纯Keras写得transformer分类代码

下面是一个使用Keras编写的Transformer分类器的示例代码：

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

def positional_encoding(seq_length, d_model):
    pos = tf.range(seq_length)[:, tf.newaxis]
    i = tf.range(d_model)[tf.newaxis, :]
    angle_rates = 1 / tf.pow(10000.0, (2 * (i // 2)) / tf.cast(d_model, 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)
    return tf.cast(pos_encoding, tf.float32)

def create_padding_mask(seq):
    seq = tf.cast(tf.math.equal(seq, 0), tf.float32)
    return seq[:, tf.newaxis, tf.newaxis, :]

def create_look_ahead_mask(size):
    mask = 1 - tf.linalg.band_part(tf.ones((size, size)), -1, 0)
    return mask

def scaled_dot_product_attention(q, k, v, mask):
    matmul_qk = tf.matmul(q, k, transpose_b=True)
    dk = tf.cast(tf.shape(k)[-1], tf.float32)
    scaled_attention_logits = matmul_qk / tf.math.sqrt(dk)
    if mask is not None:
        scaled_attention_logits += (mask * -1e9)
    attention_weights = tf.nn.softmax(scaled_attention_logits, axis=-1)
    output = tf.matmul(attention_weights, v)
    return output

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 = tf.keras.Sequential([
            Dense(dff, activation='relu'),
            Dense(d_model)
        ])
        self.layernorm1 = LayerNormalization(epsilon=1e-6)
        self.layernorm2 = LayerNormalization(epsilon=1e-6)
        self.dropout1 = Dropout(rate)
        self.dropout2 = Dropout(rate)

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

class TransformerEncoder(tf.keras.layers.Layer):
    def __init__(self, num_layers, d_model, num_heads, dff, input_vocab_size,
                 maximum_position_encoding, rate=0.1):
        super(TransformerEncoder, self).__init__()
        self.d_model = d_model
        self.num_layers = num_layers
        self.embedding = Embedding(input_vocab_size, d_model)
        self.pos_encoding = positional_encoding(maximum_position_encoding, d_model)
        self.dropout = Dropout(rate)
        self.enc_layers = [EncoderLayer(d_model, num_heads, dff, rate)
                           for _ in range(num_layers)]

    def call(self, x, training, mask):
        seq_len = tf.shape(x)[1]
        x = self.embedding(x)
        x *= tf.math.sqrt(tf.cast(self.d_model, tf.float32))
        x += self.pos_encoding[:, :seq_len, :]
        x = self.dropout(x, training=training)
        for i in range(self.num_layers):
            x = self.enc_layers[i](x, training, mask)
        return x

def transformer_classifier(num_layers, d_model, num_heads, dff, input_vocab_size,
                            maximum_position_encoding, num_classes, rate=0.1):
    inputs = Input(shape=(None,))
    padding_mask = Lambda(create_padding_mask)(inputs)
    transformer_encoder = TransformerEncoder(num_layers, d_model, num_heads, dff,
                                              input_vocab_size, maximum_position_encoding, rate)
    x = transformer_encoder(inputs, True, padding_mask)
    x = Flatten()(x)
    x = Dense(num_classes, activation='softmax')(x)
    return Model(inputs=inputs, outputs=x)

model = transformer_classifier(num_layers=4, d_model=128, num_heads=8, dff=512,
                                input_vocab_size=10000, maximum_position_encoding=1000,
                                num_classes=10)
optimizer = Adam(lr=0.001, beta_1=0.9, beta_2=0.98, epsilon=1e-9)
model.compile(optimizer=optimizer, loss='categorical_crossentropy', metrics=['accuracy'])

这段代码实现了一个Transformer编码器，用于从变长的文本序列中提取特征，然后将这些特征输入到一个全连接层中进行分类。在这个示例中，我们定义了一个包含4个编码器层的Transformer，每个编码器层包含8个注意力头和一个前馈网络。我们还使用了位置编码、嵌入层和遮挡层来处理变长的文本序列。