那transformer的代码怎么写

关于Transformer的代码，我可以提供一些基本的信息和参考资料，但具体的实现细节和代码编写需要根据具体的应用场景和需求进行设计和开发。您可以参考Google官方的TensorFlow实现或者PyTorch实现，也可以参考一些开源的Transformer代码库，例如Hugging Face的Transformers库等。同时，您也可以参考一些相关的论文和教程，例如《Attention Is All You Need》等。

相关问题

写一段transformer代码

### 回答1：
好的，以下是一个简单的 Transformer 代码示例：

import tensorflow as tf

class Transformer(tf.keras.Model):
    def __init__(self, num_layers, d_model, num_heads, dff, input_vocab_size, target_vocab_size, rate=0.1):
        super(Transformer, self).__init__()

        self.encoder = Encoder(num_layers, d_model, num_heads, dff, input_vocab_size, rate)

        self.decoder = Decoder(num_layers, d_model, num_heads, dff, target_vocab_size, rate)

        self.final_layer = tf.keras.layers.Dense(target_vocab_size)

    def call(self, inp, tar, training, enc_padding_mask, look_ahead_mask, dec_padding_mask):
        enc_output = self.encoder(inp, training, enc_padding_mask)

        dec_output, attention_weights = self.decoder(
            tar, enc_output, training, look_ahead_mask, dec_padding_mask)

        final_output = self.final_layer(dec_output)

        return final_output, attention_weights


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

        self.d_model = d_model
        self.num_layers = num_layers

        self.embedding = tf.keras.layers.Embedding(input_vocab_size, d_model)
        self.pos_encoding = positional_encoding(input_vocab_size, self.d_model)

        self.enc_layers = [EncoderLayer(d_model, num_heads, dff, rate)
                           for _ in range(num_layers)]

        self.dropout = tf.keras.layers.Dropout(rate)

    def call(self, x, training, mask):
        seq_len = tf.shape(x)[1]

        # adding embedding and position encoding.
        x = self.embedding(x)  # (batch_size, input_seq_len, d_model)
        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


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)
        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 Decoder(tf.keras.layers.Layer):
    def __init__(self, num_layers, d_model, num_heads, dff, target_vocab_size, rate=0.1):
        super(Decoder, self).__init__()

        self.d_model = d_model
        self.num_layers = num_layers

        self.embedding = tf.keras.layers  

### 回答2：
Transformer是一种用于自然语言处理的深度学习模型。以下是一个使用Python编写的简单Transformer代码段，用于进行文本分类任务：

import torch
import torch.nn as nn
import torch.optim as optim

class Transformer(nn.Module):
def __init__(self, input_dim, output_dim, hidden_dim, num_layers, num_heads):
super(Transformer, self).__init__()

self.embedding = nn.Embedding(input_dim, hidden_dim)
self.positional_encoding = PositionalEncoding(hidden_dim)
self.transformer_encoder_layer = nn.TransformerEncoderLayer(hidden_dim, num_heads)
self.transformer_encoder = nn.TransformerEncoder(self.transformer_encoder_layer, num_layers)

self.fc = nn.Linear(hidden_dim, output_dim)

def forward(self, x):
embedded = self.embedding(x)
encoded = self.positional_encoding(embedded)
transformed = self.transformer_encoder(encoded)
pooled = torch.mean(transformed, dim=1)
output = self.fc(pooled)

return output

class PositionalEncoding(nn.Module):
def __init__(self, hidden_dim, max_seq_len=300):
super(PositionalEncoding, self).__init__()

self.hidden_dim = hidden_dim
self.dropout = nn.Dropout(p=0.1)

pe = torch.zeros(max_seq_len, hidden_dim)
position = torch.arange(0, max_seq_len).unsqueeze(1)
div_term = torch.exp(torch.arange(0, hidden_dim, 2) * -(math.log(10000.0) / hidden_dim))
pe[:, 0::2] = torch.sin(position * div_term)
pe[:, 1::2] = torch.cos(position * div_term)

self.register_buffer('pe', pe.unsqueeze(0))

def forward(self, x):
x = x * math.sqrt(self.hidden_dim)
x = x + self.pe[:, :x.size(1)]
x = self.dropout(x)

return x

以上代码定义了一个Transformer模型类，包括一个词嵌入层、位置编码层、Transformer编码层和一个全连接层。其中，位置编码层使用来自论文《Attention is All You Need》中提出的方法，用于为序列中的词汇位置添加信息。模型的前向传播过程首先对输入的文本进行词嵌入，然后进行位置编码，接着使用Transformer编码层进行特征提取和表示学习，将输出进行平均池化后再通过全连接层进行分类预测。这段代码可以用于文本分类任务中，输入是一个整数序列，输出是每个类别的预测概率。  

