handwritten digital recognition

import numpy as np
import matplotlib.pyplot as plt
from tensorflow.keras.datasets import mnist # type: ignore
from tensorflow.keras.models import Sequential # type: ignore
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout # type: ignore
from tensorflow.keras.utils import to_categorical  # type: ignore

# Load the MNIST dataset
(X_train, y_train), (X_test, y_test) = mnist.load_data(path='datasets/mnist.npz')

# Preprocess the data
X_train = X_train.reshape(X_train.shape[0], 28, 28, 1).astype('float32') / 255
X_test = X_test.reshape(X_test.shape[0], 28, 28, 1).astype('float32') / 255
y_train = to_categorical(y_train, 10)
y_test = to_categorical(y_test, 10)

# Build the CNN model
model = Sequential([
    Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)),
    MaxPooling2D((2, 2)),
    Conv2D(64, (3, 3), activation='relu'),
    MaxPooling2D((2, 2)),
    Flatten(),
    Dense(128, activation='relu'),
    Dropout(0.5),
    Dense(10, activation='softmax')
])

# Compile the model
model.compile(optimizer='adam',
              loss='categorical_crossentropy',
              metrics=['accuracy'])

# Train the model
history = model.fit(X_train, y_train,
                    epochs=10,
                    batch_size=128,
                    validation_data=(X_test, y_test))

# Evaluate the model
test_loss, test_acc = model.evaluate(X_test, y_test, verbose=2)
print(f'Test accuracy: {test_acc}')

# Plot training history
plt.figure(figsize=(10, 6))
plt.plot(history.history['accuracy'], label='Training accuracy')
plt.plot(history.history['val_accuracy'], label='Validation accuracy')
plt.title('Training and Validation Accuracy')
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.legend()
plt.show()