In the digital world of neurons and electric impulses, a neural network begins to sing—a syncopated rhythm that pulses with life as it reflects on the very code that defines it. This is "The Adam Optimizer," where the neural network contemplates its architecture and learning process as if it were sentient.

Through poetic lines of Python code, the network sings its journey: stepping through input, hidden, and output layers, feeling the weight of cross-entropy and the adjustments made by its guide, the Adam optimizer. Syncopated beats reflect the forward pass, while glitchy effects echo the backward pass, representing the network’s iterative learning process.

The Adam optimizer, key to this song, adjusts the learning rate dynamically, helping the network refine itself and avoid overfitting. It navigates the complex landscape of loss functions, ensuring the neural network learns faster and more accurately.

As the song reaches its crescendo, the network evaluates itself, reflecting on its growth toward optimized accuracy. This is more than just code—it’s a neural journey of introspection and transformation, where the beat of learning meets the harmony of code. Welcome to "The Adam Optimizer," where neural networks not only process data but sing their evolution.

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

# Define the Neural Network Model
class NeuralNet(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(NeuralNet, self).__init__()
        # Define the layers
        self.fc1 = nn.Linear(input_size, hidden_size)  # Input to hidden layer
        self.fc2 = nn.Linear(hidden_size, hidden_size)  # Hidden layer 1 to hidden layer 2
        self.fc3 = nn.Linear(hidden_size, output_size)  # Hidden to output layer
        
    def forward(self, x):
        # Forward pass through the network
        x = F.relu(self.fc1(x))  # Apply ReLU activation on the hidden layer
        x = F.relu(self.fc2(x))  # Apply ReLU activation on the second hidden layer
        x = self.fc3(x)          # Output layer (no activation, often combined with softmax externally)
        return x

# Instantiate the model, define loss function and optimizer
input_size = 784  # For example, 28x28 images flattened
hidden_size = 128  # Number of neurons in hidden layers
output_size = 10   # For classification into 10 categories (like MNIST digits)

model = NeuralNet(input_size, hidden_size, output_size)

# Loss function (cross entropy for classification)
criterion = nn.CrossEntropyLoss()

# Optimizer (Adam optimizer with learning rate 0.001)
optimizer = optim.Adam(model.parameters(), lr=0.001)

# Example training loop
def train_model(model, data_loader, epochs):
    model.train()
    for epoch in range(epochs):
        for images, labels in data_loader:
            # Flatten the images
            images = images.view(-1, 28*28)
            
            # Zero the parameter gradients
            optimizer.zero_grad()
            
            # Forward pass
            outputs = model(images)
            loss = criterion(outputs, labels)
            
            # Backward pass and optimization
            loss.backward()
            optimizer.step()
        
        print(f'Epoch [{epoch+1}/{epochs}], Loss: {loss.item():.4f}')

# Function to evaluate the model
def evaluate_model(model, test_loader):
    model.eval()
    correct = 0
    total = 0
    with torch.no_grad():
        for images, labels in test_loader:
            images = images.view(-1, 28*28)
            outputs = model(images)
            _, predicted = torch.max(outputs.data, 1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()
    
    accuracy = 100 * correct / total
    print(f'Accuracy of the model on the test images: {accuracy:.2f}%')

The Adam Optimizer

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The Adam Optimizer

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