import tensorflow as tf

minist = tf.keras.datasets.mnist

(x_train,y_train),(x_test,y_test) = minist.load_data()
x_train,x_test = x_train/255,x_test/255

print(x_train.shape)
print(x_train)

model =tf.keras.models.Sequential([
    tf.keras.layers.Flatten(input_shape=(28,28)),
    tf.keras.layers.Dense(512,activation=tf.nn.relu),
    tf.keras.layers.Dropout(0.2),
    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=10)
model.evaluate(x_test,y_test)