有时候，当模型损失函数值预期的效果时，就可以结束训练了，一方面节约时间，另一方面防止过拟合
此时，设置损失函数值小于0.4，训练停止

from tensorflow import keras
import tensorflow as tf
import matplotlib.pyplot as plt
import numpy as npclass myCallback(tf.keras.callbacks.Callback):def on_epoch_end(self,epoch,logs={}):if(logs.get('loss')<0.4):print("\nLoss is low so cancelling training!")self.model.stop_training = Truecallbacks = myCallback()
mnist = tf.keras.datasets.fashion_mnist
(training_images,training_labels),(test_images,test_labels) = mnist.load_data()
training_images_y = training_images/255.0
test_images_y = test_images/255.0model = tf.keras.models.Sequential([tf.keras.layers.Flatten(),tf.keras.layers.Dense(512,activation=tf.nn.relu),tf.keras.layers.Dense(10,activation=tf.nn.softmax)
])

model.compile(optimizer='adam',loss='sparse_categorical_crossentropy',metrics=['accuracy'])
model.fit(training_images_y,training_labels,epochs=5,callbacks=[callbacks])
"""
Colocations handled automatically by placer.
Epoch 1/5
60000/60000 [==============================] - 12s 194us/sample - loss: 0.4729 - acc: 0.8303
Epoch 2/5
59712/60000 [============================>.] - ETA: 0s - loss: 0.3570 - acc: 0.8698
Loss is low so cancelling training!
60000/60000 [==============================] - 11s 190us/sample - loss: 0.3570 - acc: 0.8697
"""