import tensorflow as tf

mnist = tf.keras.datasets.mnist

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

x_train = tf.keras.utils.normalize(x_train,axis=1)
x_test = tf.keras.utils.normalize(x_test,axis=1)


model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(128,activation=tf.nn.relu))
model.add(tf.keras.layers.Dense(128,activation=tf.nn.relu))
model.add(tf.keras.layers.Dense(10,activation=tf.nn.softmax))


model.compile(optimizer='adam',loss='sparse_categorical_crossentropy',
metrics=['accuracy'])

model.fit(x_train,y_train,epochs=3)


