute_reply":"2024-06-10T05:12:45.217788Z"},"trusted":true},"execution_count":19,"outputs":[{"execution_count":19,"output_type":"execute_result","data":{"text/plain":"189"},"metadata":{}}]},{"cell_type":"code","source":"max_text_len = int(np.percentile(text_word_count, 98))\nmax_summary_len = int(np.percentile(summary_word_count, 98))\nprint(f'max_text_len        : {max_text_len}')\nprint(f'max_summary_lengths : {max_summary_len}')\n","metadata":{"execution":{"iopub.status.busy":"2024-06-10T05:12:45.220097Z","iopub.execute_input":"2024-06-10T05:12:45.220448Z","iopub.status.idle":"2024-06-10T05:12:45.230964Z","shell.execute_reply.started":"2024-06-10T05:12:45.220399Z","shell.execute_reply":"2024-06-10T05:12:45.229943Z"},"trusted":true},"execution_count":20,"outputs":[{"name":"stdout","text":"max_text_len        : 209\nmax_summary_lengths : 105\n","output_type":"stream"}]},{"cell_type":"code","source":"df = pd.DataFrame(columns=[\"ctext\", \"text\"])","metadata":{"execution":{"iopub.status.busy":"2024-06-10T05:12:45.232137Z","iopub.execute_input":"2024-06-10T05:12:45.232482Z","iopub.status.idle":"2024-06-10T05:12:45.240562Z","shell.execute_reply.started":"2024-06-10T05:12:45.232452Z","shell.execute_reply":"2024-06-10T05:12:45.239752Z"},"trusted":true},"execution_count":21,"outputs":[]},{"cell_type":"code","source":"df[\"ctext\"] = 'summarize: ' + data.cleaned_text\ndf[\"text\"] = data.cleaned_summary\n\ndf.replace('', np.nan, inplace=True)\ndf.dropna(axis=0,inplace=True)","metadata":{"execution":{"iopub.status.busy":"2024-06-10T05:12:45.241896Z","iopub.execute_input":"2024-06-10T05:12:45.242245Z","iopub.status.idle":"2024-06-10T05:12:45.257718Z","shell.execute_reply.started":"2024-06-10T05:12:45.242217Z","shell.execute_reply":"2024-06-10T05:12:45.256845Z"},"trusted":true},"execution_count":22,"outputs":[]},{"cell_type":"code","source":"config.MAX_LEN = 512\nconfig.SUMMARY_LEN = 190","metadata":{"execution":{"iopub.status.busy":"2024-06-10T05:12:45.258827Z","iopub.execute_input":"2024-06-10T05:12:45.259165Z","iopub.status.idle":"2024-06-10T05:12:45.272489Z","shell.execute_reply.started":"2024-06-10T05:12:45.259138Z","shell.execute_reply":"2024-06-10T05:12:45.271636Z"},"trusted":true},"execution_count":23,"outputs":[]},{"cell_type":"code","source":"tokenizer = T5Tokenizer.from_pretrained('t5-small')\n\ndef tokenize_data(texts, summaries, max_len_text, max_len_summary):\n    inputs = tokenizer(\n        texts, \n        add_special_tokens=True,\n        max_length=max_len_text, \n        truncation=True, \n        padding='max_length', \n        return_tensors='np'\n        )\n    \n    outputs = tokenizer(\n        summaries, \n        max_length=max_len_summary, \n        add_special_tokens=True,\n        truncation=True, \n        padding='max_length', \n        return_tensors='np'\n        )\n    \n    return inputs, outputs","metadata":{"execution":{"iopub.status.busy":"2024-06-10T05:12:45.273574Z","iopub.execute_input":"2024-06-10T05:12:45.273867Z","iopub.status.idle":"2024-06-10T05:12:46.173663Z","shell.execute_reply.started":"2024-06-10T05:12:45.273833Z","shell.execute_reply":"2024-06-10T05:12:46.172634Z"},"trusted":true},"execution_count":24,"outputs":[{"output_type":"display_data","data":{"text/plain":"Downloading spiece.model:   0%|          | 0.00/792k [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"58afc03921164ddf8f04475f40a398c2"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Downloading tokenizer_config.json:   0%|          | 0.00/2.32k [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"84d46f34d04e42f9b901d5f36ce8ca12"}},"metadata":{}},{"name":"stderr","text":"You are using the default legacy behaviour of the <class 'transformers.models.t5.tokenization_t5.T5Tokenizer'>. If you see this, DO NOT PANIC! This is expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you. If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it means, and thouroughly read the reason why this was added as explained in https://github.com/huggingface/transformers/pull/24565\n","output_type":"stream"}]},{"cell_type":"code","source":"max_len_text = config.MAX_LEN  # Define your maximum text length\nmax_len_summary = config.SUMMARY_LEN  # Define your maximum summary length","metadata":{"execution":{"iopub.status.busy":"2024-06-10T05:12:46.174997Z","iopub.execute_input":"2024-06-10T05:12:46.175376Z","iopub.status.idle":"2024-06-10T05:12:46.180407Z","shell.execute_reply.started":"2024-06-10T05:12:46.175351Z","shell.execute_reply":"2024-06-10T05:12:46.179409Z"},"trusted":true},"execution_count":25,"outputs":[]},{"cell_type":"code","source":"text_data = df[\"ctext\"].tolist()\nsummary_data = df[\"text\"].tolist()","metadata":{"execution":{"iopub.status.busy":"2024-06-10T05:12:46.181864Z","iopub.execute_input":"2024-06-10T05:12:46.182309Z","iopub.status.idle":"2024-06-10T05:12:46.192265Z","shell.execute_reply.started":"2024-06-10T05:12:46.182278Z","shell.execute_reply":"2024-06-10T05:12:46.191306Z"},"trusted":true},"execution_count":26,"outputs":[]},{"cell_type":"code","source":"inputs, outputs = tokenize_data(text_data, summary_data, max_len_text, max_len_summary)","metadata":{"execution":{"iopub.status.busy":"2024-06-10T05:12:46.193949Z","iopub.execute_input":"2024-06-10T05:12:46.194405Z","iopub.status.idle":"2024-06-10T05:12:50.385530Z","shell.execute_reply.started":"2024-06-10T05:12:46.194338Z","shell.execute_reply":"2024-06-10T05:12:50.384493Z"},"trusted":true},"execution_count":27,"outputs":[]},{"cell_type":"code","source":"from sklearn.model_selection import train_test_split\ntrain_df, test_df = train_test_split(df, test_size=0.2, random_state=42)\n\ntrain_inputs, train_outputs = tokenize_data(train_df[\"ctext\"].tolist(), train_df[\"text\"].tolist(), config.MAX_LEN, config.SUMMARY_LEN)\ntest_inputs, test_outputs = tokenize_data(test_df[\"ctext\"].tolist(), test_df[\"text\"].tolist(), config.MAX_LEN, config.SUMMARY_LEN)\n","metadata":{"execution":{"iopub.status.busy":"2024-06-10T05:12:50.387225Z","iopub.execute_input":"2024-06-10T05:12:50.387585Z","iopub.status.idle":"2024-06-10T05:12:54.684282Z","shell.execute_reply.started":"2024-06-10T05:12:50.387553Z","shell.execute_reply":"2024-06-10T05:12:54.683282Z"},"trusted":true},"execution_count":28,"outputs":[]},{"cell_type":"markdown","source":"## Model","metadata":{}},{"cell_type":"code","source":"vocab_size = tokenizer.vocab_size","metadata":{"execution":{"iopub.status.busy":"2024-06-10T05:14:04.922748Z","iopub.execute_input":"2024-06-10T05:14:04.923135Z","iopub.status.idle":"2024-06-10T05:14:04.930151Z","shell.execute_reply.started":"2024-06-10T05:14:04.923106Z","shell.execute_reply":"2024-06-10T05:14:04.929111Z"},"trusted":true},"execution_count":32,"outputs":[]},{"cell_type":"code","source":"encoder_model = TFT5EncoderModel.from_pretrained('t5-base')","metadata":{"execution":{"iopub.status.busy":"2024-06-09T14:23:05.112877Z","iopub.execute_input":"2024-06-09T14:23:05.113265Z","iopub.status.idle":"2024-06-09T14:23:11.934597Z","shell.execute_reply.started":"2024-06-09T14:23:05.113232Z","shell.execute_reply":"2024-06-09T14:23:11.933596Z"},"trusted":true},"execution_count":27,"outputs":[{"output_type":"display_data","data":{"text/plain":"Downloading config.json:   0%|          | 0.00/1.21k [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"954d60109a114e2aab00751cf0e82084"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Downloading model.safetensors:   0%|          | 0.00/892M [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"1316b08591164360b5d149b444516abf"}},"metadata":{}},{"name":"stderr","text":"Some weights of the PyTorch model were not used when initializing the TF 2.0 model TFT5EncoderModel: ['decoder.block.11.layer.0.SelfAttention.q.weight', 'decoder.block.6.layer.0.SelfAttention.o.weight', 'decoder.block.1.layer.0.SelfAttention.k.weight', 'decoder.block.3.layer.1.EncDecAttention.q.weight', 'decoder.block.8.layer.2.layer_norm.weight', 'decoder.block.1.layer.1.EncDecAttention.v.weight', 'decoder.block.2.layer.2.layer_norm.weight', 'decoder.block.2.layer.2.DenseReluDense.wo.weight', 'decoder.block.8.layer.1.EncDecAttention.q.weight', 'decoder.block.9.layer.0.SelfAttention.v.weight', 'decoder.block.5.layer.0.SelfAttention.o.weight', 'decoder.block.8.layer.0.layer_norm.weight', 'decoder.block.1.layer.2.layer_norm.weight', 'decoder.block.6.layer.2.DenseReluDense.wo.weight', 'decoder.block.6.layer.2.layer_norm.weight', 'decoder.block.5.layer.1.layer_norm.weight', 'decoder.block.9.layer.1.EncDecAttention.k.weight', 'decoder.block.6.layer.2.DenseReluDense.wi.weight', 'decoder.block.5.layer.2.layer_norm.weight', 'decoder.block.5.layer.0.layer_norm.weight', 'decoder.block.7.layer.2.DenseReluDense.wo.weight', 'decoder.block.3.layer.0.SelfAttention.o.weight', 'decoder.block.1.layer.0.SelfAttention.q.weight', 'decoder.block.10.layer.0.SelfAttention.k.weight', 'decoder.block.2.layer.1.layer_norm.weight', 'decoder.block.5.layer.1.EncDecAttention.q.weight', 'decoder.block.6.layer.1.EncDecAttention.q.weight', 'decoder.block.5.layer.0.SelfAttention.v.weight', 'decoder.block.9.layer.2.DenseReluDense.wi.weight', 'decoder.block.0.layer.0.SelfAttention.q.weight', 'decoder.block.11.layer.0.SelfAttention.k.weight', 'decoder.block.2.layer.0.layer_norm.weight', 'decoder.block.10.layer.0.SelfAttention.q.weight', 'decoder.block.0.layer.0.SelfAttention.o.weight', 'decoder.block.4.layer.0.SelfAttention.q.weight', 'decoder.block.3.layer.0.layer_norm.weight', 'decoder.block.0.layer.1.EncDecAttention.o.weight', 'decoder.block.2.layer.0.SelfAttention.o.weight', 'decoder.block.4.layer.0.layer_norm.weight', 'decoder.block.6.layer.1.EncDecAttention.o.weight', 'decoder.block.10.layer.2.layer_norm.weight', 'decoder.block.7.layer.0.layer_norm.weight', 'decoder.block.11.layer.0.layer_norm.weight', 'decoder.block.10.layer.2.DenseReluDense.wi.weight', 'decoder.block.8.layer.1.layer_norm.weight', 'decoder.block.8.layer.0.SelfAttention.q.weight', 'decoder.block.0.layer.2.layer_norm.weight', 'decoder.block.4.layer.0.SelfAttention.v.weight', 'decoder.block.4.layer.2.DenseReluDense.wo.weight', 'decoder.block.4.layer.1.EncDecAttention.k.weight', 'decoder.block.0.layer.0.layer_norm.weight', 'decoder.block.10.layer.2.DenseReluDense.wo.weight', 'decoder.block.7.layer.0.SelfAttention.o.weight', 'decoder.block.8.layer.1.EncDecAttention.o.weight', 'decoder.block.7.layer.1.EncDecAttention.o.weight', 'decoder.block.0.layer.1.layer_norm.weight', 'decoder.block.3.layer.0.SelfAttention.k.weight', 'decoder.block.7.layer.1.EncDecAttention.q.weight', 'decoder.block.7.layer.0.SelfAttention.k.weight', 'decoder.block.7.layer.2.layer_norm.weight', 'decoder.block.0.layer.1.EncDecAttention.v.weight', 'decoder.block.0.layer.0.SelfAttention.relative_attention_bias.weight', 'decoder.block.7.layer.2.DenseReluDense.wi.weight', 'decoder.block.0.layer.1.EncDecAttention.k.weight', 'decoder.block.9.layer.0.SelfAttention.o.weight', 'decoder.block.9.layer.2.layer_norm.weight', 'decoder.block.11.layer.2.DenseReluDense.wo.weight', 'decoder.block.2.layer.0.SelfAttention.q.weight', 'decoder.block.10.layer.1.layer_norm.weight', 'decoder.block.1.layer.0.layer_norm.weight', 'decoder.block.8.layer.0.SelfAttention.k.weight', 'decoder.block.0.layer.0.SelfAttention.v.weight', 'decoder.block.1.layer.1.EncDecAttention.q.weight', 'decoder.block.1.layer.2.DenseReluDense.wo.weight', 'decoder.block.10.layer.0.SelfAttention.v.weight', 'decoder.block.0.layer.0.SelfAttention.k.weight', 'decoder.block.4.layer.1.EncDecAttention.q.weight', 'decoder.block.8.layer.2.DenseReluDense.wo.weight', 'decoder.block.9.layer.1.layer_norm.weight', 'decoder.block.0.layer.2.DenseReluDense.wi.weight', 'decoder.block.10.layer.0.layer_norm.weight', 'decoder.block.3.layer.2.DenseReluDense.wi.weight', 'decoder.block.3.layer.2.DenseReluDense.wo.weight', 'decoder.block.10.layer.1.EncDecAttention.v.weight', 'decoder.block.11.layer.1.EncDecAttention.k.weight', 'decoder.block.1.layer.0.SelfAttention.o.weight', 'decoder.block.10.layer.1.EncDecAttention.k.weight', 'decoder.block.10.layer.1.EncDecAttention.o.weight', 'decoder.block.7.layer.1.layer_norm.weight', 'decoder.block.8.layer.1.EncDecAttention.v.weight', 'decoder.block.4.layer.0.SelfAttention.o.weight', 'decoder.block.3.layer.2.layer_norm.weight', 'decoder.block.5.layer.0.SelfAttention.q.weight', 'decoder.block.8.layer.0.SelfAttention.v.weight', 'decoder.block.6.layer.1.EncDecAttention.v.weight', 'decoder.block.8.layer.0.SelfAttention.o.weight', 'decoder.block.9.layer.0.layer_norm.weight', 'decoder.block.6.layer.0.SelfAttention.v.weight', 'decoder.block.2.layer.1.EncDecAttention.o.weight', 'decoder.block.11.layer.1.EncDecAttention.v.weight', 'decoder.block.7.layer.0.SelfAttention.q.weight', 'decoder.block.2.layer.2.DenseReluDense.wi.weight', 'decoder.block.5.layer.0.SelfAttention.k.weight', 'decoder.block.4.layer.1.EncDecAttention.o.weight', 'decoder.block.3.layer.1.EncDecAttention.v.weight', 'decoder.block.4.layer.0.SelfAttention.k.weight', 'decoder.block.10.layer.1.EncDecAttention.q.weight', 'decoder.block.2.layer.0.SelfAttention.v.weight', 'decoder.block.4.layer.2.layer_norm.weight', 'decoder.block.3.layer.1.layer_norm.weight', 'decoder.block.11.layer.1.EncDecAttention.q.weight', 'decoder.block.11.layer.1.layer_norm.weight', 'decoder.block.5.layer.2.DenseReluDense.wo.weight', 'decoder.block.6.layer.0.SelfAttention.q.weight', 'decoder.block.1.layer.1.EncDecAttention.o.weight', 'decoder.block.9.layer.1.EncDecAttention.q.weight', 'decoder.block.1.layer.1.layer_norm.weight', 'decoder.block.0.layer.1.EncDecAttention.q.weight', 'decoder.block.1.layer.0.SelfAttention.v.weight', 'decoder.block.4.layer.1.layer_norm.weight', 'decoder.block.2.layer.1.EncDecAttention.v.weight', 'decoder.block.9.layer.0.SelfAttention.k.weight', 'decoder.block.9.layer.0.SelfAttention.q.weight', 'decoder.block.11.layer.0.SelfAttention.o.weight', 'decoder.block.6.layer.1.layer_norm.weight', 'decoder.block.3.layer.1.EncDecAttention.o.weight', 'decoder.block.0.layer.2.DenseReluDense.wo.weight', 'decoder.block.1.layer.1.EncDecAttention.k.weight', 'decoder.block.2.layer.1.EncDecAttention.q.weight', 'decoder.block.11.layer.2.DenseReluDense.wi.weight', 'decoder.block.4.layer.2.DenseReluDense.wi.weight', 'decoder.block.8.layer.1.EncDecAttention.k.weight', 'decoder.block.1.layer.2.DenseReluDense.wi.weight', 'decoder.block.11.layer.0.SelfAttention.v.weight', 'decoder.block.5.layer.1.EncDecAttention.k.weight', 'decoder.block.9.layer.2.DenseReluDense.wo.weight', 'decoder.block.7.layer.1.EncDecAttention.v.weight', 'decoder.block.3.layer.1.EncDecAttention.k.weight', 'decoder.block.5.layer.1.EncDecAttention.v.weight', 'decoder.block.6.layer.0.layer_norm.weight', 'decoder.block.9.layer.1.EncDecAttention.o.weight', 'decoder.block.6.layer.1.EncDecAttention.k.weight', 'decoder.block.10.layer.0.SelfAttention.o.weight', 'decoder.block.8.layer.2.DenseReluDense.wi.weight', 'decoder.block.4.layer.1.EncDecAttention.v.weight', 'decoder.block.2.layer.1.EncDecAttention.k.weight', 'decoder.block.3.layer.0.SelfAttention.v.weight', 'decoder.block.3.layer.0.SelfAttention.q.weight', 'decoder.block.5.layer.2.DenseReluDense.wi.weight', 'decoder.block.2.layer.0.SelfAttention.k.weight', 'decoder.block.7.layer.0.SelfAttention.v.weight', 'decoder.block.11.layer.1.EncDecAttention.o.weight', 'decoder.block.11.layer.2.layer_norm.weight', 'decoder.block.5.layer.1.EncDecAttention.o.weight', 'decoder.block.7.layer.1.EncDecAttention.k.weight', 'decoder.block.6.layer.0.SelfAttention.k.weight', 'decoder.block.9.layer.1.EncDecAttention.v.weight', 'decoder.final_layer_norm.weight']\n- This IS expected if you are initializing TFT5EncoderModel from a PyTorch model trained on another task or with another architecture (e.g. initializing a TFBertForSequenceClassification model from a BertForPreTraining model).