transfer learning of Inception v3 returns the same predictions - tensorflow

Question

I have tried to customize inception to classify. I used the cat, dog, human and other to classify cat, dog, human ( collection of family photos) and other (mostly natural scenes) I have about 2000 dog, 1000 cat, 7000 human and 3000 images split 80:20 among the train and validate. The essence of model is as below. When I train the training accuracy is close to 97% and validation accuracy is ~90%.
import os
from tensorflow.keras import layers
from tensorflow.keras import Model
from tensorflow.keras.optimizers import RMSprop
from tensorflow.keras.applications.inception_v3 import InceptionV3
local_weights_file = 'C:/users/sethr/education/Healthcare/imagedetect/modelimageadvance /inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5'
pre_trained_model = InceptionV3(input_shape = (150, 150,3), include_top = False, weights = `enter code here`local_weights_file)
for layer in pre_trained_model.layers:
layer.trainable = False
# pre_trained_model.summary()
last_layer = pre_trained_model.get_layer('mixed7')
#print('last layer output shape: ', last_layer.output_shape)
last_output = last_layer.output
# Flatten the output layer to 1 dimension
x = tf.keras.layers.Flatten()(last_output)
# Add a fully connected layer with 1,024 hidden units and ReLU activation
x = tf.keras.layers.Dense(512, activation='relu')(x)
x = tf.keras.layers.Dense(256, activation='relu')(x)
# Add a dropout rate of 0.2
x = tf.keras.layers.Dropout(0.2)(x)
# Add a final sigmoid layer for classification
x = tf.keras.layers.Dense(4, activation='softmax')(x)
model = Model(pre_trained_model.input, x)
model.compile(optimizer = RMSprop(lr=0.0001), loss = 'categorical_crossentropy', metrics = ['accuracy'])
history = model.fit(train_generator,validation_data = validation_generator,steps_per_epoch = 20,epochs = 20,validation_steps = 25,verbose = 2)
model.save("dcho.rp5")
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import numpy as np
import cv2
import glob
from keras.preprocessing import image
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
labels= ["cat","dog","human","other"]
path='C:/Users/sethr/education/Healthcare/imagedetect/images/*.jpg'
for fim in glob.glob(path):
# predicting images
img=image.load_img(fim, target_size=(150, 150))
x=image.img_to_array(img)
x=np.expand_dims(x, axis=0)
images = np.vstack([x])
plt.figure()
plt.imshow(img)
plt.show()
classes = model.predict(images,batch_size=10)
________________________________________________________
_______________________________________________________
# All images will be rescaled by 1./255.
train_datagen = ImageDataGenerator( rescale = 1.0/255. )
test_datagen = ImageDataGenerator( rescale = 1.0/255. )
# --------------------
# Flow training images in batches of 20 using train_datagen generator
# --------------------
train_generator = train_datagen.flow_from_directory(train_dir,
batch_size=20,
shuffle='True',
class_mode='categorical',
target_size=(150, 150))
# --------------------
# Flow validation images in batches of 20 using test_datagen generator
# --------------------
validation_generator = test_datagen.flow_from_directory(validation_dir,
target_size = (150, 150),
batch_size=20,
class_mode='categorical',
shuffle='True',
)
_______________________________________________________________
The problem is it is returning always human as prediction.. I played around with Adam, Adjusted learning rate but still the prediction remains the same. Any insight.

Answer 1

The issue is you have not done the rescaling part when you predicting images.
You are doing rescaling only for training and validation
train_datagen = ImageDataGenerator( rescale = 1.0/255. )
test_datagen = ImageDataGenerator( rescale = 1.0/255. )
# Note - this test_datagen used for validation
In order to get correct predictions, we should do the same preprocessing steps as we did for training and validation. So you need to rescale the image.
You can rescale the image by using this code
x = x* 1.0/255.0
So the correct predicting image part is shown below.
for fim in glob.glob(path):
# predicting images
img=image.load_img(fim, target_size=(150, 150))
x=image.img_to_array(img)
x=np.expand_dims(x, axis=0)
# Add this line
x = x* 1.0/255.0
images = np.vstack([x])
plt.figure()
plt.imshow(img)
plt.show()
classes = model.predict(images,batch_size=10)