Spaces:
Running
Running
| """ | |
| VGG-style CNN for EMNIST character classification. | |
| See the README for a more detailed description. | |
| The .pth file (weights) for this model will be downloaded from HuggingFace by app.py | |
| It's hosted at https://huggingface.co/compendious/EMNIST-OCR-WEIGHTS/ | |
| The file is EMNIST_CNN.pth | |
| Go here to download directly: | |
| https://huggingface.co/compendious/EMNIST-OCR-WEIGHTS/resolve/main/EMNIST_CNN.pth?download=true | |
| """ | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| class ConvBlock(nn.Module): | |
| """Convolutional block: 2 conv layers, LeakyReLU, MaxPool""" | |
| def __init__(self, in_channels, out_channels, padding=1, pool_kernel=2, pool_stride=2): | |
| super(ConvBlock, self).__init__() | |
| self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=padding) | |
| self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=padding) | |
| self.pool = nn.MaxPool2d(kernel_size=pool_kernel, stride=pool_stride) | |
| def forward(self, x): | |
| # CHANGE 1: LeakyReLU prevents "dead neurons," critical for 62-class differentiation. | |
| x = F.leaky_relu(self.conv1(x), negative_slope=0.1) | |
| x = F.leaky_relu(self.conv2(x), negative_slope=0.1) | |
| x = self.pool(x) | |
| return x | |
| class EMNIST_VGG(nn.Module): | |
| """ | |
| The actual CNN that will be trained. | |
| Brought to you by composition. | |
| """ | |
| def __init__(self, num_classes=62): | |
| super(EMNIST_VGG, self).__init__() | |
| # The four blocks | |
| self.conv1 = ConvBlock(in_channels=1, out_channels=32, pool_kernel=2, pool_stride=2) | |
| self.bn1 = nn.BatchNorm2d(32) | |
| self.conv2 = ConvBlock(in_channels=32, out_channels=64, pool_stride=2) | |
| self.bn2 = nn.BatchNorm2d(64) | |
| self.conv3 = ConvBlock(in_channels=64, out_channels=128, pool_stride=1) | |
| self.bn3 = nn.BatchNorm2d(128) | |
| self.conv4 = ConvBlock(in_channels=128, out_channels=256, pool_stride=1) | |
| self.bn4 = nn.BatchNorm2d(256) | |
| # CHANGE 2: Spatial Dropout. | |
| # Drops entire feature maps to force redundancy, unlike standard dropout. | |
| self.spatial_drop = nn.Dropout2d(p=0.1) | |
| # Flatten layer (no parameters needed, only reshaping) | |
| self.flatten = nn.Flatten() | |
| # Two fully-connected layers | |
| # CHANGE 3: Expanded Width (256 -> 512). | |
| # Your Keras model used 512; 256 is a bottleneck for 62 classes. | |
| self.fc1 = nn.Linear(256 * 5 * 5, 512) | |
| self.bn_fc = nn.BatchNorm1d(512) # Added BN to the dense layer for stability | |
| self.dropout = nn.Dropout(p=0.5) | |
| # Classifier | |
| self.fc2 = nn.Linear(512, num_classes) | |
| def forward(self, x): | |
| x = self.conv1(x) | |
| x = self.bn1(x) | |
| x = self.conv2(x) | |
| x = self.bn2(x) | |
| x = self.spatial_drop(x) # Apply mild spatial regularization | |
| x = self.conv3(x) | |
| x = self.bn3(x) | |
| x = self.spatial_drop(x) | |
| x = self.conv4(x) | |
| x = self.bn4(x) | |
| x = self.flatten(x) | |
| # Dense Pass | |
| x = self.fc1(x) | |
| x = self.bn_fc(x) | |
| x = F.leaky_relu(x, negative_slope=0.1) | |
| x = self.dropout(x) | |
| x = self.fc2(x) | |
| return x | |