### 回答3：
Transformer是一种深度学习模型架构，适用于自然语言处理任务，例如机器翻译、文本生成等。下面是一个简单的Transformer代码示例：

import torch
import torch.nn as nn
import torch.nn.functional as F

class TransformerEncoder(nn.Module):
def __init__(self, input_size, hidden_size, num_layers, num_heads):
super(TransformerEncoder, self).__init__()

self.embedding = nn.Embedding(input_size, hidden_size)
self.position_encoding = PositionalEncoding(hidden_size)
self.encoder_layer = TransformerEncoderLayer(hidden_size, num_heads)

def forward(self, inputs):
embeddings = self.embedding(inputs)
encoded = self.position_encoding(embeddings)
output = self.encoder_layer(encoded)
return output

class PositionalEncoding(nn.Module):
def __init__(self, hidden_size, max_sequence_length=1000):
super(PositionalEncoding, self).__init__()

position_encoding = torch.zeros(max_sequence_length, hidden_size)
position = torch.arange(0, max_sequence_length, dtype=torch.float).unsqueeze(1)
div_term = torch.exp(torch.arange(0, hidden_size, 2).float() * (-math.log(10000.0) / hidden_size))

position_encoding[:, 0::2] = torch.sin(position * div_term)
position_encoding[:, 1::2] = torch.cos(position * div_term)

self.register_buffer('position_encoding', position_encoding)

def forward(self, inputs):
seq_length = inputs.size(1)
position_encoding = self.position_encoding[:seq_length, :]
return inputs + position_encoding

class TransformerEncoderLayer(nn.Module):
def __init__(self, hidden_size, num_heads, dropout=0.1):
super(TransformerEncoderLayer, self).__init__()

self.self_attention = MultiHeadAttention(hidden_size, num_heads)
self.feed_forward = FeedForward(hidden_size)
self.dropout = nn.Dropout(dropout)

def forward(self, inputs):
attended = self.self_attention(inputs)
attended = self.dropout(attented)
output = attended + inputs
output = self.feed_forward(output)
output = self.dropout(output)
return output

class MultiHeadAttention(nn.Module):
def __init__(self, hidden_size, num_heads):
super(MultiHeadAttention, self).__init__()

self.hidden_size = hidden_size
self.num_heads = num_heads
self.head_size = hidden_size // num_heads

self.W_q = nn.Linear(hidden_size, hidden_size)
self.W_k = nn.Linear(hidden_size, hidden_size)
self.W_v = nn.Linear(hidden_size, hidden_size)
self.W_o = nn.Linear(hidden_size, hidden_size)

def forward(self, inputs):
batch_size, seq_length, _ = inputs.size()

query = self.W_q(inputs)
key = self.W_k(inputs)
value = self.W_v(inputs)

query = query.view(batch_size, seq_length, self.num_heads, self.head_size).transpose(1, 2)
key = key.view(batch_size, seq_length, self.num_heads, self.head_size).transpose(1, 2)
value = value.view(batch_size, seq_length, self.num_heads, self.head_size).transpose(1, 2)

attention_scores = torch.matmul(query, key.transpose(-1, -2)) / math.sqrt(self.head_size)
attention_probs = F.softmax(attention_scores, dim=-1)
attended = torch.matmul(attention_probs, value).transpose(1, 2).contiguous().view(batch_size, seq_length, self.hidden_size)

output = self.W_o(attended)
return output

class FeedForward(nn.Module):
def __init__(self, hidden_size, dropout=0.1):
super(FeedForward, self).__init__()

self.linear1 = nn.Linear(hidden_size, 4 * hidden_size)
self.linear2 = nn.Linear(4 * hidden_size, hidden_size)
self.dropout = nn.Dropout(dropout)

def forward(self, inputs):
output = self.linear1(inputs)
output = F.relu(output)
output = self.dropout(output)
output = self.linear2(output)
return output

