def get_uncompiled_model(self):
self.correct_params()
last_lay_num = self.num_of_layers - 1
self.model = Sequential()
if self.num_of_layers == 1:
self.model.add(
GRU(self.num_of_neurons[0],
input_shape=(1, self.num_of_words),
activation=self.activation_functions[0]))
else:
self.model.add(
GRU(self.num_of_neurons[0],
input_shape=(1, self.num_of_words),
return_sequences=True,
activation=self.activation_functions[0]))
for i in range(1, last_lay_num):
if self.dropouts[i]:
self.model.add(Dropout(rate=self.dropout_values[i]))
self.model.add(
GRU(self.num_of_neurons[i],
return_sequences=True,
activation=self.activation_functions[i]))
if self.num_of_layers != 1:
self.model.add(
GRU(self.num_of_neurons[last_lay_num],
activation=self.activation_functions[last_lay_num]))
#self.model.add(GRU(self.num_of_neurons[last_lay_num], activation=self.activation_functions[last_lay_num]))
self.model.add(Dense(self.topic_nums, activation='softmax'))
return self.model
def get_uncompiled_model(self):
self.correct_params()
last_lay_num = self.num_of_layers - 1
self.model = Sequential()
self.model.add(Embedding(self.num_of_words, self.embedding_dim))
if self.num_of_layers != 1:
self.model.add(
LSTM(self.num_of_neurons[0],
return_sequences=True,
activation=self.activation_functions[0]))
for i in range(1, last_lay_num):
if self.dropouts[i]:
self.model.add(Dropout(rate=self.dropout_values[i]))
self.model.add(
LSTM(self.num_of_neurons[i],
return_sequences=True,
activation=self.activation_functions[i]))
self.model.add(
LSTM(self.num_of_neurons[last_lay_num],
activation=self.activation_functions[last_lay_num]))
self.model.add(Dense(self.topic_nums, activation='softmax'))
self.model.layers[0].set_weights([
get_embedding_matrix(self.num_of_words, self.embedding_dim,
self.tokenizer.word_index)
])
self.model.layers[0].trainable = False
return self.model
def __init__(self):
super().__init__()
self.model = Sequential()
self.model_name = "GRU-"
self.activation_functions = 'tanh'
self.train_text_generator = TrainingTextGeneratorRNN
self.preprocess = True
def __init__(self):
super().__init__()
self.model = Sequential()
self.model_name = "Bidirectional-"
self.activation_functions = 'tanh'
self.train_text_generator = TrainingTextGeneratorRNN
self.inner_network_type = 'LSTM'
self.preprocess = True
def __init__(self):
super().__init__()
self.model = Sequential()
self.model_name = "LSTM-Embedding-"
self.activation_functions = 'tanh'
self.train_text_generator = TrainingTextGeneratorRNNEmbedding
self.embedding_matrix = []
self.embedding_dim = 100
self.preprocess = True
self.tokenizer = None
def get_uncompiled_static_model(self):
if self.topic_nums is None or self.enhanced_num_of_topics is None or self.num_of_words is None:
raise Exception(
"Base argument were not set. Network cannot be created.")
self.model = Sequential()
self.model.add(
GRU(self.enhanced_num_of_topics,
input_shape=(1, self.num_of_words),
return_sequences=True))
self.model.add(GRU(self.enhanced_num_of_topics))
self.model.add(Dense(self.topic_nums, activation='softmax'))
return self.model
def get_uncompiled_static_model(self):
if self.topic_nums is None or self.enhanced_num_of_topics is None or self.num_of_words is None:
raise Exception(
"Base argument were not set. Network cannot be created.")
self.model = Sequential()
self.model.add(Embedding(self.num_of_words, self.embedding_dim))
self.model.add(
LSTM(self.enhanced_num_of_topics,
input_shape=(1, self.num_of_words),
return_sequences=True))
self.model.add(LSTM(self.enhanced_num_of_topics))
self.model.add(Dense(self.topic_nums, activation='softmax'))
self.model.layers[0].set_weights([self.embedding_matrix])
self.model.layers[0].trainable = False
return self.model
def get_uncompiled_model(self):
self.correct_params()
last_lay_num = self.num_of_layers - 1
self.model = Sequential()
self.model_name += self.inner_network_type + "-"
if self.inner_network_type.lower() == 'lstm':
inner_network = LSTM
elif self.inner_network_type.lower() == 'gru':
inner_network = GRU
else:
raise Exception('Unknown network type')
if self.num_of_layers == 1:
self.model.add(
Bidirectional(
inner_network(self.num_of_neurons[0],
input_shape=(1, self.num_of_words),
activation=self.activation_functions[0])))
else:
self.model.add(
Bidirectional(
inner_network(self.num_of_neurons[0],
input_shape=(1, self.num_of_words),
return_sequences=True,
activation=self.activation_functions[0])))
for i in range(1, last_lay_num):
if self.dropouts[i]:
self.model.add(Dropout(rate=self.dropout_values[i]))
self.model.add(
Bidirectional(
inner_network(self.num_of_neurons[i],
return_sequences=True,
activation=self.activation_functions[i])))
if self.num_of_layers != 1:
self.model.add(
Bidirectional(
inner_network(
self.num_of_neurons[last_lay_num],
activation=self.activation_functions[last_lay_num])))
#self.model.add(Bidirectional(inner_network(self.num_of_neurons[last_lay_num], activation=self.activation_functions[last_lay_num])))
self.model.add(Dense(self.topic_nums, activation='softmax'))
return self.model
class EmbeddingLSTMModel(Model):
def __init__(self):
super().__init__()
self.model = Sequential()
self.model_name = "LSTM-Embedding-"
self.activation_functions = 'tanh'
self.train_text_generator = TrainingTextGeneratorRNNEmbedding
self.embedding_matrix = []
self.embedding_dim = 100
self.preprocess = True
self.tokenizer = None
def get_uncompiled_static_model(self):
if self.topic_nums is None or self.enhanced_num_of_topics is None or self.num_of_words is None:
raise Exception(
"Base argument were not set. Network cannot be created.")
