Comprehensive comparison for AI technology in Deep Learning applications

import jax
import jax.numpy as jnp
from jax import grad, jit, vmap, random
from typing import Tuple, Dict, Any
import optax
from functools import partial

# Neural Network for Image Classification using JAX
# Production-ready pattern with proper initialization, training loop, and error handling

class ConvNet:
    """Convolutional Neural Network for MNIST-like image classification."""
    
    @staticmethod
    def initialize_params(key: jax.random.PRNGKey, input_shape: Tuple[int, ...]) -> Dict[str, Any]:
        """Initialize network parameters with proper Xavier initialization."""
        keys = random.split(key, 4)
        
        # Conv layer: (out_channels, in_channels, height, width)
        conv1_w = random.normal(keys[0], (32, 1, 3, 3)) * jnp.sqrt(2.0 / (1 * 3 * 3))
        conv1_b = jnp.zeros((32,))
        
        # Dense layers
        dense1_w = random.normal(keys[1], (32 * 13 * 13, 128)) * jnp.sqrt(2.0 / (32 * 13 * 13))
        dense1_b = jnp.zeros((128,))
        
        dense2_w = random.normal(keys[2], (128, 10)) * jnp.sqrt(2.0 / 128)
        dense2_b = jnp.zeros((10,))
        
        return {
            'conv1': {'w': conv1_w, 'b': conv1_b},
            'dense1': {'w': dense1_w, 'b': dense1_b},
            'dense2': {'w': dense2_w, 'b': dense2_b}
        }
    
    @staticmethod
    @jit
    def forward(params: Dict[str, Any], x: jnp.ndarray, training: bool = True) -> jnp.ndarray:
        """Forward pass with conv, pooling, and dense layers."""
        # Conv + ReLU + MaxPool
        x = jnp.expand_dims(x, axis=1) if x.ndim == 3 else x
        conv1 = jax.lax.conv(x, params['conv1']['w'], (1, 1), 'SAME')
        conv1 = conv1 + params['conv1']['b'].reshape(1, -1, 1, 1)
        conv1 = jax.nn.relu(conv1)
        pool1 = jax.lax.reduce_window(conv1, -jnp.inf, jax.lax.max, (1, 1, 2, 2), (1, 1, 2, 2), 'VALID')
        
        # Flatten
        flat = pool1.reshape(pool1.shape[0], -1)
        
        # Dense layers
        dense1 = jnp.dot(flat, params['dense1']['w']) + params['dense1']['b']
        dense1 = jax.nn.relu(dense1)
        
        # Output layer
        logits = jnp.dot(dense1, params['dense2']['w']) + params['dense2']['b']
        return logits

@jit
def cross_entropy_loss(params: Dict[str, Any], x: jnp.ndarray, y: jnp.ndarray) -> jnp.ndarray:
    """Compute cross-entropy loss with numerical stability."""
    logits = ConvNet.forward(params, x, training=True)
    log_probs = jax.nn.log_softmax(logits, axis=-1)
    one_hot_labels = jax.nn.one_hot(y, num_classes=10)
    loss = -jnp.mean(jnp.sum(one_hot_labels * log_probs, axis=-1))
    return loss

@jit
def compute_accuracy(params: Dict[str, Any], x: jnp.ndarray, y: jnp.ndarray) -> float:
    """Compute classification accuracy."""
    logits = ConvNet.forward(params, x, training=False)
    predictions = jnp.argmax(logits, axis=-1)
    return jnp.mean(predictions == y)

@partial(jit, static_argnums=(3,))
def train_step(params: Dict[str, Any], opt_state: Any, batch: Tuple[jnp.ndarray, jnp.ndarray], optimizer: optax.GradientTransformation) -> Tuple[Dict[str, Any], Any, float]:
    """Single training step with gradient computation and parameter update."""
    x, y = batch
    loss_value, grads = jax.value_and_grad(cross_entropy_loss)(params, x, y)
    updates, opt_state = optimizer.update(grads, opt_state, params)
    params = optax.apply_updates(params, updates)
    return params, opt_state, loss_value

def train_model(key: jax.random.PRNGKey, num_epochs: int = 10, batch_size: int = 32, learning_rate: float = 0.001):
    """Complete training pipeline with error handling."""
    try:
        # Initialize model
        params = ConvNet.initialize_params(key, (batch_size, 28, 28))
        
        # Setup optimizer
        optimizer = optax.adam(learning_rate)
        opt_state = optimizer.init(params)
        
        # Simulate training data (replace with real data loader)
        data_key, key = random.split(key)
        x_train = random.normal(data_key, (1000, 28, 28))
        y_train = random.randint(data_key, (1000,), 0, 10)
        
        print(f"Starting training for {num_epochs} epochs...")
        
        for epoch in range(num_epochs):
            epoch_loss = 0.0
            num_batches = len(x_train) // batch_size
            
            for i in range(num_batches):
                batch_x = x_train[i * batch_size:(i + 1) * batch_size]
                batch_y = y_train[i * batch_size:(i + 1) * batch_size]
                
                params, opt_state, loss = train_step(params, opt_state, (batch_x, batch_y), optimizer)
                epoch_loss += loss
            
            avg_loss = epoch_loss / num_batches
            accuracy = compute_accuracy(params, x_train[:batch_size], y_train[:batch_size])
            print(f"Epoch {epoch + 1}/{num_epochs} - Loss: {avg_loss:.4f}, Accuracy: {accuracy:.4f}")
        
        return params
    
    except Exception as e:
        print(f"Training error: {str(e)}")
        raise

# Example usage
if __name__ == "__main__":
    key = random.PRNGKey(42)
    trained_params = train_model(key, num_epochs=5, batch_size=32, learning_rate=0.001)