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python - 比较 tf.keras.callbacks.Callback 中回调实例中单个类的精度

问题描述

我需要帮助来设计我的回调，我有以下架构：

def CNN_exctractor(input_img):
    l2_loss_lambda = 0.01 # the definintion of l2 regaluraiation 

    l2 = None if l2_loss_lambda is None else regularizers.l2(l2_loss_lambda)
    if l2 is not None:
        print('Using L2 regularization - l2_loss_lambda = %.7f' % l2_loss_lambda)

    conv1 = Conv2D(filters=64, kernel_size=(3,3), padding="same", activation="relu", kernel_regularizer=l2)(input_img)
    conv11 = BatchNormalization()(conv1)
    pool2 = MaxPooling2D(pool_size=(2, 2))(conv11)

    
    conv10 = Conv2D(filters=64, kernel_size=(3,3), padding="same", activation="relu", kernel_regularizer=l2)(pool2) 
    conv110 = BatchNormalization()(conv10)
    pool21 = MaxPooling2D(pool_size=(2, 2))(conv110)

         
    conv3 = Conv2D(filters=128, kernel_size=(3,3), padding="same", activation="relu", kernel_regularizer=l2)( pool21)#conv21)
    conv31 = BatchNormalization()(conv3)

    conv5 = Conv2D(filters=256, kernel_size=(3,3), padding="same", activation="relu", kernel_regularizer=l2)( conv31)#conv41)
    conv51 = BatchNormalization()(conv5)

    conv511 = Conv2D(filters=256, kernel_size=(3,3), padding="same", activation="relu", kernel_regularizer=l2)( conv51)#conv41)
    conv5111 = BatchNormalization()(conv511)
    #pool3 = MaxPooling2D(pool_size=(2, 2))(conv51)
    return conv5111

    
    
  
def fc1(enco):
    l2_loss_lambda = 0.01

    l2 = None if l2_loss_lambda is None else regularizers.l2(l2_loss_lambda)
    if l2 is not None:
        print('Using L2 regularization - l2_loss_lambda = %.7f' % l2_loss_lambda)

    flat = Flatten()(enco)

    
    den = Dense(256, activation='relu',kernel_regularizer=l2)(flat)#(den_n)#(den_n)
    den_n= BatchNormalization()(den)


    den1 = Dense(128, activation='relu',kernel_regularizer=l2)(den_n)#(den_n)#(den_n)
    den1_n= BatchNormalization()(den1)

    
    out = Dense(2, activation='softmax')(den1_n)
    return out

如您所见，我在输出端有两个神经元，我使用这个简单的代码进行回调：

class myCallback(tf.keras.callbacks.Callback): 
    def on_epoch_end(self, epoch, logs={}): 
        if((logs.get('val_accuracy')>= 0.92) and (logs.get('accuracy')>= 0.96) and  ):   
          print("\nReached %2.2f%% accuracy, so stopping training!!" %(0.96*100))   
          self.model.stop_training = True

我正在比较训练和验证的准确性，我想做的不是比较整个验证的准确性，而是比较单个类的精度，例如（如果存在）

logs.get('class_1_precision')>= 0.8

标签： pythonkerasdeep-learning

解决方案

您可以将验证数据传递给回调，然后针对特定类对其进行过滤。我不知道您是如何构建验证数据的，但在这里我假设它分为两组（val_x和val_y）。在回调中，您将获得包含您需要的类的行（也许过滤您需要val_y的类的索引，然后获取相同的索引val_x） - 我已经把这一点留给您了。

from sklearn.metrics import precision_score


class myCallback(tf.keras.callbacks.Callback):
    def __init__(self, val_x, val_y):
        super(myCallback, self).__init__()
        self.val_x = val_x
        self.val_y = val_y

    def on_epoch_end(self, epoch, logs={}):
        # Filter validation data for your required class
        val_x_class_1 = #filter self.val_x for your class
        val_y_class_1 = #filter self.val_y for your class>

        # Get predictions for the filtered val data
        class1_scores = self.model.predict(val_x_class_1)

        # Get indices of best predictions - you might need to alter this
        y_pred_class1 = tf.argmax(class1_scores, axis=1)
        y_true_class1 = tf.argmax(val_y_class_1, axis=1)
      
        # Calculate precision
        precision_class1 = precision_score(y_true_class1, y_pred_class1)

        # Rest of your code
        <....>

要将验证数据传递给回调，您需要在 fit 函数中添加如下内容：

cbs = myCallback(val_x,val_y)

model.fit(...., callbacks=[cbs])

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