import tensorflow as tf

class ACCCallback(tf.keras.callbacks.Callback):
    def on_epoch_end(self, epoch, logs={}):
        if logs.get('acc')>0.8:
            print('\n Reach 80% accuracy, so early stop.')
            self.model.stop_training = True


(x_train, y_train), (x_test, y_test) = tf.keras.datasets.fashion_mnist.load_data()
x_train,x_test = x_train/255., x_test/255.
acc_callback =  ACCCallback()
model = keras.Sequential([keras.layers.Flatten(),
                          keras.layers.Dense(128,activation=tf.nn.relu),
                          keras.layers.Dense(10,activation='softmax')])
model.compile(loss='sparse_categorical_crossentropy',optimizer=tf.train.AdamOptimizer(),metrics=['accuracy'])
model.fit(x_train,y_train,epochs=10,callbacks=[acc_callback])

"""
Epoch 1/10
59744/60000 [============================>.] - ETA: 0s - loss: 0.4633 - acc: 0.8371Reach 80% accuracy, so early stop.
60000/60000 [==============================] - 6s 106us/sample - loss: 0.4630 - acc: 0.8372
"""