positionwise

"Positionwise"通常是指在特定位置或序列中的特定位置上的数据或信息。在许多领域中，特别是在文本处理和计算机视觉中，位置信息是非常重要的。例如，在自然语言处理中，句子中的单词或字符的位置可能会影响句子的意义。在计算机视觉中，图像中的对象位置可能会影响其识别和理解。

"Positionwise"也可能指特定的训练模式或策略，例如在深度学习中，将每个输入样本的所有输入特征视为同一位置的元素，并将其整合为表示输入样本的整体向量。这种方法称为"positionwise"表示，它可以与全局平均池化（Global Average Pooling）结合使用，以便从整个输入中获取有用的信息。

在计算机科学和相关领域中，"positionwise"是一个常见术语，用于描述在处理序列数据时，关注或处理数据在序列中的位置或顺序。这个概念也常用于生成模型中，特别是在自然语言处理和语音识别中。

请注意，以上解释可能不完全准确或适用于所有情况，因为"positionwise"在不同的上下文中可能有不同的含义。如果您有特定的上下文或问题，我可以为您提供更具体的答案。

相关问题

def ff(inputs, num_units, scope="positionwise_feedforward"): ''' inputs: A 3d tensor with shape of [N, T, C]. num_units: A list of two integers. scope: Optional scope for `variable_scope`. Returns: A 3d tensor with the same shape and dtype as inputs ''' # Inner layer outputs = tf.keras.layers.Dense(num_units[0], activation=tf.nn.relu)(inputs) # Outer layer outputs = tf.keras.layers.Dense(num_units[1])(outputs) # Residual connection outputs = tf.keras.layers.concatenate([outputs, inputs],axis=-1) # Normalize outputs = tf.keras.layers.LayerNormalization()(outputs) return outputs

这是一个实现位置编码器中的前馈神经网络的函数。输入是一个形状为[N, T, C]的3D张量，其中N表示批量大小，T表示序列长度，C表示每个元素的特征数。该函数使用两个Dense层，其中第一个层使用ReLU激活函数，第二个层没有激活函数。在这两个层之间，使用残差连接将输入添加到输出中，并对输出进行层归一化以减少内部协变量位移。最终输出与输入具有相同的形状和数据类型。

手把手教你用pytorch代码实现Transformer模型

以下是手把手教你用PyTorch代码实现Transformer模型的步骤：

1.导入必要的库和模块

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

2.定义Transformer模型的编码器和解码器

class Encoder(nn.Module):
    def __init__(self, input_dim, hid_dim, n_layers, n_heads, pf_dim, dropout, device):
        super().__init__()

        self.device = device

        self.tok_embedding = nn.Embedding(input_dim, hid_dim)
        self.pos_embedding = nn.Embedding(1000, hid_dim)
        self.layers = nn.ModuleList([EncoderLayer(hid_dim, n_heads, pf_dim, dropout, device) for _ in range(n_layers)])

        self.dropout = nn.Dropout(dropout)

        self.scale = torch.sqrt(torch.FloatTensor([hid_dim])).to(device)

    def forward(self, src, src_mask):
        # src = [batch size, src len]
        # src_mask = [batch size, 1, 1, src len]

        batch_size = src.shape[0]
        src_len = src.shape[1]

        pos = torch.arange(0, src_len).unsqueeze(0).repeat(batch_size, 1).to(self.device)

        src = self.dropout((self.tok_embedding(src) * self.scale) + self.pos_embedding(pos))

        for layer in self.layers:
            src = layer(src, src_mask)

        return src


class EncoderLayer(nn.Module):
    def __init__(self, hid_dim, n_heads, pf_dim, dropout, device):
        super().__init__()

        self.self_attn_layer_norm = nn.LayerNorm(hid_dim)
        self.ff_layer_norm = nn.LayerNorm(hid_dim)
        self.self_attention = MultiHeadAttentionLayer(hid_dim, n_heads, dropout, device)
        self.positionwise_feedforward = PositionwiseFeedforwardLayer(hid_dim, pf_dim, dropout)
        self.dropout = nn.Dropout(dropout)

    def forward(self, src, src_mask):
        # src = [batch size, src len, hid dim]
        # src_mask = [batch size, 1, 1, src len]

