TensorFlow训练模型时，自定义数据处理函数，方便快速加载数据训练模型

ゝ一世哀愁。 2022-12-20 11:26 168阅读 0赞

一：自定义数据处理函数

#txt：txt文件里面存放图像名称
    def read_images(txt, batch_size):
        imagepaths, labels = list(), list() #分别存放图像路径和label
        images_names = []
    
        with open('/data/' + txt, 'r') as r:
            images_names.extend(r.readlines())
        for name in images_names:
            name = name.replace('\n', '')
            imagepaths.append(os.path.join(cover_path, name))
            labels.append(0)
            imagepaths.append(os.path.join(stego_path, name))
            labels.append(1)
    
        # Convert to Tensor
        imagepaths = tf.convert_to_tensor(imagepaths, dtype=tf.string)
        labels = tf.convert_to_tensor(labels, dtype=tf.int32)
        # Build a TF Queue, shuffle data
        image, label = tf.train.slice_input_producer([imagepaths, labels],
                                                     shuffle=True)
    
        # Read images from disk
        image = tf.read_file(image)
        image = tf.image.decode_jpeg(image, channels=CHANNELS)
    
        # Resize images to a common size
        image = tf.image.resize_images(image, [IMG_HEIGHT, IMG_WIDTH])
    
        # Normalize
        image = image * 1.0 / 127.5 - 1.0
    
        # Create batches
        X, Y = tf.train.batch([image, label], batch_size=batch_size,
                              capacity=batch_size * 8,
                              num_threads=4)
    
        return X, Y

二：训练模型

# -*- coding: utf-8 -*-
    
    import scipy.misc
    import os
    
    import tensorflow as tf
    import numpy as np
    # import pandas as pd
    import cv2
    from random import shuffle
    from data_utils import *
    from utils import read_images
    from os import walk
    import os
    
    os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
    os.environ["CUDA_VISIBLE_DEVICES"] = "6"
    os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
    
    tf.logging.set_verbosity(tf.logging.ERROR)
    batch_size = 16
    
    
    def train(save_model_path):
        train_x, train_y = read_images('train.txt', batch_size)
        val_x, val_y = read_images('val.txt', batch_size)
        test_x, test_y = read_images('test.txt', batch_size)
    
        # model setup
        with tf.variable_scope('xuNet'):
            logits_train = discriminator(train_x, is_training=True)
    
        with tf.variable_scope('xuNet', reuse=tf.AUTO_REUSE):
            logits_val = discriminator(val_x, is_training=False)
    
        with tf.variable_scope('xuNet', reuse=tf.AUTO_REUSE):
            logits_test = discriminator(test_x, is_training=False)
    
        # calculate loss
        loss_op = tf.reduce_mean(
            tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits_train, labels=train_y))
        optimizer = tf.train.AdamOptimizer(learning_rate=0.001, beta1=0.5)
        train_op = optimizer.minimize(loss_op)
    
        train_correct_pred = tf.equal(tf.argmax(logits_train, 1), tf.cast(train_y, tf.int64))
        train_acc = tf.reduce_mean(tf.cast(train_correct_pred, tf.float32))
    
        val_correct_pred = tf.equal(tf.argmax(logits_val, 1), tf.cast(val_y, tf.int64))
        val_acc = tf.reduce_mean(tf.cast(val_correct_pred, tf.float32))
    
        test_correct_pred = tf.equal(tf.argmax(logits_test, 1), tf.cast(test_y, tf.int64))
        test_acc = tf.reduce_mean(tf.cast(test_correct_pred, tf.float32))
    
        init = tf.global_variables_initializer()
        saver = tf.train.Saver(max_to_keep=50)  # maximum 100 latest models are saved
        with tf.Session() as sess:
            # saver.restore(sess, '/data/zhouzl/de_attack/model/xu_spatial_02/xu_spatial_model_860.ckpt')
            sess.run(init)
            # Start the data queue
            tf.train.start_queue_runners()
    
            pre_acc = 0
    
            for epoch in range(200):
                train_acc_per_epoch = np.array([])
                for step in range(round(18000*2 / batch_size)):
                    _, train_accuracy, train_loss = sess.run([train_op, train_acc, loss_op])
                    train_acc_per_epoch = np.insert(train_acc_per_epoch, 0, train_accuracy)
                    if step % 200 == 0:
                        print("epoch " + str(epoch) + ", step " + str(step) + ", train mean Accuracy= " + str(
                            np.mean(train_acc_per_epoch)
                            + ", train loss " + str(train_loss)))
    
                if epoch % 5 == 0:
                    val_acc_per_epoch = np.array([])
                    for step in range(round(9000 * 2 / batch_size)):
                        val_accuracy = sess.run(val_acc)
                        val_acc_per_epoch = np.insert(val_acc_per_epoch, 0, val_accuracy)
                    print('epoch {}, val mean accuracy {}'.format(epoch, np.mean(val_acc_per_epoch)))
    
                if epoch % 10 == 0:
                    test_acc_per_epoch = np.array([])
                    for step in range(round(3000 * 2 / batch_size)):
                        test_accuracy = sess.run(test_acc)
                        test_acc_per_epoch = np.insert(test_acc_per_epoch, 0, test_accuracy)
                    test_mean_acc = np.mean(test_acc_per_epoch)
                    print('epoch {}, test mean accuracy {}'.format(epoch, test_mean_acc))
                    if test_mean_acc > pre_acc and epoch % 10 == 0:
                        saver.save(sess, save_model_path + '/xu_model_' + str(test_mean_acc) + '.ckpt')
                pre_acc = test_mean_acc
    
    
    if __name__ == '__main__':
    
        save_model_path = '/model'
        if not os.path.exists(save_model_path):
            os.mkdir(save_model_path)
        train(save_model_path)

[reference][]

[reference]: https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v1/examples/5_DataManagement/build_an_image_dataset.py