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import torch
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
# First Conv Block
self.conv1_1 = nn.Conv2d(3, 32, 3, padding=1)
self.conv1_2 = nn.Conv2d(32, 32, 3, padding=1)
self.pool = nn.MaxPool2d(2, 2)
self.dropout1 = nn.Dropout(0.2)
# Second Conv Block
self.conv2_1 = nn.Conv2d(32, 64, 3, padding=1)
self.conv2_2 = nn.Conv2d(64, 64, 3, padding=1)
self.dropout2 = nn.Dropout(0.3)
# Third Conv Block
self.conv3_1 = nn.Conv2d(64, 128, 3, padding=1)
self.conv3_2 = nn.Conv2d(128, 128, 3, padding=1)
self.dropout3 = nn.Dropout(0.4)
# Dense Layers
self.fc1 = nn.Linear(128 * 4 * 4, 128)
self.dropout4 = nn.Dropout(0.5)
self.fc2 = nn.Linear(128, 10)
def forward(self, x):
# Block 1
x = F.relu(self.conv1_1(x))
x = F.relu(self.conv1_2(x))
x = self.pool(x)
x = self.dropout1(x)
# Block 2
x = F.relu(self.conv2_1(x))
x = F.relu(self.conv2_2(x))
x = self.pool(x)
x = self.dropout2(x)
# Block 3
x = F.relu(self.conv3_1(x))
x = F.relu(self.conv3_2(x))
x = self.pool(x)
x = self.dropout3(x)
# Flatten
x = x.view(-1, 128 * 4 * 4)
# Dense
x = F.relu(self.fc1(x))
x = self.dropout4(x)
x = self.fc2(x)
return x
def create_model():
return Net()
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