keras_mnist_digit_classification_sequential_model_quickstart.py

1import tensorflow as tf
2
3# Load and prepare the MNIST dataset
4mnist = tf.keras.datasets.mnist
5
6(x_train, y_train), (x_test, y_test) = mnist.load_data()
7x_train, x_test = x_train / 255.0, x_test / 255.0
8
9# Build the tf.keras.Sequential model by stacking layers
10model = tf.keras.models.Sequential([
11  tf.keras.layers.Flatten(input_shape=(28, 28)),
12  tf.keras.layers.Dense(128, activation='relu'),
13  tf.keras.layers.Dropout(0.2),
14  tf.keras.layers.Dense(10)
15])
16
17# Define a loss function for training
18loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
19
20# Compile the model
21model.compile(optimizer='adam',
22              loss=loss_fn,
23              metrics=['accuracy'])
24
25# Train the model
26model.fit(x_train, y_train, epochs=5)
27
28# Evaluate the model performance
29model.evaluate(x_test,  y_test, verbose=2)