\n- This IS NOT expected if you are initializing TFT5EncoderModel from a PyTorch model that you expect to be exactly identical (e.g. initializing a TFBertForSequenceClassification model from a BertForSequenceClassification model).\nAll the weights of TFT5EncoderModel were initialized from the PyTorch model.\nIf your task is similar to the task the model of the checkpoint was trained on, you can already use TFT5EncoderModel for predictions without further training.\n","output_type":"stream"}]},{"cell_type":"code","source":"input_ids = tf.keras.Input(shape=(config.MAX_LEN,), dtype=tf.int32, name=\"input_ids\")\nattention_mask = tf.keras.Input(shape=(config.MAX_LEN,), dtype=tf.int32, name=\"attention_mask\")\n\nencoder_outputs = encoder_model(input_ids, attention_mask=attention_mask).last_hidden_state","metadata":{"execution":{"iopub.status.busy":"2024-06-09T14:29:25.109277Z","iopub.execute_input":"2024-06-09T14:29:25.109702Z","iopub.status.idle":"2024-06-09T14:29:26.688502Z","shell.execute_reply.started":"2024-06-09T14:29:25.109667Z","shell.execute_reply":"2024-06-09T14:29:26.687381Z"},"trusted":true},"execution_count":37,"outputs":[]},{"cell_type":"code","source":"# Embedding layer\ndecoder_inputs = Input(shape=(config.SUMMARY_LEN,), dtype=tf.int32, name=\"decoder_inputs\")\nembedding_layer = tf.keras.layers.Embedding(\n    input_dim=vocab_size, output_dim=300, \n    input_length=config.SUMMARY_LEN, trainable=False, \n    name=\"embedding\"\n)\ndecoder_inputs_embedded = embedding_layer(decoder_inputs)\n\n# Decoder LSTM\ndecoder_lstm = tf.keras.layers.LSTM(768, return_sequences=True, return_state=True, name=\"decoder_lstm\")\ndecoder_outputs, _, _ = decoder_lstm(\n    decoder_inputs_embedded, initial_state=[encoder_outputs[:, 0, :], encoder_outputs[:, 0, :]]\n)\n\n# Decoder Dense layer\ndecoder_dense = tf.keras.layers.TimeDistributed(Dense(vocab_size, activation='softmax', name=\"TD_dec_dense\"))\ndecoder_outputs = decoder_dense(decoder_outputs)\n\n# Define model\nmodel = tf.keras.Model(inputs=[input_ids, attention_mask, decoder_inputs], outputs=decoder_outputs)\n\nmodel.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n\nmodel.summary()","metadata":{"execution":{"iopub.status.busy":"2024-06-09T15:01:51.022194Z","iopub.execute_input":"2024-06-09T15:01:51.022634Z","iopub.status.idle":"2024-06-09T15:01:51.803195Z","shell.execute_reply.started":"2024-06-09T15:01:51.022601Z","shell.execute_reply":"2024-06-09T15:01:51.802074Z"},"trusted":true},"execution_count":66,"outputs":[{"name":"stdout","text":"Model: \"model_8\"\n__________________________________________________________________________________________________\n Layer (type)                   Output Shape         Param #     Connected to                     \n==================================================================================================\n input_ids (InputLayer)         [(None, 512)]        0           []                               \n                                                                                                  \n attention_mask (InputLayer)    [(None, 512)]        0           []                               \n                                                                                                  \n decoder_inputs (InputLayer)    [(None, 190)]        0           []                               \n                                                                                                  \n tft5_encoder_model (TFT5Encode  TFBaseModelOutput(l  109628544  ['input_ids[0][0]',              \n rModel)                        ast_hidden_state=(N               'attention_mask[0][0]']         \n                                one, 512, 768),                                                   \n                                 hidden_states=None                                               \n                                , attentions=None)                                                \n                                                                                                  \n embedding (Embedding)          (None, 190, 300)     9630000     ['decoder_inputs[0][0]']         \n                                                                                                  \n tf.__operators__.getitem_18 (S  (None, 768)         0           ['tft5_encoder_model[3][0]']     \n licingOpLambda)                                                                                  \n                                                                                                  \n tf.__operators__.getitem_19 (S  (None, 768)         0           ['tft5_encoder_model[3][0]']     \n licingOpLambda)                                                                                  \n                                                                                                  \n decoder_lstm (LSTM)            [(None, 190, 768),   3283968     ['embedding[0][0]',              \n                                 (None, 768),                     'tf.__operators__.getitem_18[0][\n                                 (None, 768)]                    0]',                             \n                                                                  'tf.__operators__.getitem_19[0][\n                                                                 0]']                             \n                                                                                                  \n time_distributed_4 (TimeDistri  (None, 190, 32100)  24684900    ['decoder_lstm[0][0]']           \n buted)                                                                                           \n                                                                                                  \n==================================================================================================\nTotal params: 147,227,412\nTrainable params: 137,597,412\nNon-trainable params: 9,630,000\n__________________________________________________________________________________________________\n","output_type":"stream"}]},{"cell_type":"code","source":"# Shifted summary tokens for decoder input during training\ndecoder_inputs_train = np.zeros_like(train_outputs['input_ids'])\ndecoder_inputs_train[:, 1:] = train_outputs['input_ids'][:, :-1]\ndecoder_inputs_train[:, 0] = tokenizer.pad_token_id\n\n# Shifted summary tokens for decoder input during testing\ndecoder_inputs_test = np.zeros_like(test_outputs['input_ids'])\ndecoder_inputs_test[:, 1:] = test_outputs['input_ids'][:, :-1]\ndecoder_inputs_test[:, 0] = tokenizer.pad_token_id\n","metadata":{"execution":{"iopub.status.busy":"2024-06-09T15:10:03.434412Z","iopub.execute_input":"2024-06-09T15:10:03.435142Z","iopub.status.idle":"2024-06-09T15:10:03.447034Z","shell.execute_reply.started":"2024-06-09T15:10:03.435107Z","shell.execute_reply":"2024-06-09T15:10:03.444946Z"},"trusted":true},"execution_count":69,"outputs":[]},{"cell_type":"code","source":"callbacks = [\n    tf.keras.callbacks.EarlyStopping(patience=4, monitor='val_loss', restore_best_weights=True),\n    tf.keras.callbacks.ReduceLROnPlateau(factor=0.2, patience=2, min_lr=1e-6)\n]\n\nhistory = model.fit(\n    x=[train_inputs['input_ids'], train_inputs['attention_mask'], decoder_inputs_train],\n    y=train_outputs['input_ids'],\n    validation_data=(\n        [test_inputs['input_ids'], test_inputs['attention_mask'], decoder_inputs_test],\n        test_outputs['input_ids']\n    ),\n    batch_size=8,\n    epochs=50,\n    callbacks=callbacks\n)\n","metadata":{"execution":{"iopub.status.busy":"2024-06-09T15:11:45.544190Z","iopub.execute_input":"2024-06-09T15:11:45.544583Z","iopub.status.idle":"2024-06-09T16:52:41.099440Z","shell.execute_reply.started":"2024-06-09T15:11:45.544551Z","shell.execute_reply":"2024-06-09T16:52:41.098327Z"},"trusted":true},"execution_count":72,"outputs":[{"name":"stdout","text":"Epoch 1/50\n267/267 [==============================] - 280s 1s/step - loss: 2.5232 - accuracy: 0.6907 - val_loss: 2.1613 - val_accuracy: 0.6995 - lr: 0.0010\nEpoch 2/50\n267/267 [==============================] - 276s 1s/step - loss: 2.1515 - accuracy: 0.6979 - val_loss: 2.1427 - val_accuracy: 0.7030 - lr: 0.0010\nEpoch 3/50\n267/267 [==============================] - 276s 1s/step - loss: 2.0986 - accuracy: 0.7002 - val_loss: 2.0782 - val_accuracy: 0.7048 - lr: 0.0010\nEpoch 4/50\n267/267 [==============================] - 276s 1s/step - loss: 2.0504 - accuracy: 0.7019 - val_loss: 2.0388 - val_accuracy: 0.7075 - lr: 0.0010\nEpoch 5/50\n267/267 [==============================] - 276s 1s/step - loss: 1.9937 - accuracy: 0.7048 - val_loss: 1.9883 - val_accuracy: 0.7107 - lr: 0.0010\nEpoch 6/50\n267/267 [==============================] - 275s 1s/step - loss: 1.9354 - accuracy: 0.7073 - val_loss: 1.9450 - val_accuracy: 0.7125 - lr: 0.0010\nEpoch 7/50\n267/267 [==============================] - 275s 1s/step - loss: 1.8726 - accuracy: 0.7108 - val_loss: 1.9034 - val_accuracy: 0.7171 - lr: 0.0010\nEpoch 8/50\n267/267 [==============================] - 275s 1s/step - loss: 1.8041 - accuracy: 0.7153 - val_loss: 1.8666 - val_accuracy: 0.7206 - lr: 0.0010\nEpoch 9/50\n267/267 [==============================] - 275s 1s/step - loss: 1.7293 - accuracy: 0.7195 - val_loss: 1.8232 - val_accuracy: 0.7248 - lr: 0.0010\nEpoch 10/50\n267/267 [==============================] - 275s 1s/step - loss: 1.6531 - accuracy: 0.7238 - val_loss: 1.7945 - val_accuracy: 0.7275 - lr: 0.0010\nEpoch 11/50\n267/267 [==============================] - 275s 1s/step - loss: 1.5768 - accuracy: 0.7283 - val_loss: 1.7712 - val_accuracy: 0.7311 - lr: 0.0010\nEpoch 12/50\n267/267 [==============================] - 275s 1s/step - loss: 1.4979 - accuracy: 0.7337 - val_loss: 1.7527 - val_accuracy: 0.7334 - lr: 0.0010\nEpoch 13/50\n267/267 [==============================] - 275s 1s/step - loss: 1.4220 - accuracy: 0.7402 - val_loss: 1.7410 - val_accuracy: 0.7358 - lr: 0.0010\nEpoch 14/50\n267/267 [==============================] - 275s 1s/step - loss: 1.3428 - accuracy: 0.7482 - val_loss: 1.7325 - val_accuracy: 0.7380 - lr: 0.0010\nEpoch 15/50\n267/267 [==============================] - 275s 1s/step - loss: 1.2645 - accuracy: 0.7573 - val_loss: 1.7252 - val_accuracy: 0.7396 - lr: 0.0010\nEpoch 16/50\n267/267 [==============================] - 275s 1s/step - loss: 1.1893 - accuracy: 0.7677 - val_loss: 1.7272 - val_accuracy: 0.7404 - lr: 0.0010\nEpoch 17/50\n267/267 [==============================] - 274s 1s/step - loss: 1.1179 - accuracy: 0.7775 - val_loss: 1.7279 - val_accuracy: 0.7421 - lr: 0.0010\nEpoch 18/50\n267/267 [==============================] - 275s 1s/step - loss: 0.9847 - accuracy: 0.7994 - val_loss: 1.7171 - val_accuracy: 0.7449 - lr: 2.0000e-04\nEpoch 19/50\n267/267 [==============================] - 274s 1s/step - loss: 0.9468 - accuracy: 0.8061 - val_loss: 1.7197 - val_accuracy: 0.7452 - lr: 2.0000e-04\nEpoch 20/50\n267/267 [==============================] - 274s 1s/step - loss: 0.9239 - accuracy: 0.8103 - val_loss: 1.7204 - val_accuracy: 0.7454 - lr: 2.0000e-04\nEpoch 21/50\n267/267 [==============================] - 274s 1s/step - loss: 0.8909 - accuracy: 0.8169 - val_loss: 1.7215 - val_accuracy: 0.7454 - lr: 4.0000e-05\nEpoch 22/50\n267/267 [==============================] - 275s 1s/step - loss: 0.8849 - accuracy: 0.8180 - val_loss: 1.7225 - val_accuracy: 0.7455 - lr: 4.0000e-05\n","output_type":"stream"}]},{"cell_type":"code","source":"from matplotlib import pyplot\npyplot.plot(history.history['loss'], label='train')\npyplot.plot(history.history['val_loss'], label='test')\npyplot.legend()\npyplot.show()\n\npyplot.plot(history.history['accuracy'], label='train')\npyplot.plot(history.history['val_accuracy'], label='test')\npyplot.legend()\npyplot.show()","metadata":{"execution":{"iopub.status.busy":"2024-06-09T16:52:41.101718Z","iopub.execute_input":"2024-06-09T16:52:41.102117Z","iopub.status.idle":"2024-06-09T16:53:08.000934Z","shell.execute_reply.started":"2024-06-09T16:52:41.102078Z","shell.execute_reply":"2024-06-09T16:53:07.999752Z"},"trusted":true},"execution_count":73,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 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"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure 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"},"metadata":{}}]},{"cell_type":"markdown","source":"### Inference Code","metadata":{}},{"cell_type":"code","source":"def summarize_text(text, max_len_text=config.MAX_LEN, max_len_summary=config.SUMMARY_LEN):\n#     Preprocess input text\n    input_text = text\n    inputs = tokenizer(\n        input_text,\n        add_special_tokens=True,\n        max_length=max_len_text,\n        truncation=True,\n        padding='max_length',\n        return_tensors='tf'\n    )\n    \n    # Initialize decoder input with pad token\n    decoder_input = np.zeros((1, max_len_summary), dtype=np.int32)\n    decoder_input[:, 0] = tokenizer.pad_token_id\n\n    # Perform inference step-by-step\n    for i in range(1, max_len_summary):\n        predictions = model.predict([inputs['input_ids'], inputs['attention_mask'], decoder_input])\n        next_token = np.argmax(predictions[0, i-1, :])\n        decoder_input[0, i] = next_token\n        if next_token == tokenizer.eos_token_id:\n            break\n    \n    # Decode the generated ids to text\n    summary_ids = decoder_input[0]\n    summary = tokenizer.decode(summary_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)\n    return summary\n\n# # Example usage\n# example_text = \"Your example text goes here.