基于TensorFlow编写transformer代码

好的，关于基于TensorFlow编写transformer代码的问题，我可以向您提供一些参考资料和代码示例。首先，您可以参考TensorFlow官方提供的Transformers API文档，其中包含了transformer模型实现的详细说明和代码示例。另外，您还可以参考GitHub上大量的开源transformer代码示例，比如Hugging Face团队的transformers库、Google Research团队的tensor2tensor库等等。这些代码示例可以帮助您更深入地理解transformer模型的实现原理和具体实现方法。

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基于TensorFlow编写transformer代码示例

以下是基于TensorFlow编写的Transformer代码示例：

import tensorflow as tf
from tensorflow.keras import layers


class MultiHeadSelfAttention(layers.Layer):
    def __init__(self, embed_dim, num_heads):
        super(MultiHeadSelfAttention, self).__init__()
        self.embed_dim = embed_dim
        self.num_heads = num_heads
        if embed_dim % num_heads != 0:
            raise ValueError(f"embed_dim {embed_dim} should be divisible by num_heads {num_heads}")
        self.projection_dim = embed_dim // num_heads
        self.query_dense = layers.Dense(embed_dim)
        self.key_dense = layers.Dense(embed_dim)
        self.value_dense = layers.Dense(embed_dim)
        self.combine_heads = layers.Dense(embed_dim)

    def attention(self, query, key, value):
        score = tf.matmul(query, key, transpose_b=True)
        dim_key = tf.cast(tf.shape(key)[-1], tf.float32)
        scaled_score = score / tf.math.sqrt(dim_key)
        weights = tf.nn.softmax(scaled_score, axis=-1)
        output = tf.matmul(weights, value)
        return output, weights

    def separate_heads(self, x, batch_size):
        x = tf.reshape(x, (batch_size, -1, self.num_heads, self.projection_dim))
        return tf.transpose(x, perm=[0, 2, 1, 3])

    def call(self, inputs):
        batch_size = tf.shape(inputs)[0]
        query = self.query_dense(inputs)
        key = self.key_dense(inputs)
        value = self.value_dense(inputs)
        query = self.separate_heads(query, batch_size)
        key = self.separate_heads(key, batch_size)
        value = self.separate_heads(value, batch_size)
        attention, weights = self.attention(query, key, value)
        attention = tf.transpose(attention, perm=[0, 2, 1, 3])
        concat_attention = tf.reshape(attention, (batch_size, -1, self.embed_dim))
        output = self.combine_heads(concat_attention)
        return output


class TransformerBlock(layers.Layer):
    def __init__(self, embed_dim, num_heads, ff_dim, rate=0.1):
        super(TransformerBlock, self).__init__()
        self.att = MultiHeadSelfAttention(embed_dim, num_heads)
        self.ffn = tf.keras.Sequential(
            [layers.Dense(ff_dim, activation="relu"), layers.Dense(embed_dim)]
        )
        self.layernorm1 = layers.LayerNormalization(epsilon=1e-6)
        self.layernorm2 = layers.LayerNormalization(epsilon=1e-6)
        self.dropout1 = layers.Dropout(rate)
        self.dropout2 = layers.Dropout(rate)

    def call(self, inputs, training):
        attn_output = self.att(inputs)
        attn_output = self.dropout1(attn_output, training=training)
        out1 = self.layernorm1(inputs + attn_output)
        ffn_output = self.ffn(out1)
        ffn_output = self.dropout2(ffn_output, training=training)
        out2 = self.layernorm2(out1 + ffn_output)
        return out2


class TokenAndPositionEmbedding(layers.Layer):
    def __init__(self, maxlen, vocab_size, embed_dim):
        super(TokenAndPositionEmbedding, self).__init__()
        self.token_embedding = layers.Embedding(input_dim=vocab_size, output_dim=embed_dim)
        self.position_embedding = layers.Embedding(input_dim=maxlen, output_dim=embed_dim)

    def call(self, x):
        maxlen = tf.shape(x)[-1]
        positions = tf.range(start=0, limit=maxlen, delta=1)
        positions = self.position_embedding(positions)
        x = self.token_embedding(x)
        return x + positions


class TransformerModel(tf.keras.Model):
    def __init__(self, maxlen, vocab_size, embed_dim, num_heads, ff_dim, num_layers):
        super(TransformerModel, self).__init__()
        self.embedding = TokenAndPositionEmbedding(maxlen, vocab_size, embed_dim)
        self.transformer_blocks = [TransformerBlock(embed_dim, num_heads, ff_dim) for _ in range(num_layers)]
        self.dropout = layers.Dropout(0.1)
        self.dense = layers.Dense(vocab_size, activation="softmax")

    def call(self, inputs, training):
        x = self.embedding(inputs)
        for transformer_block in self.transformer_blocks:
            x = transformer_block(x, training)
        x = self.dropout(x, training)
        x = self.dense(x)
        return x

