CNN multi class classification for American Sign Language digits.py

import os
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.transforms as transforms
from torch.utils.data import DataLoader, Dataset
from sklearn.model_selection import train_test_split
from PIL import Image




# Define a custom dataset class
class ASLDataset(Dataset):
    def __init__(self, root_dir, transform=None):
        self.root_dir = root_dir
        self.classes = os.listdir(root_dir)
        self.transform = transform
        self.images = []
        self.labels = []

        for class_idx, class_name in enumerate(self.classes):
            class_dir = os.path.join(root_dir, class_name)
            for image_name in os.listdir(class_dir):
                image_path = os.path.join(class_dir, image_name)
                image = Image.open(image_path)
                image = image.resize((64, 64))  # Resize to your desired size
                image_array = np.array(image)
                self.images.append(image_array)
                self.labels.append(class_idx)

    def __len__(self):
        return len(self.images)
    
    def __getitem__(self, idx):
        image = self.images[idx]
        label = self.labels[idx]
        
        if self.transform:
            image = self.transform(image)
        
        return image, label

# Define data transforms
transform = transforms.Compose([
    transforms.ToTensor()
])


import matplotlib.pyplot as plt

# Create dataset and data loaders for training and testing
train_dataset = ASLDataset(root_dir="C:\\Users\\sha\\Desktop\\ASL Digits gray\\asl_dataset_digits_gray", transform=transform) 
test_dataset = ASLDataset(root_dir="C:\\Users\\sha\\Desktop\\ASL Digits gray\\test_gray", transform=transform)

train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False)


# Get the number of examples in the train and test datasets
num_train_examples = len(train_dataset)
num_test_examples = len(test_dataset)

print("Number of examples in train dataset:", num_train_examples)
print("Number of examples in test dataset:", num_test_examples)



class_counts = [0] * 10  # Initialize a list to store the counts for each class

# Count the occurrences of each class in the train dataset
for _, label in train_dataset:
    class_counts[label] += 1

# Print the counts for each class
for class_idx, count in enumerate(class_counts):
    print(f"Class {class_idx}: {count} examples")



test_class_counts = [0] * 10  # Initialize a list to store the counts for each class in the test dataset

# Count the occurrences of each class in the test dataset
for _, label in test_dataset:
    test_class_counts[label] += 1

# Print the counts for each class in the test dataset
for class_idx, count in enumerate(test_class_counts):
    print(f"Class {class_idx}: {count} examples")



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

class CNNModel(nn.Module):
    def __init__(self):
        super(CNNModel, self).__init__()
        self.conv1 = nn.Conv2d(in_channels=1, out_channels=32, kernel_size=3, padding=1)
        self.conv2 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, padding=1)
        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
        self.fc1 = nn.Linear(64 * 16 * 16, 128)
        self.fc2 = nn.Linear(128, 10)  # 10 classes for digits 0-9
        
    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = x.view(-1, 64 * 16 * 16)  # Flatten
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        return x

# Initialize the model and other training settings
model = CNNModel()
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)


# Training loop
num_epochs = 20
for epoch in range(num_epochs):
    model.train()
    running_loss = 0.0
    correct = 0
    total = 0
    
    for images, labels in train_loader:
        optimizer.zero_grad()
        outputs = model(images)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        
        # Compute training loss
        running_loss += loss.item()
        
        # Compute training accuracy
        _, predicted = torch.max(outputs, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

    epoch_loss = running_loss / len(train_loader)
    epoch_accuracy = correct / total
    
    print(f"Epoch {epoch+1}/{num_epochs} completed. Training Loss: {epoch_loss:.4f}, Training Accuracy: {epoch_accuracy:.4f}")




# Evaluate the model on the test set
model.eval()
test_loss = 0.0
correct = 0
total = 0
with torch.no_grad():
    for images, labels in test_loader:
        outputs = model(images)
        loss = criterion(outputs, labels)
        test_loss += loss.item()
        _, predicted = torch.max(outputs, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

print(f"Test Loss: {test_loss/len(test_loader):.4f}, Test Accuracy: {correct/total:.4f}")