\"\n# summary = summarize_text(example_text)\n# print(\"Generated Summary:\", summary)","metadata":{"execution":{"iopub.status.busy":"2024-06-10T08:01:11.216272Z","iopub.execute_input":"2024-06-10T08:01:11.217327Z","iopub.status.idle":"2024-06-10T08:01:11.227090Z","shell.execute_reply.started":"2024-06-10T08:01:11.217289Z","shell.execute_reply":"2024-06-10T08:01:11.226082Z"},"trusted":true},"execution_count":40,"outputs":[]},{"cell_type":"code","source":"!pip install rouge-score","metadata":{"execution":{"iopub.status.busy":"2024-06-10T08:01:12.889246Z","iopub.execute_input":"2024-06-10T08:01:12.889967Z","iopub.status.idle":"2024-06-10T08:01:24.471673Z","shell.execute_reply.started":"2024-06-10T08:01:12.889936Z","shell.execute_reply":"2024-06-10T08:01:24.470394Z"},"trusted":true},"execution_count":41,"outputs":[{"name":"stdout","text":"Requirement already satisfied: rouge-score in /opt/conda/lib/python3.10/site-packages (0.1.2)\nRequirement already satisfied: absl-py in /opt/conda/lib/python3.10/site-packages (from rouge-score) (1.4.0)\nRequirement already satisfied: nltk in /opt/conda/lib/python3.10/site-packages (from rouge-score) (3.2.4)\nRequirement already satisfied: numpy in /opt/conda/lib/python3.10/site-packages (from rouge-score) (1.23.5)\nRequirement already satisfied: six>=1.14.0 in /opt/conda/lib/python3.10/site-packages (from rouge-score) (1.16.0)\n","output_type":"stream"}]},{"cell_type":"code","source":"","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from tqdm import tqdm\nfrom rouge_score import rouge_scorer\n\ndef evaluate_model(test_texts, test_summaries, max_len_text=config.MAX_LEN, max_len_summary=config.SUMMARY_LEN):\n    scorer = rouge_scorer.RougeScorer(['rouge1', 'rouge2', 'rougeL'], use_stemmer=True)\n    scores = {\n        'rouge1': {'precision': [], 'recall': [], 'fmeasure': []},\n        'rouge2': {'precision': [], 'recall': [], 'fmeasure': []},\n        'rougeL': {'precision': [], 'recall': [], 'fmeasure': []}\n    }\n    \n    for text, summary in tqdm(zip(test_texts, test_summaries), \"Evaluation\"):\n        generated_summary = summarize_text(text, max_len_text, max_len_summary)\n        score = scorer.score(summary, generated_summary)\n        \n        for metric in ['rouge1', 'rouge2', 'rougeL']:\n            scores[metric]['precision'].append(score[metric].precision)\n            scores[metric]['recall'].append(score[metric].recall)\n            scores[metric]['fmeasure'].append(score[metric].fmeasure)\n    \n    print(\"Calculating Mean Scores\")\n    mean_scores = {\n        metric: {\n            'precision': np.mean(values['precision']),\n            'recall': np.mean(values['recall']),\n            'fmeasure': np.mean(values['fmeasure'])\n        }\n        for metric, values in scores.items()\n    }\n    \n    return mean_scores\n\ntest_texts = test_df['ctext'].tolist()\ntest_summaries = test_df['text'].tolist()\nmean_scores = evaluate_model(test_texts, test_summaries)\n\nprint(\"ROUGE scores:\")\nfor metric, values in mean_scores.items():\n    print(f\"{metric.upper()}: Precision: {values['precision']:.4f}, Recall: {values['recall']:.4f}, F-measure: {values['fmeasure']:.4f}\")\n","metadata":{"execution":{"iopub.status.busy":"2024-06-09T19:30:46.989807Z","iopub.execute_input":"2024-06-09T19:30:46.990179Z","iopub.status.idle":"2024-06-09T19:30:46.997793Z","shell.execute_reply.started":"2024-06-09T19:30:46.990148Z","shell.execute_reply":"2024-06-09T19:30:46.996742Z"},"trusted":true},"execution_count":92,"outputs":[]},{"cell_type":"code","source":"print(\"ROUGE scores:\")\nfor metric, values in mean_scores.items():\n    print(f\"{metric.upper()}: Precision: {values['precision']:.4f}, Recall: {values['recall']:.4f}, F-measure: {values['fmeasure']:.4f}\")\n","metadata":{"execution":{"iopub.status.busy":"2024-06-09T19:24:51.480921Z","iopub.execute_input":"2024-06-09T19:24:51.481664Z","iopub.status.idle":"2024-06-09T19:24:51.489586Z","shell.execute_reply.started":"2024-06-09T19:24:51.481631Z","shell.execute_reply":"2024-06-09T19:24:51.488218Z"},"trusted":true},"execution_count":81,"outputs":[{"name":"stdout","text":"ROUGE scores:\nROUGE1: Precision: 0.2628, Recall: 0.1929, F-measure: 0.2114\nROUGE2: Precision: 0.0450, Recall: 0.0338, F-measure: 0.0366\nROUGEL: Precision: 0.1887, Recall: 0.1388, F-measure: 0.1520\n","output_type":"stream"}]},{"cell_type":"code","source":"print(\"With Attention Model\")\nprint(\"ROUGE scores:\")\nfor metric, values in mean_scores.items():\n    print(f\"{metric.upper()}: Precision: {values['precision']:.4f}, Recall: {values['recall']:.4f}, F-measure: {values['fmeasure']:.4f}\")\n","metadata":{"execution":{"iopub.status.busy":"2024-06-10T10:17:48.334366Z","iopub.execute_input":"2024-06-10T10:17:48.334772Z","iopub.status.idle":"2024-06-10T10:17:48.344177Z","shell.execute_reply.started":"2024-06-10T10:17:48.334735Z","shell.execute_reply":"2024-06-10T10:17:48.343069Z"},"trusted":true},"execution_count":45,"outputs":[{"name":"stdout","text":"With Attention Model\nROUGE scores:\nROUGE1: Precision: 0.1824, Recall: 0.1731, F-measure: 0.1658\nROUGE2: Precision: 0.0275, Recall: 0.0282, F-measure: 0.0264\nROUGEL: Precision: 0.1479, Recall: 0.1406, F-measure: 0.1340\n","output_type":"stream"}]},{"cell_type":"markdown","source":"## T5-LSTM Att","metadata":{}},{"cell_type":"code","source":"from transformers import T5Tokenizer\nimport tensorflow as tf\nfrom tensorflow.keras.layers import Input, Dense, LSTM, Attention, Concatenate, TimeDistributed, Dropout","metadata":{"execution":{"iopub.status.busy":"2024-06-10T05:13:33.426879Z","iopub.execute_input":"2024-06-10T05:13:33.427237Z","iopub.status.idle":"2024-06-10T05:13:33.433776Z","shell.execute_reply.started":"2024-06-10T05:13:33.427209Z","shell.execute_reply":"2024-06-10T05:13:33.432697Z"},"trusted":true},"execution_count":30,"outputs":[]},{"cell_type":"code","source":"encoder_model = TFT5EncoderModel.from_pretrained('t5-base')\n\ninput_ids = tf.keras.Input(shape=(config.MAX_LEN,), dtype=tf.int32, name=\"input_ids\")\nattention_mask = tf.keras.Input(shape=(config.MAX_LEN,), dtype=tf.int32, name=\"attention_mask\")\n\nencoder_outputs = encoder_model(input_ids, attention_mask=attention_mask).last_hidden_state\n\n# Embedding layer\ndecoder_inputs = Input(shape=(config.SUMMARY_LEN,), dtype=tf.int32, name=\"decoder_inputs\")\nembedding_layer = tf.keras.layers.Embedding(\n    input_dim=vocab_size, output_dim=300, \n    input_length=config.SUMMARY_LEN, trainable=False, \n    name=\"embedding\"\n)\ndecoder_inputs_embedded = embedding_layer(decoder_inputs)\n\n# Decoder LSTM\ndecoder_lstm = tf.keras.layers.LSTM(768, return_sequences=True, return_state=True, name=\"decoder_lstm\")\ndecoder_outputs_1, _, _ = decoder_lstm(\n    decoder_inputs_embedded, initial_state=[encoder_outputs[:, 0, :], encoder_outputs[:, 0, :]]\n)\ndecoder_outputs_1 = tf.keras.layers.Dropout(0.2)(decoder_outputs_1)\n\ndecoder_lstm2 = tf.keras.layers.LSTM(768, return_sequences=True, return_state=True, name=\"decoder_lstm2\")\ndecoder_outputs_2, _, _ = decoder_lstm2(\n    decoder_outputs_1, initial_state=[encoder_outputs[:, 0, :], encoder_outputs[:, 0, :]]\n)\ndecoder_outputs_2 = tf.keras.layers.Dropout(0.2)(decoder_outputs_2)\n\nattn_layer = AttentionLayer(name='attention_layer')\nattn_out, attn_states = attn_layer([encoder_outputs, decoder_outputs_2])\n\ndecoder_concat_input = tf.keras.layers.Concatenate(axis=-1, name='concat_layer')([decoder_outputs_2, attn_out])\n\n# Decoder Dense layer\ndecoder_dense = tf.keras.layers.TimeDistributed(Dense(320, name=\"TD_dec_dense\"))\ndecoder_outputs = decoder_dense(decoder_concat_input)\n\ndecoder_dense = tf.keras.layers.TimeDistributed(Dense(vocab_size, activation='softmax', name=\"TD_dec_dense\"))\ndecoder_outputs = decoder_dense(decoder_outputs)\n\n# Define model\nmodel = tf.keras.Model(inputs=[input_ids, attention_mask, decoder_inputs], outputs=decoder_outputs)\n\n# model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\nmodel.compile(optimizer='rmsprop', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n\nmodel.summary()","metadata":{"execution":{"iopub.status.busy":"2024-06-10T05:15:11.188124Z","iopub.execute_input":"2024-06-10T05:15:11.188839Z","iopub.status.idle":"2024-06-10T05:15:15.712176Z","shell.execute_reply.started":"2024-06-10T05:15:11.188807Z","shell.execute_reply":"2024-06-10T05:15:15.711110Z"},"trusted":true},"execution_count":35,"outputs":[{"name":"stderr","text":"Some weights of the PyTorch model were not used when initializing the TF 2.0 model TFT5EncoderModel: ['decoder.block.6.layer.1.EncDecAttention.k.weight', 'decoder.block.0.layer.0.SelfAttention.v.weight', 'decoder.block.10.layer.1.layer_norm.weight', 'decoder.block.1.layer.1.EncDecAttention.q.weight', 'decoder.block.10.layer.2.layer_norm.weight', 'decoder.block.5.layer.0.SelfAttention.k.weight', 'decoder.block.5.layer.0.layer_norm.weight', 'decoder.block.7.layer.1.EncDecAttention.v.weight', 'decoder.block.9.layer.1.EncDecAttention.q.weight', 'decoder.block.5.layer.2.DenseReluDense.wo.weight', 'decoder.block.4.layer.0.SelfAttention.o.weight', 'decoder.block.1.layer.1.EncDecAttention.o.weight', 'decoder.block.9.layer.0.SelfAttention.v.weight', 'decoder.block.7.layer.1.EncDecAttention.q.weight', 'decoder.block.7.layer.0.SelfAttention.v.weight', 'decoder.block.8.layer.0.SelfAttention.v.weight', 'decoder.block.8.layer.1.EncDecAttention.o.weight', 'decoder.block.1.layer.0.SelfAttention.k.weight', 'decoder.block.11.layer.1.EncDecAttention.v.weight', 'decoder.block.0.layer.2.DenseReluDense.wi.weight', 'decoder.block.10.layer.2.DenseReluDense.wo.weight', 'decoder.block.1.layer.0.SelfAttention.q.weight', 'decoder.block.2.layer.0.SelfAttention.q.weight', 'decoder.block.3.layer.0.layer_norm.weight', 'decoder.block.8.layer.1.EncDecAttention.v.weight', 'decoder.block.1.layer.2.layer_norm.weight', 'decoder.block.11.layer.0.SelfAttention.k.weight', 'decoder.block.1.layer.2.DenseReluDense.wo.weight', 'decoder.block.4.layer.1.EncDecAttention.k.weight', 'decoder.block.10.layer.0.SelfAttention.v.weight', 'decoder.block.5.layer.0.SelfAttention.v.weight', 'decoder.block.0.layer.1.layer_norm.weight', 'decoder.block.4.layer.0.layer_norm.weight', 'decoder.block.6.layer.1.EncDecAttention.v.weight', 'decoder.block.2.layer.1.layer_norm.weight', 'decoder.block.2.layer.2.layer_norm.weight', 'decoder.block.10.layer.1.EncDecAttention.k.weight', 'decoder.block.11.layer.1.layer_norm.weight', 'decoder.block.2.layer.1.EncDecAttention.v.weight', 'decoder.block.3.layer.0.SelfAttention.v.weight', 'decoder.block.6.layer.1.EncDecAttention.o.weight', 'decoder.block.10.layer.1.EncDecAttention.v.weight', 'decoder.block.2.layer.2.DenseReluDense.wi.weight', 'decoder.block.5.layer.1.EncDecAttention.o.weight', 'decoder.block.5.layer.2.DenseReluDense.wi.weight', 'decoder.block.4.layer.0.SelfAttention.v.weight', 'decoder.block.0.layer.0.SelfAttention.k.weight', 'decoder.block.6.layer.0.SelfAttention.v.weight', 'decoder.block.5.layer.0.SelfAttention.o.weight', 'decoder.block.4.layer.1.EncDecAttention.v.weight', 'decoder.block.8.layer.0.SelfAttention.k.weight', 'decoder.block.1.layer.2.DenseReluDense.wi.weight', 'decoder.block.9.layer.2.DenseReluDense.wi.weight', 'decoder.block.6.layer.2.DenseReluDense.wo.weight', 'decoder.block.7.layer.1.EncDecAttention.o.weight', 'decoder.block.6.layer.0.layer_norm.weight', 'decoder.block.0.layer.2.DenseReluDense.wo.weight', 'decoder.block.11.layer.0.SelfAttention.q.weight', 'decoder.block.5.layer.1.EncDecAttention.k.weight', 'decoder.final_layer_norm.weight', 'decoder.block.6.layer.0.SelfAttention.o.weight', 'decoder.block.8.layer.0.SelfAttention.o.weight', 'decoder.block.7.layer.2.DenseReluDense.wo.weight', 'decoder.block.1.layer.1.EncDecAttention.k.weight', 'decoder.block.6.layer.2.layer_norm.weight', 'decoder.block.10.layer.1.EncDecAttention.q.weight', 'decoder.block.9.layer.0.SelfAttention.o.weight', 'decoder.block.0.layer.1.EncDecAttention.o.weight', 'decoder.block.5.layer.2.layer_norm.weight', 'decoder.block.1.layer.1.layer_norm.weight', 'decoder.block.11.layer.1.EncDecAttention.k.weight', 'decoder.block.3.layer.1.layer_norm.weight', 'decoder.block.9.layer.1.EncDecAttention.o.weight', 'decoder.block.1.layer.1.EncDecAttention.v.weight', 'decoder.block.11.layer.0.SelfAttention.o.weight', 'decoder.block.6.layer.1.layer_norm.weight', 'decoder.block.8.layer.0.layer_norm.weight', 'decoder.block.3.layer.0.SelfAttention.k.weight', 'decoder.block.11.layer.2.DenseReluDense.wo.weight', 'decoder.block.0.layer.1.EncDecAttention.k.weight', 'decoder.block.8.layer.1.layer_norm.weight', 'decoder.block.10.layer.0.SelfAttention.q.weight', 'decoder.block.0.layer.2.layer_norm.weight', 'decoder.block.1.layer.0.layer_norm.weight', 'decoder.block.3.layer.2.DenseReluDense.wo.weight', 'decoder.block.5.layer.0.SelfAttention.q.weight', 'decoder.block.4.layer.1.EncDecAttention.q.weight', 'decoder.block.2.layer.0.layer_norm.weight', 'decoder.block.7.layer.0.SelfAttention.k.weight', 'decoder.block.11.layer.1.EncDecAttention.q.weight', 'decoder.block.1.layer.0.SelfAttention.o.weight', 'decoder.block.4.layer.2.DenseReluDense.wo.weight', 'decoder.block.2.layer.0.SelfAttention.o.weight', 'decoder.block.3.layer.1.EncDecAttention.k.weight', 'decoder.block.8.layer.2.DenseReluDense.wi.weight', 'decoder.block.0.layer.0.SelfAttention.o.weight', 'decoder.block.7.layer.2.DenseReluDense.wi.weight', 'decoder.block.6.layer.0.SelfAttention.q.weight', 'decoder.block.3.layer.2.layer_norm.weight', 'decoder.block.7.layer.2.layer_norm.weight', 'decoder.block.2.layer.0.SelfAttention.k.weight', 'decoder.block.4.layer.1.layer_norm.weight', 'decoder.block.10.layer.0.layer_norm.weight', 'decoder.block.2.layer.0.SelfAttention.v.weight', 'decoder.block.10.layer.0.SelfAttention.o.weight', 'decoder.block.9.layer.0.layer_norm.weight', 'decoder.block.4.layer.0.SelfAttention.k.weight', 'decoder.block.7.layer.0.layer_norm.weight', 'decoder.block.11.layer.0.layer_norm.weight', 'decoder.block.4.layer.0.SelfAttention.q.weight', 'decoder.block.7.layer.0.SelfAttention.o.weight', 'decoder.block.8.layer.2.DenseReluDense.wo.weight', 'decoder.block.4.layer.2.DenseReluDense.wi.weight', 'decoder.block.2.layer.1.EncDecAttention.k.weight', 'decoder.block.3.layer.1.EncDecAttention.q.weight', 'decoder.block.5.layer.1.EncDecAttention.q.weight', 'decoder.block.2.layer.2.DenseReluDense.wo.weight', 'decoder.block.5.layer.1.layer_norm.weight', 'decoder.block.8.layer.1.EncDecAttention.k.weight', 'decoder.block.3.layer.0.SelfAttention.q.weight', 'decoder.block.11.layer.2.layer_norm.weight', 'decoder.block.9.layer.1.layer_norm.weight', 'decoder.block.4.layer.2.layer_norm.weight', 'decoder.block.11.layer.1.EncDecAttention.o.weight', 'decoder.block.6.layer.1.EncDecAttention.q.weight', 'decoder.block.8.layer.2.layer_norm.weight', 'decoder.block.7.layer.0.SelfAttention.q.weight', 'decoder.block.3.layer.1.EncDecAttention.v.weight', 'decoder.block.4.layer.1.EncDecAttention.o.weight', 'decoder.block.9.layer.0.SelfAttention.k.weight', 'decoder.block.11.layer.0.SelfAttention.v.weight', 'decoder.block.3.layer.2.DenseReluDense.wi.weight', 'decoder.block.10.layer.1.EncDecAttention.o.weight', 'decoder.block.9.layer.1.EncDecAttention.v.weight', 'decoder.block.9.layer.2.DenseReluDense.wo.weight', 'decoder.block.5.layer.1.EncDecAttention.v.weight', 'decoder.block.6.layer.2.DenseReluDense.wi.weight', 'decoder.block.3.layer.1.EncDecAttention.o.weight', 'decoder.block.0.layer.0.SelfAttention.q.weight', 'decoder.block.9.layer.2.layer_norm.weight', 'decoder.block.8.layer.0.SelfAttention.q.weight', 'decoder.block.10.layer.0.SelfAttention.k.weight', 'decoder.block.8.layer.1.EncDecAttention.q.weight', 'decoder.block.3.layer.0.SelfAttention.o.weight', 'decoder.block.0.layer.0.SelfAttention.relative_attention_bias.weight', 'decoder.block.7.layer.1.EncDecAttention.k.weight', 'decoder.block.9.layer.1.EncDecAttention.k.weight', 'decoder.block.2.layer.1.EncDecAttention.o.weight', 'decoder.block.0.layer.1.EncDecAttention.v.weight', 'decoder.block.2.layer.1.EncDecAttention.q.weight', 'decoder.block.0.layer.0.layer_norm.weight', 'decoder.block.6.layer.0.SelfAttention.k.weight', 'decoder.block.9.layer.0.SelfAttention.q.weight', 'decoder.block.1.layer.0.SelfAttention.v.weight', 'decoder.block.10.layer.2.DenseReluDense.wi.weight', 'decoder.block.11.layer.2.DenseReluDense.wi.weight', 'decoder.block.0.layer.1.EncDecAttention.q.weight', 'decoder.block.7.layer.1.layer_norm.weight']\n- This IS expected if you are initializing TFT5EncoderModel from a PyTorch model trained on another task or with another architecture (e.g. initializing a TFBertForSequenceClassification model from a BertForPreTraining model).