main.py

import numpy as np
import tensorflow
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten, BatchNormalization, Activation
from keras.layers.convolutional import Conv2D, MaxPooling2D
from keras.constraints import maxnorm
from keras.utils import np_utils
import cv2
from keras.preprocessing.image import ImageDataGenerator, img_to_array, load_img
import warnings
warnings.filterwarnings('ignore')

train_path = "C:/Users/Manab/Waste_Recognition_System/DATASET/TRAIN"
test_path = "C:/Users/Manab/Waste_Recognition_System/DATASET/TEST"

model = Sequential()

model.add(Conv2D(32, (3, 3), input_shape=(224,224,3), padding='same'))
model.add(Activation('relu'))
model.add(Dropout(0.2))
model.add(BatchNormalization())

model.add(Conv2D(64, (3, 3), padding='same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.2))
model.add(BatchNormalization())

model.add(Conv2D(64, (3, 3), padding='same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.2))
model.add(BatchNormalization())

model.add(Conv2D(128, (3, 3), padding='same'))
model.add(Activation('relu'))
model.add(Dropout(0.2))
model.add(BatchNormalization())

model.add(Conv2D(256, (3, 3), padding='same'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.2))
model.add(BatchNormalization())

model.add(Flatten())
model.add(Dropout(0.2))

model.add(Dense(256, kernel_constraint=maxnorm(3)))
model.add(Activation('relu'))
model.add(Dropout(0.2))
model.add(BatchNormalization())

model.add(Dense(128, kernel_constraint=maxnorm(3)))
model.add(Activation('relu'))
model.add(Dropout(0.2))
model.add(BatchNormalization())

model.add(Dense(64, kernel_constraint=maxnorm(3)))
model.add(Activation('relu'))
model.add(Dropout(0.2))
model.add(BatchNormalization())

model.add(Dense(64, kernel_constraint=maxnorm(3)))
model.add(Activation('relu'))
model.add(Dropout(0.2))
model.add(BatchNormalization())

model.add(Dense(256))
model.add(Activation('softmax'))

optimizer = 'adam'

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

train_data = ImageDataGenerator(rescale= 1./255)
test_data = ImageDataGenerator(rescale= 1./255)
train_generator = train_data.flow_from_directory(train_path,target_size= (224,224),batch_size = 256,color_mode= "rgb",class_mode= "categorical")
test_generator = test_data.flow_from_directory(test_path,target_size= (224,224),batch_size = 256,color_mode= "rgb",class_mode= "categorical")

model.fit_generator(train_generator, 25 ,test_generator)

model.save('CNN_Waste_Recognition_Systems.h5')