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Python layers.Concatenate方法代碼示例

本文整理匯總了Python中tensorflow.python.keras.layers.Concatenate方法的典型用法代碼示例。如果您正苦於以下問題:Python layers.Concatenate方法的具體用法?Python layers.Concatenate怎麽用?Python layers.Concatenate使用的例子?那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在tensorflow.python.keras.layers的用法示例。


在下文中一共展示了layers.Concatenate方法的3個代碼示例,這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚,您的評價將有助於係統推薦出更棒的Python代碼示例。

示例1: concat_fun

# 需要導入模塊: from tensorflow.python.keras import layers [as 別名]
# 或者: from tensorflow.python.keras.layers import Concatenate [as 別名]
def concat_fun(inputs, axis=-1):
    if len(inputs) == 1:
        return inputs[0]
    else:
        return Concatenate(axis=axis)(inputs) 
開發者ID:ShenDezhou,項目名稱:icme2019,代碼行數:7,代碼來源:utils.py

示例2: define_nmt

# 需要導入模塊: from tensorflow.python.keras import layers [as 別名]
# 或者: from tensorflow.python.keras.layers import Concatenate [as 別名]
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 
開發者ID:thushv89,項目名稱:attention_keras,代碼行數:63,代碼來源:model.py

示例3: define_nmt

# 需要導入模塊: from tensorflow.python.keras import layers [as 別名]
# 或者: from tensorflow.python.keras.layers import Concatenate [as 別名]
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 
開發者ID:thushv89,項目名稱:attention_keras,代碼行數:63,代碼來源:model.py


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