explain.txt

part 1: 

importing dataset 'Vedic dataset'

into datapath, the dataset have two categories Vedic symbols & Vedic worshipping  device.

part 2:

now diving into data prepocessing:

1.for loop of categories.

in this section we are going through each and every image in the data folder & loading the images

2. then images converted into grey scale.

try:
            gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)           
           
            resized=cv2.resize(gray,(img_size,img_size))
          

resizing into 100X100

  data.append(resized)
            target.append(label_dict[category])  

image append {appending the resized images}

exception case in case image isn't loading properly :-

    
        except Exception as e:
            print('Exception:',e)
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import numpy as np

data=np.array(data)/255.0

in this section diving by 255 converting the pixel range into 0 & 1. by diving with 255.

read the article for mor3e info roll no 78:- https://machinelearningmastery.com/how-to-manually-scale-image-pixel-data-for-deep-learning/


data=np.reshape(data,(data.shape[0],img_size,img_size,1))  

You always have to feed a 4D array of shape (batch_size, height, width, depth) to the CNN. Output data from CNN is also a 4D array of shape (batch_size, height, width, depth). To add a Dense layer on top of the CNN layer, we have to change the 4D output of CNN to 2D using a Flatten layer

target=np.array(target)

from keras.utils import np_utils

new_target=np_utils.to_categorical(target)...



..............................


np.save('data',data) -- containing images


np.save('target',new_target) ---- vedic tools, or vedic symbols  



part 3: training the cnn:-

the prepocessed data is now load for training  using two layers of cnn 200*3*3 & 100*3*3



from keras.models import Sequential
from keras.layers import Dense,Activation,Flatten,Dropout
from keras.layers import Conv2D,MaxPooling2D
from keras.callbacks import ModelCheckpoint

model=Sequential()

model.add(Conv2D(200,(3,3),input_shape=data.shape[1:]))
model.add(Activation('relu'))   -- part of cnn
model.add(MaxPooling2D(pool_size=(2,2)))  -- part of cnn


model.add(Conv2D(100,(3,3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2,2)))


model.add(Flatten())   -- flattining 
model.add(Dropout(0.6))   - avoiding overfitting.

model.add(Dense(50,activation='relu'))  - 50 neurons dense layer.

model.add(Dense(2,activation='softmax'))


model.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['accuracy'])  


about adam optimizer :-

What is the Adam optimization algorithm?

Adam is an optimization algorithm that can be used instead of the classical stochastic gradient descent procedure to update network weights iterative based in training data.


output layer for two category........


..................................................................


from sklearn.model_selection import train_test_split

train_data,test_data,train_target,test_target=train_test_split(data,target,test_size=0.1)


test & train split.

.......................................................................


checkpoint = ModelCheckpoint('model-{epoch:03d}.model',monitor='val_loss',verbose=0,save_best_only=True,mode='auto')
history=model.fit(train_data,train_target,epochs=12,callbacks=[checkpoint],validation_split=0.2)  


this is model checkpoint epoches and monintering things...


.....................................................................


from matplotlib import pyplot as plt

plt.plot(history.history['loss'],'r',label='training loss')
plt.plot(history.history['val_loss'],label='validation loss')
plt.xlabel('# epochs')
plt.ylabel('loss')
plt.legend()
plt.show() 

plotting the val & trainig loss.



...................................................

printing accuracy 


print(model.evaluate(test_data,test_target))


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 problem faced & key idea:-

the main problem was the randomness of the data and overfitting problem at the starting the model was badly overfitting so we decided to arrange things in two part tools & symbols... 2 layer of cnn & 10 epoches makes it working. Then we added more dataset.




note :- 1.relu & maxpoll read or watch any video about it for your knwledge ..  it is a part of cnn