tensorflow随笔—

这次用slim搭个稍微大一点的网络VGG16，VGG16和VGG19实际上差不多，所以本例程的代码以VGG16来做5类花的分类任务。

VGG网络相比之前的LeNet，AlexNet引入如下几个特点：

1. 堆叠3×3的小卷积核替代了5×5，7×7的大卷积核。

虽然5×5的卷积核感受野大，但是参数多。2个3×3的卷积堆叠感受野等同于5×5，并且进行了2次非线性变换。总结一下：相比于大卷积核，小卷积核的堆叠一方面减少了参数; 另一方面进行了更多的非线性映射，增加了网络表达能力。

2.网络层数加深。我们先不谈深层网络难以训练又或者梯度弥散等缺点，在特征的抽象化或者网络的表达能力范畴上，深层网络比浅层网络更加能够拟合数据的分布。

3.VGG网络的原作还引入了数据增广，图像预处理等trick。

开始贴代码阶段，工程分为三个文件：

vgg.py: 搭建16层的VGG网络。

import tensorflow as tf
import tensorflow.contrib.slim as slimdef build_vgg(rgb, num_classes, keep_prob, train=True):with slim.arg_scope([slim.conv2d, slim.fully_connected], activation_fn=tf.nn.relu, normalizer_fn=slim.batch_norm):# block_1net = slim.repeat(rgb, 2, slim.conv2d, 64, [3, 3], padding='SAME', scope='conv1')net = slim.max_pool2d(net, [2, 2], scope='pool1')# block_2net = slim.repeat(net, 2, slim.conv2d, 128, [3, 3], padding='SAME', scope='conv2')net = slim.max_pool2d(net, [2, 2], scope='pool2')# block_3net = slim.repeat(net, 3, slim.conv2d, 256, [3, 3], padding='SAME', scope='conv3')net = slim.max_pool2d(net, [2, 2], scope='pool3')# block_4net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3], padding='SAME', scope='conv4')net = slim.max_pool2d(net, [2, 2], scope='pool4')# block_5net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3], padding='SAME', scope='conv5')net = slim.max_pool2d(net, [2, 2], scope='pool5')# flattenfeature_shape = net.get_shape()flattened_shape = feature_shape[1].value * feature_shape[2].value * feature_shape[3].valuepool5_flatten = tf.reshape(net, [-1, flattened_shape])# fc6net = slim.fully_connected(pool5_flatten, 4096, scope='fc6')if train:net = slim.dropout(net, keep_prob=keep_prob, scope='dropout6')# fc7net = slim.fully_connected(net, 4096, scope='fc7')if train:net = slim.dropout(net, keep_prob=keep_prob, scope='dropout7')# fc8net = slim.fully_connected(net, num_classes, activation_fn=tf.nn.softmax, scope='fc8')return net

tfrecords.py：用于数据的编码和解码，本例程不同与之前的文章采用feed_dict向网络喂数据，而是使用tensorflow自己的TFRecord结构编码数据集。

import tensorflow as tf
import numpy as np
import os
import glob
from PIL import Imagepath_tfrecord = '/home/danny/chenwei/CSDN_blog/VGG/tfrecords/'def convert_to_tfrecord(images, labels, filename):print("Converting data into %s ..." % filename)writer = tf.python_io.TFRecordWriter(path_tfrecord + filename)for index, img in enumerate(images):img_raw = Image.open(img)if img_raw.mode != "RGB":continueimg_raw = img_raw.resize((256, 256))img_raw = img_raw.tobytes()label = int(labels[index])example = tf.train.Example(features=tf.train.Features(feature={"label": tf.train.Feature(int64_list=tf.train.Int64List(value=[label])),"img_raw": tf.train.Feature(bytes_list=tf.train.BytesList(value=[img_raw])),}))writer.write(example.SerializeToString())writer.close()def read_and_decode(filename, is_train=None):filename_queue = tf.train.string_input_producer([filename], num_epochs=400)reader = tf.TFRecordReader()_, serialized_example = reader.read(filename_queue)features = tf.parse_single_example(serialized_example,features={'label': tf.FixedLenFeature([], tf.int64),'img_raw': tf.FixedLenFeature([], tf.string),})img = tf.decode_raw(features['img_raw'], tf.uint8)img = tf.reshape(img, [256, 256, 3])img = tf.cast(img, tf.float32) * (1. / 255) - 0.5if is_train == True:img = tf.random_crop(img, [224, 224, 3])img = tf.image.random_flip_left_right(img)img = tf.image.random_brightness(img, max_delta=63)img = tf.image.random_contrast(img, lower=0.2, upper=1.8)img = tf.image.per_image_standardization(img)else:img = tf.image.resize_image_with_crop_or_pad(img, 224, 224)img = tf.image.per_image_standardization(img)label = tf.cast(features['label'], tf.int32)return img, labeldef get_file(path):cate = [path+x for x in os.listdir(path) if os.path.isdir(path+x)]images = []labels = []for idx, folder in enumerate(cate):for img in glob.glob(folder+'/*.jpg'):print('reading the images:%s' % (img))images.append(img)labels.append(idx)image_list = np.asarray(images, np.string_)label_list = np.asarray(labels, np.int32)# shufflenum_example = image_list.shape[0]arr = np.arange(num_example)np.random.shuffle(arr)image_list = image_list[arr]label_list = label_list[arr]# divide train_data and val_datanum_example = image_list.shape[0]split = np.int(num_example * 0.8)train_images = image_list[:split]train_labels = label_list[:split]val_images = image_list[split:]val_labels = label_list[split:]return train_images, train_labels, val_images, val_labelsif __name__ == '__main__':train_images, train_labels, val_images, val_labels = get_file('/home/danny/chenwei/CSDN_blog/VGG/datasets/')convert_to_tfrecord(images=train_images, labels=train_labels, filename="train.tfrecords")convert_to_tfrecord(images=val_images, labels=val_labels, filename="test.tfrecords")