\n- This IS NOT expected if you are initializing TFT5EncoderModel from a PyTorch model that you expect to be exactly identical (e.g. initializing a TFBertForSequenceClassification model from a BertForSequenceClassification model).\nAll the weights of TFT5EncoderModel were initialized from the PyTorch model.\nIf your task is similar to the task the model of the checkpoint was trained on, you can already use TFT5EncoderModel for predictions without further training.\n","output_type":"stream"},{"name":"stdout","text":"Model: \"model_1\"\n__________________________________________________________________________________________________\n Layer (type)                   Output Shape         Param #     Connected to                     \n==================================================================================================\n input_ids (InputLayer)         [(None, 512)]        0           []                               \n                                                                                                  \n attention_mask (InputLayer)    [(None, 512)]        0           []                               \n                                                                                                  \n decoder_inputs (InputLayer)    [(None, 190)]        0           []                               \n                                                                                                  \n tft5_encoder_model_2 (TFT5Enco  TFBaseModelOutput(l  109628544  ['input_ids[0][0]',              \n derModel)                      ast_hidden_state=(N               'attention_mask[0][0]']         \n                                one, 512, 768),                                                   \n                                 hidden_states=None                                               \n                                , attentions=None)                                                \n                                                                                                  \n embedding (Embedding)          (None, 190, 300)     9630000     ['decoder_inputs[0][0]']         \n                                                                                                  \n tf.__operators__.getitem_4 (Sl  (None, 768)         0           ['tft5_encoder_model_2[0][0]']   \n icingOpLambda)                                                                                   \n                                                                                                  \n tf.__operators__.getitem_5 (Sl  (None, 768)         0           ['tft5_encoder_model_2[0][0]']   \n icingOpLambda)                                                                                   \n                                                                                                  \n decoder_lstm (LSTM)            [(None, 190, 768),   3283968     ['embedding[0][0]',              \n                                 (None, 768),                     'tf.__operators__.getitem_4[0][0\n                                 (None, 768)]                    ]',                              \n                                                                  'tf.__operators__.getitem_5[0][0\n                                                                 ]']                              \n                                                                                                  \n dropout_149 (Dropout)          (None, 190, 768)     0           ['decoder_lstm[0][0]']           \n                                                                                                  \n tf.__operators__.getitem_6 (Sl  (None, 768)         0           ['tft5_encoder_model_2[0][0]']   \n icingOpLambda)                                                                                   \n                                                                                                  \n tf.__operators__.getitem_7 (Sl  (None, 768)         0           ['tft5_encoder_model_2[0][0]']   \n icingOpLambda)                                                                                   \n                                                                                                  \n decoder_lstm2 (LSTM)           [(None, 190, 768),   4721664     ['dropout_149[0][0]',            \n                                 (None, 768),                     'tf.__operators__.getitem_6[0][0\n                                 (None, 768)]                    ]',                              \n                                                                  'tf.__operators__.getitem_7[0][0\n                                                                 ]']                              \n                                                                                                  \n dropout_150 (Dropout)          (None, 190, 768)     0           ['decoder_lstm2[0][0]']          \n                                                                                                  \n attention_layer (AttentionLaye  ((None, 190, 768),  1180416     ['tft5_encoder_model_2[0][0]',   \n r)                              (None, 190, 512))                'dropout_150[0][0]']            \n                                                                                                  \n concat_layer (Concatenate)     (None, 190, 1536)    0           ['dropout_150[0][0]',            \n                                                                  'attention_layer[0][0]']        \n                                                                                                  \n time_distributed_2 (TimeDistri  (None, 190, 320)    491840      ['concat_layer[0][0]']           \n buted)                                                                                           \n                                                                                                  \n time_distributed_3 (TimeDistri  (None, 190, 32100)  10304100    ['time_distributed_2[0][0]']     \n buted)                                                                                           \n                                                                                                  \n==================================================================================================\nTotal params: 139,240,532\nTrainable params: 129,610,532\nNon-trainable params: 9,630,000\n__________________________________________________________________________________________________\n","output_type":"stream"}]},{"cell_type":"code","source":"# Shifted summary tokens for decoder input during training\ndecoder_inputs_train = np.zeros_like(train_outputs['input_ids'])\ndecoder_inputs_train[:, 1:] = train_outputs['input_ids'][:, :-1]\ndecoder_inputs_train[:, 0] = tokenizer.pad_token_id\n\n# Shifted summary tokens for decoder input during testing\ndecoder_inputs_test = np.zeros_like(test_outputs['input_ids'])\ndecoder_inputs_test[:, 1:] = test_outputs['input_ids'][:, :-1]\ndecoder_inputs_test[:, 0] = tokenizer.pad_token_id\n\ncallbacks = [\n    tf.keras.callbacks.EarlyStopping(patience=4, monitor='val_loss', restore_best_weights=True),\n    tf.keras.callbacks.ReduceLROnPlateau(factor=0.2, patience=2, min_lr=1e-6)\n]\n\nhistory = model.fit(\n    x=[train_inputs['input_ids'], train_inputs['attention_mask'], decoder_inputs_train],\n    y=train_outputs['input_ids'],\n    validation_data=(\n        [test_inputs['input_ids'], test_inputs['attention_mask'], decoder_inputs_test],\n        test_outputs['input_ids']\n    ),\n    batch_size=2,\n    epochs=50,\n    callbacks=callbacks\n)","metadata":{"execution":{"iopub.status.busy":"2024-06-10T05:15:35.630528Z","iopub.execute_input":"2024-06-10T05:15:35.631144Z","iopub.status.idle":"2024-06-10T07:43:30.842795Z","shell.execute_reply.started":"2024-06-10T05:15:35.631114Z","shell.execute_reply":"2024-06-10T07:43:30.841729Z"},"trusted":true},"execution_count":36,"outputs":[{"name":"stdout","text":"Epoch 1/50\n1068/1068 [==============================] - 553s 498ms/step - loss: 2.4020 - accuracy: 0.6933 - val_loss: 2.2148 - val_accuracy: 0.7041 - lr: 0.0010\nEpoch 2/50\n1068/1068 [==============================] - 522s 489ms/step - loss: 2.1921 - accuracy: 0.6994 - val_loss: 2.1235 - val_accuracy: 0.7033 - lr: 0.0010\nEpoch 3/50\n1068/1068 [==============================] - 522s 488ms/step - loss: 2.1082 - accuracy: 0.7008 - val_loss: 2.0672 - val_accuracy: 0.7039 - lr: 0.0010\nEpoch 4/50\n1068/1068 [==============================] - 521s 488ms/step - loss: 2.0474 - accuracy: 0.7023 - val_loss: 2.0495 - val_accuracy: 0.7020 - lr: 0.0010\nEpoch 5/50\n1068/1068 [==============================] - 521s 488ms/step - loss: 1.9934 - accuracy: 0.7035 - val_loss: 2.0070 - val_accuracy: 0.7045 - lr: 0.0010\nEpoch 6/50\n1068/1068 [==============================] - 521s 488ms/step - loss: 1.9399 - accuracy: 0.7042 - val_loss: 1.9884 - val_accuracy: 0.7076 - lr: 0.0010\nEpoch 7/50\n1068/1068 [==============================] - 522s 489ms/step - loss: 1.8958 - accuracy: 0.7057 - val_loss: 1.9736 - val_accuracy: 0.7074 - lr: 0.0010\nEpoch 8/50\n1068/1068 [==============================] - 521s 488ms/step - loss: 1.8500 - accuracy: 0.7069 - val_loss: 1.9705 - val_accuracy: 0.7084 - lr: 0.0010\nEpoch 9/50\n1068/1068 [==============================] - 520s 487ms/step - loss: 1.7979 - accuracy: 0.7091 - val_loss: 1.9793 - val_accuracy: 0.7093 - lr: 0.0010\nEpoch 10/50\n1068/1068 [==============================] - 519s 486ms/step - loss: 1.7491 - accuracy: 0.7113 - val_loss: 1.9604 - val_accuracy: 0.7103 - lr: 0.0010\nEpoch 11/50\n1068/1068 [==============================] - 519s 486ms/step - loss: 1.7007 - accuracy: 0.7139 - val_loss: 1.9793 - val_accuracy: 0.7126 - lr: 0.0010\nEpoch 12/50\n1068/1068 [==============================] - 518s 485ms/step - loss: 1.6517 - accuracy: 0.7168 - val_loss: 1.9828 - val_accuracy: 0.7125 - lr: 0.0010\nEpoch 13/50\n1068/1068 [==============================] - 519s 486ms/step - loss: 1.4197 - accuracy: 0.7298 - val_loss: 1.8431 - val_accuracy: 0.7211 - lr: 2.0000e-04\nEpoch 14/50\n1068/1068 [==============================] - 519s 486ms/step - loss: 1.3366 - accuracy: 0.7350 - val_loss: 1.8499 - val_accuracy: 0.7212 - lr: 2.0000e-04\nEpoch 15/50\n1068/1068 [==============================] - 519s 486ms/step - loss: 1.3124 - accuracy: 0.7366 - val_loss: 1.8686 - val_accuracy: 0.7212 - lr: 2.0000e-04\nEpoch 16/50\n1068/1068 [==============================] - 518s 485ms/step - loss: 1.2420 - accuracy: 0.7433 - val_loss: 1.8460 - val_accuracy: 0.7225 - lr: 4.0000e-05\nEpoch 17/50\n1068/1068 [==============================] - 519s 486ms/step - loss: 1.2295 - accuracy: 0.7449 - val_loss: 1.8478 - val_accuracy: 0.7229 - lr: 4.0000e-05\n","output_type":"stream"}]},{"cell_type":"code","source":"from matplotlib import pyplot\npyplot.plot(history.history['loss'], label='train')\npyplot.plot(history.history['val_loss'], label='test')\npyplot.legend()\npyplot.show()\n\npyplot.plot(history.history['accuracy'], label='train')\npyplot.plot(history.history['val_accuracy'], label='test')\npyplot.legend()\npyplot.show()","metadata":{"execution":{"iopub.status.busy":"2024-06-10T07:43:30.845075Z","iopub.execute_input":"2024-06-10T07:43:30.845519Z","iopub.status.idle":"2024-06-10T07:43:31.868514Z","shell.execute_reply.started":"2024-06-10T07:43:30.845483Z","shell.execute_reply":"2024-06-10T07:43:31.863496Z"},"trusted":true},"execution_count":37,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure 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"},"metadata":{}}]},{"cell_type":"code","source":"from tqdm import tqdm\nfrom rouge_score import rouge_scorer\n\ndef evaluate_model(test_texts, test_summaries, max_len_text=config.MAX_LEN, max_len_summary=config.SUMMARY_LEN):\n    scorer = rouge_scorer.RougeScorer(['rouge1', 'rouge2', 'rougeL'], use_stemmer=True)\n    scores = {\n        'rouge1': {'precision': [], 'recall': [], 'fmeasure': []},\n        'rouge2': {'precision': [], 'recall': [], 'fmeasure': []},\n        'rougeL': {'precision': [], 'recall': [], 'fmeasure': []}\n    }\n    \n    for text, summary in tqdm(zip(test_texts, test_summaries), \"Evaluation\"):\n        generated_summary = summarize_text(text, max_len_text, max_len_summary)\n        score = scorer.score(summary, generated_summary)\n        \n        for metric in ['rouge1', 'rouge2', 'rougeL']:\n            scores[metric]['precision'].append(score[metric].precision)\n            scores[metric]['recall'].append(score[metric].recall)\n            scores[metric]['fmeasure'].append(score[metric].fmeasure)\n    \n    print(\"Calculating Mean Scores\")\n    mean_scores = {\n        metric: {\n            'precision': np.mean(values['precision']),\n            'recall': np.mean(values['recall']),\n            'fmeasure': np.mean(values['fmeasure'])\n        }\n        for metric, values in scores.items()\n    }\n    \n    print(mean_scores)\n    return mean_scores\n\ntest_texts = test_df['ctext'].tolist()\ntest_summaries = test_df['text'].tolist()\nmean_scores = evaluate_model(test_texts, test_summaries)\n\nprint(\"ROUGE scores:\")\nfor metric, values in mean_scores.items():\n    print(f\"{metric.upper()}: Precision: {values['precision']:.4f}, Recall: {values['recall']:.4f}, F-measure: {values['fmeasure']:.4f}\")\n","metadata":{"execution":{"iopub.status.busy":"2024-06-10T08:01:24.473870Z","iopub.execute_input":"2024-06-10T08:01:24.474207Z","iopub.status.idle":"2024-06-10T10:06:48.354573Z","shell.execute_reply.started":"2024-06-10T08:01:24.474178Z","shell.execute_reply":"2024-06-10T10:06:48.353295Z"},"trusted":true},"execution_count":42,"outputs":[{"name":"stderr","text":"Evaluation: 0it [00:00, ?it/s]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 3s 3s/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 97ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 1it [00:09,  9.99s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 106ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 113ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 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97ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 2it [00:19,  