python - Error during training on ResNet50: Expected 4-dimensional input for 4-dimensional weight [64, 3, 7, 7], but got input of size [8, 196608] instead

Question

python v 3.7.3, pytorch v 0.4.1, using jupyter

I am working on creating an image classifier using transfer learning with ResNet50 as the base model. I've run across an error that I don't understand how to debug, even after searching for solutions online. I'm not sure why my dimensions are so off. Any help on what to do would be appreciated. Also, any book recs on Deep Learning using Pytorch would be great :)

Code for final layer of ResNet:

fc_inputs = model.fc.in_features

model.fc = nn.Sequential(OrderedDict([
            ('fc1', nn.Linear(fc_inputs, 256)), 
            ('relu', nn.ReLU()),
            ('dropout', nn.Dropout(0.2)),
            ('fc2', nn.Linear(256, 25)),
            ('output', nn.LogSoftmax(dim=1))
]))

model.classifier = classifier

print(model)
print(model.classifier[0])
print(model.classifier[3])

optimizer = torch.optim.Adam(model.classifier.parameters(), lr = 0.001) 
criterion = nn.CrossEntropyLoss()
scheduler = lr_scheduler.StepLR(optimizer, step_size=5, gamma=0.1)

code for training:

epochs = 1
steps = 0
running_loss = 0
print_every = 10


for epoch in range(epochs):

    for images, labels in dataloaders['train']:

        images = images.view(images.shape[0], -1)  #this flattens it?
        steps += 1

        images, labels = images.to(device), labels.to(device)

        optimizer.zero_grad()

        logps = model(images)

        loss = criterion(logps, labels)
        loss.backward()
        optimizer.step()

        running_loss += loss.item()

        if step % print_every == 0:
            model.eval()
            test_loss = 0
            accuracy = 0

            for images, labels in dataloader:
                images, labels = images.to(device), labels.to(device)

                logps = model(images)
                loss = criterion(logps, labels)
                test_loss += loss.item()

                ps = torch.exp(logps)

                top_ps, top_class = ps.topk(1, dim=1)
                equality = top_class == labels.view(*top_class.shape)
                accuracy += torch.mean(equality.type(torch.FloatTensor)).item()

            train_losses.append(running_loss / len(dataloader['train']))
            test_losses.append(test_loss / len(dataloader['test']))

            print(f"Epoch {epoch + 1}/{epochs}.."
                  f"Train loss: {running_loss / print_every:.3f}.."
                  f"Test loss: {test_loss/len(dataloader['test']):.3f}.."
                  f"Test accuracy: {accuracy/len(dataloader['test']):.3f}")


            running_loss = 0
            model.train()            

error:

RuntimeError                              Traceback (most recent call last)
<ipython-input-47-8f88694d7909> in <module>
     16         optimizer.zero_grad()
     17 
---> 18         logps = model(images)
     19 
     20         loss = criterion(logps, labels)

~\Anaconda3\lib\site-packages\torch\nn\modules\module.py in __call__(self, *input, **kwargs)
    475             result = self._slow_forward(*input, **kwargs)
    476         else:
--> 477             result = self.forward(*input, **kwargs)
    478         for hook in self._forward_hooks.values():
    479             hook_result = hook(self, input, result)

~\Anaconda3\lib\site-packages\torchvision\models\resnet.py in forward(self, x)
    137 
    138     def forward(self, x):
--> 139         x = self.conv1(x)
    140         x = self.bn1(x)
    141         x = self.relu(x)

~\Anaconda3\lib\site-packages\torch\nn\modules\module.py in __call__(self, *input, **kwargs)
    475             result = self._slow_forward(*input, **kwargs)
    476         else:
--> 477             result = self.forward(*input, **kwargs)
    478         for hook in self._forward_hooks.values():
    479             hook_result = hook(self, input, result)

~\Anaconda3\lib\site-packages\torch\nn\modules\conv.py in forward(self, input)
    299     def forward(self, input):
    300         return F.conv2d(input, self.weight, self.bias, self.stride,
--> 301                         self.padding, self.dilation, self.groups)
    302 
    303 

RuntimeError: Expected 4-dimensional input for 4-dimensional weight [64, 3, 7, 7], but got input of size [8, 196608] instead

EDIT: I removed images = images.view.shape[0], -1, which was converting my tensor to [8, 196608]. But now I am receiving a new error:

RuntimeError: size mismatch, m1: [8 x 8192], m2: [2048 x 256] at c:\programdata\miniconda3\conda-bld\pytorch_1532509700152\work\aten\src\th\generic/THTensorMath.cpp:2070