attn_model = 'dot' # attn_model = 'general' # attn_model = 'concat'
时间: 2024-01-16 11:05:41 浏览: 20
这段代码是为 `attn_model` 变量设置了一个字符串值。根据给定的注释,它可以是 `'dot'`、`'general'` 或 `'concat'` 中的一个。
这些字符串值代表了注意力机制(attention mechanism)的不同类型,常用于序列到序列(sequence-to-sequence)模型中。注意力机制用于在每个时间步选择输入序列中的哪些部分需要被模型关注和处理。
这里是对每种类型的简要解释:
- `'dot'`:简单的点积注意力机制,通过计算查询向量和键向量之间的点积来评估注意力权重。
- `'general'`:使用额外的线性层将查询向量和键向量映射到相同的空间维度,然后计算它们之间的点积来评估注意力权重。
- `'concat'`:将查询向量和键向量连接在一起,然后通过一个线性层将其映射到一个新的空间,最后计算注意力权重。
根据您的需要,您可以选择其中一种注意力机制,并将其赋值给 `attn_model` 变量。例如:
```python
attn_model = 'dot'
# or
attn_model = 'general'
# or
attn_model = 'concat'
```
这样,您就可以根据选择的注意力机制类型在后续代码中使用 `attn_model` 变量。
相关问题
请在这个DeepCFD的网络添加attention机制,并给出示例代码:import paddle import paddle.nn as nn class Attention(nn.Layer): def __init__(self, input_size, hidden_size): super(Attention, self).__init__() self.input_size = input_size self.hidden_size = hidden_size self.attn = nn.Linear(self.input_size + self.hidden_size, 1) self.softmax = nn.Softmax(axis=1) def forward(self, input, hidden): max_len = input.shape[0] attn_energies = paddle.zeros([max_len, 1]) for i in range(max_len): attn_energies[i] = self.score(input[i], hidden) attn_weights = self.softmax(attn_energies) context = paddle.sum(attn_weights * input, axis=0) return context.unsqueeze(0) def score(self, input, hidden): energy = self.attn(paddle.concat([input, hidden], axis=1)) return energy class DeepCFD(nn.Layer): def __init__(self, input_size, hidden_size, output_size): super(DeepCFD, self).__init__() self.input_size = input_size self.hidden_size = hidden_size self.output_size = output_size self.lstm = nn.LSTM(input_size, hidden_size, num_layers=2, batch_first=True) self.attention = Attention(input_size, hidden_size) self.fc = nn.Linear(hidden_size, output_size) def forward(self, input): output, (hidden, cell) = self.lstm(input) context = self.attention(output, hidden[-1]) output = self.fc(context) return output
好的,下面是添加了Attention机制的DeepCFD网络的示例代码:
import paddle
import paddle.nn as nn
class Attention(nn.Layer):
def __init__(self, input_size, hidden_size):
super(Attention, self).__init__()
self.input_size = input_size
self.hidden_size = hidden_size
self.attn = nn.Linear(self.input_size + self.hidden_size, 1)
self.softmax = nn.Softmax(axis=1)
def forward(self, input, hidden):
max_len = input.shape[1]
attn_energies = paddle.zeros([input.shape[0], max_len, 1])
for i in range(max_len):
attn_energies[:, i] = self.score(input[:, i, :], hidden)
attn_weights = self.softmax(attn_energies)
context = paddle.sum(attn_weights * input, axis=1)
return context
def score(self, input, hidden):
energy = self.attn(paddle.concat([input, hidden], axis=1))
return energy
class DeepCFD(nn.Layer):
def __init__(self, input_size, hidden_size, output_size):
super(DeepCFD, self).__init__()
self.input_size = input_size
self.hidden_size = hidden_size
self.output_size = output_size
self.lstm = nn.LSTM(input_size, hidden_size, num_layers=2, batch_first=True)
self.attention = Attention(input_size, hidden_size)
self.fc = nn.Linear(hidden_size, output_size)
def forward(self, input):
output, (hidden, cell) = self.lstm(input)