9.60s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 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96ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 105ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 100ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 20it [05:18, 20.10s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 104ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 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122ms/step\n1/1 [==============================] - 0s 108ms/step\n1/1 [==============================] - 0s 115ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 27it [07:42, 22.86s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 117ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 105ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 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100ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 112ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 101ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 28it [07:53, 19.34s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 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98ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 94ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 29it [08:01, 16.03s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 109ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 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112ms/step\n1/1 [==============================] - 0s 106ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 30it [08:13, 14.85s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 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100ms/step\n1/1 [==============================] - 0s 95ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 35it [08:54,  9.96s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 107ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 105ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 106ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 104ms/step\n1/1 [==============================] - 0s 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99ms/step\n1/1 [==============================] - 0s 94ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 38it [09:46, 16.79s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 121ms/step\n1/1 [==============================] - 0s 113ms/step\n1/1 [==============================] - 0s 107ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 105ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 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101ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 105ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 97ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 39it [09:56, 14.65s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 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96ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 40it [10:05, 13.04s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 105ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 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103ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 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96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 104ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 104ms/step\n1/1 [==============================] - 0s 118ms/step\n1/1 [==============================] - 0s 102ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 42it [10:22, 10.45s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 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95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 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103ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 96ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 43it [10:37, 11.92s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 104ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 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99ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 105ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 112ms/step\n1/1 [==============================] - 0s 108ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 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[==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 106ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 100ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 46it [11:01,  9.42s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 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102ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 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8.58s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] 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98ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 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95ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 95ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 51it [12:10, 13.29s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 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96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 53it [12:21,  9.19s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 118ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 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17.43s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 104ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 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102ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 61it [14:28, 12.78s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 105ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 113ms/step\n1/1 [==============================] - 0s 119ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 109ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 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95ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 66it [16:20, 20.23s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 108ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 117ms/step\n1/1 [==============================] - 0s 117ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 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100ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 95ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 70it [16:50, 10.34s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 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108ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 72it [17:00,  7.58s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 111ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 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95ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 113ms/step\n1/1 [==============================] - 0s 106ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 104ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 74it [17:40, 14.73s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 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95ms/step\n1/1 [==============================] - 0s 100ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 75it [17:45, 12.04s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 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98ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 95ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 89it [21:26,  9.22s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 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96ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 102it [25:11, 15.93s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 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96ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 100ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 103it [25:20, 14.05s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 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96ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 105it [25:39, 11.89s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 104ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 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100ms/step\n1/1 [==============================] - 0s 100ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 106it [25:47, 10.96s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 107ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 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95ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 108it [26:28, 14.90s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 116ms/step\n1/1 [==============================] - 0s 106ms/step\n1/1 [==============================] - 0s 117ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 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100ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 111it [27:15, 17.34s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 106ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 104ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 107ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 107ms/step\n1/1 [==============================] - 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97ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 118it [29:15, 18.14s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 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102ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 120it [29:28, 12.34s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 93ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 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95ms/step\n1/1 [==============================] - 0s 102ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 123it [30:12, 11.97s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 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100ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 124it [30:21, 10.95s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 105ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 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95ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 125it [30:37, 12.47s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 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106ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 129it [31:42, 15.02s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 113ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 105ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 107ms/step\n1/1 [==============================] - 0s 109ms/step\n1/1 [==============================] - 0s 109ms/step\n1/1 [==============================] - 0s 105ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 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97ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 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101ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 97ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 131it [32:01, 12.04s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 105ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 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98ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 93ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 97ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 133it [32:21, 11.08s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 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100ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 116ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 95ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 134it [32:53, 17.45s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 104ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 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95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 105ms/step\n1/1 [==============================] - 0s 101ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 135it [33:11, 17.60s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 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96ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 121ms/step\n1/1 [==============================] - 0s 109ms/step\n1/1 [==============================] - 0s 128ms/step\n1/1 [==============================] - 0s 104ms/step\n1/1 [==============================] - 0s 109ms/step\n1/1 [==============================] - 0s 106ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 136it [33:20, 15.13s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 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100ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 137it [33:27, 12.55s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 105ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 99ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 138it [33:32, 10.49s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 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[==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 104ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 139it [33:41,  9.85s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 104ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 105ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 106ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 105ms/step\n1/1 [==============================] - 0s 105ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 106ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 110ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 107ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 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100ms/step\n1/1 [==============================] - 0s 110ms/step\n1/1 [==============================] - 0s 104ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 104ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 107ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 105ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 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102ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 108ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 100ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 143it [34:36, 15.13s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 144it [34:36, 10.78s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 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94ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 145it [34:46, 10.56s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 104ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 105ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 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100ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 100ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 148it [35:07,  