I am very new to this pytorch and neural networks.I am stuck in training one model since last 1 week. My model paramters are not getting updated after each epoch. Also, ‘’‘list(model.paramteres()[-1].grad)’’’ returns ‘’‘None’’’.
This is the code I wrote.
for epoch in range(10): running_loss = 0.0 print(list(net.parameters()).grad ) outputs = net(inputs.float()) a=torch.max(outputs,1).indices a=a.float() labels=labels.float() print(list(net.parameters()).grad ) loss = criterion(a,labels.squeeze(1)) loss = torch.tensor(loss, requires_grad = True) print(list(net.parameters()).grad ) optimizer.zero_grad() loss.backward() optimizer.step() print(list(net.parameters()).grad ) print('Finished Training')
also, this is the optimizer I have used,
import torch.optim as optim criterion = nn.BCELoss() optimizer = optim.Adam(net.parameters(), lr=0.001,weight_decay=0.0001) from torch.autograd import Variable
Basically, what I did is I tried to train the model on only one batch of batch size=16 in order to see if my weights are getting updated or not.And I found that my loss, and the weights all remain same for 100 iterations.I really can’t figure out why is this happening.
I also have another question as well. My dataset contains grayscale images.And I need to perform a binary classification on them. This is the model I have made.It takes as an input tensor of shape (N,1,64,64) where N is the batch_size.
import torch import torch.nn as nn import torch.nn.functional as F class Net(nn.Module): def __init__(self): super(Net, self).__init__() self.conv1 = nn.Conv2d(1, 16, 5) self.conv2 = nn.Conv2d(16, 32, 7) self.dropout1 = nn.Dropout2d(0.25) self.dropout2 = nn.Dropout2d(0.5) self.fc1 = nn.Linear(4608,128) self.fc2 = nn.Linear(128,16) self.fc3 = nn.Linear(16, 2) def forward(self, x): # Max pooling over a (2, 2) window x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2)) # If the size is a square, you can specify with a single number x = F.max_pool2d(F.relu(self.conv2(x)), 2) x = self.dropout1(x) # x = F.max_pool2d(F.relu(self.conv3(x)), 2) x = torch.flatten(x, 1) # flatten all dimensions except the batch dimension x = F.relu(self.fc1(x)) x = F.relu(self.fc2(x)) x = self.dropout2(x) x =self.fc3(x) #print(x,torch.max(x)) return x net = Net() net = net.float() print(net)