import tensorflow as tf
from tensorflow.keras import layers, models
from tensorflow.keras.datasets import mnist
from tensorflow.keras.utils import to_categorical
 
(X_train, y_train), (X_test, y_test) = mnist.load_data()
X_train = X_train.reshape(-1,28,28,1).astype('float32')/255.0
X_test = X_test.reshape(-1,28,28,1).astype('float32')/255.0
y_train = to_categorical(y_train, 10)
y_test = to_categorical(y_test, 10)
 
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(64, activation='relu'),
    layers.Dense(10, activation='softmax')
])
 
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
history = model.fit(X_train, y_train, epochs=5, batch_size=128, validation_split=0.1)
 
test_loss, test_acc = model.evaluate(X_test, y_test)
print("Test accuracy:", test_acc)

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