Python 多层感知器神经网络模型权值获取误差

我正在尝试访问torch在databricks上构建的神经网络模型的权重

守则：

 import torch
 import torch.nn as nn
 import numpy 

 class Feedforward(nn.Module):

     def __init__(self, input_size, hidden_size):
         super(Feedforward, self).__init__()
         self.input_size = input_size
         self.hidden_size  = hidden_size
         self.fc1 = nn.Linear(self.input_size, self.hidden_size)
         self.relu = nn.ReLU()
         self.fc2 = nn.Linear(self.hidden_size, 1)
         self.sigmoid = nn.Sigmoid()

     def forward(self, x):
         hidden = self.fc1(x)
         relu = self.relu(hidden)
         output = self.fc2(relu)
         output = self.sigmoid(output)
         return output

from sklearn.datasets import make_blobs
def blob_label(y, label, loc): # assign labels
    target = numpy.copy(y)
    for l in loc:
        target[y == l] = label
    return target

x_train, y_train = make_blobs(n_samples=40, n_features=2, cluster_std=1.5, shuffle=True)
x_train = torch.FloatTensor(x_train)
y_train = torch.FloatTensor(blob_label(y_train, 0, [0]))
y_train = torch.FloatTensor(blob_label(y_train, 1, [1,2,3]))

x_test, y_test = make_blobs(n_samples=10, n_features=2, cluster_std=1.5, shuffle=True)
x_test = torch.FloatTensor(x_test)
y_test = torch.FloatTensor(blob_label(y_test, 0, [0]))
y_test = torch.FloatTensor(blob_label(y_test, 1, [1,2,3]))

model = Feedforward(2, 10)
criterion = torch.nn.BCELoss()
optimizer = torch.optim.SGD(model.parameters(), lr = 0.01)

y_pred = model(x_test)
before_train = criterion(y_pred.squeeze(), y_test)
print('Test loss before training' , before_train.item())

model.train()
epoch = 20
for epoch in range(epoch):
    optimizer.zero_grad()
    # Forward pass
    y_pred = model(x_train)
    # Compute Loss
    loss = criterion(y_pred.squeeze(), y_train)

print('Epoch {}: train loss: {}'.format(epoch, loss.item()))
# Backward pass
loss.backward()
optimizer.step()

model.eval()
y_pred = model(x_test)
after_train = criterion(y_pred.squeeze(), y_test) 
print('Test loss after Training' , after_train.item())

代码运行良好。但是，当我尝试访问模型的权重时，我得到了错误：

 model.weight # ModuleAttributeError: 'Feedforward' object has no attribute 'weight'

但是，如果我试着

model.fc1.weight

它工作得很好

如何获取多层感知器构建的模型的权重

本帖 不适合我

谢谢

因为该属性是

nn.Linear（）

而不是

前馈

，请尝试更改

class Feedforward(nn.Module):

     def __init__(self, input_size, hidden_size):
         super(Feedforward, self).__init__()
         self.input_size = input_size
         self.hidden_size  = hidden_size
         self.fc1 = nn.Linear(self.input_size, self.hidden_size)
         self.relu = nn.ReLU()
         self.fc2 = nn.Linear(self.hidden_size, 1)
         self.sigmoid = nn.Sigmoid()

到

---

嗨，我试过你的建议了。我得到错误“TypeError:super（type，obj）：obj必须是type的实例或子类型”。谢谢

class Feedforward(nn.Module):

     def __init__(self, input_size, hidden_size):
         super(nn.Linear, self).__init__()
         self.input_size = input_size
         self.hidden_size  = hidden_size
         self.fc1 = nn.Linear(self.input_size, self.hidden_size)
         self.relu = nn.ReLU()
         self.fc2 = nn.Linear(self.hidden_size, 1)
         self.sigmoid = nn.Sigmoid()