8.14s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 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96ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 149it [35:38, 14.96s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 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12.94s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 104ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 95ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 151it [35:51, 10.54s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 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119ms/step\n1/1 [==============================] - 0s 119ms/step\n1/1 [==============================] - 0s 107ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 94ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 152it [35:57,  9.00s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 104ms/step\n1/1 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103ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 104ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 104ms/step\n1/1 [==============================] - 0s 107ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 104ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 105ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 110ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 105ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 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[==============================] - 0s 106ms/step\n1/1 [==============================] - 0s 101ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 155it [36:24,  9.30s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 106ms/step\n1/1 [==============================] - 0s 105ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 106ms/step\n1/1 [==============================] - 0s 105ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 104ms/step\n1/1 [==============================] - 0s 117ms/step\n1/1 [==============================] - 0s 105ms/step\n1/1 [==============================] - 0s 107ms/step\n1/1 [==============================] - 0s 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96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 107ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 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96ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 104ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 109ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 101ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 157it [37:03, 13.33s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 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[==============================] - 0s 104ms/step\n1/1 [==============================] - 0s 106ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 96ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 158it [37:14, 12.57s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 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95ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 95ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 159it [37:18, 10.16s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 108ms/step\n1/1 [==============================] - 0s 112ms/step\n1/1 [==============================] - 0s 105ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 94ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 160it [37:25,  9.23s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 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99ms/step\n1/1 [==============================] - 0s 107ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 105ms/step\n1/1 [==============================] - 0s 110ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 161it [37:33,  8.64s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 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[==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 115ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 100ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 162it [37:43,  9.22s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 98ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 163it [37:50,  8.52s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 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106ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 104ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 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96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 95ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 167it [38:45, 14.94s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 104ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 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95ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 105ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 95ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 168it [38:53, 12.89s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 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98ms/step\n1/1 [==============================] - 0s 93ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 115ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 98ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 169it [39:02, 11.69s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 123ms/step\n1/1 [==============================] - 0s 104ms/step\n1/1 [==============================] - 0s 108ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 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99ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 105ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 100ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 170it [39:15, 11.86s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 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95ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 173it [39:47, 11.13s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 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16.39s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 108ms/step\n1/1 [==============================] - 0s 106ms/step\n1/1 [==============================] - 0s 107ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 104ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 105ms/step\n1/1 [==============================] - 0s 107ms/step\n1/1 [==============================] - 0s 106ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 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107ms/step\n1/1 [==============================] - 0s 104ms/step\n1/1 [==============================] - 0s 110ms/step\n1/1 [==============================] - 0s 108ms/step\n1/1 [==============================] - 0s 105ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 105ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 106ms/step\n1/1 [==============================] - 0s 107ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 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[==============================] - 0s 102ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 179it [40:58, 10.30s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 106ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 104ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 108ms/step\n1/1 [==============================] - 0s 104ms/step\n1/1 [==============================] - 0s 106ms/step\n1/1 [==============================] - 0s 107ms/step\n1/1 [==============================] - 0s 108ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 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[==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 181it [41:38, 16.02s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 109ms/step\n1/1 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94ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 100ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 182it [41:44, 13.16s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 93ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 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11.29s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 116ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 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95ms/step\n1/1 [==============================] - 0s 95ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 184it [41:57,  9.70s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 104ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 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102ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 185it [42:02,  8.41s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 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97ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 188it [42:25,  7.80s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 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95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 115ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 189it [42:33,  7.77s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 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106ms/step\n1/1 [==============================] - 0s 105ms/step\n1/1 [==============================] - 0s 104ms/step\n1/1 [==============================] - 0s 109ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 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95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 93ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 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93ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 100ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 193it [43:07,  8.47s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 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95ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 95ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 196it [43:33,  8.12s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 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100ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 198it [44:20, 15.28s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 104ms/step\n1/1 [==============================] - 0s 106ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 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95ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 111ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 100ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 199it [44:33, 14.60s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 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97ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 205it [46:20, 17.80s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 109ms/step\n1/1 [==============================] - 0s 117ms/step\n1/1 [==============================] - 0s 111ms/step\n1/1 [==============================] - 0s 104ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 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95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 95ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 209it [47:17, 17.61s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 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95ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 213it [48:12, 13.41s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 104ms/step\n1/1 [==============================] - 0s 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11.75s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 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95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 95ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 217it [48:50, 10.01s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 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96ms/step\n1/1 [==============================] - 0s 111ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 97ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 219it [49:08,  9.41s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] 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[==============================] - 0s 96ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 220it [49:15,  8.47s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 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103ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 104ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 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[==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 103ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 223it [49:52, 10.59s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 106ms/step\n1/1 [==============================] - 0s 104ms/step\n1/1 [==============================] - 0s 109ms/step\n1/1 [==============================] - 0s 107ms/step\n1/1 [==============================] - 0s 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99ms/step\n1/1 [==============================] - 0s 105ms/step\n1/1 [==============================] - 0s 106ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 104ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 97ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 224it [50:08, 12.14s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 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96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 226it [50:20,  8.96s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 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108ms/step\n1/1 [==============================] - 0s 107ms/step\n1/1 [==============================] - 0s 104ms/step\n1/1 [==============================] - 0s 109ms/step\n1/1 [==============================] - 0s 98ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 227it [50:34, 10.50s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 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95ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 104ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 228it [50:45, 10.70s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 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96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 104ms/step\n1/1 [==============================] - 0s 121ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 229it [50:55, 10.46s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 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96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 101ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 230it [51:01,  8.90s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 114ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 117ms/step\n1/1 [==============================] - 0s 108ms/step\n1/1 [==============================] - 0s 108ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 94ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 