        # self attention
        _src, _ = self.self_attention(src, src, src, src_mask)

        # dropout, residual connection and layer norm
        src = self.self_attn_layer_norm(src + self.dropout(_src))

        # positionwise feedforward
        _src = self.positionwise_feedforward(src)

        # dropout, residual and layer norm
        src = self.ff_layer_norm(src + self.dropout(_src))

        return src


class MultiHeadAttentionLayer(nn.Module):
    def __init__(self, hid_dim, n_heads, dropout, device):
        super().__init__()

        assert hid_dim % n_heads == 0

        self.hid_dim = hid_dim
        self.n_heads = n_heads
        self.head_dim = hid_dim // n_heads

        self.fc_q = nn.Linear(hid_dim, hid_dim)
        self.fc_k = nn.Linear(hid_dim, hid_dim)
        self.fc_v = nn.Linear(hid_dim, hid_dim)

        self.fc_o = nn.Linear(hid_dim, hid_dim)

        self.dropout = nn.Dropout(dropout)

        self.scale = torch.sqrt(torch.FloatTensor([self.head_dim])).to(device)

    def forward(self, query, key, value, mask=None):
        batch_size = query.shape[0]

        Q = self.fc_q(query)
        K = self.fc_k(key)
        V = self.fc_v(value)

        # Q = [batch size, query len, hid dim]
        # K = [batch size, key len, hid dim]
        # V = [batch size, value len, hid dim]

        Q = Q.view(batch_size, -1, self.n_heads, self.head_dim).permute(0, 2, 1, 3)
        K = K.view(batch_size, -1, self.n_heads, self.head_dim).permute(0, 2, 1, 3)
        V = V.view(batch_size, -1, self.n_heads, self.head_dim).permute(0, 2, 1, 3)

        # Q = [batch size, n heads, query len, head dim]
        # K = [batch size, n heads, key len, head dim]
        # V = [batch size, n heads, value len, head dim]

        energy = torch.matmul(Q, K.permute(0, 1, 3, 2)) / self.scale

        # energy = [batch size, n heads, query len, key len]

        if mask is not None:
            energy = energy.masked_fill(mask == 0, -1e10)

        attention = torch.softmax(energy, dim=-1)

        # attention = [batch size, n heads, query len, key len]

        x = torch.matmul(self.dropout(attention), V)

        # x = [batch size, n heads, query len, head dim]

        x = x.permute(0, 2, 1, 3).contiguous()

        # x = [batch size, query len, n heads, head dim]

        x = x.view(batch_size, -1, self.hid_dim)

        # x = [batch size, query len, hid dim]

        x = self.fc_o(x)

        # x = [batch size, query len, hid dim]

        return x, attention


class PositionwiseFeedforwardLayer(nn.Module):
    def __init__(self, hid_dim, pf_dim, dropout):
        super().__init__()

        self.fc_1 = nn.Linear(hid_dim, pf_dim)
        self.fc_2 = nn.Linear(pf_dim, hid_dim)

        self.dropout = nn.Dropout(dropout)

    def forward(self, x):
        # x = [batch size, seq len, hid dim]

        x = self.dropout(torch.relu(self.fc_1(x)))

        # x = [batch size, seq len, pf dim]

        x = self.fc_2(x)

        # x = [batch size, seq len, hid dim]

        return x


class Decoder(nn.Module):
    def __init__(self, output_dim, hid_dim, n_layers, n_heads, pf_dim, dropout, device):
        super().__init__()

        self.device = device

        self.tok_embedding = nn.Embedding(output_dim, hid_dim)
        self.pos_embedding = nn.Embedding(1000, hid_dim)

        self.layers = nn.ModuleList([DecoderLayer(hid_dim, n_heads, pf_dim, dropout, device) for _ in range(n_layers)])

        self.fc_out = nn.Linear(hid_dim, output_dim)

        self.dropout = nn.Dropout(dropout)

        self.scale = torch.sqrt(torch.FloatTensor([hid_dim])).to(device)

    def forward(self, trg, enc_src, trg_mask, src_mask):
        # trg = [batch size, trg len]
        # enc_src = [batch size, src len, hid dim]
        # trg_mask = [batch size, 1, trg len, trg len]
        # src_mask = [batch size, 1, 1, src len]

        batch_size = trg.shape[0]
        trg_len = trg.shape[1]

        pos = torch.arange(0, trg_len).unsqueeze(0).repeat(batch_size, 1).to(self.device)

        trg = self.dropout((self.tok_embedding(trg) * self.scale) + self.pos_embedding(pos))

        for layer in self.layers:
            trg, attention = layer(trg, enc_src, trg_mask, src_mask)

        output = self.fc_out(trg)

        return output, attention


class DecoderLayer(nn.Module):
    def __init__(self, hid_dim, n_heads, pf_dim, dropout, device):
        super().__init__()

        self.self_attn_layer_norm = nn.LayerNorm(hid_dim)
        self.enc_attn_layer_norm = nn.LayerNorm(hid_dim)
        self.ff_layer_norm = nn.LayerNorm(hid_dim)
        self.self_attention = MultiHeadAttentionLayer(hid_dim, n_heads, dropout, device)
        self.encoder_attention = MultiHeadAttentionLayer(hid_dim, n_heads, dropout, device)
        self.positionwise_feedforward = PositionwiseFeedforwardLayer(hid_dim, pf_dim, dropout)
        self.dropout = nn.Dropout(dropout)

    def forward(self, trg, enc_src, trg_mask, src_mask):
        # trg = [batch size, trg len, hid dim]
        # enc_src = [batch size, src len, hid dim]
        # trg_mask = [batch size, 1, trg len, trg len]
        # src_mask = [batch size, 1, 1, src len]

