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train image data with cnn
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| import glob | |
| from importlib.resources import path | |
| from os import listdir | |
| from os.path import isfile, join | |
| import cv2 | |
| import os | |
| import numpy as np | |
| from random import shuffle | |
| # from tqdm import tqdm | |
| import pandas as pd | |
| train_data=[] | |
| path="/home/myn/Computer_Vision/Age detection/face_age" | |
| for label in os.listdir(path): | |
| images = glob.glob(path+'/'+label+'/*.png') | |
| # df = pd.DataFrame(list(zip(images, label)), | |
| # columns =['Image', 'Label']) | |
| for i in images: | |
| img = cv2.imread(i, cv2.IMREAD_GRAYSCALE) | |
| train_data.append([np.array(img), np.array(label)]) | |
| # print(train_data[1]) | |
| np.save('train_data.npy', train_data) | |
| # Splitting the testing data and training data | |
| train = train_data[:-6000] | |
| test = train_data[-6000:] | |
| IMG_SIZE = 200 | |
| '''Setting up the features and labels''' | |
| # X-Features & Y-Labels | |
| X = np.array([i[0] for i in train]).reshape(-1, IMG_SIZE, IMG_SIZE, 1) | |
| Y = [i[1] for i in train] | |
| test_x = np.array([i[0] for i in test]).reshape(-1, IMG_SIZE, IMG_SIZE, 1) | |
| test_y = [i[1] for i in test] | |
| LR = 1e-3 | |
| MODEL_NAME = 'dogsvscats-{}-{}.model'.format(LR, '6conv-basic') | |
| 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 | |
| tf.reset_default_graph() | |
| convnet = input_data(shape =[None, IMG_SIZE, IMG_SIZE , 1], name ='input') | |
| convnet = conv_2d(convnet, 32, 5, activation ='relu') | |
| convnet = max_pool_2d(convnet, 5) | |
| convnet = conv_2d(convnet, 64, 5, activation ='relu') | |
| convnet = max_pool_2d(convnet, 5) | |
| convnet = conv_2d(convnet, 128, 5, activation ='relu') | |
| convnet = max_pool_2d(convnet, 5) | |
| convnet = conv_2d(convnet, 64, 5, activation ='relu') | |
| convnet = max_pool_2d(convnet, 5) | |
| convnet = conv_2d(convnet, 32, 5, activation ='relu') | |
| convnet = max_pool_2d(convnet, 5) | |
| convnet = fully_connected(convnet, 1024, activation ='relu') | |
| convnet = dropout(convnet, 0.8) | |
| convnet = fully_connected(convnet, 2, activation ='softmax') | |
| convnet = regression(convnet, optimizer ='adam', learning_rate = LR, | |
| loss ='categorical_crossentropy', name ='targets') | |
| model = tflearn.DNN(convnet, tensorboard_dir ='log') | |
| # '''Fitting the data into our model''' | |
| # epoch = 5 taken | |
| model.fit({'input': X}, {'targets': Y}, n_epoch = 5, | |
| validation_set =({'input': test_x}, {'targets': test_y}), | |
| snapshot_step = 6000, show_metric = True, run_id = MODEL_NAME) | |
| model.save(MODEL_NAME) |
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