mirror of
https://github.com/WallyS02/Song-Lyrics-Generator.git
synced 2024-10-03 11:26:10 +00:00
204 lines
9.4 KiB
Python
204 lines
9.4 KiB
Python
import os.path
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import numpy as np
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from keras import Sequential
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from keras.callbacks import ModelCheckpoint, EarlyStopping, CSVLogger
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from keras.layers import LSTM, Dense, GRU, Embedding
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from keras.metrics import Precision, Recall
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from keras_nlp.metrics import EditDistance
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#from keras_nlp.metrics import Perplexity
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from data_processor import DataProcessor
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from quality_evaluator import QualityEvaluatorCallback
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from song_generator import GeneratorCallback
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class DefaultLSTMModel:
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def __init__(self, X, y, v_X, v_y):
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self.keras_model = Sequential()
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self.keras_model.add(LSTM(256, input_shape=(X.shape[1], X.shape[2]), return_sequences=True, dropout=0.2))
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self.keras_model.add(LSTM(256, dropout=0.2))
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self.keras_model.add(Dense(y.shape[1], activation='softmax'))
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self.keras_model.compile(loss='categorical_crossentropy',
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metrics=['accuracy', Precision(), Recall(name="recall")],
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optimizer='adam')
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self.X = X
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self.y = y
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self.validation_X = v_X
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self.validation_y = v_y
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def train(self, generator=None, max_epochs=512):
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filepath = os.path.join("trained_models", "default-lstm-weights.hdf5")
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checkpoint = ModelCheckpoint(filepath, monitor='loss', verbose=1, save_best_only=True, mode='min')
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early_stop = EarlyStopping(monitor="loss", min_delta=0.01, patience=64)
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callbacks_list = [checkpoint, early_stop, CSVLogger('default_lstm.log')]
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if generator:
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callbacks_list.append(GeneratorCallback(generator))
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self.keras_model.fit(self.X, self.y, epochs=max_epochs, batch_size=64, callbacks=callbacks_list,
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validation_data=(self.validation_X, self.validation_y))
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class BetaLSTMModel:
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def __init__(self, X, y, v_X, v_y):
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self.keras_model = Sequential()
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self.keras_model.add(LSTM(256, input_shape=(X.shape[1], X.shape[2]), return_sequences=True, dropout=0.36))
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self.keras_model.add(LSTM(256, dropout=0.2))
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self.keras_model.add(Dense(y.shape[1], activation='softmax'))
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self.keras_model.compile(loss='categorical_crossentropy',
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metrics=['accuracy', Precision(), Recall(name="recall")],
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optimizer='adam')
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self.X = X
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self.y = y
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self.validation_X = v_X
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self.validation_y = v_y
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def train(self, generator=None, max_epochs=512):
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filepath = os.path.join("trained_models", "beta-lstm-weights.hdf5")
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checkpoint = ModelCheckpoint(filepath, monitor='loss', verbose=1, save_best_only=True, mode='min')
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early_stop = EarlyStopping(monitor="loss", min_delta=0.01, patience=64)
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callbacks_list = [checkpoint, early_stop, CSVLogger('beta_lstm.log')]
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if generator:
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callbacks_list.append(GeneratorCallback(generator))
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self.keras_model.fit(self.X, self.y, epochs=max_epochs, batch_size=64, callbacks=callbacks_list,
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validation_data=(self.validation_X, self.validation_y))
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class GammaLSTMModel:
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def __init__(self, X, y, v_X, v_y):
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self.keras_model = Sequential()
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self.keras_model.add(LSTM(512, input_shape=(X.shape[1], X.shape[2]), return_sequences=True, dropout=0.2))
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self.keras_model.add(LSTM(512, dropout=0.2))
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self.keras_model.add(Dense(y.shape[1], activation='softmax'))
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self.keras_model.compile(loss='categorical_crossentropy',
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metrics=['accuracy', Precision(), Recall(name="recall")],
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optimizer='adam')
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self.X = X
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self.y = y
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self.validation_X = v_X
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self.validation_y = v_y
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def train(self, generator=None, max_epochs=512):
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filepath = os.path.join("trained_models", "gamma-lstm-weights.hdf5")
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checkpoint = ModelCheckpoint(filepath, monitor='loss', verbose=1, save_best_only=True, mode='min')
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early_stop = EarlyStopping(monitor="loss", min_delta=0.01, patience=64)
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#quality_eval = QualityEvaluatorCallback(self.validation_X, self.validation_y, self.keras_model)
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callbacks_list = [checkpoint, early_stop, CSVLogger('gamma_lstm.log')]
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if generator:
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callbacks_list.append(GeneratorCallback(generator))
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self.keras_model.fit(self.X, self.y, epochs=max_epochs, batch_size=64, callbacks=callbacks_list,
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validation_data=(self.validation_X, self.validation_y))
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class DefaultGRUModel:
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def __init__(self, X, y, v_X, v_y):
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self.keras_model = Sequential()
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self.keras_model.add(GRU(256, input_shape=(X.shape[1], X.shape[2]), return_sequences=True, dropout=0.2))
