CNN Model Predicting Only First Class - tensorflow

Question

I am working on a fine-grained classification to classify car models. So I have used transfer learning ResNet50. As per my knowledge it is performing fine while training. But when I try new images it is always predicting a single class. Below is my code.
For training:
from tensorflow.keras.layers import Input, Lambda, Dense, Flatten
from tensorflow.keras.models import Model
from tensorflow.keras.applications.resnet50 import ResNet50
from keras.applications.vgg16 import VGG16
from tensorflow.keras.applications.resnet50 import preprocess_input
from tensorflow.keras.preprocessing import image
from tensorflow.keras.preprocessing.image import ImageDataGenerator,load_img
from tensorflow.keras.models import Sequential
import matplotlib.pyplot as plt
import numpy as np
from glob import glob
IMAGE_SIZE = [224, 224]
train_path = 'Datasets/train'
valid_path = 'Datasets/test'
resnet = ResNet50(input_shape = IMAGE_SIZE + [3], weights='imagenet', include_top = False)
for layer in resnet.layers:
layer.trainable = False
folders = glob('Datasets/train/*') #training folders
x = Flatten()(resnet.output)
prediction = Dense(len(folders), activation='softmax') (x)
model = Model(inputs = resnet.input, outputs = prediction)
model.compile(
loss = 'categorical_crossentropy',
optimizer = 'adam',
metrics = ['accuracy']
)
train_datagen = ImageDataGenerator(rescale = 1./255,
shear_range = 0.2,
zoom_range = 0.2,
horizontal_flip = True)
test_datagen = ImageDataGenerator(rescale = 1./255)
training_set = train_datagen.flow_from_directory('Datasets/train',
target_size = (224, 224),
batch_size = 32,
class_mode = 'categorical')
test_set = test_datagen.flow_from_directory('Datasets/test',
target_size = (224, 224),
batch_size = 32,
class_mode = 'categorical')
r = model.fit_generator(
training_set,
validation_data=test_set,
epochs=200,
steps_per_epoch=len(training_set),
validation_steps=len(test_set)
)
from tensorflow.keras.models import load_model
model.save('model_updateV1.h5')
y_pred = model.predict(test_set)
import numpy as np
y_pred = np.argmax(y_pred, axis=1)
For Trying New Images:
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing import image
import numpy as np
from tensorflow.keras.applications.resnet50 import preprocess_input
model = load_model('model_updateV1.h5')
img = image.load_img('Datasets/test/mercedes/45.jpg', target_size=(224,224))
x = image.img_to_array(img)
x = x/255.
x = np.expand_dims(x, axis = 0)
img_data = preprocess_input(x)
img_data.shape
model.predict(img_data)
a = np.argmax(model.predict(img_data), axis=1)
a

Answer 1

I think your problem is that you are rescaling the images twice. You have code
x=x/255
then you expand the dimensions which is fine. However you then have code
img_data = preprocess_input(x)
The preprocess_input functon I believe rescales the pixel values between -1 and +1 with the code
x=x/127.5-1.
So now your pixel value have been scaled down twice. So just delete the code
x=x/255