231it [51:07,  8.12s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 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94ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 108ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 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97ms/step\n1/1 [==============================] - 0s 99ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 233it [51:20,  7.26s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 105ms/step\n1/1 [==============================] - 0s 104ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 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100ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 114ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 236it [52:07, 14.69s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 108ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 113ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 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96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 94ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 237it [52:15, 12.42s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 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93ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 93ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 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96ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 97ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 240it [52:35,  8.94s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 105ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 119ms/step\n1/1 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109ms/step\n1/1 [==============================] - 0s 113ms/step\n1/1 [==============================] - 0s 121ms/step\n1/1 [==============================] - 0s 105ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 95ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 245it [53:44, 16.35s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 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13.19s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 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100ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 255it [56:43, 23.80s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 104ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 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101ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 101ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 260it [58:05, 19.37s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 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21.95s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 107ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 105ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 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104ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 105ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 104ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 269it [1:00:31, 13.35s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 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96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 94ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 271it [1:00:47, 10.37s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 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96ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 105ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 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103ms/step\n1/1 [==============================] - 0s 96ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 274it [1:01:31, 12.89s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 121ms/step\n1/1 [==============================] - 0s 146ms/step\n1/1 [==============================] - 0s 111ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 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95ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 292it [1:07:00, 22.36s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 109ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 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96ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 293it [1:07:10, 18.56s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 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[==============================] - 0s 100ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 294it [1:07:21, 16.25s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 111ms/step\n1/1 [==============================] - 0s 115ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 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100ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 99ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 295it [1:07:29, 13.67s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 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[==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 101ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 298it [1:07:54, 10.74s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 107ms/step\n1/1 [==============================] - 0s 106ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 105ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 107ms/step\n1/1 [==============================] - 0s 106ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 108ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 104ms/step\n1/1 [==============================] - 0s 107ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 106ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 101ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 299it [1:07:59,  9.06s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 109ms/step\n1/1 [==============================] - 0s 108ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 109ms/step\n1/1 [==============================] - 0s 134ms/step\n1/1 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101ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 94ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 300it [1:08:31, 15.81s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 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101ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 95ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 301it [1:08:38, 13.42s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 93ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 93ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 93ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 93ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 112ms/step\n1/1 [==============================] - 0s 93ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 106ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 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96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 303it [1:08:49,  9.34s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 122ms/step\n1/1 [==============================] - 0s 113ms/step\n1/1 [==============================] - 0s 113ms/step\n1/1 [==============================] - 0s 104ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 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95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 307it [1:09:25,  9.10s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 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[==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 96ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 308it [1:09:38, 10.43s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 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108ms/step\n1/1 [==============================] - 0s 107ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 104ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 104ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 95ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 309it [1:09:57, 13.03s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 93ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 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99ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 98ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 327it [1:15:17, 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101ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 334it [1:17:46, 20.64s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 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94ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 93ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 94ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 337it [1:18:08, 11.72s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 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93ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 106ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 94ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 345it [1:20:42, 18.85s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 93ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 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104ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 350it [1:21:26,  9.46s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 105ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 107ms/step\n1/1 [==============================] - 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[==============================] - 0s 102ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 351it [1:21:32,  8.43s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 108ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 106ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 107ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 105ms/step\n1/1 [==============================] - 0s 107ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 107ms/step\n1/1 [==============================] - 0s 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100ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 93ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 94ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 353it [1:22:19, 16.67s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 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95ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 97ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 354it [1:22:26, 13.89s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 114ms/step\n1/1 [==============================] - 0s 110ms/step\n1/1 [==============================] - 0s 108ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 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96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 95ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 355it [1:22:37, 12.98s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 [==============================] - 0s 94ms/step\n1/1 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97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 105ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 97ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 358it [1:22:58,  9.18s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 123ms/step\n1/1 [==============================] - 0s 112ms/step\n1/1 [==============================] - 0s 104ms/step\n1/1 [==============================] - 0s 105ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 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[==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 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98ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 104ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 117ms/step\n1/1 [==============================] - 0s 105ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 100ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 361it [1:23:23,  8.41s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 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117ms/step\n1/1 [==============================] - 0s 108ms/step\n1/1 [==============================] - 0s 114ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 104ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 106ms/step\n1/1 [==============================] - 0s 102ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 362it [1:23:32,  8.48s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 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0s 101ms/step\n1/1 [==============================] - 0s 104ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 111ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 99ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 365it [1:23:53,  7.38s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 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97ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 99ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 366it [1:24:01,  7.59s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 121ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 109ms/step\n1/1 [==============================] - 0s 130ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 98ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 367it [1:24:06,  6.81s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 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97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 117ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 113ms/step\n1/1 [==============================] - 0s 111ms/step\n1/1 [==============================] - 0s 107ms/step\n1/1 [==============================] - 0s 104ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 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102ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 97ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 368it [1:24:37, 14.14s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 105ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 104ms/step\n1/1 [==============================] - 0s 107ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 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113ms/step\n1/1 [==============================] - 0s 106ms/step\n1/1 [==============================] - 0s 110ms/step\n1/1 [==============================] - 0s 106ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 380it [1:27:11, 12.72s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 110ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 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96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 381it [1:27:20, 11.36s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 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[==============================] - 0s 100ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 382it [1:27:25,  9.43s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 