AttributeError: module 'tensorflow.keras.layers' has no attribute 'transformer'这个问题如何解决，添加transformer层

如果你想手动添加transformer层，可以参考以下代码实现：

import tensorflow as tf

class MultiHeadSelfAttention(tf.keras.layers.Layer):
    def __init__(self, embed_dim, num_heads, **kwargs):
        super().__init__(**kwargs)
        self.embed_dim = embed_dim
        self.num_heads = num_heads
        if embed_dim % num_heads != 0:
            raise ValueError(
                f"embedding dimension = {embed_dim} should be divisible by number of heads = {num_heads}"
            )
        self.projection_dim = embed_dim // num_heads
        self.query_dense = tf.keras.layers.Dense(embed_dim)
        self.key_dense = tf.keras.layers.Dense(embed_dim)
        self.value_dense = tf.keras.layers.Dense(embed_dim)
        self.combine_heads = tf.keras.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(tf.keras.layers.Layer):
    def __init__(self, embed_dim, num_heads, ff_dim, rate=0.1, **kwargs):
        super().__init__(**kwargs)
        self.att = MultiHeadSelfAttention(embed_dim, num_heads)
        self.ffn = tf.keras.Sequential(
            [tf.keras.layers.Dense(ff_dim, activation="relu"), tf.keras.layers.Dense(embed_dim),]
        )
        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, 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)
        return self.layernorm2(out1 + ffn_output)


class TokenAndPositionEmbedding(tf.keras.layers.Layer):
    def __init__(self, maxlen, vocab_size, embed_dim, **kwargs):
        super().__init__(**kwargs)
        self.token_emb = tf.keras.layers.Embedding(input_dim=vocab_size, output_dim=embed_dim)
        self.pos_emb = tf.keras.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.pos_emb(positions)
        x = self.token_emb(x)
        return x + positions


class Transformer(tf.keras.Model):
    def __init__(self, vocab_size, maxlen, embed_dim, num_heads, ff_dim, num_layers=4, **kwargs):
        super().__init__(**kwargs)
        self.embedding = TokenAndPositionEmbedding(maxlen, vocab_size, embed_dim)
        self.transformer_blocks = [TransformerBlock(embed_dim, num_heads, ff_dim) for _ in range(num_layers)]
        self.out = tf.keras.layers.Dense(vocab_size, activation="softmax")

    def call(self, inputs, training):
        x = self.embedding(inputs)
        for block in self.transformer_blocks:
            x = block(x, training)
        x = self.out(x)
        return x

这里实现了一个基本的Transformer模型，包括多头自注意力机制（MultiHeadSelfAttention）、Transformer块（TransformerBlock）、位置编码嵌入层（TokenAndPositionEmbedding）和Transformer模型（Transformer）。你可以根据需要调整其中的参数来满足你的需求。