本文整理汇总了Python中keras.layers.core.RepeatVector方法的典型用法代码示例。如果您正苦于以下问题:Python core.RepeatVector方法的具体用法?Python core.RepeatVector怎么用?Python core.RepeatVector使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类keras.layers.core
的用法示例。
在下文中一共展示了core.RepeatVector方法的12个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: create
# 需要导入模块: from keras.layers import core [as 别名]
# 或者: from keras.layers.core import RepeatVector [as 别名]
def create(self):
self.textual_embedding(self, mask_zero=True)
self.stacked_RNN(self)
self.add(self._config.recurrent_encoder(
self._config.hidden_state_dim,
return_sequences=False,
go_backwards=self._config.go_backwards))
self.add(Dropout(0.5))
self.add(RepeatVector(self._config.max_output_time_steps))
self.add(self._config.recurrent_decoder(
self._config.hidden_state_dim, return_sequences=True))
self.add(Dropout(0.5))
self.add(TimeDistributedDense(self._config.output_dim))
self.add(Activation('softmax'))
###
# Multimodal models
###
示例2: _buildDecoder
# 需要导入模块: from keras.layers import core [as 别名]
# 或者: from keras.layers.core import RepeatVector [as 别名]
def _buildDecoder(self, z, latent_rep_size, max_length, charset_length):
h = Dense(latent_rep_size, name='latent_input', activation='relu')(z)
h = RepeatVector(max_length, name='repeat_vector')(h)
h = GRU(501, return_sequences=True, name='gru_1')(h)
h = GRU(501, return_sequences=True, name='gru_2')(h)
h = GRU(501, return_sequences=True, name='gru_3')(h)
return TimeDistributed(
Dense(charset_length, activation='softmax'), name='decoded_mean')(h)
示例3: test_repeat_vector
# 需要导入模块: from keras.layers import core [as 别名]
# 或者: from keras.layers.core import RepeatVector [as 别名]
def test_repeat_vector(self):
layer = core.RepeatVector(10)
self._runner(layer)
示例4: build_model
# 需要导入模块: from keras.layers import core [as 别名]
# 或者: from keras.layers.core import RepeatVector [as 别名]
def build_model(input_size, seq_len, hidden_size):
"""建立一个 sequence to sequence 模型"""
model = Sequential()
model.add(GRU(input_dim=input_size, output_dim=hidden_size, return_sequences=False))
model.add(Dense(hidden_size, activation="relu"))
model.add(RepeatVector(seq_len))
model.add(GRU(hidden_size, return_sequences=True))
model.add(TimeDistributed(Dense(output_dim=input_size, activation="linear")))
model.compile(loss="mse", optimizer='adam')
return model
示例5: build_model
# 需要导入模块: from keras.layers import core [as 别名]
# 或者: from keras.layers.core import RepeatVector [as 别名]
def build_model(input_size, seq_len, hidden_size):
"""建立一个 seq2seq 模型"""
model = Sequential()
model.add(GRU(input_dim=input_size, output_dim=hidden_size, return_sequences=False))
model.add(Dense(hidden_size, activation="relu"))
model.add(RepeatVector(seq_len))
model.add(GRU(hidden_size, return_sequences=True))
model.add(TimeDistributed(Dense(output_dim=input_size, activation="softmax")))
model.compile(loss="categorical_crossentropy", optimizer='adam')
return model
示例6: base_model
# 需要导入模块: from keras.layers import core [as 别名]
# 或者: from keras.layers.core import RepeatVector [as 别名]
def base_model(feature_len=1, after_day=1, input_shape=(20, 1)):
model = Sequential()
model.add(Conv1D(10, kernel_size=5, input_shape=input_shape, activation='relu', padding='valid', strides=1))
model.add(LSTM(100, return_sequences=False, input_shape=input_shape))
model.add(Dropout(0.25))
# one to many
model.add(RepeatVector(after_day))
model.add(LSTM(200, return_sequences=True))
model.add(Dropout(0.25))
