keras中padding层数

Keras中的padding层数是由padding参数控制的。padding参数可以设置为“valid”或“same”，分别表示不进行padding和进行padding使输出大小与输入大小相同。如果要进行多层padding，可以使用多个padding层，但通常不建议这样做，因为会增加计算量和参数数量。

相关问题

keras使用ZeroPadding2D层的Conv2d怎么写

使用`ZeroPadding2D`层来实现自定义填充，可以将其作为`Conv2D`的输入层。下面是一个使用`ZeroPadding2D`层来实现自定义填充的示例：

from keras.layers import Input, Conv2D, ZeroPadding2D

# 定义输入张量
input_tensor = Input(shape=(height, width, channels))

# 自定义填充
padding_height = 2
padding_width = 3
padding_tensor = ZeroPadding2D(padding=((padding_height, padding_height), (padding_width, padding_width)))(input_tensor)

# 定义卷积层
filters = 32
kernel_size = (3, 3)
strides = (1, 1)
conv_tensor = Conv2D(filters=filters, kernel_size=kernel_size, strides=strides)(padding_tensor)

在上述代码中，`ZeroPadding2D`层的输入是`input_tensor`，`padding`参数是一个元组，用于指定要在每个维度上填充的大小。例如，`padding=((2, 2), (3, 3))`表示在第1和第3个维度上分别填充2个像素，第2和第4个维度上分别填充3个像素。

然后，使用`Conv2D`层对填充后的张量`padding_tensor`进行卷积。注意，`Conv2D`层不需要指定填充方式，因为填充已经在`ZeroPadding2D`层中完成了。

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)