import tensorflow as tf mnist = tf.keras.datasets.mnist # Load and prepare the MNIST dataset. Convert the samples from integers to floating-point numbers (x_train, y_train), (x_test, y_test) = mnist.load_data(path='/bin/mnist.npz') x_train, x_test = x_train / 255.0, x_test / 255.0 # Build the tf.keras.Sequential model by stacking layers. Choose an optimizer and loss function for training model = tf.keras.models.Sequential([ tf.keras.layers.Flatten(input_shape=(28, 28)), tf.keras.layers.Dense(128, activation='relu'), tf.keras.layers.Dropout(0.2), tf.keras.layers.Dense(10, activation='softmax') ]) model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) # Train and evaluate model model.fit(x_train, y_train, epochs=5) model.evaluate(x_test, y_test, verbose=2)