本文整理汇总了Python中tensorflow.python.keras.layers.Concatenate方法的典型用法代码示例。如果您正苦于以下问题：Python layers.Concatenate方法的具体用法？Python layers.Concatenate怎么用？Python layers.Concatenate使用的例子？那么恭喜您, 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tensorflow.python.keras.layers的用法示例。
在下文中一共展示了layers.Concatenate方法的3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: concat_fun
# 需要导入模块: from tensorflow.python.keras import layers [as 别名]
# 或者: from tensorflow.python.keras.layers import Concatenate [as 别名]
def concat_fun(inputs, axis=-1):
if len(inputs) == 1:
return inputs[0]
else:
return Concatenate(axis=axis)(inputs) 示例2: define_nmt
# 需要导入模块: from tensorflow.python.keras import layers [as 别名]
# 或者: from tensorflow.python.keras.layers import Concatenate [as 别名]
def define_nmt(hidden_size, batch_size, en_timesteps, en_vsize, fr_timesteps, fr_vsize):
""" Defining a NMT model """
# Define an input sequence and process it.
if batch_size:
encoder_inputs = Input(batch_shape=(batch_size, en_timesteps, en_vsize), name='encoder_inputs')
decoder_inputs = Input(batch_shape=(batch_size, fr_timesteps - 1, fr_vsize), name='decoder_inputs')
else:
encoder_inputs = Input(shape=(en_timesteps, en_vsize), name='encoder_inputs')
if fr_timesteps:
decoder_inputs = Input(shape=(fr_timesteps - 1, fr_vsize), name='decoder_inputs')
else:
decoder_inputs = Input(shape=(None, fr_vsize), name='decoder_inputs')
# Encoder GRU
encoder_gru = GRU(hidden_size, return_sequences=True, return_state=True, name='encoder_gru')
encoder_out, encoder_state = encoder_gru(encoder_inputs)
# Set up the decoder GRU, using `encoder_states` as initial state.
decoder_gru = GRU(hidden_size, return_sequences=True, return_state=True, name='decoder_gru')
decoder_out, decoder_state = decoder_gru(decoder_inputs, initial_state=encoder_state)
# Attention layer
attn_layer = AttentionLayer(name='attention_layer')
attn_out, attn_states = attn_layer([encoder_out, decoder_out])
# Concat attention input and decoder GRU output
decoder_concat_input = Concatenate(axis=-1, name='concat_layer')([decoder_out, attn_out])
# Dense layer
dense = Dense(fr_vsize, activation='softmax', name='softmax_layer')
dense_time = TimeDistributed(dense, name='time_distributed_layer')
decoder_pred = dense_time(decoder_concat_input)
# Full model
full_model = Model(inputs=[encoder_inputs, decoder_inputs], outputs=decoder_pred)
full_model.compile(optimizer='adam', loss='categorical_crossentropy')
full_model.summary()
""" Inference model """
batch_size = 1
""" Encoder (Inference) model """
encoder_inf_inputs = Input(batch_shape=(batch_size, en_timesteps, en_vsize), name='encoder_inf_inputs')
encoder_inf_out, encoder_inf_state = encoder_gru(encoder_inf_inputs)
encoder_model = Model(inputs=encoder_inf_inputs, outputs=[encoder_inf_out, encoder_inf_state])
""" Decoder (Inference) model """
decoder_inf_inputs = Input(batch_shape=(batch_size, 1, fr_vsize), name='decoder_word_inputs')
encoder_inf_states = Input(batch_shape=(batch_size, en_timesteps, hidden_size), name='encoder_inf_states')
decoder_init_state = Input(batch_shape=(batch_size, hidden_size), name='decoder_init')
decoder_inf_out, decoder_inf_state = decoder_gru(decoder_inf_inputs, initial_state=decoder_init_state)
attn_inf_out, attn_inf_states = attn_layer([encoder_inf_states, decoder_inf_out])
decoder_inf_concat = Concatenate(axis=-1, name='concat')([decoder_inf_out, attn_inf_out])
decoder_inf_pred = TimeDistributed(dense)(decoder_inf_concat)
decoder_model = Model(inputs=[encoder_inf_states, decoder_init_state, decoder_inf_inputs],
outputs=[decoder_inf_pred, attn_inf_states, decoder_inf_state])
return full_model, encoder_model, decoder_model 示例3: define_nmt
# 需要导入模块: from tensorflow.python.keras import layers [as 别名]
# 或者: from tensorflow.python.keras.layers import Concatenate [as 别名]
def define_nmt(hidden_size, batch_size, en_timesteps, en_vsize, fr_timesteps, fr_vsize):
""" Defining a NMT model """
