train_mnist.py

import tensorflow as tf
import numpy as np
from tensorflow.keras import layers, models

# Load MNIST dataset
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()

# Normalize pixel values to 0-1
x_train = x_train / 255.0
x_test = x_test / 255.0

# Add channel dimension (28, 28) -> (28, 28, 1)
x_train = x_train[..., tf.newaxis]
x_test = x_test[..., tf.newaxis]

# Build simple CNN model
model = models.Sequential([
    layers.Conv2D(32, (3,3), activation='relu', input_shape=(28,28,1)),
    layers.MaxPooling2D(2,2),
    layers.Conv2D(64, (3,3), activation='relu'),
    layers.MaxPooling2D(2,2),
    layers.Flatten(),
    layers.Dense(128, activation='relu'),
    layers.Dense(10, activation='softmax')  # 10 digits (0-9)
])

model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

model.summary()

# Train
model.fit(x_train, y_train, epochs=5, validation_split=0.1)

# Evaluate
test_loss, test_acc = model.evaluate(x_test, y_test)
print(f"\nTest accuracy: {test_acc:.4f}")

# Save model
model.save("mnist_model.h5")
print("Model saved as mnist_model.h5")