human
I am working on the file "DEEP LEARNING/Pytorch from scratch/MLP/Part 4 - Fashion-MNIST (Solution).ipynb".
The first task is:
Building the network
Here you should define your network. As with MNIST, each image is 28x28 which is a total of 784 pixels, and there are 10 classes. You should include at least one hidden layer. We suggest you use ReLU activations for the layers and to return the logits or log-softmax from the forward pass. It's up to you how many layers you add and the size of those layers.
Can you write Python program for it?

Can you provide a the Python program you requested?
assistant
#Here is the Python program you requested.

from torch import nn, optim
import torch.nn.functional as F

# TODO: Define your network architecture here
class Classifier(nn.Module):
    def __init__(self):
        super().__init__()
        self.fc1 = nn.Linear(784, 256)
        self.fc2 = nn.Linear(256, 128)
        self.fc3 = nn.Linear(128, 64)
        self.fc4 = nn.Linear(64, 10)
        
    def forward(self, x):
        # make sure input tensor is flattened
        x = x.view(x.shape[0], -1)
        
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = F.relu(self.fc3(x))
        x = F.log_softmax(self.fc4(x), dim=1)
        
        return x