self.model = Sequential()
self.model.add(Embedding(self.num_of_words, self.embedding_dim))
self.model.add(
LSTM(self.enhanced_num_of_topics,
input_shape=(1, self.num_of_words),
return_sequences=True))
self.model.add(LSTM(self.enhanced_num_of_topics))
self.model.add(Dense(self.topic_nums, activation='softmax'))
self.model.layers[0].set_weights([self.embedding_matrix])
self.model.layers[0].trainable = False
return self.model
def get_compiled_static_model(self):
self.get_uncompiled_static_model().compile(
optimizer=self.optimizer,
loss='categorical_crossentropy',
metrics=['accuracy'])
return self.model
def get_uncompiled_model(self):
self.correct_params()
last_lay_num = self.num_of_layers - 1
self.model = Sequential()
self.model.add(Embedding(self.num_of_words, self.embedding_dim))
if self.num_of_layers != 1:
self.model.add(
LSTM(self.num_of_neurons[0],
return_sequences=True,
activation=self.activation_functions[0]))
for i in range(1, last_lay_num):
if self.dropouts[i]:
self.model.add(Dropout(rate=self.dropout_values[i]))
self.model.add(
LSTM(self.num_of_neurons[i],
return_sequences=True,
activation=self.activation_functions[i]))
self.model.add(
LSTM(self.num_of_neurons[last_lay_num],
activation=self.activation_functions[last_lay_num]))
self.model.add(Dense(self.topic_nums, activation='softmax'))
self.model.layers[0].set_weights([
get_embedding_matrix(self.num_of_words, self.embedding_dim,
self.tokenizer.word_index)
])
self.model.layers[0].trainable = False
return self.model
def get_compiled_model(self):
self.get_uncompiled_model().compile(optimizer=self.optimizer,
loss='categorical_crossentropy',
metrics=['accuracy'])
return self.model
class GRUModel(Model):
def __init__(self):
super().__init__()
self.model = Sequential()
self.model_name = "GRU-"
self.activation_functions = 'tanh'
self.train_text_generator = TrainingTextGeneratorRNN
self.preprocess = True
def get_uncompiled_static_model(self):
if self.topic_nums is None or self.enhanced_num_of_topics is None or self.num_of_words is None:
raise Exception(
"Base argument were not set. Network cannot be created.")
self.model = Sequential()
self.model.add(
GRU(self.enhanced_num_of_topics,
input_shape=(1, self.num_of_words),
return_sequences=True))
self.model.add(GRU(self.enhanced_num_of_topics))
self.model.add(Dense(self.topic_nums, activation='softmax'))
return self.model
def get_compiled_static_model(self):
self.get_uncompiled_model().compile(optimizer='rmsprop',
loss='categorical_crossentropy',
metrics=['accuracy'])
return self.model
def get_uncompiled_model(self):
self.correct_params()
last_lay_num = self.num_of_layers - 1
self.model = Sequential()
if self.num_of_layers == 1:
self.model.add(
GRU(self.num_of_neurons[0],
input_shape=(1, self.num_of_words),
activation=self.activation_functions[0]))
else:
self.model.add(
GRU(self.num_of_neurons[0],
input_shape=(1, self.num_of_words),
return_sequences=True,
activation=self.activation_functions[0]))
for i in range(1, last_lay_num):
if self.dropouts[i]:
self.model.add(Dropout(rate=self.dropout_values[i]))
self.model.add(
GRU(self.num_of_neurons[i],
return_sequences=True,
activation=self.activation_functions[i]))
if self.num_of_layers != 1:
self.model.add(
GRU(self.num_of_neurons[last_lay_num],
activation=self.activation_functions[last_lay_num]))
#self.model.add(GRU(self.num_of_neurons[last_lay_num], activation=self.activation_functions[last_lay_num]))
self.model.add(Dense(self.topic_nums, activation='softmax'))
return self.model
def get_compiled_model(self):
self.get_uncompiled_model().compile(optimizer=self.optimizer,
loss='categorical_crossentropy',
metrics=['accuracy'])
return self.model
def __init__(self):
super().__init__()
self.model = Sequential()
self.model_name = "Dense-"
self.train_text_generator = TrainingTextGenerator
self.preprocess = True