这是一个基于Transformer的语言模型，使用了多头自注意力机制和前馈神经网络等内容，可以用于文本生成或者机器翻译等任务。

基于TensorFlow编写transformer预测模型代码示例

可以通过以下代码示例来基于TensorFlow编写transformer预测模型代码：

import tensorflow as tf
from tensorflow.keras.layers import Input, Dense, Dropout, LayerNormalization
from tensorflow.keras.layers import MultiHeadAttention, Embedding, Flatten
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import Adam

def get_transformer_model(seq_length, vocab_size, num_layers=4, d_model=128, num_heads=4, dff=512, dropout_rate=0.1):
    # 输入层
    input_layer = Input(shape=(seq_length,), name="input")
    
    # 词嵌入层
    embedding_layer = Embedding(input_dim=vocab_size, output_dim=d_model, name="embedding")(input_layer)
    
    # 位置编码层
    position_encoding_layer = get_position_encoding(vocab_size, d_model)
    position_encoded_layer = position_encoding_layer[:seq_length, :]
    position_encoded_layer = tf.expand_dims(position_encoded_layer, axis=0)
    position_embedding_layer = Embedding(input_dim=vocab_size, output_dim=d_model, name="position_embedding")(input_layer)
    embeddings = embedding_layer + position_embedding_layer
    
    # 编码器层，包括多头注意力层、点前全连接层和残差连接与归一化层
    encoder_layer = embeddings
    for i in range(num_layers):
        multi_head_attention_layer = MultiHeadAttention(num_heads=num_heads, key_dim=d_model, name="multi_head_attention_{}".format(i))(encoder_layer, encoder_layer)
        multi_head_attention_layer = Dropout(rate=dropout_rate, name="multi_head_attention_dropout_{}".format(i))(multi_head_attention_layer)
        multi_head_attention_layer += encoder_layer
        multi_head_attention_layer = LayerNormalization(name="multi_head_attention_layer_normalization_{}".format(i))(multi_head_attention_layer)
        
        point_wise_feed_forward_layer = Dense(units=dff, activation="relu", name="point_wise_feed_forward_{}".format(i))(multi_head_attention_layer)
        point_wise_feed_forward_layer = Dense(units=d_model, name="point_wise_feed_forward_2_{}".format(i))(point_wise_feed_forward_layer)
        point_wise_feed_forward_layer = Dropout(rate=dropout_rate, name="point_wise_feed_forward_dropout_{}".format(i))(point_wise_feed_forward_layer)
        point_wise_feed_forward_layer += multi_head_attention_layer
        point_wise_feed_forward_layer = LayerNormalization(name="point_wise_feed_forward_layer_normalization_{}".format(i))(point_wise_feed_forward_layer)
        
        encoder_layer = point_wise_feed_forward_layer
    
    # 将编码器层输出的张量展平
    flatten_layer = Flatten(name="flatten")(encoder_layer)
    
    # 输出层
    output_layer = Dense(units=vocab_size, activation="softmax", name="output")(flatten_layer)
    
    # 建立模型
    model = Model(inputs=input_layer, outputs=output_layer)
    
    # 编译模型
    optimizer = Adam(lr=0.0001)
    model.compile(loss="sparse_categorical_crossentropy", optimizer=optimizer, metrics=["accuracy"])
    
    return model

def get_position_encoding(vocab_size, d_model):
    angle_rates = 1 / np.power(10000, (2 * np.arange(d_model)) / np.float32(d_model))
    angle_rads = np.arange(vocab_size)[:, np.newaxis] * angle_rates[np.newaxis, :]
    pos_encoding = np.zeros((vocab_size, d_model))
    pos_encoding[:, 0::2] = np.sin(angle_rads[:, 0::2])
    pos_encoding[:, 1::2] = np.cos(angle_rads[:, 1::2])
    pos_encoding = pos_encoding[np.newaxis, ...]
    return tf.cast(pos_encoding, dtype=tf.float32)

# 测试
model = get_transformer_model(seq_length=20, vocab_size=1000)
model.summary()