X = torch.normal(0, 1, (num_examples, len(w)))
时间: 2023-10-22 12:10:09 浏览: 59
As an AI language model, I don't have access to the full context of your code. However, I can explain what this line does based on the information provided.
This line creates a tensor `X` with dimensions `(num_examples, len(w))` and fills it with random values drawn from a normal distribution with mean 0 and standard deviation 1 (i.e. a standard normal distribution). The values in `X` will be used as inputs to a neural network model.
The `len(w)` part of the dimensions likely refers to the number of input features in the model, while `num_examples` is the number of training examples.
相关问题
不使用任何库写 X = torch.normal(0, 1, (num_examples, len(w))) y = torch.matmul(X, w) + b y += torch.normal(0, 0.01, y.shape)
要在不使用任何库的情况下执行这个操作,你可以按照以下步骤手动实现:
1. 初始化一个形状为 `(num_examples, len(w))` 的零张量 `X`。
2. 对于 `i` 从 0 到 `num_examples-1`:
- 对于 `j` 从 0 到 `len(w)-1`:
- 生成一个从均值为 0,标准差为 1 的正态分布中采样得到的随机数,并将其赋值给 `X[i][j]`。
3. 初始化一个形状为 `(num_examples, 1)` 的零张量 `y`。
4. 对于 `i` 从 0 到 `num_examples-1`:
- 计算 `X[i]` 与 `w` 的矩阵乘法,并将结果存储在 `y[i]` 中。
- 将 `b` 加到 `y[i]` 上。
- 生成一个从均值为 0,标准差为 0.01 的正态分布中采样得到的随机数,并将其加到 `y[i]` 上。
以下是使用 Python 代码实现上述步骤的例子:
```python
import random
# 假设 num_examples 是样本数量
# 假设 w 是一个形状为 (n, p) 的张量
# 假设 b 是一个形状为 (p,) 的张量
n, p = w.shape
# 初始化 X
X = torch.zeros((num_examples, n))
for i in range(num_examples):
for j in range(n):
X[i][j] = random.normalvariate(0, 1)
# 初始化 y
y = torch.zeros((num_examples, 1))
for i in range(num_examples):
for j in range(n):
for k in range(p):
y[i][0] += X[i][j] * w[j][k]
for j in range(p):
y[i][0] += b[j]
noise = random.normalvariate(0, 0.01)
y[i][0] += noise
```
请注意,这只是一个示例,实际使用时可能需要根据具体情况进行调整。
这段代码有什么错误def forward(self,x): num_nodes = x.size(1) # sub_graph size batch_size = x.size(0) W = torch.cat([self.W] * batch_size, dim=0) representation = torch.matmul(x, W) r_sum = torch.sum(representation, dim=-1, keepdim=False) b = torch.zeros([batch_size, num_nodes]) b = Variable(b) one = torch.ones_like(r_sum) zero = torch.zeros_like(r_sum) label = torch.clone(r_sum) label = torch.where(label == 0, one, zero) b.data.masked_fill_(label.bool(), -float('inf')) num_iterations = 3 for i in range(num_iterations): c = torch.nn.functional.softmax(b, dim=-1) weight_coeff = c.unsqueeze(dim=1) representation_global = torch.matmul(weight_coeff, representation) representation_global_all = torch.cat([representation_global] * num_nodes, dim=1) representation_similarity = torch.nn.functional.cosine_similarity(representation, representation_global_all, dim=-1) representation_similarity.data.masked_fill_(label.bool(), -float('inf')) b = representation_similarity return representation_global.squeeze(dim=1)
这段代码中存在一个错误。在for循环中,代码更新了变量b,但是在更新后没有再次进行softmax操作,导致后续的计算结果不正确。因此,需要在for循环中,在更新b后再次进行softmax操作,如下所示:
```
for i in range(num_iterations):
c = torch.nn.functional.softmax(b, dim=-1)
weight_coeff = c.unsqueeze(dim=1)
representation_global = torch.matmul(weight_coeff, representation)
representation_global_all = torch.cat([representation_global] * num_nodes, dim=1)
representation_similarity = torch.nn.functional.cosine_similarity(representation, representation_global_all, dim=-1)
representation_similarity.data.masked_fill_(label.bool(), -float('inf'))
b = torch.nn.functional.softmax(representation_similarity, dim=-1) # 进行softmax操作
```
这样修改后,该段代码的功能应该能够正常运行。