print ("weight:", net[0].weight.data.numpy())
时间: 2024-06-03 15:12:45 浏览: 9
这是一行 Python 代码,用于输出神经网络模型的第一层的权重参数。假设 `net` 是一个神经网络模型,`net[0]` 表示该模型的第一层,`.weight` 表示该层的权重参数,`.data` 表示该参数的数据,`.numpy()` 表示将该数据转换为 NumPy 数组,最后用 `print` 函数输出该数组。
相关问题
解释代码: elif isinstance(m0, nn.Conv2d): idx0 = np.squeeze(np.argwhere(np.asarray(start_mask.cpu().numpy()))) idx1 = np.squeeze(np.argwhere(np.asarray(end_mask.cpu().numpy()))) print('In shape: {:d} Out shape:{:d}'.format(idx0.shape[0], idx1.shape[0])) w = m0.weight.data[:, idx0, :, :].clone() w = w[idx1, :, :, :].clone() m1.weight.data = w.clone()
这是一个 PyTorch 模型中网络层权重剪枝的代码段,主要功能是根据指定的起始和终止剪枝掩码,将卷积层的输入通道和输出通道进行筛选和更新。具体来说,首先从起始和终止掩码中取出相关的通道索引,然后根据这些索引对卷积层的权重张量进行裁剪和更新,最终得到一个新的卷积层。
import torch import matplotlib.pyplot as plt def features(x): x = x.unsqueeze(1) return torch.cat([x ** i for i in range(1, 5)], 1) x_weight = torch.Tensor([-1.13, -2.14, 3.15, -0.01]).unsqueeze(1) b = torch.Tensor([0.512]) def target(x): return x.mm(x_weight) + b.item() def get_batch_data(batch_size): batch_x = torch.rand(batch_size) features_x = features(batch_x) target_y = target(features_x) return features_x, target_y class PR(torch.nn.Module): def __init__(self): super(PR, self).__init__() self.poly = torch.nn.Linear(4, 1) def forward(self, x): return self.poly(x) epochs = 10000 batch_size = 32 model = PR() criterion = torch.nn.MSELoss() optimizer = torch.optim.SGD(model.parameters(), 0.01) for i in range(epochs): batch_x, batch_y = get_batch_data(batch_size) out = model(batch_x) loss = criterion(out, batch_y) optimizer.zero_grad() loss.backward() optimizer.step() if(i % 100 == 0): print(f"Epoch:{i+1}/{epochs}, loss:{loss.item}") if(i%1000 == 0): predict = model(features(batch_x)) plt.plot(batch_x.data.numpy(), predict.squeeze().data.numpy(), "r") loss = criterion(predict, batch_y) plt.title(f"Loss:{loss.item()}") plt.xlabel("X") plt.ylabel("Y") plt.scatter(batch_x, batch_y) plt.show()哪里出错了
代码有一个小错误,第26行应该是`print(f"Epoch:{i+1}/{epochs}, loss:{loss.item()}")`,需要加上括号。另外,在第34行,应该是`loss = criterion(out, batch_y)`,而不是`loss = criterion(predict, batch_y)`。正确的代码如下所示:
```
import torch
import matplotlib.pyplot as plt
def features(x):
x = x.unsqueeze(1)
return torch.cat([x ** i for i in range(1, 5)], 1)
x_weight = torch.Tensor([-1.13, -2.14, 3.15, -0.01]).unsqueeze(1)
b = torch.Tensor([0.512])
def target(x):
return x.mm(x_weight) + b.item()
def get_batch_data(batch_size):
batch_x = torch.rand(batch_size)
features_x = features(batch_x)
target_y = target(features_x)
return features_x, target_y
class PR(torch.nn.Module):
def __init__(self):
super(PR, self).__init__()
self.poly = torch.nn.Linear(4, 1)
def forward(self, x):
return self.poly(x)
epochs = 10000
batch_size = 32
model = PR()
criterion = torch.nn.MSELoss()
optimizer = torch.optim.SGD(model.parameters(), 0.01)
for i in range(epochs):
batch_x, batch_y = get_batch_data(batch_size)
out = model(batch_x)
loss = criterion(out, batch_y)
optimizer.zero_grad()
loss.backward()
optimizer.step()
if(i % 100 == 0):
print(f"Epoch:{i+1}/{epochs}, loss:{loss.item()}")
if(i%1000 == 0):
predict = model(features(batch_x))
plt.plot(batch_x.data.numpy(), predict.squeeze().data.numpy(), "r")
loss = criterion(out, batch_y)
plt.title(f"Loss:{loss.item()}")
plt.xlabel("X")
plt.ylabel("Y")
plt.scatter(batch_x, batch_y)
plt.show()
```
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