context = self.attention(output, hidden[-1])
output = self.fc(context)
return output
在这个示例代码中,我们将Attention机制应用到了LSTM的输出上。在Attention中,我们计算了每个时间步的注意力能量,然后使用softmax函数计算注意力权重。然后,我们将这些权重与LSTM输出相乘并求和,得到上下文向量作为Attention机制的输出。
在DeepCFD中,我们使用了两层LSTM,然后将LSTM输出和最后一个时刻的隐藏状态作为Attention机制的输入。最后,我们将Attention机制的输出传递到一个全连接层中,得到最终的输出。
transformer代码
以下是一个简单的Transformer模型的代码实现,包括Encoder和Decoder部分:
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
class MultiHeadAttention(nn.Module):
def __init__(self, d_model, n_heads):
super(MultiHeadAttention, self).__init__()
self.d_model = d_model
self.n_heads = n_heads
self.d_k = d_model // n_heads
self.q_linear = nn.Linear(d_model, d_model)
self.v_linear = nn.Linear(d_model, d_model)
self.k_linear = nn.Linear(d_model, d_model)
self.out_linear = nn.Linear(d_model, d_model)
def forward(self, q, k, v, mask=None):
bs = q.size(0)
# Linear projections
k = self.k_linear(k).view(bs, -1, self.n_heads, self.d_k)
q = self.q_linear(q).view(bs, -1, self.n_heads, self.d_k)
v = self.v_linear(v).view(bs, -1, self.n_heads, self.d_k)
# Transpose to get dimensions bs * n_heads * sl * d_model
k = k.transpose(1,2)
q = q.transpose(1,2)
v = v.transpose(1,2)
# Attention
scores = torch.matmul(q, k.transpose(-2,-1)) / math.sqrt(self.d_k)
if mask is not None:
mask = mask.unsqueeze(1)
scores = scores.masked_fill(mask == 0, -1e9)
scores = F.softmax(scores, dim=-1)
attention = torch.matmul(scores, v)
# Concatenate and linear projection
concat_attention = attention.transpose(1,2).contiguous().view(bs, -1, self.d_model)
output = self.out_linear(concat_attention)
return output
class FeedForward(nn.Module):
def __init__(self, d_model, d_ff=2048, dropout=0.1):
super(FeedForward, self).__init__()
self.linear_1 = nn.Linear(d_model, d_ff)
self.dropout = nn.Dropout(dropout)
self.linear_2 = nn.Linear(d_ff, d_model)
def forward(self, x):
x = F.relu(self.linear_1(x))
x = self.dropout(x)
x = self.linear_2(x)
return x
class EncoderLayer(nn.Module):
def __init__(self, d_model, n_heads, dropout=0.1):
super(EncoderLayer, self).__init__()
self.multi_head_attention = MultiHeadAttention(d_model, n_heads)
self.feed_forward = FeedForward(d_model)
self.layer_norm_1 = nn.LayerNorm(d_model)
self.layer_norm_2 = nn.LayerNorm(d_model)
self.dropout_1 = nn.Dropout(dropout)
self.dropout_2 = nn.Dropout(dropout)
def forward(self, x, mask=None):
# Multi-head attention
attn_output = self.multi_head_attention(x, x, x, mask=mask)
attn_output = self.dropout_1(attn_output)
# Residual connection and layer normalization
out1 = self.layer_norm_1(x + attn_output)
# Feed-forward layer
ff_output = self.feed_forward(out1)
ff_output = self.dropout_2(ff_output)
# Residual connection and layer normalization
out2 = self.layer_norm_2(out1 + ff_output)
return out2
class DecoderLayer(nn.Module):
def __init__(self, d_model, n_heads, dropout=0.1):
super(DecoderLayer, self).__init__()
self.multi_head_attention_1 = MultiHeadAttention(d_model, n_heads)
self.multi_head_attention_2 = MultiHeadAttention(d_model, n_heads)
self.feed_forward = FeedForward(d_model)
self.layer_norm_1 = nn.LayerNorm(d_model)
