having a very large loss when I am training a regression loss

Question

I want to predict the center of the pupil from an image. so I used a CNN with 3 Dence layer. so the input is an image and the output is a coordinate (X,Y). my model is :

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 

and I got a very large error with "MSE" in training. what is the problem? Is the problem with my data? it's my link in colab: https://colab.research.google.com/drive/12hjlT6JG8IlEXYISKw5zFJE6qBDuuVi1?usp=sharing than you for your help

Answer 1

(Thanks @amina for the update)

Adding the solution here in the Answer Section though it is present in the comment section for the benefit of the community.

I used " tf.keras.losses.MeanSquaredLogarithmicError() " loss function. It makes the amount of error smaller (because of Log )and you can understand whether training is doing well or not.