Transformer跟用attention的Seq2Seq的模型有著一樣的pattern

• 輸入的句子通過N個Encoder layer，把序列中的每一個token都標記成輸出
• Decoder用Encoder的輸出和自己的輸入來預測下一個文字

Encoder layer

每個encoder layer都是由幾個子層組成的

1. Multi-head attention
2. Point wise feed forward network

這些子層的周圍都有一個殘差連接，然後做正規化

殘差連接有助於避免深度網路中的梯度消失問題

每一個子層的輸出是LayerNorm(x + Sublayer(x))

正規化是在d_model的軸上完成的

Transformer中共有N個Encoder layer

class EncoderLayer(tf.keras.layers.Layer):
  def __init__(self, d_model, num_heads, dff, rate=0.1):
    super(EncoderLayer, self).__init__()

    self.mha = MultiHeadAttention(d_model, num_heads)
    self.ffn = point_wise_feed_forward_network(d_model, dff)

    self.layernorm1 = tf.keras.layers.LayerNormalization(epsilon=1e-6)
    self.layernorm2 = tf.keras.layers.LayerNormalization(epsilon=1e-6)

    self.dropout1 = tf.keras.layers.Dropout(rate)
    self.dropout2 = tf.keras.layers.Dropout(rate)

  def call(self, x, training, mask):

    attn_output, _ = self.mha(x, x, x, mask)  # (batch_size, input_seq_len, d_model)
    attn_output = self.dropout1(attn_output, training=training)
    out1 = self.layernorm1(x + attn_output)  # (batch_size, input_seq_len, d_model)

    ffn_output = self.ffn(out1)  # (batch_size, input_seq_len, d_model)
    ffn_output = self.dropout2(ffn_output, training=training)
    out2 = self.layernorm2(out1 + ffn_output)  # (batch_size, input_seq_len, d_model)

    return out2

sample_encoder_layer = EncoderLayer(512, 8, 2048)

sample_encoder_layer_output = sample_encoder_layer(
    tf.random.uniform((64, 43, 512)), False, None)

sample_encoder_layer_output.shape  # (batch_size, input_seq_len, d_model)

TensorShape([64, 43, 512])