分析代码class N_need: def __init__(self, max_len): self.max_len = max_len self.set_all = set([i for i in range(max_len)]) # 初始化全部情况 name_list = ["nc", "nu", "ncf", "nuf", "ncs", "nus"] self.nc_res = [] for i in range(max_len): self.nc_res.append(dict()) for _ in name_list: self.nc_res[i][_] = 0 self._temp_process = set() def process(self, number): self._temp_process.add(number) def update(self, flag): diff_set = self.set_all - self._temp_process for _num in list(self._temp_process): self.nc_res[_num]["nc"] += 1 if flag: self.nc_res[_num]["ncs"] += 1 else: self.nc_res[_num]["ncf"] += 1 for _num in list(diff_set): self.nc_res[_num]["nu"] += 1 if flag: self.nc_res[_num]["nus"] += 1 else: self.nc_res[_num]["nuf"] += 1 self._temp_process = set() def __getitem__(self, item): res = self.nc_res[item] return [item, res['nc'], res['nu'], res['ncf'], res['nuf'], res['ncs'], res['nus']] def __len__(self): return self.max_len nc = N_need(21)

这段代码定义了一个名为 `N_need` 的类，它包含以下方法：

- `__init__(self, max_len)`：构造函数，初始化 `max_len` 和 `set_all` 属性，并初始化一个 `nc_res` 列表，包含 `max_len` 个字典，每个字典代表一个数字对应的统计结果，同时将 `_temp_process` 属性初始化为空集合。
- `process(self, number)`：将 `number` 添加到 `_temp_process` 集合中。
- `update(self, flag)`：根据 `flag` 更新统计结果，将 `_temp_process` 集合中的数字加入到 `nc_res` 列表中对应的字典中，同时将未处理的数字加入到对应字典的其他字段中。
- `__getitem__(self, item)`：根据 `item` 返回对应数字的统计结果。
- `__len__(self)`：返回 `max_len` 属性的值。

在代码最后，创建了一个名为 `nc` 的 `N_need` 对象，`max_len` 属性值为 21。

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。

语言翻译transformer代码

以下是一个简单的语言翻译Transformer的代码示例，使用PyTorch实现：

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

class Transformer(nn.Module):
    def __init__(self, input_dim, output_dim, d_model, nhead, num_layers, dim_feedforward, dropout):
        super().__init__()
        self.encoder = nn.Embedding(input_dim, d_model)
        self.decoder = nn.Embedding(output_dim, d_model)
        self.pos_encoder = PositionalEncoding(d_model, dropout)
        self.pos_decoder = PositionalEncoding(d_model, dropout)
        self.transformer = nn.Transformer(d_model=d_model, nhead=nhead, num_encoder_layers=num_layers, num_decoder_layers=num_layers, dim_feedforward=dim_feedforward, dropout=dropout)
        self.fc = nn.Linear(d_model, output_dim)

    def forward(self, src, tgt):
        src_mask = self._generate_square_subsequent_mask(len(src)).to(src.device)
        tgt_mask = self._generate_square_subsequent_mask(len(tgt)).to(tgt.device)
        src_embed = self.pos_encoder(self.encoder(src))
        tgt_embed = self.pos_decoder(self.decoder(tgt))
        memory = self.transformer.encoder(src_embed, src_mask)
        output = self.transformer.decoder(tgt_embed, memory, tgt_mask)
        output = self.fc(output)
        return output

    def _generate_square_subsequent_mask(self, sz):
        mask = (torch.triu(torch.ones(sz, sz)) == 1).transpose(0, 1)
        mask = mask.float().masked_fill(mask == 0, float('-inf')).masked_fill(mask == 1, float(0.0))
        return mask
        

class PositionalEncoding(nn.Module):
    def __init__(self, d_model, dropout=0.1, max_len=5000):
        super().__init__()
        self.dropout = nn.Dropout(p=dropout)
        pe = torch.zeros(max_len, d_model)
        position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
        div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))
        pe[:, 0::2] = torch.sin(position * div_term)
        pe[:, 1::2] = torch.cos(position * div_term)
        pe = pe.unsqueeze(0).transpose(0, 1)
        self.register_buffer('pe', pe)

    def forward(self, x):
        x = x + self.pe[:x.size(0), :]
        return self.dropout(x)

在这个例子中，我们定义了一个名为Transformer的类，它继承自nn.Module，并包含以下组件：

- 一个嵌入层，用于将输入和输出序列中的标记转换为特征向量。
- 一个位置编码层，用于为每个输入和输出序列的标记添加位置编码。
- 一个Transformer模型，其中包含编码器和解码器，用于将输入序列转换为输出序列。
- 一个线性层，用于将Transformer输出转换为输出标记的概率分布。

在forward方法中，我们首先使用生成的掩码来掩盖输入和输出序列中的填充标记，并将嵌入后的序列传递到位置编码层中。然后，我们使用编码器来生成内存表示，并将其用于解码器，以生成最终的输出。最后，我们将Transformer模型的输出馈入线性层，以将其转换为输出标记的概率分布。

此外，我们还定义了一个名为PositionalEncoding的辅助类，用于为输入和输出序列中的标记添加位置编码。在这个类中，我们使用了一个类似于Attention is All You Need论文中的公式来生成位置编码。