CNN Network.py

import json 
import numpy as np
from sklearn.model_selection import train_test_split
import tensorflow.keras as keras
import tensorflow as tf

DATA_PATH = 'image_test_NT.json'
SAVED_MODEL_PATH = 'test_model.h5'
LEARNING_RATE = 0.0001
EPOCHS = 10
BATCH_SIZE = 1
NUM_IMG_CLASSES = 2

def load_data(data_path):
    with open(data_path,'r') as fp:
        data = json.load(fp)

        X = np.array(data['PIXs'])
        Y = np.array(data['labels'])
        
        print(f'X shape : {X.shape}')
        print(f'Y shape: {Y.shape}')
        return X ,Y



def get_data_splits(data_path , test_size=0.1 , val_size=0.1):

    X,Y = load_data(data_path)

    X_train , X_test, Y_train , Y_test = train_test_split(X,Y,test_size=test_size)
    X_train , X_val, Y_train , Y_val = train_test_split(X_train,Y_train,test_size=val_size)

    print(f'X_train shape : {X_train.shape}')
    print(f'X_test shape : {X_test.shape}')
    print(f'X_val shape : {X_val.shape}')


    #X_train = X_train[...,np.newaxis]
    #X_val = X_val[...,np.newaxis]
    #X_test = X_test[...,np.newaxis]

    print(f'new X_train shape : {X_train.shape}')
    print(f'new X_test shape : {X_test.shape}')
    print(f'new X_val shape : {X_val.shape}')
    


    return X_train , X_test , X_val , Y_train ,Y_test , Y_val


def build_model(input_shape,learning_rate,error='sparse_categorical_crossentropy')    :
    model = tf.keras.Sequential()

    model.add(tf.keras.layers.Conv2D(64,(3,3),activation='relu',input_shape=input_shape, kernel_regularizer=tf.keras.regularizers.l2(0.001)))
    model.add(tf.keras.layers.BatchNormalization())
    model.add(tf.keras.layers.MaxPool2D(2,2))


    model.add(tf.keras.layers.Conv2D(32,(3,3),activation='relu',kernel_regularizer = tf.keras.regularizers.l2(0.001)))
    model.add(tf.keras.layers.BatchNormalization())
    model.add(tf.keras.layers.MaxPool2D(2,2))


    model.add(tf.keras.layers.Conv2D(32,(3,3),activation='relu',kernel_regularizer = keras.regularizers.l2(0.001)))
    model.add(tf.keras.layers.BatchNormalization())
    model.add(tf.keras.layers.MaxPool2D(2,2))

    model.add(tf.keras.layers.Flatten())
    model.add(tf.keras.layers.Dense(64,activation='relu'))
    model.add(tf.keras.layers.Dropout(0.3))

    model.add(tf.keras.layers.Dense(NUM_IMG_CLASSES,activation='softmax'))

    optimizer = tf.optimizers.Adam(learning_rate=learning_rate)
    model.compile(optimizer=optimizer,loss=error,metrics=['accuracy'])
    model.summary()
    return model


def main():
    X_train,X_test,X_val,Y_train,Y_test,Y_val = get_data_splits(DATA_PATH)

    input_shape = (X_train.shape[1],X_train.shape[2],X_train.shape[3])
    print(input_shape)
    print(f"X_train_1 : {X_train.shape[1]}")
    print(f"X_train_2 : {X_train.shape[2]}")
    print(f"X_train_3 : {X_train.shape[3]}")


    model = build_model(input_shape,LEARNING_RATE)
    model.fit(X_train,Y_train,epochs=EPOCHS,batch_size=BATCH_SIZE,validation_data=(X_val,Y_val))

    test_err , test_acc = model.evaluate(X_test,Y_test)
    print(f'Test error : {test_err} , Test Acc : {test_acc}')    
    model.save(SAVED_MODEL_PATH)


if __name__ == '__main__':
    main()