RuntimeError: Expected 4-dimensional input for 4-dimensional weight X, but got 3-dimensional input of size Y instead
I am building a CNN to do image classification on the EMNIST dataset. 

To do so, I have the following datasets:

import scipy .io
emnist = scipy.io.loadmat(DIRECTORY + '/emnist-letters.mat')
data = emnist ['dataset']
X_train = data ['train'][0, 0]['images'][0, 0]
X_train = X_train.reshape((-1,28,28), order='F')

y_train = data ['train'][0, 0]['labels'][0, 0]

X_test = data ['test'][0, 0]['images'][0, 0]
X_test = X_test.reshape((-1,28,28), order = 'F')

y_test = data ['test'][0, 0]['labels'][0, 0]


With shape:


X_train = (124800, 28, 28)
y_train = (124800, 1)
X_test = (20800, 28, 28)
y_test = (20800, 1)


Note that the pictures are grayscale, so the colors are represented with only one number. 

Which I prepare further as follows:

train_dataset = torch.utils.data.TensorDataset(torch.from_numpy(X_train), torch.from_numpy(y_train))
test_dataset = torch.utils.data.TensorDataset(torch.from_numpy(X_test), torch.from_numpy(y_test))

train_loader = torch.utils.data.DataLoader(dataset=train_dataset, 
                                           batch_size=batch_size, 
                                           shuffle=True)

test_loader = torch.utils.data.DataLoader(dataset=test_dataset, 
                                          batch_size=batch_size, 
                                          shuffle=False)


My model looks as follows:

class CNNModel(nn.Module):
    def __init__(self):
        super(CNNModel, self).__init__()

        self.cnn_layers = Sequential(
            # Defining a 2D convolution layer
            Conv2d(1, 4, kernel_size=3, stride=1, padding=1),
            BatchNorm2d(4),
            ReLU(inplace=True),
            MaxPool2d(kernel_size=2, stride=2),
            # Defining another 2D convolution layer
            Conv2d(4, 4, kernel_size=3, stride=1, padding=1),
            BatchNorm2d(4),
            ReLU(inplace=True),
            MaxPool2d(kernel_size=2, stride=2),
        )

        self.linear_layers = Sequential(
            Linear(4 * 7 * 7, 10)
        )

    # Defining the forward pass    
    def forward(self, x):
        x = self.cnn_layers(x)
        x = x.view(x.size(0), -1)
        x = self.linear_layers(x)
        return x

model = CNNModel()


The code below is part of the code that I use to train my model:

for epoch in range(num_epochs):
    for i, (images, labels) in enumerate(train_loader):

    images = Variable(images)
    labels = Variable(labels)

    # Forward pass to get output/logits
    outputs = model(images)


However, by excuting my code, I get the following error:

RuntimeError: Expected 4-dimensional input for 4-dimensional weight [4, 1, 3, 3], but got 3-dimensional input of size [100, 28, 28] instead


So a 4D input is expected while my input is 3D. What can I do so a 3D model is expected rather than a 4D model?

Here a similar question is asked, however I dont see how I can translate this to my code

The convolution expects the input to have size [batch_size, channels, height, width], but your images have size [batch_size, height, width], the channel dimension is missing. Greyscale is represented with a single channel and you have correctly set the in_channels of the first convolutions to 1, but your images don't have the matching dimension.

You can easily add the singular dimension with torch.unsqueeze.

Also, please don't use Variable, it was deprecated with PyTorch 0.4.0, which was released over 2 years ago, and all of its functionality has been merged into the tensors.

for i, (images, labels) in enumerate(train_loader):
    # Add a single channel dimension
    # From: [batch_size, height, width]
    # To: [batch_size, 1, height, width]
    images = images.unsqueeze(1)

    # Forward pass to get output/logits
    outputs = model(images)

