cnn.py

import cv2                 
import numpy as np         
import os                  
from random import shuffle 
from tqdm import tqdm
import matplotlib.pyplot as plt 
from tensorflow.python.framework import ops
# Visualize training history
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense

# TRAIN_DIR = 'E:\\diagnetic_Retinopathy\\train'
TRAIN_DIR = 'C:\\project_folder\\diabetes_Retinopathy\\train'
# TEST_DIR = 'E:\\diagnetic_Retinopathy\\test'
TEST_DIR = 'C:\\project_folder\\diabetes_Retinopathy\\test'

IMG_SIZE = 50
LR = 1e-3
MODEL_NAME = 'diabeticRetinopathy-{}-{}.model'.format(LR, '2conv-basic')

  
def label_img(img):
    word_label = img[0]
    print(word_label)
  
    if word_label == 'm':
        print('Mild')
        return [1,0,0,0,0]   
    elif word_label == 'd':
        print('Moderate')
        return [0,1,0,0,0]
    elif word_label == 'n':
        print('NO_DR')
        return [0,0,1,0,0]
    elif word_label == 'p':
        print('proliferate')
        return [0,0,0,1,0]
    elif word_label == 's':
        print('Severe')
        return [0,0,0,0,1]

def create_train_data():
    training_data = []
    for img in tqdm(os.listdir(TRAIN_DIR)):
        label = label_img(img)
        print('##############')
        print(label)
        path = os.path.join(TRAIN_DIR,img)
        img = cv2.imread(path,cv2.IMREAD_COLOR)
        img = cv2.resize(img, (IMG_SIZE,IMG_SIZE))
        #img = cv2.resize(img,None,fx=0.5,fy=0.5)
        training_data.append([np.array(img),np.array(label)])
    shuffle(training_data)
    np.save('train_data.npy', training_data)
    return training_data

def process_test_data():
    testing_data = []
    for img in tqdm(os.listdir(TEST_DIR)):
        path = os.path.join(TEST_DIR,img)
        img_num = img.split('.')[0]
        img = cv2.imread(path,cv2.IMREAD_COLOR)
        img = cv2.resize(img, (IMG_SIZE,IMG_SIZE))
        testing_data.append([np.array(img), img_num])
        
    shuffle(testing_data)
    np.save('test_data.npy', testing_data)
    return testing_data

train_data = create_train_data()
# If you have already created the dataset:
#train_data = np.load('train_data.npy')


import tflearn
from tflearn.layers.conv import conv_2d, max_pool_2d
from tflearn.layers.core import input_data, dropout, fully_connected
from tflearn.layers.estimator import regression
import tensorflow as tf

from tensorflow.python.framework import ops
import matplotlib.pyplot as plt
#tf.reset_default_graph()
ops.reset_default_graph()

convnet = input_data(shape=[None, IMG_SIZE, IMG_SIZE, 3], name='input')

convnet = conv_2d(convnet, 32, 3, activation='relu')
convnet = max_pool_2d(convnet, 3)

convnet = conv_2d(convnet, 64, 3, activation='relu')
convnet = max_pool_2d(convnet, 3)

convnet = conv_2d(convnet, 128, 3, activation='relu')
convnet = max_pool_2d(convnet, 3)

convnet = conv_2d(convnet, 32, 3, activation='relu')
convnet = max_pool_2d(convnet, 3)

convnet = conv_2d(convnet, 64, 3, activation='relu')
convnet = max_pool_2d(convnet, 3)

convnet = fully_connected(convnet, 1024, activation='relu')
convnet = dropout(convnet, 0.8)

convnet = fully_connected(convnet, 5, activation='softmax')
convnet = regression(convnet, optimizer='adam', learning_rate=LR, loss='categorical_crossentropy', name='targets')

model = tflearn.DNN(convnet, tensorboard_dir='log')

if os.path.exists('{}.meta'.format(MODEL_NAME)):
    model.load(MODEL_NAME)
    print('model loaded!')

train = train_data[:-1]
test = train_data[:-1]

X = np.array([i[0] for i in train]).reshape(-1,IMG_SIZE,IMG_SIZE,3)
Y = [i[1] for i in train]
print(X.shape)
test_x = np.array([i[0] for i in test]).reshape(-1,IMG_SIZE,IMG_SIZE,3)
test_y = [i[1] for i in test]
print(test_x.shape)

history=model.fit({'input': X}, {'targets': Y},n_epoch=50, validation_set=({'input': test_x}, {'targets': test_y}),snapshot_step=20, show_metric=True, run_id=MODEL_NAME)
model.save(MODEL_NAME)
h=history.history
plt.plot(h['accuracy'])
plt.plot(h['val_accuracy'], c="red")
plt.title("acc vs v-acc")
plt.show()
plt.savefig("accuracy plot")

plt.plot(h['loss'])
plt.plot(h['val_loss'], c="red")
plt.title("loss vs v-loss")
plt.show()
plt.savefig("loss plot")