keras实现transformer层

Keras已经提供了一个现成的transformer模块，包括transformer encoder和transformer decoder。在Keras中使用transformer的步骤如下：

1. 导入相关库：

from tensorflow.keras.layers import Input, Dense
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.layers import LayerNormalization, Dropout
from tensorflow.keras.layers import Add, Concatenate

2. 定义encoder和decoder的参数：

num_layers = 6  # transformer的层数
d_model = 512   # transformer中各层的维度（即embedding的维度）
dff = 1024      # feedforward层的维度
num_heads = 8   # multi-head attention的头数
input_vocab_size = 10000   # 输入词汇表的大小
target_vocab_size = 10000  # 输出词汇表的大小
dropout_rate = 0.1  # dropout概率

3. 构建transformer encoder：

def get_encoder_layer(d_model, num_heads, dff, rate=0.1):
    inputs = Input(shape=(None, d_model))
    padding_mask = Input(shape=(1, 1, None))

    attn_output, _ = MultiHeadAttention(
        d_model, num_heads)(inputs, inputs, inputs, padding_mask)
    attn_output = Dropout(rate)(attn_output)
    out1 = LayerNormalization(epsilon=1e-6)(Add()([inputs, attn_output]))

    ffn = Sequential([
        Dense(dff, activation='relu'),
        Dense(d_model),
    ])
    ffn_output = ffn(out1)
    ffn_output = Dropout(rate)(ffn_output)
    out2 = LayerNormalization(epsilon=1e-6)(Add()([out1, ffn_output]))

    return Model(inputs=[inputs, padding_mask], outputs=out2)

4. 构建transformer decoder：

def get_decoder_layer(d_model, num_heads, dff, rate=0.1):
    inputs = Input(shape=(None, d_model))
    enc_outputs = Input(shape=(None, d_model))
    look_ahead_mask = Input(shape=(1, None, None))
    padding_mask = Input(shape=(1, 1, None))

    attn1, attn_weights_block1 = MultiHeadAttention(
        d_model, num_heads)(inputs, inputs, inputs, look_ahead_mask)
    attn1 = Dropout(rate)(attn1)
    out1 = LayerNormalization(epsilon=1e-6)(Add()([inputs, attn1]))

    attn2, attn_weights_block2 = MultiHeadAttention(
        d_model, num_heads)(enc_outputs, enc_outputs, out1, padding_mask)
    attn2 = Dropout(rate)(attn2)
    out2 = LayerNormalization(epsilon=1e-6)(Add()([out1, attn2]))

    ffn = Sequential([
        Dense(dff, activation='relu'),
        Dense(d_model),
    ])
    ffn_output = ffn(out2)
    ffn_output = Dropout(rate)(ffn_output)
    out3 = LayerNormalization(epsilon=1e-6)(Add()([out2, ffn_output]))

    return Model(inputs=[inputs, enc_outputs,
                   look_ahead_mask, padding_mask],
            outputs=[out3, attn_weights_block1, attn_weights_block2])

5. 构建Transformer模型：

def get_transformer_model():
    inputs = Input(shape=(None,), name='inputs')
    dec_inputs = Input(shape=(None,), name='dec_inputs')

    enc_padding_mask = Lambda(
        create_padding_mask, output_shape=(1, 1, None),
        name='enc_padding_mask')(inputs)
    # mask the future tokens for decoder inputs at the 1st attention block
    look_ahead_mask = Lambda(
        create_look_ahead_mask,
        output_shape=(1, None, None),
        name='look_ahead_mask')(dec_inputs)
    # mask the encoder outputs for the 2nd attention block
    dec_padding_mask = Lambda(
        create_padding_mask, output_shape=(1, 1, None),
        name='dec_padding_mask')(inputs)

    encoder = get_encoder_layer(d_model, num_heads, dff, dropout_rate)
    decoder = get_decoder_layer(d_model, num_heads, dff, dropout_rate)

    enc_outputs = encoder(inputs=[inputs, enc_padding_mask])

    # dec_inputs are passed through embedding
    dec_outputs = Embedding(target_vocab_size, d_model)(dec_inputs)
    dec_outputs = PositionalEncoding(
        target_vocab_size, d_model)(dec_outputs)

    # dec_outputs are passed and passed through next layers
    dec_outputs, attention_weights_block1, attention_weights_block2 = \
        decoder(inputs=[dec_outputs, enc_outputs, look_ahead_mask, dec_padding_mask])

    dec_outputs = Dense(target_vocab_size, activation='softmax')(dec_outputs)

    model = Model(inputs=[inputs, dec_inputs], outputs=dec_outputs)

    return model

6. 编译和训练模型：

model = get_transformer_model()
optimizer = Adam(lr=0.001, beta_1=0.9, beta_2=0.98, epsilon=1e-9)
model.compile(optimizer=optimizer, loss='categorical_crossentropy')

model.fit([x_train, y_train[:, :-1]], y_train[:, 1:], batch_size=64, epochs=20, validation_split=0.2)