# Define an input sequence and process it.
if batch_size:
encoder_inputs = Input(batch_shape=(batch_size, en_timesteps, en_vsize), name='encoder_inputs')
decoder_inputs = Input(batch_shape=(batch_size, fr_timesteps - 1, fr_vsize), name='decoder_inputs')
else:
encoder_inputs = Input(shape=(en_timesteps, en_vsize), name='encoder_inputs')
decoder_inputs = Input(shape=(fr_timesteps - 1, fr_vsize), name='decoder_inputs')
# Encoder GRU
encoder_gru = Bidirectional(GRU(hidden_size, return_sequences=True, return_state=True, name='encoder_gru'), name='bidirectional_encoder')
encoder_out, encoder_fwd_state, encoder_back_state = encoder_gru(encoder_inputs)
# Set up the decoder GRU, using `encoder_states` as initial state.
decoder_gru = GRU(hidden_size*2, return_sequences=True, return_state=True, name='decoder_gru')
decoder_out, decoder_state = decoder_gru(
decoder_inputs, initial_state=Concatenate(axis=-1)([encoder_fwd_state, encoder_back_state])
)
# Attention layer
attn_layer = AttentionLayer(name='attention_layer')
attn_out, attn_states = attn_layer([encoder_out, decoder_out])
# Concat attention input and decoder GRU output
decoder_concat_input = Concatenate(axis=-1, name='concat_layer')([decoder_out, attn_out])
# Dense layer
dense = Dense(fr_vsize, activation='softmax', name='softmax_layer')
dense_time = TimeDistributed(dense, name='time_distributed_layer')
decoder_pred = dense_time(decoder_concat_input)
# Full model
full_model = Model(inputs=[encoder_inputs, decoder_inputs], outputs=decoder_pred)
full_model.compile(optimizer='adam', loss='categorical_crossentropy')
full_model.summary()
""" Inference model """
batch_size = 1
""" Encoder (Inference) model """
encoder_inf_inputs = Input(batch_shape=(batch_size, en_timesteps, en_vsize), name='encoder_inf_inputs')
encoder_inf_out, encoder_inf_fwd_state, encoder_inf_back_state = encoder_gru(encoder_inf_inputs)
encoder_model = Model(inputs=encoder_inf_inputs, outputs=[encoder_inf_out, encoder_inf_fwd_state, encoder_inf_back_state])
""" Decoder (Inference) model """
decoder_inf_inputs = Input(batch_shape=(batch_size, 1, fr_vsize), name='decoder_word_inputs')
encoder_inf_states = Input(batch_shape=(batch_size, en_timesteps, 2*hidden_size), name='encoder_inf_states')
decoder_init_state = Input(batch_shape=(batch_size, 2*hidden_size), name='decoder_init')
decoder_inf_out, decoder_inf_state = decoder_gru(
decoder_inf_inputs, initial_state=decoder_init_state)
attn_inf_out, attn_inf_states = attn_layer([encoder_inf_states, decoder_inf_out])
decoder_inf_concat = Concatenate(axis=-1, name='concat')([decoder_inf_out, attn_inf_out])
decoder_inf_pred = TimeDistributed(dense)(decoder_inf_concat)
decoder_model = Model(inputs=[encoder_inf_states, decoder_init_state, decoder_inf_inputs],
outputs=[decoder_inf_pred, attn_inf_states, decoder_inf_state])
return full_model, encoder_model, decoder_model