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self.keras_model.add(GRU(256, dropout=0.2))
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self.keras_model.add(Dense(y.shape[1], activation='softmax'))
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self.keras_model.compile(loss='categorical_crossentropy',
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metrics=['accuracy', Precision(), Recall(name="recall")],
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optimizer='adam')
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self.X = X
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self.y = y
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self.validation_X = v_X
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self.validation_y = v_y
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def train(self, generator=None, max_epochs=512):
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filepath = os.path.join("trained_models", "default-gru-weights.hdf5")
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checkpoint = ModelCheckpoint(filepath, monitor='loss', verbose=1, save_best_only=True, mode='min')
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early_stop = EarlyStopping(monitor="loss", min_delta=0.01, patience=64)
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callbacks_list = [checkpoint, early_stop, CSVLogger('default_gru.log')]
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if generator:
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callbacks_list.append(GeneratorCallback(generator))
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self.keras_model.fit(self.X, self.y, epochs=max_epochs, batch_size=64, callbacks=callbacks_list,
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validation_data=(self.validation_X, self.validation_y))
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class BetaGRUModel:
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def __init__(self, X, y, v_X, v_y):
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self.keras_model = Sequential()
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self.keras_model.add(GRU(128, input_shape=(X.shape[1], X.shape[2]), return_sequences=True, dropout=0.2))
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self.keras_model.add(GRU(128, dropout=0.2))
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self.keras_model.add(Dense(y.shape[1], activation='softmax'))
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self.keras_model.compile(loss='categorical_crossentropy',
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metrics=['accuracy', Precision(), Recall(name="recall")],
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optimizer='adam')
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self.X = X
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self.y = y
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self.validation_X = v_X
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self.validation_y = v_y
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def train(self, generator=None, max_epochs=512):
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filepath = os.path.join("trained_models", "beta-gru-weights.hdf5")
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checkpoint = ModelCheckpoint(filepath, monitor='loss', verbose=1, save_best_only=True, mode='min')
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early_stop = EarlyStopping(monitor="loss", min_delta=0.01, patience=64)
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callbacks_list = [checkpoint, early_stop, CSVLogger('beta_gru.log')]
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if generator:
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callbacks_list.append(GeneratorCallback(generator))
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self.keras_model.fit(self.X, self.y, epochs=max_epochs, batch_size=64, callbacks=callbacks_list,
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validation_data=(self.validation_X, self.validation_y))
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class OmicronLSTM:
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def __init__(self, X, y, data_processor: DataProcessor, v_X, v_y):
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self.keras_model = Sequential()
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embeddings_index = {}
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f = open("glove.6B.100d.txt")
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for line in f:
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values = line.split()
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word = values[0]
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coefficients = np.asarray(values[1:], dtype="float32")
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embeddings_index[word] = coefficients
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f.close()
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word_index = data_processor.tokenizer.word_index
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max_length = data_processor.max_length(data_processor.train_lyrics)
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embedding_matrix = np.random.normal(0, 1, size=(len(word_index) + 1, 100))
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for word, i in word_index.items():
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embedding_vector = embeddings_index.get(word)
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if embedding_vector is not None:
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# words not found in embedding index will be all-zeros.
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embedding_matrix[i] = embedding_vector
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embedding_layer = Embedding(input_dim=len(word_index) + 1,
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output_dim=100,
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weights=[embedding_matrix],
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input_length=max_length,
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trainable=True)
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self.keras_model.add(embedding_layer)
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self.keras_model.add(LSTM(256, input_shape=(100, 1), return_sequences=True, dropout=0.2))
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self.keras_model.add(LSTM(256, dropout=0.2))
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self.keras_model.add(Dense(y.shape[1], activation='softmax'))
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self.keras_model.compile(loss='categorical_crossentropy',
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metrics=['accuracy', Precision(), Recall(name="recall")],
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optimizer='adam')
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self.X = X
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self.y = y
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self.validation_X = v_X
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self.validation_y = v_y
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def train(self, generator=None, max_epochs=512):
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filepath = os.path.join("trained_models", "trained_models/omicron-lstm-weights.hdf5")
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checkpoint = ModelCheckpoint(filepath, monitor='loss', verbose=1, save_best_only=True, mode='min')
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early_stop = EarlyStopping(monitor="loss", min_delta=0.01, patience=64)
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callbacks_list = [checkpoint, early_stop, CSVLogger('omicron_lstm.log')]
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if generator:
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callbacks_list.append(GeneratorCallback(generator))
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self.keras_model.fit(self.X, self.y, epochs=max_epochs, batch_size=64, callbacks=callbacks_list,
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validation_data=(self.validation_X, self.validation_y))
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