model.add(TimeDistributed(Dense(100, activation='relu', kernel_initializer='uniform')))
model.add(TimeDistributed(Dense(feature_len, activation='linear', kernel_initializer='uniform')))
return model
示例7: base_model
# 需要导入模块: from keras.layers import core [as 别名]
# 或者: from keras.layers.core import RepeatVector [as 别名]
def base_model(feature_len=1, after_day=1, input_shape=(20, 1)):
model = Sequential()
model.add(LSTM(units=100, return_sequences=False, input_shape=input_shape))
#model.add(LSTM(units=100, return_sequences=False, input_shape=input_shape))
# one to many
model.add(RepeatVector(after_day))
model.add(LSTM(200, return_sequences=True))
#model.add(LSTM(50, return_sequences=True))
model.add(TimeDistributed(Dense(units=feature_len, activation='linear')))
return model
示例8: build_CNN_LSTM
# 需要导入模块: from keras.layers import core [as 别名]
# 或者: from keras.layers.core import RepeatVector [as 别名]
def build_CNN_LSTM(channels, width, height, lstm_output_size, nb_classes):
model = Sequential()
# 1 conv
model.add(Convolution2D(64, 3, 3, border_mode='same', activation='relu',
input_shape=(channels, height, width)))
model.add(BatchNormalization(mode=0, axis=1))
# 2 conv
model.add(Convolution2D(64, 3, 3, border_mode='same', activation='relu'))
model.add(BatchNormalization(mode=0, axis=1))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2,2)))
# 3 conv
model.add(Convolution2D(128, 3, 3, border_mode='same', activation='relu'))
model.add(BatchNormalization(mode=0, axis=1))
# 4 conv
model.add(Convolution2D(128, 3, 3, border_mode='same', activation='relu'))
model.add(BatchNormalization(mode=0, axis=1))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2,2)))
# flaten
a = model.add(Flatten())
# 1 dense
model.add(Dense(512, activation='relu'))
model.add(BatchNormalization())
model.add(Dropout(0.5))
# 2 dense
model.add(Dense(512, activation='relu'))
model.add(BatchNormalization())
model.add(Dropout(0.5))
# lstm
model.add(RepeatVector(lstm_output_size))
model.add(LSTM(512, return_sequences=True))
model.add(TimeDistributed(Dropout(0.5)))
model.add(TimeDistributed(Dense(nb_classes, activation='softmax')))
model.summary()
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=[categorical_accuracy_per_sequence],
sample_weight_mode='temporal'
)
return model
示例9: _buildDecoder
# 需要导入模块: from keras.layers import core [as 别名]
# 或者: from keras.layers.core import RepeatVector [as 别名]
def _buildDecoder(self, z, latent_rep_size, max_length, charset_length):
h = Dense(latent_rep_size, name='latent_input', activation = 'relu')(z)
h = RepeatVector(max_length, name='repeat_vector')(h)
h = GRU(501, return_sequences = True, name='gru_1')(h)
h = GRU(501, return_sequences = True, name='gru_2')(h)
h = GRU(501, return_sequences = True, name='gru_3')(h)
return TimeDistributed(Dense(charset_length, activation='softmax'), name='decoded_mean')(h)
示例10: construct_model
# 需要导入模块: from keras.layers import core [as 别名]
# 或者: from keras.layers.core import RepeatVector [as 别名]
def construct_model(maxlen, input_dimension, output_dimension, lstm_vector_output_dim):
"""
Склеены три слова
"""
input = Input(shape=(maxlen, input_dimension), name='input')
# lstm_encode = LSTM(lstm_vector_output_dim)(input)
lstm_encode = SimpleRNN(lstm_vector_output_dim, activation='sigmoid')(input)
encoded_copied = RepeatVector(n=maxlen)(lstm_encode)
# lstm_decode = LSTM(output_dim=output_dimension, return_sequences=True, activation='softmax')(encoded_copied)
lstm_decode = SimpleRNN(output_dim=output_dimension, return_sequences=True, activation='softmax')(encoded_copied)
