def forward_with_weights(self, v, q, w): v_ = self.v_net(v) q_ = self.q_net(q) logits = torch.einsum('bvk,bvq,bqk->bk', (v_, w, q_)) if 1 < self.k: logits = logits.unsqueeze(1) logits = self.p_net(logits).squeeze(1) * self.k return logits有什么作用
时间: 2023-05-19 08:06:01 浏览: 71
这段代码是一个神经网络的前向传播函数,用于计算输入的三个张量 v、q、w 经过神经网络后的输出 logits。具体来说,它首先将 v 和 q 分别通过两个全连接层 v_net 和 q_net 进行线性变换,然后将它们与 w 进行矩阵乘法,得到一个大小为 [batch_size, k] 的矩阵,其中 k 是一个超参数。如果 k 大于 1,那么还会通过一个全连接层 p_net 对这个矩阵进行处理,最后得到一个大小为 [batch_size] 的向量 logits。这个向量的每个元素都表示对应输入的一个样本的输出值。最后,这个向量会乘以 k,得到最终的输出。
相关问题
class NeuralNetwork: def init(self, input_dim, hidden_dim, output_dim): self.input_dim = input_dim self.hidden_dim = hidden_dim self.output_dim = output_dim self.weights1 = np.random.randn(input_dim, hidden_dim) self.bias1 = np.zeros((1, hidden_dim)) self.weights2 = np.random.randn(hidden_dim, output_dim) self.bias2 = np.zeros((1, output_dim)) def relu(self, x): return np.maximum(0, x) def relu_derivative(self, x): return np.where(x >= 0, 1, 0) def forward(self, x): self.z1 = np.dot(x, self.weights1) + self.bias1 self.a1 = self.relu(self.z1) self.z2 = np.dot(self.a1, self.weights2) + self.bias2 self.y_hat = self.z2 return self.y_hat def backward(self, x, y, learning_rate): error = self.y_hat - y delta2 = error delta1 = np.dot(delta2, self.weights2.T) * self.relu_derivative(self.a1) grad_weights2 = np.dot(self.a1.T, delta2) grad_bias2 = np.sum(delta2, axis=0, keepdims=True) grad_weights1 = np.dot(x.T, delta1) grad_bias1 = np.sum(delta1, axis=0) self.weights2 -= learning_rate * grad_weights2 self.bias2 -= learning_rate * grad_bias2 self.weights1 -= learning_rate * grad_weights1 根据代码加上损失函数和优化
函数,可以将这个神经网络变为完整的模型。一种常见的损失函数是均方误差(Mean Squared Error),可以用于回归问题:
```python
class NeuralNetwork:
def __init__(self, input_dim, hidden_dim, output_dim):
self.input_dim = input_dim
self.hidden_dim = hidden_dim
self.output_dim = output_dim
self.weights1 = np.random.randn(input_dim, hidden_dim)
self.bias1 = np.zeros((1, hidden_dim))
self.weights2 = np.random.randn(hidden_dim, output_dim)
self.bias2 = np.zeros((1, output_dim))
def relu(self, x):
return np.maximum(0, x)
def relu_derivative(self, x):
return np.where(x >= 0, 1, 0)
def forward(self, x):
self.z1 = np.dot(x, self.weights1) + self.bias1
self.a1 = self.relu(self.z1)
self.z2 = np.dot(self.a1, self.weights2) + self.bias2
self.y_hat = self.z2
return self.y_hat
def backward(self, x, y, learning_rate):
error = self.y_hat - y
delta2 = error
delta1 = np.dot(delta2, self.weights2.T) * self.relu_derivative(self.a1)
grad_weights2 = np.dot(self.a1.T, delta2)
grad_bias2 = np.sum(delta2, axis=0, keepdims=True)
grad_weights1 = np.dot(x.T, delta1)
grad_bias1 = np.sum(delta1, axis=0)
self.weights2 -= learning_rate * grad_weights2
self.bias2 -= learning_rate * grad_bias2
self.weights1 -= learning_rate * grad_weights1
self.bias1 -= learning_rate * grad_bias1
def mse_loss(self, y, y_hat):
return np.mean((y - y_hat)**2)
def sgd_optimizer(self, x, y, learning_rate):
y_hat = self.forward(x)
loss = self.mse_loss(y, y_hat)
self.backward(x, y, learning_rate)
return loss
```
在这个模型中,我们添加了 `mse_loss` 函数,用于计算均方误差,同时添加了 `sgd_optimizer` 函数,用于执行随机梯度下降优化算法。在每次迭代中,我们计算预测值 `y_hat`,然后计算损失值并执行反向传播算法更新神经网络的权重和偏置。最后,我们返回损失值作为当前迭代的结果。根据需要,我们可以使用其他损失函数和优化器来训练这个神经网络。
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。