在训练中使用early stoping终止模型和使用checkpoint保存模型

当我们训练模型时，如果epoch设置太长，常常希望可以在loss不再下降或者accuracy不再提高时终止训练，获得模型，避免模型浪费时间，这时可以使用tensorflow 中的early stoping终止模型和使用checkpoint保存模型：

import tensorflow as tf
from tensorflow.keras import datasets, layers, optimizers, Sequential, metrics
import pandas as pd
import numpy as np
from scipy import sparse
from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint #导入early stopping，checkpoint
import os

os.environ["CUDA_VISIBLE_DEVICES"] = "0"

batchsz = 256
....
....
.... # 用过pandas读取数据并构建你自己的x_train,y_train,x_val,y_val,x_test,y_test
db = tf.data.Dataset.from_tensor_slices((x_train, y_train))
db = db.shuffle(6000).batch(batchsz)
ds_val = tf.data.Dataset.from_tensor_slices((x_val, y_val))
ds_val = ds_val.batch(batchsz)
db_test = tf.data.Dataset.from_tensor_slices((x_test, y_test))
db_test = db_test.batch(batchsz)

sample = next(iter(db))
print(sample[0].shape, sample[1].shape)

network = Sequential([layers.Conv1D(512, kernel_size=5, strides=3,padding='same',activation=tf.nn.relu),
                     layers.Dropout(0.4),
                     layers.Conv1D(256, kernel_size=5, strides=3,padding='same',activation=tf.nn.relu),
                     layers.Dropout(0.4),
                     layers.Conv1D(128, kernel_size=5, strides=3,padding='same',activation=tf.nn.relu),
                     layers.Dropout(0.4),
                     layers.Flatten(),
                     layers.Dense(128, activation=tf.nn.relu),
                     layers.Dropout(0.4),
                     layers.Dense(1,activation='sigmoid')]) # 构建你自己network

network.build(input_shape=(None, 2000,4))
network.summary()

# 构建early-stopping，监测目标是测试集的accuracy，8个epoch如果提高不到0。001即终止
early_stopping = EarlyStopping(monitor='val_acc',min_delta=0.001,patience=8) 

#构建checkpoint，保存model名字为‘conv.h5‘，监测指标为测试集准确率val_acc，model=‘max’保存val_acc最大的的，save_best_only=true,保存最好的模型
checkpoint=ModelCheckpoint('conv.h5', monitor='val_acc',model='max',verbose=1,save_best_only=True) 

network.compile(optimizer=optimizers.Adam(lr=0.001),
                loss='binary_crossentropy',
                metrics=['accuracy']
               )

# 在callback中使用early-stopping和checkpoint。
network.fit(db, epochs=100, validation_data=ds_val, validation_steps=2,callbacks=[early_stopping，checkpoint]) 
network.evaluate(db_test)

#测试
sample = next(iter(db_test))
x = sample[0]
y = sample[1] 
pred = network.predict(x)  

如果想进一步了解early stoppong，可以查看jason的文章Use Early Stopping to Halt the Training of Neural Networks At the Right Time