cnn.py

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

# class FontClassifierCNN(nn.Module):
#     def __init__(self,edge=True):
#         super(FontClassifierCNN, self).__init__()
#         if edge:
#             self.conv1 = nn.Conv2d(1, 16, 5, padding=2)
#         else:
#             self.conv1 = nn.Conv2d(3, 16, 5, padding=2)
#         self.pool = nn.MaxPool2d(2, 2)
#         self.conv2 = nn.Conv2d(16, 32, 5, padding=2)
#         self.conv3 = nn.Conv2d(32, 64, 3, padding=1)
#         self.conv4 = nn.Conv2d(64, 128, 3, padding=1)
#         self.conv5 = nn.Conv2d(128, 256, 3, padding=1)
#         self.fc1 = nn.Linear(4096, 1024)
#         self.fc2 = nn.Linear(1024, 128)
#         self.fc3 = nn.Linear(128, 10)
#         # self.features = None

#     def forward(self, x):
#         x = self.pool(F.relu(self.conv1(x)))
#         x = self.pool(F.relu(self.conv2(x)))
#         x = self.pool(F.relu(self.conv3(x)))
#         x = self.pool(F.relu(self.conv4(x)))
#         x = self.pool(F.relu(self.conv5(x)))
#         x = x.view(-1, 256 * x.size(2) * x.size(3))
#         x = F.relu(self.fc1(x))
#         x = F.relu(self.fc2(x))
#         x = self.fc3(x)
#         return x

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

class FontClassifierCNN(nn.Module):
    def __init__(self, edge=True):
        super(FontClassifierCNN, self).__init__()
        in_channels = 1 if edge else 3
        self.conv1 = nn.Conv2d(in_channels, 16, 5, padding=2)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(16, 32, 5, padding=2)
        self.conv3 = nn.Conv2d(32, 64, 3, padding=1)
        self.fc1 = nn.Linear(64 * 16 * 16, 512)
        self.dropout = nn.Dropout(0.3)  # Adjusted to 0.3
        self.fc2 = nn.Linear(512, 128)
        self.fc3 = nn.Linear(128, 10)

    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = self.pool(F.relu(self.conv3(x)))
        x = x.view(-1, 64 * 16 * 16)
        x = F.relu(self.fc1(x))
        x = self.dropout(x)
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x