model data parameters load the data and split it between train and test sets scale images to the 0 1 range make sure images have shape 28 28 1 convert class vectors to binary class matrices model data parameters load the data and split it between train and test sets scale images to the 0 1 range make sure images have shape 28 28 1 convert class vectors to binary class matrices
import numpy as np
import keras
from keras import layers
from keras.utils import to_categorical


num_classes = 10
input_shape = (28, 28, 1)


(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()


x_train = x_train.astype("float32") / 255
x_test = x_test.astype("float32") / 255

x_train = np.expand_dims(x_train, -1)
x_test = np.expand_dims(x_test, -1)
print("x_train shape:", x_train.shape)
print(x_train.shape[0], "train samples")
print(x_test.shape[0], "test samples")



y_train = to_categorical(y_train, num_classes)
y_test = to_categorical(y_test, num_classes)

batch_size = 128
epochs = 3

model = keras.Sequential(
    [
        layers.Input(shape=input_shape),
        layers.Conv2D(32, kernel_size=(3, 3), activation="relu"),
        layers.MaxPooling2D(pool_size=(2, 2)),
        layers.Conv2D(64, kernel_size=(3, 3), activation="relu"),
        layers.MaxPooling2D(pool_size=(2, 2)),
        layers.Flatten(),
        layers.Dropout(0.5),
        layers.Dense(num_classes, activation="softmax"),
    ]
)

model.summary()

model.compile(
    loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"]
)

model.fit(
    x_train, y_train, batch_size=batch_size, epochs=epochs, validation_split=0.1
)

score = model.evaluate(x_test, y_test, verbose=0)
print("Test loss:", score[0])
print("Test accuracy:", score[1])