以上是一个简单的Transformer模型代码示例，包括了TransformerEncoder、PositionalEncoding、TransformerEncoderLayer、MultiHeadAttention和FeedForward等模块的实现。这段代码可以作为一个Transformer模型的基本框架，但是其中的具体实现可能需要根据具体任务需求进行调整和优化。

用tensorflow写 transformer代码

好的，下面是使用TensorFlow 2.0实现Transformer的代码示例，主要使用了tf.keras.layers和tf.data来构建和训练模型：

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

# 定义超参数
max_seq_len = 50 # 最大序列长度
vocab_size = 10000 # 词汇表大小
embedding_dim = 128 # 词嵌入维度
num_heads = 8 # 多头注意力头数
num_encoder_layers = 2 # 编码器层数
num_decoder_layers = 2 # 解码器层数
dff = 512 # 前馈网络隐藏层维度
dropout_rate = 0.1 # dropout率

# 定义输入层
encoder_inputs = Input(shape=(max_seq_len,), name='encoder_inputs')
decoder_inputs = Input(shape=(max_seq_len,), name='decoder_inputs')

# 定义词嵌入层
embedding_layer = Embedding(vocab_size, embedding_dim)

# 编码器
def encoder_layer(units, dff, num_heads, dropout_rate):
    inputs = Input(shape=(None, embedding_dim), name='encoder_inputs')
    attention = MultiHeadAttention(num_heads=num_heads, key_dim=embedding_dim)(inputs, inputs)
    attention = Dropout(dropout_rate)(attention)
    attention = LayerNormalization(epsilon=1e-6)(inputs + attention)
    outputs = Dense(units, activation='relu')(attention)
    outputs = Dense(dff, activation='relu')(outputs)
    outputs = Dropout(dropout_rate)(outputs)
    outputs = LayerNormalization(epsilon=1e-6)(attention + outputs)
    return Model(inputs=inputs, outputs=outputs)

# 解码器
def decoder_layer(units, dff, num_heads, dropout_rate):
    inputs = Input(shape=(None, embedding_dim), name='decoder_inputs')
    attention1 = MultiHeadAttention(num_heads=num_heads, key_dim=embedding_dim)(inputs, inputs)
    attention1 = Dropout(dropout_rate)(attention1)
    attention1 = LayerNormalization(epsilon=1e-6)(inputs + attention1)
    attention2 = MultiHeadAttention(num_heads=num_heads, key_dim=embedding_dim)(attention1, encoder_outputs)
    attention2 = Dropout(dropout_rate)(attention2)
    attention2 = LayerNormalization(epsilon=1e-6)(attention1 + attention2)
    outputs = Dense(units, activation='relu')(attention2)
    outputs = Dense(dff, activation='relu')(outputs)
    outputs = Dropout(dropout_rate)(outputs)
    outputs = LayerNormalization(epsilon=1e-6)(attention2 + outputs)
    return Model(inputs=[inputs, encoder_outputs], outputs=outputs)

# 定义编码器
encoder_outputs = embedding_layer(encoder_inputs)
for i in range(num_encoder_layers):
    encoder_outputs = encoder_layer(embedding_dim, dff, num_heads, dropout_rate)(encoder_outputs)

# 定义解码器
decoder_outputs = embedding_layer(decoder_inputs)
for i in range(num_decoder_layers):
    decoder_outputs = decoder_layer(embedding_dim, dff, num_heads, dropout_rate)(decoder_outputs, encoder_outputs)

# 定义输出层
outputs = Dense(vocab_size, activation='softmax')(decoder_outputs)

# 定义模型
model = Model(inputs=[encoder_inputs, decoder_inputs], outputs=outputs)

# 定义损失函数
def loss_function(y_true, y_pred):
    mask = tf.math.logical_not(tf.math.equal(y_true, 0))
    loss_ = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)(y_true, y_pred)
    mask = tf.cast(mask, dtype=loss_.dtype)
    loss_ *= mask
    return tf.reduce_mean(loss_)

# 定义学习率
learning_rate = CustomSchedule(embedding_dim)

# 定义优化器
optimizer = Adam(learning_rate, beta_1=0.9, beta_2=0.98, epsilon=1e-9)

# 编译模型
model.compile(optimizer=optimizer, loss=loss_function)

# 训练模型
model.fit(train_dataset, epochs=num_epochs)

以上是一个简单的Transformer模型实现，其中还需要定义一些其他的辅助函数和数据预处理部分。这里只是提供了一个大致的代码框架，实际应用中还需要根据具体任务进行调整和优化。