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from tensorflow import keras
import tensorflow as tf
import matplo…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,activationtf.nn.relu),tf.keras.layers.Dense(10,activationtf.nn.softmax)
])model.compile(optimizeradam,losssparse_categorical_crossentropy,metrics[accuracy])
model.fit(training_images_y,training_labels,epochs5,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
