Hello I am trying to build an image classifier using Keras and CNN
I already trained a model for Binary classification and it works really well.
I applied the same knowledge to build a Image classification using multiple categories(Which is failing miserably) I have 5 classes I have created 5 folders inside jpeg dir and the directoy structure is as follows
C:\Users\jpeg
1.train
2.test
Inside train folder I have 5 subfolders each folder corresponding to a class
C:\Users\jpeg\train
1.Auth_Docs
2.Certificates_Reports
3.Document
4.Title
5.communication
and i placed appropriate images in each folder
Followed the exact same structure in test folder as well
source code:
import matplotlib.pyplot as plt
import cv2
%matplotlib inline
from keras.preprocessing.image import ImageDataGenerator
image_gen.flow_from_directory('C://Users/Jpeg/train')
image_gen.flow_from_directory('C://Users/jpeg/test')
image_shape = (150,150,3)
from keras.models import Sequential
from keras.layers import Activation, Dropout, Flatten, Dense, Conv2D, MaxPooling2D
model = Sequential()
model.add(Conv2D(filters=32, kernel_size=(3,3),input_shape=(150,150,3), activation='relu',))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(filters=64, kernel_size=(3,3),input_shape=(150,150,3), activation='relu',))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(filters=64, kernel_size=(3,3),input_shape=(150,150,3), activation='relu',))
model.add(MaxPooling2D(pool_size=(2, 2)))
#
model.add(Flatten())
#hidden layer number of neurons
model.add(Dense(256, activation='relu'))
# Here we say randomly turn off 30% of neurons.
model.add(Dropout(0.3))
# Last layer(add number of layers based on number of categories)
model.add(Dense(5, activation='softmax'))
model.compile(loss='categorical_crossentropy',
optimizer='rmsprop',
metrics=['accuracy'])
#Training the model
batch_size = 16
train_image_gen = image_gen.flow_from_directory('C://Users/jpeg/train',
target_size=image_shape[:2],
batch_size=batch_size,
class_mode='categorical'
)
#Found 2434 images belonging to 5 classes.
test_image_gen = image_gen.flow_from_directory('C://Users/jpeg/test',
target_size=image_shape[:2],
batch_size=batch_size,
class_mode='categorical'
)
#Found 60 images belonging to 5 classes.
train_image_gen.class_indices
#o/p
{'Auth_Docs': 0,
'Certificates_Reports': 1,
'Document': 2,
'Title': 3,
'communication': 4}
#Fitting the model
from PIL import Image
Image.MAX_IMAGE_PIXELS = None
results = model.fit_generator(train_image_gen,epochs=50,
steps_per_epoch=100,
validation_data=test_image_gen,
validation_steps=12)
#saving the model
model.save('Document_Classification.h5')
#results.accuracy for my model gives around 80% of accuracy
Now the issue with testing the model
from keras.models import load_model
new_model = load_model('Document_Classification.h5')
import numpy as np
from keras.preprocessing import image
import os,sys
from PIL import Image
Image.MAX_IMAGE_PIXELS = None
for a,b,c in os.walk("C:/Users/jpeg/test/communication"):
for i in c:
doc_img = image.load_img(os.path.join(a,i), target_size=(150, 150))
doc_img = image.img_to_array(doc_img)
doc_img = np.expand_dims(doc_img, axis=0)
doc_img = doc_img/255
#print (a,i)
prediction_prob = new_model.predict_classes(doc_img)
print(prediction_prob )
only output I get is
[2]
[2]
[2]
[2]
no matter which folder i use to test the o/p is the same a i.e in above example i used the communication folder images and the o/p is 2
same o/p when i test images from Auth_Docs,Title etc.
I don not see anything wrong in my code as this code worked for binary classification. Please advise
Also, I want to find what is the associated label with the output I am getting.
Please advise.
Thanks.