self.layer_norm_2 = nn.LayerNorm(d_model)
self.layer_norm_3 = nn.LayerNorm(d_model)
self.dropout_1 = nn.Dropout(dropout)
self.dropout_2 = nn.Dropout(dropout)
self.dropout_3 = nn.Dropout(dropout)
def forward(self, x, enc_output, src_mask=None, tgt_mask=None):
# Masked multi-head attention
attn_output_1 = self.multi_head_attention_1(x, x, x, mask=tgt_mask)
attn_output_1 = self.dropout_1(attn_output_1)
# Residual connection and layer normalization
out1 = self.layer_norm_1(x + attn_output_1)
# Multi-head attention with encoder output
attn_output_2 = self.multi_head_attention_2(out1, enc_output, enc_output, mask=src_mask)
attn_output_2 = self.dropout_2(attn_output_2)
# Residual connection and layer normalization
out2 = self.layer_norm_2(out1 + attn_output_2)
# Feed-forward layer
ff_output = self.feed_forward(out2)
ff_output = self.dropout_3(ff_output)
# Residual connection and layer normalization
out3 = self.layer_norm_3(out2 + ff_output)
return out3
class Encoder(nn.Module):
def __init__(self, input_dim, d_model, n_layers, n_heads, dropout=0.1):
super(Encoder, self).__init__()
self.d_model = d_model
self.n_layers = n_layers
self.embedding = nn.Embedding(input_dim, d_model)
self.pos_embedding = nn.Embedding(1000, d_model)
self.layers = nn.ModuleList([EncoderLayer(d_model, n_heads, dropout) for _ in range(n_layers)])
self.dropout = nn.Dropout(dropout)
def forward(self, x, mask=None):
# Embedding and position encoding
x = self.embedding(x) * math.sqrt(self.d_model)
pos = torch.arange(0, x.size(1), device=x.device).unsqueeze(0).repeat(x.size(0), 1)
pos = self.pos_embedding(pos)
x = x + pos
x = self.dropout(x)
# Encoder layers
for layer in self.layers:
x = layer(x, mask)
return x
class Decoder(nn.Module):
def __init__(self, output_dim, d_model, n_layers, n_heads, dropout=0.1):
super(Decoder, self).__init__()
self.d_model = d_model
self.n_layers = n_layers
self.embedding = nn.Embedding(output_dim, d_model)
self.pos_embedding = nn.Embedding(1000, d_model)
self.layers = nn.ModuleList([DecoderLayer(d_model, n_heads, dropout) for _ in range(n_layers)])
self.out_linear = nn.Linear(d_model, output_dim)
self.dropout = nn.Dropout(dropout)
def forward(self, x, enc_output, src_mask=None, tgt_mask=None):
# Embedding and position encoding
x = self.embedding(x) * math.sqrt(self.d_model)
pos = torch.arange(0, x.size(1), device=x.device).unsqueeze(0).repeat(x.size(0), 1)
pos = self.pos_embedding(pos)
x = x + pos
x = self.dropout(x)
# Decoder layers
for layer in self.layers:
x = layer(x, enc_output, src_mask, tgt_mask)
# Linear projection to output vocabulary
output = self.out_linear(x)
return output
class Transformer(nn.Module):
def __init__(self, input_dim, output_dim, d_model, n_layers, n_heads, dropout=0.1):
super(Transformer, self).__init__()
self.encoder = Encoder(input_dim, d_model, n_layers, n_heads, dropout)
self.decoder = Decoder(output_dim, d_model, n_layers, n_heads, dropout)
def forward(self, src, tgt, src_mask=None, tgt_mask=None):
enc_output = self.encoder(src, src_mask)
output = self.decoder(tgt, enc_output, src_mask, tgt_mask)
return output
```
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