ModIA_TP1/mnist_net.py at master · clemtarge/ModIA_TP1 · GitHub

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

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


class MNISTNet(nn.Module):
    def __init__(self):
        super(MNISTNet, self).__init__()
        self.conv1 = nn.Conv2d(1, 8, kernel_size=5)
        self.conv2 = nn.Conv2d(8, 16, kernel_size=5)
        self.pool = nn.MaxPool2d(kernel_size=2)
        self.fc1 = nn.Linear(256, 128)
        self.fc2 = nn.Linear(128, 64)
        self.fc3 = nn.Linear(64, 10)

    def forward(self, x):
        x = F.relu(self.conv1(x))       # First convolution followed by
        x = self.pool(x)                # a relu activation and a max pooling#
        x = F.relu(self.conv2(x))
        x = self.pool(x)
        x = torch.flatten(x, start_dim=1)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x

    def get_features(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = x.view(-1, 16 * 4 * 4)
        return x


if __name__=='__main__':
    x = torch.rand(16,1,28,28)
    net = MNISTNet()
    y = net(x)
    assert y.shape == (16,10)