tensorflow 2.x系列03 模型断点续训，自动停训
本期文章是一个系列课程，本文是这个系列的第3篇复习笔记
(1)Build and train neural network models using TensorFlow 2.x
(2)Image classification
(3)Natural language processing(NLP)
(4)Time series, sequences and predictions
断点续训
断点续传主要是模型序列化，然后重新加载模型继续训练，这个对于实际生产应用非常有帮助，可以实现在线实时训练．而不用丢失之前的训练进度． 主要通过model.laod_weights函数来完成模型的加载，tensorflow的模型文件格式一般是ckpt文件
读取模型
checkponit_save_path="./checkponit/fashion.ckpt"ifos.path.exists(checkponit_save_path+".index"):print("------------load the model -------------") model.load_weights(checkponit_save_path)
保存模型
tf.keras.callbacks.ModelCheckpoint( filepath="路径文件名", save_weights_only=True/False, save_best_only=True/False)
训练时调用
cp_callback=tf.keras.callbacks.ModelCheckpoint(filepath=checkponit_save_pathsave_weights_only=True,save_best_only=True)history=model.fit(x,y,batch_size=32,epochs=5,validation_data=(x_test,y_test),validation_freq=1,callbacks=[cp_callback])
自动停止训练
主要是实现一个callback的子类来获取实际中的参数来控制stop_training变量达到停止训练目的．
classMyCallback(tf.keras.callbacks.Callback):defon_epoch_end(self,epoch,logs={}):if(logs.get("loss")<0.25): print("\n loss is low so cancel train") self.model.stop_training=True
代码实战
importtensorflowastfimportnumpyasnpimportmatplotlib.pyplotaspltprint(tf.__version__)(train_x,train_y),(test_x,test_y)=tf.keras.datasets.fashion_mnist.load_data()model=tf.keras.models.Sequential()model.add(tf.keras.layers.Flatten(input_shape=(28,28)))model.add(tf.keras.layers.Dense(128,activation=tf.nn.relu))model.add(tf.keras.layers.Dense(10,activation=tf.nn.softmax))model.compile(optimizer="adam",loss=tf.keras.losses.sparse_categorical_crossentropy,metrics=["accuracy"])checkponit_save_path="/tmp/checkponit/fashion.ckpt"ifos.path.exists(checkponit_save_path+".index"): print("------------load the model -------------") model.load_weights(checkponit_save_path)classMyCallback(tf.keras.callbacks.Callback):defon_epoch_end(self,epoch,logs={}):iflogs.get("loss")<0.3: print("\n loss is low so cancel train") self.model.stop_training=Truemodel_save_callback=tf.keras.callbacks.ModelCheckpoint(filepath=checkponit_save_path, monitor ='val_loss', save_weights_only=True, save_best_only=True)auto_stop_callback=MyCallback()history=model.fit(train_x/255,train_y,batch_size=32,epochs=50,validation_data=(test_x,test_y),validation_freq=1,callbacks=[auto_stop_callback,model_save_callback])model.evaluate(test_x/255,test_y)t=np.array(test_x[0]/255).reshape(1,28,28)print(np.argmax(model.predict(t)))plt.imshow(test_x[0])
2.3.0------------load the model -------------Epoch1/501853/1875[============================>.] - ETA:0s - loss:0.2806- accuracy:0.8959loss is low so cancel train1875/1875[==============================] -4s2ms/step - loss:0.2801- accuracy:0.8960- val_loss:54.3266- val_accuracy:0.8685313/313[==============================] -0s1ms/step - loss:0.3418- accuracy:0.8796WARNING:tensorflow:7out of the last7calls to .predict_functionat0x7f76704549d8>triggeredtf.functionretracing.Tracingisexpensiveandtheexcessivenumberoftracingscouldbedueto(1)creating @tf.functionrepeatedlyinaloop,(2)passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.functionoutsideoftheloop.For(2), @tf.functionhasexperimental_relax_shapes=Trueoptionthatrelaxesargumentshapesthatcanavoidunnecessaryretracing.For(3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_argsandhttps://www.tensorflow.org/api_docs/python/tf/functionformoredetails.9
总结
通过实现不同的callback，可以对模型训练期间进行精确控制． 包括模型的断点续训和自动停止训练