CNN for MNIST

ConvNet_for_mnist.py

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

class Net(nn.Module):
        def __init__(self):
            super(Net, self).__init__()
            self.conv1 = nn.Conv2d(1, 20, kernel_size=5)
            self.conv2 = nn.Conv2d(20, 40, kernel_size=5)
            self.conv2_drop = nn.Dropout2d()
            self.fc1 = nn.Linear(640, 150)
            self.fc2 = nn.Linear(150, 10)
            self.log_softmax = nn.LogSoftmax(dim = 1)

        def forward(self, x):
            x = x.view(-1,1,28,28)
            x = F.relu(F.max_pool2d(self.conv1(x), 2))
            x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
            x = x.view(-1, 640)
            x = F.relu(self.fc1(x))
            x = F.dropout(x, training=self.training)
            x = F.relu(self.fc2(x))
            x = self.log_softmax(x)
            return x
        
net = Net().cuda()