回叫在张量流中不起作用以停止训练

debugcn 发表于 Dev

斯塔克2022

我写了回电电话，当准确性达到99％时会停止训练。但是问题是我遇到了这个错误。有时，如果我解决了这个错误，即使acuurqacy变成100％也不会回电。

“ NoneType”和“ float”的实例之间不支持“>”

    class myCallback(tf.keras.callbacks.Callback):
        
        def on_epoch_end(self, epoch, logs={}):
            
            if(logs.get('accuracy') > 0.99):
                
                
               
               self.model.stop_training = True


def train_mnist():
    # Please write your code only where you are indicated.
    # please do not remove # model fitting inline comments.

    # YOUR CODE SHOULD START HERE

    # YOUR CODE SHOULD END HERE
    call = myCallback()
    mnist = tf.keras.datasets.mnist

    (x_train, y_train),(x_test, y_test) = mnist.load_data(path=path)
    # YOUR CODE SHOULD START
    x_train = x_train/255
    y_train = y_train/255
    # YOUR CODE SHOULD END HERE
    model = tf.keras.models.Sequential([
        # YOUR CODE SHOULD START HERE
          keras.layers.Flatten(input_shape=(28,28)),
          keras.layers.Dense(128,activation='relu'),
          keras.layers.Dense(10,activation='softmax')
        # YOUR CODE SHOULD END HERE
    ])

    model.compile(optimizer='adam',
                  loss='sparse_categorical_crossentropy',
                  metrics=['accuracy'])
    
    # model fitting
    history = model.fit(# YOUR CODE SHOULD START HERE
          x_train,y_train,epochs=9,callbacks=[call] )
    # model fitting
    return history.epoch, history.history['acc'][-1]

龙尼可

上面的代码有两个主要问题：

在训练集上达到100％的准确性几乎总是意味着您的模型过度拟合。那是坏的。您要做的是validation_split=.2在.fit方法中指定参数，并在验证集上寻找高精度。
在自定义回调中尝试构建的内容已在中完成keras.callbacks.EarlyStopping，它甚至可以选择在每个时期恢复到最佳总体模型。而且，默认情况下，如果您有验证分组，它会寻找验证准确性，而不是训练准确性。