Spaces:
Running
Running
File size: 1,740 Bytes
3a12dd0 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 |
import pandas as pd
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split
from numpy import argmax
import tensorflow as tf
from tensorflow.keras import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.preprocessing import sequence
from tensorflow.keras.models import save_model
from tensorflow.keras.preprocessing.text import Tokenizer
import pickle
dataset = pd.read_csv(r"IMDB Dataset.csv")
dataset['sentiment'] = dataset['sentiment'].map( {'negative': 1, 'positive': 0} )
X = dataset['review'].values
y = dataset['sentiment'].values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3,random_state=42)
tokeniser = tf.keras.preprocessing.text.Tokenizer()
tokeniser.fit_on_texts(X_train)
X_train = tokeniser.texts_to_sequences(X_train)
X_test = tokeniser.texts_to_sequences(X_test)
vocab_size = len(tokeniser.word_index)+1
max_review_length = 500
X_train = sequence.pad_sequences(X_train, maxlen=max_review_length, padding = 'post')
X_test = sequence.pad_sequences(X_test, maxlen=max_review_length, padding = 'post')
n_features = X_train.shape[1]
#Modelling a sample DNN
model = Sequential()
model.add(Dense(64, activation='relu',input_shape=(500,)))
model.add(Dense(32, activation='relu'))
model.add(Dense(16, activation='relu'))
model.add(Dense(1,activation='sigmoid'))
opt=Adam(learning_rate=0.01)
model.compile(optimizer=opt, loss='binary_crossentropy', metrics=['accuracy'])
history=model.fit(X_train, y_train, epochs=50, batch_size=16)
loss, acc = model.evaluate(X_test, y_test)
model.save("dnn_model.h5")
with open("dnn_tokeniser.pkl",'wb') as file:
pickle.dump(tokeniser, file)
|