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97ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 397it [1:32:27, 17.22s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 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103ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 105ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 96ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 399it [1:32:47, 13.30s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 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96ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 106ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 405it [1:34:20, 11.62s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 107ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 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96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 429it [1:40:59, 15.19s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 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96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 99ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 431it [1:41:17, 11.75s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 109ms/step\n1/1 [==============================] - 0s 114ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 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96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 434it [1:42:25, 20.86s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 104ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 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101ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 439it [1:43:09, 10.88s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 104ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 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9.29s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 107ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 105ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 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[==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 97ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 442it [1:43:33,  9.31s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 104ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 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101ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 101ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 447it [1:45:00, 14.97s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 105ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 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101ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 453it [1:45:55, 10.34s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 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97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 105ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 98ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 460it [1:47:53, 14.50s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 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103ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 96ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 478it [1:53:04, 18.19s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 107ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 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15.34s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 104ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 104ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 105ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 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16.34s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 105ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 104ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 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11.42s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 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[==============================] - 0s 97ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 485it [1:54:28, 10.74s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 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0s 103ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 97ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 486it [1:54:34,  9.56s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 104ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 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[==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 97ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 487it [1:54:41,  8.74s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 104ms/step\n1/1 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97ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 107ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 494it [1:56:22, 12.90s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 104ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 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98ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 104ms/step\n1/1 [==============================] - 0s 101ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 495it [1:56:30, 11.48s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 112ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 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99ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 102ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 496it [1:56:41, 11.40s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 116ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 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102ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 97ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 497it [1:57:07, 15.63s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 104ms/step\n1/1 [==============================] - 0s 104ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 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0s 99ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 107ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 498it [1:57:19, 14.68s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 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0s 97ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 106ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 98ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 499it [1:57:29, 13.13s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 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- 0s 105ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 103ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 504it [1:58:17, 10.41s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 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101ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 510it [1:59:35, 11.37s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 106ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 108ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 105ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 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[==============================] - 0s 102ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 511it [1:59:47, 11.60s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 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96ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 104ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 105ms/step\n1/1 [==============================] - 0s 101ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 512it [1:59:55, 10.64s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 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15.38s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 106ms/step\n1/1 [==============================] - 0s 104ms/step\n1/1 [==============================] - 0s 105ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 106ms/step\n1/1 [==============================] - 0s 109ms/step\n1/1 [==============================] - 0s 108ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 106ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 105ms/step\n1/1 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[==============================] - 0s 98ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 523it [2:03:11, 10.72s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 118ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 104ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 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96ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 112ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 97ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 524it [2:03:20, 10.22s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 107ms/step\n1/1 [==============================] - 0s 113ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 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8.70s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 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105ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 110ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 98ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 526it [2:03:32,  8.20s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 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[==============================] - 0s 118ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 105ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 528it [2:03:47,  7.87s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 105ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 104ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 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101ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 95ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 104ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 100ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 529it [2:03:55,  7.96s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 106ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 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97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 117ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 104ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 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96ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 97ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 531it [2:04:13,  8.29s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 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100ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 532it [2:04:40, 14.04s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 117ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 99ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 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13.23s/it]","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 119ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 106ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 102ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 101ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 103ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 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0s 96ms/step\n1/1 [==============================] - 0s 100ms/step\n1/1 [==============================] - 0s 97ms/step\n1/1 [==============================] - 0s 96ms/step\n1/1 [==============================] - 0s 98ms/step\n1/1 [==============================] - 0s 103ms/step\n","output_type":"stream"},{"name":"stderr","text":"Evaluation: 534it [2:05:23, 14.09s/it]","output_type":"stream"},{"name":"stdout","text":"Calculating Mean Scores\n{'rouge1': {'precision': 0.18237303322499374, 'recall': 0.1730843023951982, 'fmeasure': 0.16577834182301804}, 'rouge2': {'precision': 0.027540391066902876, 'recall': 0.02820362230593347, 'fmeasure': 0.02637000058962339}, 'rougeL': {'precision': 0.14792701880747436, 'recall': 0.14057317974627012, 'fmeasure': 0.13403174655251932}}\nROUGE scores:\nROUGE1: Precision: 0.1824, Recall: 0.1731, F-measure: 0.1658\nROUGE2: Precision: 0.0275, Recall: 0.0282, F-measure: 0.0264\nROUGEL: Precision: 0.1479, Recall: 0.1406, F-measure: 0.1340\n","output_type":"stream"},{"name":"stderr","text":"\n","output_type":"stream"}]},{"cell_type":"code","source":"","metadata":{"execution":{"iopub.status.busy":"2024-06-10T10:06:48.356337Z","iopub.execute_input":"2024-06-10T10:06:48.356645Z","iopub.status.idle":"2024-06-10T10:06:48.365636Z","shell.execute_reply.started":"2024-06-10T10:06:48.356619Z","shell.execute_reply":"2024-06-10T10:06:48.364488Z"},"trusted":true},"execution_count":43,"outputs":[{"name":"stdout","text":"ROUGE scores:\nROUGE1: Precision: 0.1824, Recall: 0.1731, F-measure: 0.1658\nROUGE2: Precision: 0.0275, Recall: 0.0282, F-measure: 0.0264\nROUGEL: Precision: 0.1479, Recall: 0.1406, F-measure: 0.1340\n","output_type":"stream"}]},{"cell_type":"markdown","source":"## T5-GRU","metadata":{}},{"cell_type":"code","source":"import tensorflow as tf\n\n# Define inputs\ninput_ids = tf.keras.Input(shape=(config.MAX_LEN,), dtype=tf.int32, name=\"input_ids\")\nattention_mask = tf.keras.Input(shape=(config.MAX_LEN,), dtype=tf.int32, name=\"attention_mask\")\ndecoder_inputs = tf.keras.Input(shape=(config.SUMMARY_LEN,), dtype=tf.int32, name=\"decoder_inputs\")\n\n# Encoder model (T5)\nencoder_model = TFT5EncoderModel.from_pretrained('t5-base')\nencoder_outputs = encoder_model(input_ids, attention_mask=attention_mask).last_hidden_state\n\n# Embedding layer for decoder inputs\nembedding_layer = tf.keras.layers.Embedding(\n    input_dim=vocab_size, output_dim=300, \n    input_length=config.SUMMARY_LEN, trainable=False, \n    name=\"embedding\"\n)\ndecoder_inputs_embedded = embedding_layer(decoder_inputs)\n\n# GRU layer in decoder\ndecoder_gru = tf.keras.layers.GRU(768, return_sequences=True, return_state=True, name=\"decoder_gru\")\ndecoder_outputs, _ = decoder_gru(\n    decoder_inputs_embedded, initial_state=encoder_outputs[:, 0, :]\n)\n\nattn_layer = AttentionLayer(name='attention_layer')\nattn_out, attn_states = attn_layer([encoder_outputs, decoder_outputs])\n\ndecoder_concat_input = tf.keras.layers.Concatenate(axis=-1, name='concat_layer')([decoder_outputs, attn_out])\n\n# Dense layer for predictions\ndecoder_dense = tf.keras.layers.TimeDistributed(tf.keras.layers.Dense(vocab_size, activation='softmax', name=\"TD_dec_dense\"))\ndecoder_outputs = decoder_dense(decoder_concat_input)\n\n# Define model\ngru_model = tf.keras.Model(inputs=[input_ids, attention_mask, decoder_inputs], outputs=decoder_outputs)\n\ngru_model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n\ngru_model.summary()\n","metadata":{},"execution_count":null,"outputs":[]}]}