All the predictions have the same values (wrong predictions). TensorFlow, Keras, EfficientNetV2L

I am trying to use EfficientNetV2L for my simple regression problem. I will use my lab experiment data later in replacement of this current simple regression problem. I am using 100 examples of 75x75 size. Example one contains all 1’s in 75x75 matrix, example 2 contains all 2’s in 75x75 matrix and so on. Correspondingly, labels are in a vector with a range from 1 to 100. The problem I am facing is that all the predictions have the same value (wrong predictions). Someone please help me in this problem. Thank you.

EfficientNetV2L Example with Plain Matrix

import tensorflow as tf
from tensorflow import keras
from keras import Sequential
from keras import layers
from keras.layers import Dense, Dropout
import numpy as np
from keras import optimizers, Model
from keras.optimizers import SGD, RMSprop
from keras import losses
from tensorflow.keras.applications import EfficientNetV2L
import os
import matplotlib.pyplot as plt

dum = np.ones(shape=(75,75),dtype=float)
for i in range(1,101):
x = np.vstack((x,dum*i))
y = np.append(y,i)

x = np.delete(x, range(0,75), axis = 0)

x_reshp = np.reshape(x, (-1,75,75,1))

x_train = x_reshp/np.max(x_reshp)
y_train = y/np.max(y)

base_model = tf.keras.applications.efficientnet_v2.EfficientNetV2L(
# input_tensor=None,
pooling= ‘avg’,
# classes=1,
# classifier_activation=None,

x = base_model.output

x = layers.Flatten()(x)

x = Dense(50, activation=‘relu’)(x)

x = Dropout(0.5)(x)

x = Dense(10, activation=‘relu’)(x)
Preds = Dense(1)(x)

model = Model(inputs=base_model.input, outputs = Preds)

model.compile(keras.optimizers.Adam(learning_rate= 0.0001),loss=‘mse’,metrics=[‘mae’])


Train the model

history =, y_train, epochs=50,verbose=2)

x_pred = x_train[0:100]
z = model.predict(x_pred)
print('Predicted Value is = ', z*(np.max(y)))

acc = history.history[‘mae’]
loss = history.history[‘loss’]
epochs = range(len(acc))
plt.plot(epochs, acc, ‘r’, label=‘Training MAE’)
plt.plot(epochs, loss, ‘b’, label=‘Training Loss’)


As you have mentioned there are 100 labels you need to mention the classes = 100 in the final Dense layer for the correct prediction.

Please change this line as below:
Preds = Dense(100)(x)