decoded = TimeDistributed(Dense(output_dimension, activation='softmax'))(lstm_decode)
encoder_decoder = Model(input, decoded)
adam = Adam()
encoder_decoder.compile(loss='categorical_crossentropy', optimizer=adam)
return encoder_decoder
示例11: construct_model
# 需要导入模块: from keras.layers import core [as 别名]
# 或者: from keras.layers.core import RepeatVector [as 别名]
def construct_model(maxlen, input_dimension, output_dimension, lstm_vector_output_dim):
"""
Склеены три слова
"""
input = Input(shape=(maxlen, input_dimension), name='input')
# lstm_encode = LSTM(lstm_vector_output_dim)(input)
lstm_encode = SimpleRNN(lstm_vector_output_dim, activation='relu')(input)
encoded_copied = RepeatVector(n=maxlen)(lstm_encode)
# lstm_decode = LSTM(output_dim=output_dimension, return_sequences=True, activation='softmax')(encoded_copied)
lstm_decode = SimpleRNN(output_dim=output_dimension, return_sequences=True, activation='softmax')(encoded_copied)
encoder = Model(input, lstm_decode)
adam = Adam()
encoder.compile(loss='categorical_crossentropy', optimizer=adam)
return encoder
示例12: build
# 需要导入模块: from keras.layers import core [as 别名]
# 或者: from keras.layers.core import RepeatVector [as 别名]
def build(self):
enc_size = self.size_of_env_observation()
argument_size = IntegerArguments.size_of_arguments
input_enc = InputLayer(batch_input_shape=(self.batch_size, enc_size), name='input_enc')
input_arg = InputLayer(batch_input_shape=(self.batch_size, argument_size), name='input_arg')
input_prg = Embedding(input_dim=PROGRAM_VEC_SIZE, output_dim=PROGRAM_KEY_VEC_SIZE, input_length=1,
batch_input_shape=(self.batch_size, 1))
f_enc = Sequential(name='f_enc')
f_enc.add(Merge([input_enc, input_arg], mode='concat'))
f_enc.add(MaxoutDense(128, nb_feature=4))
self.f_enc = f_enc
program_embedding = Sequential(name='program_embedding')
program_embedding.add(input_prg)
f_enc_convert = Sequential(name='f_enc_convert')
f_enc_convert.add(f_enc)
f_enc_convert.add(RepeatVector(1))
f_lstm = Sequential(name='f_lstm')
f_lstm.add(Merge([f_enc_convert, program_embedding], mode='concat'))
f_lstm.add(LSTM(256, return_sequences=False, stateful=True, W_regularizer=l2(0.0000001)))
f_lstm.add(Activation('relu', name='relu_lstm_1'))
f_lstm.add(RepeatVector(1))
f_lstm.add(LSTM(256, return_sequences=False, stateful=True, W_regularizer=l2(0.0000001)))
f_lstm.add(Activation('relu', name='relu_lstm_2'))
# plot(f_lstm, to_file='f_lstm.png', show_shapes=True)
f_end = Sequential(name='f_end')
f_end.add(f_lstm)
f_end.add(Dense(1, W_regularizer=l2(0.001)))
f_end.add(Activation('sigmoid', name='sigmoid_end'))
f_prog = Sequential(name='f_prog')
f_prog.add(f_lstm)
f_prog.add(Dense(PROGRAM_KEY_VEC_SIZE, activation="relu"))
f_prog.add(Dense(PROGRAM_VEC_SIZE, W_regularizer=l2(0.0001)))
f_prog.add(Activation('softmax', name='softmax_prog'))
# plot(f_prog, to_file='f_prog.png', show_shapes=True)
f_args = []
for ai in range(1, IntegerArguments.max_arg_num+1):
f_arg = Sequential(name='f_arg%s' % ai)
f_arg.add(f_lstm)
f_arg.add(Dense(IntegerArguments.depth, W_regularizer=l2(0.0001)))
f_arg.add(Activation('softmax', name='softmax_arg%s' % ai))
f_args.append(f_arg)
# plot(f_arg, to_file='f_arg.png', show_shapes=True)
self.model = Model([input_enc.input, input_arg.input, input_prg.input],
[f_end.output, f_prog.output] + [fa.output for fa in f_args],
name="npi")
self.compile_model()
plot(self.model, to_file='model.png', show_shapes=True)