tensorflow2实现transformer模型

要实现Transformer模型，需要使用TensorFlow 2中的Keras API，以下是基本步骤：

1. 导入所需的库：

import tensorflow as tf
from tensorflow.keras.layers import Input, Dense, Dropout, LayerNormalization
from tensorflow.keras.layers import Embedding, GlobalAveragePooling1D, Concatenate
from tensorflow.keras.models import Model

2. 定义Transformer的编码器和解码器层：

class EncoderLayer(tf.keras.layers.Layer):
    def __init__(self, num_heads, hidden_size, dropout_rate=0.1):
        super(EncoderLayer, self).__init__()

        self.multihead_attention = tf.keras.layers.MultiHeadAttention(num_heads, hidden_size)
        self.dropout1 = tf.keras.layers.Dropout(dropout_rate)
        self.layer_norm1 = tf.keras.layers.LayerNormalization()

        self.feed_forward = tf.keras.Sequential([
            Dense(hidden_size * 4, activation='relu'),
            Dense(hidden_size),
        ])
        self.dropout2 = tf.keras.layers.Dropout(dropout_rate)
        self.layer_norm2 = tf.keras.layers.LayerNormalization()

    def call(self, inputs, training):
        attn_output = self.multihead_attention(inputs, inputs)
        attn_output = self.dropout1(attn_output, training=training)
        out1 = self.layer_norm1(inputs + attn_output)

        ffn_output = self.feed_forward(out1)
        ffn_output = self.dropout2(ffn_output, training=training)
        out2 = self.layer_norm2(out1 + ffn_output)

        return out2


class DecoderLayer(tf.keras.layers.Layer):
    def __init__(self, num_heads, hidden_size, dropout_rate=0.1):
        super(DecoderLayer, self).__init__()

        self.masked_multihead_attention = tf.keras.layers.MultiHeadAttention(num_heads, hidden_size)
        self.dropout1 = tf.keras.layers.Dropout(dropout_rate)
        self.layer_norm1 = tf.keras.layers.LayerNormalization()

        self.multihead_attention = tf.keras.layers.MultiHeadAttention(num_heads, hidden_size)
        self.dropout2 = tf.keras.layers.Dropout(dropout_rate)
        self.layer_norm2 = tf.keras.layers.LayerNormalization()

        self.feed_forward = tf.keras.Sequential([
            Dense(hidden_size * 4, activation='relu'),
            Dense(hidden_size),
        ])
        self.dropout3 = tf.keras.layers.Dropout(dropout_rate)
        self.layer_norm3 = tf.keras.layers.LayerNormalization()

    def call(self, inputs, enc_output, training):
        attn1_output = self.masked_multihead_attention(inputs, inputs)
        attn1_output = self.dropout1(attn1_output, training=training)
        out1 = self.layer_norm1(inputs + attn1_output)

        attn2_output = self.multihead_attention(out1, enc_output)
        attn2_output = self.dropout2(attn2_output, training=training)
        out2 = self.layer_norm2(out1 + attn2_output)

        ffn_output = self.feed_forward(out2)
        ffn_output = self.dropout3(ffn_output, training=training)
        out3 = self.layer_norm3(out2 + ffn_output)

        return out3

3. 定义Transformer模型：

class Transformer(tf.keras.models.Model):
    def __init__(self, num_layers, num_heads, hidden_size, input_vocab_size, target_vocab_size, dropout_rate=0.1):
        super(Transformer, self).__init__()

        self.embedding_size = hidden_size
        self.num_layers = num_layers

        self.embedding = Embedding(input_vocab_size, self.embedding_size, mask_zero=True)
        self.pos_encoding = self.positional_encoding(input_vocab_size, self.embedding_size)

        self.encoder_layers = [EncoderLayer(num_heads, hidden_size, dropout_rate)
                               for _ in range(num_layers)]
        self.decoder_layers = [DecoderLayer(num_heads, hidden_size, dropout_rate)
                               for _ in range(num_layers)]

        self.final_dense = Dense(target_vocab_size, activation='softmax')

    def call(self, inputs, targets, training):
        inputs = self.embedding(inputs)
        inputs *= tf.math.sqrt(tf.cast(self.embedding_size, dtype=tf.float32))
        inputs += self.pos_encoding[:, :tf.shape(inputs)[1], :]

        enc_output = inputs
        for i in range(self.num_layers):
            enc_output = self.encoder_layers[i](enc_output, training)

        targets = self.embedding(targets)
        targets *= tf.math.sqrt(tf.cast(self.embedding_size, dtype=tf.float32))
        targets += self.pos_encoding[:, :tf.shape(targets)[1], :]

        dec_output = targets
        for i in range(self.num_layers):
            dec_output = self.decoder_layers[i](dec_output, enc_output, training)

        final_output = self.final_dense(dec_output)

        return final_output

    def positional_encoding(self, seq_len, hidden_size):
        pos = tf.range(seq_len, dtype=tf.float32)[:, tf.newaxis]
        i = tf.range(hidden_size, dtype=tf.float32)[tf.newaxis, :]
        angle_rates = 1 / tf.pow(10000, (2 * (i // 2)) / tf.cast(hidden_size, dtype=tf.float32))

        angle_rads = pos * angle_rates

        sines = tf.math.sin(angle_rads[:, 0::2])
        cosines = tf.math.cos(angle_rads[:, 1::2])

        pos_encoding = tf.concat([sines, cosines], axis=-1)
        pos_encoding = pos_encoding[tf.newaxis, ...]

        return tf.cast(pos_encoding, dtype=tf.float32)

4. 编译和训练模型：

num_layers = 4
num_heads = 8
hidden_size = 512
input_vocab_size = len(input_vocab)
target_vocab_size = len(target_vocab)
dropout_rate = 0.1
learning_rate = 0.001

transformer = Transformer(num_layers, num_heads, hidden_size, input_vocab_size, target_vocab_size, dropout_rate)
transformer.compile(optimizer=tf.keras.optimizers.Adam(learning_rate),
                    loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
                    metrics=['accuracy'])

transformer.fit(train_dataset, epochs=50)

以上是基本的实现过程，可以根据实际需求进行修改。