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import torch
import torch.nn as nn
import torch.optim as optim
import matplotlib.pyplot as plt
import numpy as np
# 🔥 Deep Neural Network Model
class DeepNeuralNetwork(nn.Module):
def __init__(self, input_size, hidden_sizes, output_size):
super(DeepNeuralNetwork, self).__init__()
layers = []
in_size = input_size
for hidden_size in hidden_sizes:
layers.append(nn.Linear(in_size, hidden_size))
layers.append(nn.ReLU())
in_size = hidden_size
layers.append(nn.Linear(in_size, output_size))
self.model = nn.Sequential(*layers)
def forward(self, x):
return self.model(x) # ✅ Apply the model layers properly
# 🔥 Training Function
def train_model(model, criterion, optimizer, x_train, y_train, epochs=100):
model.train()
for epoch in range(epochs):
optimizer.zero_grad()
# Forward pass
y_pred = model(x_train)
# Loss calculation
loss = criterion(y_pred, y_train)
# Backward pass
loss.backward()
optimizer.step()
if (epoch + 1) % 10 == 0:
print(f'Epoch [{epoch + 1}/{epochs}], Loss: {loss.item():.4f}')
# ✅ Example Usage
if __name__ == "__main__":
# 🔥 Sample Data
x_train = torch.randn(100, 10, requires_grad=True) # ✅ Require gradient tracking
y_train = torch.randint(0, 2, (100,), dtype=torch.long) # ✅ Ensure LongTensor for CrossEntropyLoss
# Plotting the input data
plt.scatter(x_train[:, 0].detach().numpy(), x_train[:, 1].detach().numpy(), c=y_train.numpy(), cmap='viridis')
plt.title('Deep Neural Network Input Data')
plt.xlabel('Input Feature 1')
plt.ylabel('Input Feature 2')
plt.colorbar(label='Output Class')
plt.show()
# Initialize Model
model = DeepNeuralNetwork(input_size=10, hidden_sizes=[20, 10], output_size=2)
# Criterion and Optimizer
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.01)
# Train the model
train_model(model, criterion, optimizer, x_train, y_train, epochs=100)
# ✅ Plotting the predictions with softmax
model.eval()
with torch.no_grad():
y_pred = torch.softmax(model(x_train), dim=1).detach().numpy()
plt.scatter(x_train[:, 0].detach().numpy(), x_train[:, 1].detach().numpy(), c=np.argmax(y_pred, axis=1), cmap='viridis')
plt.title('Deep Neural Network Predictions')
plt.xlabel('Input Feature 1')
plt.ylabel('Input Feature 2')
plt.colorbar(label='Predicted Class')
plt.show()
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