        # self attention
        _trg, _ = self.self_attention(trg, trg, trg, trg_mask)

        # dropout, residual connection and layer norm
        trg = self.self_attn_layer_norm(trg + self.dropout(_trg))

        # encoder attention
        _trg, attention = self.encoder_attention(trg, enc_src, enc_src, src_mask)

        # dropout, residual connection and layer norm
        trg = self.enc_attn_layer_norm(trg + self.dropout(_trg))

        # positionwise feedforward
        _trg = self.positionwise_feedforward(trg)

        # dropout, residual and layer norm
        trg = self.ff_layer_norm(trg + self.dropout(_trg))

        return trg, attention

3.定义完模型后，我们需要定义一些辅助函数，如下所示：

def get_pad_mask(seq, pad_idx):
    return (seq != pad_idx).unsqueeze(-2)


def get_subsequent_mask(seq):
    sz_b, len_s = seq.size()
    subsequent_mask = (1 - torch.triu(torch.ones((1, len_s, len_s), device=seq.device), diagonal=1)).bool()
    return subsequent_mask


def get_clones(module, N):
    return nn.ModuleList([copy.deepcopy(module) for i in range(N)])


def greedy_decode(model, src, src_mask, max_len, start_symbol):
    memory = model.encode(src, src_mask)
    ys = torch.ones(1, 1).fill_(start_symbol).type_as(src.data)
    for i in range(max_len - 1):
        out = model.decode(memory, src_mask, ys, subsequent_mask)
        prob = model.generator(out[:, -1])
        _, next_word = torch.max(prob, dim=1)
        next_word = next_word.item()
        ys = torch.cat([ys, torch.ones(1, 1).type_as(src.data).fill_(next_word)], dim=1)
        if next_word == 2:
            break
    return ys

4.定义完辅助函数后，我们需要定义完整的Transformer模型，如下所示：

class Transformer(nn.Module):
    def __init__(self, src_vocab, trg_vocab, hid_dim, n_layers, n_heads, pf_dim, dropout, device, max_length=100):
        super().__init__()

        self.device = device
        self.src_vocab = src_vocab
        self.trg_vocab = trg_vocab
        self.hid_dim = hid_dim
        self.n_layers = n_layers
        self.n_heads = n_heads
        self.pf_dim = pf_dim
        self.dropout = dropout
        self.max_length = max_length

        self.encoder = Encoder(src_vocab, hid_dim, n_layers, n_heads, pf_dim, dropout, device)
        self.decoder = Decoder(trg_vocab, hid_dim, n_layers, n_heads, pf_dim, dropout, device)
        self.src_pad_idx = src_vocab.stoi['<pad>']
        self.trg_pad_idx = trg_vocab.stoi['<pad>']

        self.device = device

    def make_src_mask(self, src):
        # src = [batch size, src len]

        src_mask = (src != self.src_pad_idx).unsqueeze(1).unsqueeze(2)

        # src_mask = [batch size, 1, 1, src len]

        return src_mask

    def make_trg_mask(self, trg):
        # trg = [batch size, trg len]

        trg_pad_mask = (trg != self.trg_pad_idx).unsqueeze(1).unsqueeze(2)

        # trg_pad_mask = [batch size, 1, 1, trg len]

        trg_len = trg.shape[1]

        trg_sub_mask = torch.tril(torch.ones((trg_len, trg_len), device=self.device)).bool()

        # trg_sub_mask = [trg len, trg len]

        trg_mask = trg_pad_mask & trg_sub_mask

        # trg_mask = [batch size, 1, trg len, trg len]

        return trg_mask

    def forward(self, src, trg):
        # src = [batch size, src len]
        # trg = [batch size, trg len]

        src_mask = self.make_src_mask(src)
        trg_mask = self.make_trg_mask(trg)

        enc_src = self.encoder(src, src_mask)

        output, attention = self.decoder(trg, enc_src, trg_mask, src_mask)

        return output, attention

    def encode(self, src, src_mask):
        # src = [batch size, src len]
        # src_mask = [batch size, 1, 1, src len]

        enc_src = self.encoder(src, src_mask)

        return enc_src

    def decode(self, memory, src_mask, trg, trg_mask):
        # memory = [batch size, src len, hid dim]
        # src_mask = [batch size, 1, 1, src len]
        # trg = [batch size, trg