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"""
This function manages the LSTM model with lyrics only
"""
import os
from keras import Input, Model
from keras import backend as K
from keras.layers import Dense, Dropout, Embedding, Bidirectional, LSTM, Masking
from keras.optimizers import Adam
# Environment settings
IS_COLAB = (os.name == 'posix')
LOAD_DATA = not (os.name == 'posix')
if not IS_COLAB:
from rnn import RecurrentNeuralNetwork
class LSTMLyrics(RecurrentNeuralNetwork):
def __init__(self, seed, loss, metrics, optimizer, learning_rate, total_words, seq_length, vector_size,
word2vec_matrix, units):
"""
Seed - The seed used to initialize the weights
width, height, cells - used for defining the tensors used for the input images
loss, metrics, optimizer, dropout_rate - settings used for compiling the siamese model (e.g., 'Accuracy' and 'ADAM)
:return Nothing
"""
super().__init__(seed)
K.clear_session()
self.seed = seed
self.initialize_seed()
self.initialize_model(learning_rate, loss, metrics, optimizer, seq_length, total_words, units, vector_size,
word2vec_matrix)
def initialize_model(self, learning_rate, loss, metrics, optimizer, seq_length, total_words, units, vector_size,
word2vec_matrix):
"""
This function initializes the architecture and builds the model
:param learning_rate: a tuning parameter in an optimization algorithm that determines the step size
:param loss: the loss function we want to use
:param metrics: the metrics we want to use, such as Loss
:param optimizer: the optimizer function, such as Adam
:param seq_length: the length of the sequence (the sentence in this case)
:param total_words: total number of words we have (used for the output dense)
:param units: number of LSTM units
:param vector_size: the size of the embedding vector
:param word2vec_matrix: the embedding matrix
:return: Nothing
"""
lyrics_features_input = Input((seq_length,))
embedding_layer = Embedding(input_dim=total_words, # the size of the vocabulary in the text data
input_length=seq_length, # the length of input sequences
output_dim=vector_size,
# the size of the vector space in which words will be embedded
weights=[word2vec_matrix],
trainable=False,
# the model must be informed that some part of
# the data is actually padding and should be ignored.
mask_zero=True,
name='MelodiesLyrics')(lyrics_features_input)
masking_layer = Masking(mask_value=0.)(embedding_layer)
# Bidirectional Recurrent layer
b_rnn_layer = Bidirectional(LSTM(units=units, activation='relu'))(masking_layer)
dropout_layer = Dropout(0.6)(b_rnn_layer)
output_dense = Dense(units=total_words, activation='softmax')(dropout_layer)
self.model = Model(inputs=lyrics_features_input, outputs=output_dense)
if optimizer == 'adam':
optimizer = Adam(lr=learning_rate)
self.model.compile(optimizer=optimizer, loss=loss, metrics=metrics)
print("Loaded Successfully")
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