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python - 检查 val_acc 饱和度的回调

问题描述

通常，如果准确度达到一定水平，我们可以为模型定义一个回调以停止 epoch。

我正在调整参数。如图所示，val_acc 非常不稳定acc_graphs。

def LSTM_model(X_train, y_train, X_test, y_test, num_classes, batch_size=68, units=128, learning_rate=0.005, epochs=20,
               dropout=0.2, recurrent_dropout=0.2):
    class myCallback(tf.keras.callbacks.Callback):

        def on_epoch_end(self, epoch, logs={}):
            if (logs.get('acc') > 0.90):
                print("\nReached 90% accuracy so cancelling training!")
                self.model.stop_training = True

    callbacks = myCallback()

如图所示，val_acc(orange) 在一个范围内波动，并且不再真正上升。

一旦 val_acc 的总体趋势停止增加，有没有办法自动停止训练？

标签： pythonmachine-learningdeep-learning

解决方案

您可以通过这样的方式实现此callback目的

class terminate_on_plateau(keras.callbacks.Callback):
    
    def __init__(self):
        self.patience = 10
        self.val_loss = deque([],self.patience)
        self.std_threshold = 1e-2
        
    def on_epoch_end(self,epoch,logs=None):
        val_loss,val_mae = model.evaluate(x_val,y_val)
        self.val_loss.append(val_loss)
        if len(self.val_loss) >= self.patience:
            std = np.std(self.val_loss)
            if std < self.std_threshold:
                print('\n\n EarlyStopping on std invoked! \n\n')
                # clear the deque
                self.val_loss = deque([],self.patience)
                model.stop_training = True

如您所见， in terminate_on_plateau, val_lossof epoch 存储在 adeque的 max lengthself.patience中。deque一旦达到的长度，将为每个新的 epoch计算的self.patience标准差，如果计算的值小于阈值，则终止训练过程（ 的也将被清除） 。val_lossdequeval_lossstd

下面是一个简单的脚本，向您展示如何使用它

from collections import deque
import numpy as np

import tensorflow as tf 
from tensorflow import keras 
import tensorflow.keras.backend as K
from sklearn.model_selection import train_test_split
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input,Dense

x = np.linspace(0,10,1000)
np.random.shuffle(x)
y = np.sin(x) + x

x_train,x_val,y_train,y_val = train_test_split(x,y,test_size=0.3)

input_x = Input(shape=(1,))
y = Dense(10,activation='relu')(input_x)
y = Dense(10,activation='relu')(y)
y = Dense(1,activation='relu')(y)
model = Model(inputs=input_x,outputs=y)

adamopt = tf.keras.optimizers.Adam(lr=0.01, beta_1=0.9, beta_2=0.999, epsilon=1e-8)

class terminate_on_plateau(keras.callbacks.Callback):
    
    def __init__(self):
        self.patience = 10
        self.val_loss = deque([],self.patience)
        self.std_threshold = 1e-2
        
    def on_epoch_end(self,epoch,logs=None):
        val_loss,val_mae = model.evaluate(x_val,y_val)
        self.val_loss.append(val_loss)
        if len(self.val_loss) >= self.patience:
            std = np.std(self.val_loss)
            if std < self.std_threshold:
                print('\n\n EarlyStopping on std invoked! \n\n')
                # clear the deque
                self.val_loss = deque([],self.patience)
                model.stop_training = True
    
model.compile(loss='mse',optimizer=adamopt,metrics=['mae'])
history = model.fit(x_train,y_train,
                    batch_size=8,
                    epochs=100,
                    validation_data=(x_val, y_val),
                    verbose=1,
                    callbacks=[terminate_on_plateau()])

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