当我训练回归损失时，损失很大

Question

我想从图像中预测学生的中心。因此，我使用了带有3个凹层的CNN。 因此输入是图像，输出是坐标（x，y）。 我的模型是：

from keras.layers import Layer, Conv2D, MaxPooling2D, UpSampling2D, Dropout,Input ,concatenate, Dense
from keras.models import Model
tf.keras.layers.GlobalAveragePooling2D(
    data_format=None, keepdims=False
)
 
def get_model():
    img = Input(shape=(None, None, 3 ))
    conv1_1 = Conv2D(16, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(img)
    conv1_2 = Conv2D(16, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(conv1_1)
    pool1 = MaxPooling2D((2, 2))(conv1_2)
    
    conv2_1 = Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(pool1)
    conv2_2 = Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(conv2_1)
    
    conv3_1 = Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(conv2_2)
    conv3_2 = Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(conv3_1)
    pool3 = MaxPooling2D((2, 2))(conv3_2)
        
    conv4_1 = Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(pool3)
    conv4_2 = Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(conv4_1)
    pool4 = MaxPooling2D((2, 2))(conv4_2)
    
    conv5_1 = Conv2D(256, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(pool4)
    conv5_2 = Conv2D(256, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(conv5_1)
    conv5_2 = Dropout(0.5)(conv5_2)
    conv5_3 = Conv2D(256, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(conv5_2)
    pool5 = MaxPooling2D((2, 2))(conv5_3)
    
    conv6_1 = Conv2D(512, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(pool5)
    conv6_1 = Dropout(0.5)(conv6_1)
    conv6_2 = Conv2D(512, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(conv6_1)
    pool6 = MaxPooling2D((2, 2))(conv6_2)
    
    conv7_1 = Conv2D(1024, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(pool6)
    pool7 = MaxPooling2D((2, 2))(conv7_1)

    conv8_1 = Conv2D(1024, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(pool7)
    Global_pooling = tf.keras.layers.GlobalAveragePooling2D()(conv8_1)

    x = Dense(500, activation='relu')(Global_pooling)
    x = Dense(256, activation='relu')(x)
    x = Dense(128, activation='relu')(x)
    prediction = Dense(2, activation='linear')(x) 
 
    model = Model(inputs=[img], outputs=[prediction])
    #model.summary()
    return model 

我在培训中的“ MSE”遇到了很大的错误。问题是什么？ 我的数据问题吗？ 这是我在COLAB中的链接： 比你的帮助

Answer 1

（感谢 @amina 的更新）

在答案部分中添加解决方案，尽管它存在于评论部分中为了社区的利益。

我使用了“ tf.keras.losses.MeanSquaredLogarithmicError() ”损失
功能。它使错误量更小（因为 Log ）并且
您可以了解培训效果是否良好。