<gh_stars>0
import tensorflow as tf

mnist = tf.keras.datasets.mnist

(x_train,y_train),(x_test,y_test) = mnist.load_data()
x_train = x_train / 255.0
x_test = x_test / 255.0

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'])

model.fit(x_train,y_train,epochs=5)

model.evaluate(x_test,y_test,verbose=2)