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import torch
import torch.nn as nn
import torch.optim as optim
# Convolutional Neural Network Model
class ConvolutionalNeuralNetwork(nn.Module):
def __init__(self):
super(ConvolutionalNeuralNetwork, self).__init__()
self.conv1 = nn.Conv2d(1, 16, kernel_size=5)
self.conv2 = nn.Conv2d(16, 32, kernel_size=5)
self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
self.fc1 = nn.Linear(32 * 4 * 4, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10)
def forward(self, x):
x = self.pool(torch.relu(self.conv1(x)))
x = self.pool(torch.relu(self.conv2(x)))
x = x.view(-1, 32 * 4 * 4)
x = torch.relu(self.fc1(x))
x = torch.relu(self.fc2(x))
return x # Return raw output without applying softmax
# Training Function
def train_model(model, criterion, optimizer, x_train, y_train, epochs=100):
for epoch in range(epochs):
model.train()
optimizer.zero_grad()
y_pred = model(x_train)
loss = criterion(y_pred, y_train)
loss.backward()
optimizer.step()
if (epoch+1) % 10 == 0:
print(f'Epoch [{epoch+1}/{epochs}], Loss: {loss.item():.4f}')
import matplotlib.pyplot as plt # Added import for plotting
# Example Usage
if __name__ == "__main__":
# Sample Data
x_train = torch.randn(100, 1, 28, 28) # Example for MNIST
y_train = torch.randint(0, 10, (100,))
# Plotting the input data
for i in range(6):
plt.subplot(2, 3, i + 1)
plt.imshow(x_train[i].squeeze(), cmap='gray')
plt.title(f'Label: {y_train[i].item()}')
plt.axis('off')
plt.show()
model = ConvolutionalNeuralNetwork()
# Plotting the predictions
y_pred = model(x_train).detach().numpy()
plt.figure(figsize=(12, 6))
for i in range(6):
plt.subplot(2, 3, i + 1)
plt.imshow(x_train[i].squeeze(), cmap='gray')
plt.title(f'Predicted: {np.argmax(y_pred[i])}')
plt.axis('off')
plt.show()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.01)
train_model(model, criterion, optimizer, x_train, y_train)
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