网站建设的博客,wordpress会员介绍页,如何制作自己的网址链接,python 开发手机app有时候#xff0c;当模型损失函数值预期的效果时#xff0c;就可以结束训练了#xff0c;一方面节约时间#xff0c;另一方面防止过拟合 此时#xff0c;设置损失函数值小于0.4#xff0c;训练停止 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