Predicting score problem

[Ildar5]

Hello, I am trying to use your code without kitura and swift just with python, but it doesn't matter what image I use, each of the predicting images has at least ~0.79 memorability score, even completely white image has ~0.83 score. Please help me, here is my code:

...
model = Sequential()
model.add(Conv2D(96, (11, 11), (4, 4), activation="relu", input_shape=(227, 227, 3)))
model.add(MaxPooling2D((3, 3), (2, 2)))
model.add(BatchNormalization())
model.add(Conv2D(256, (5, 5), activation="relu"))
model.add(ZeroPadding2D((2, 2)))
model.add(MaxPooling2D((3, 3), (2, 2)))
model.add(BatchNormalization())
model.add(Conv2D(384, (3, 3), activation="relu"))
model.add(ZeroPadding2D((1, 1)))
model.add(Conv2D(384, (3, 3), activation="relu"))
model.add(ZeroPadding2D((1, 1)))
model.add(Conv2D(256, (3, 3), activation="relu"))
model.add(ZeroPadding2D((1, 1)))
model.add(MaxPooling2D((3, 3), (2, 2)))
model.add(GlobalAveragePooling2D())
model.add(Dense(4096, activation="relu"))
model.add(Dropout(0.5))
model.add(Dense(4096, activation="relu"))
model.add(Dropout(0.5))
model.add(Dense(1))

train_split = load_split("../lamem/splits/train_1.txt")
test_split = load_split("../lamem/splits/test_1.txt")
batch_size = 64 * 4

train_gen = lamem_generator(train_split, batch_size=batch_size)
test_gen = lamem_generator(test_split, batch_size=batch_size)

model.compile("adam", euclidean_distance_loss)
model.fit(train_gen, steps_per_epoch=int(len(train_split) / batch_size), epochs=5, verbose=1, validation_data=test_gen, 
validation_steps=int(len(test_split) / batch_size))

model.save("memnet_model2.h5")
// I am trying also with separate weights saving.
model.save_weights('memnet_model2_w')

then in other script:

model = tf.keras.models.load_model('memnet_model2.h5', custom_objects={'euclidean_distance_loss': 
euclidean_distance_loss})
// tried also with load_weights, I have hoped this may help(
//model.load_weights('memnet_model2_w')

def load_image(image_file):
return np.array(Image.open(image_file).resize((227, 227)).convert("RGB"), dtype="float32") / 255.

test_img = mp.Pool().map(load_image, ['predict/7.png'])
test_img = np.array(test_img)
print(test_img.shape)
# test_img.reshape(-1, 227, 227, 3)
print(np.array(test_img).shape)

prediction = model.predict(np.array(test_img))
print(prediction)