train.py：用于训练的文件，与之间不同之处在于使用队列的方式多线程取数据进行训练。

# -*- coding: utf-8 -*-
import tensorflow as tf
from utils.tfrecords import *
from model.vgg import *tf.app.flags.DEFINE_integer('num_classes', 5, 'classification number.')
tf.app.flags.DEFINE_integer('crop_width', 256, 'width of input image.')
tf.app.flags.DEFINE_integer('crop_height', 256, 'height of input image.')
tf.app.flags.DEFINE_integer('channels', 3, 'channel number of image.')
tf.app.flags.DEFINE_integer('batch_size', 2, 'num of each batch')
tf.app.flags.DEFINE_integer('num_epochs', 400, 'number of epoch')
tf.app.flags.DEFINE_bool('continue_training', False, 'whether is continue training')
tf.app.flags.DEFINE_float('learning_rate', 0.01, 'learning rate')
tf.app.flags.DEFINE_string('dataset_path', './datasets/', 'path of dataset')
tf.app.flags.DEFINE_string('checkpoints', './checkpoints/model.ckpt', 'path of checkpoints')
tf.app.flags.DEFINE_string('train_tfrecords', '/home/danny/chenwei/CSDN_blog/VGG/tfrecords/train.tfrecords', 'train tfrecord')
tf.app.flags.DEFINE_string('test_tfrecords', '/home/danny/chenwei/CSDN_blog/VGG/tfrecords/test.tfrecords', 'test tfrecord')FLAGS = tf.app.flags.FLAGSdef main(_):# data processtrain_images, train_labels = read_and_decode(FLAGS.train_tfrecords, True)val_images, val_labels = read_and_decode(FLAGS.test_tfrecords, False)train_labels = tf.one_hot(indices=tf.cast(train_labels, tf.int32), depth=FLAGS.num_classes)train_images_batch, train_labels_batch = tf.train.shuffle_batch([train_images, train_labels], batch_size=FLAGS.batch_size, capacity=20000, min_after_dequeue=3000, num_threads=16)  # 这里设置线程数val_labels = tf.one_hot(indices=tf.cast(val_labels, tf.int32), depth=FLAGS.num_classes)val_images_batch, val_labels_batch = tf.train.shuffle_batch([val_images, val_labels], batch_size=FLAGS.batch_size, capacity=20000, min_after_dequeue=3000, num_threads=16)  # 这里设置线程数# define network inputinput = tf.placeholder(tf.float32, shape=[FLAGS.batch_size, FLAGS.crop_height, FLAGS.crop_width, FLAGS.channels], name='input')output = tf.placeholder(tf.int32, shape=[FLAGS.batch_size, FLAGS.num_classes], name='output')# control GPU resource utilizationconfig = tf.ConfigProto(allow_soft_placement=True)config.gpu_options.allow_growth = Truesess = tf.Session(config=config)# build networklogits = build_vgg(input, FLAGS.num_classes, 0.5, True)# losscross_entropy_loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=output))regularization_loss = tf.reduce_sum(tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES))loss = cross_entropy_loss + regularization_loss# optimizertrain_op = tf.train.AdamOptimizer(FLAGS.learning_rate).minimize(loss)# calculate correctcorrect_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(output, 1))accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))with sess.as_default():# init all paramterssaver = tf.train.Saver(max_to_keep=1000)sess.run(tf.local_variables_initializer())sess.run(tf.global_variables_initializer())# restore weightif FLAGS.continue_training:saver.restore(sess, FLAGS.checkpoints)# begin trainingcoord = tf.train.Coordinator()threads = tf.train.start_queue_runners(sess=sess, coord=coord)epoch = 0try:while not coord.should_stop():# begin trainingtrain_images, train_labels = sess.run([train_images_batch, train_labels_batch])_, err, acc = sess.run([train_op, loss, accuracy], feed_dict={input: train_images, output: train_labels})print("[Train] Step: %d, loss: %.4f, accuracy: %.4f%%" % (epoch, err, acc))epoch += 1if epoch % 10 == 0 or (epoch + 1) == FLAGS.num_epochs:val_images, val_labels = sess.run([val_images_batch, val_labels_batch])val_err, val_acc = sess.run([loss, accuracy], feed_dict={input:val_imagesh, output: val_labels})print("[validation] Step: %d, loss: %.4f, accuracy: %.4f%%" % (epoch, val_err, val_acc))if (epoch + 1) == FLAGS.num_epochs:checkpoint_path = FLAGS.checkpointssaver.save(sess, save_path=checkpoint_path, global_step=epoch)except tf.errors.OutOfRangeError:print('Done training -- epoch limited reached')finally:coord.request_stop()coord.join(threads)sess.close()if __name__ == '__main__':tf.app.run()

训练结果：大约在96%左右

[Train] Step: 19985, loss: 1.1098, accuracy: 1.0000%
[Train] Step: 19986, loss: 1.1302, accuracy: 1.0000%
[Train] Step: 19987, loss: 1.1232, accuracy: 1.0000%
[Train] Step: 19988, loss: 1.1299, accuracy: 1.0000%
[Train] Step: 19989, loss: 1.1220, accuracy: 1.0000%
[validation] Step: 19990, loss: 1.1634, accuracy: 0.9688%

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