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 slim def 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_1 net = slim.repeat(rgb, 2, slim.conv2d, 64, [3, 3], padding='SAME', scope='conv1') net = slim.max_pool2d(net, [2, 2], scope='pool1') # block_2 net = slim.repeat(net, 2, slim.conv2d, 128, [3, 3], padding='SAME', scope='conv2') net = slim.max_pool2d(net, [2, 2], scope='pool2') # block_3 net = slim.repeat(net, 3, slim.conv2d, 256, [3, 3], padding='SAME', scope='conv3') net = slim.max_pool2d(net, [2, 2], scope='pool3') # block_4 net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3], padding='SAME', scope='conv4') net = slim.max_pool2d(net, [2, 2], scope='pool4') # block_5 net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3], padding='SAME', scope='conv5') net = slim.max_pool2d(net, [2, 2], scope='pool5') # flatten feature_shape = net.get_shape() flattened_shape = feature_shape[1].value * feature_shape[2].value * feature_shape[3].value pool5_flatten = tf.reshape(net, [-1, flattened_shape]) # fc6 net = slim.fully_connected(pool5_flatten, 4096, scope='fc6') if train: net = slim.dropout(net, keep_prob=keep_prob, scope='dropout6') # fc7 net = slim.fully_connected(net, 4096, scope='fc7') if train: net = slim.dropout(net, keep_prob=keep_prob, scope='dropout7') # fc8 net = 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 Image path_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": continue img_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) - 0.5 if 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, label def 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) # shuffle num_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_data num_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_labels if __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.FLAGS def main(_): # data process train_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 input input = 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 utilization config = tf.ConfigProto(allow_soft_placement=True) config.gpu_options.allow_growth = True sess = tf.Session(config=config) # build network logits = build_vgg(input, FLAGS.num_classes, 0.5, True) # loss cross_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 # optimizer train_op = tf.train.AdamOptimizer(FLAGS.learning_rate).minimize(loss) # calculate correct correct_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 paramters saver = tf.train.Saver(max_to_keep=1000) sess.run(tf.local_variables_initializer()) sess.run(tf.global_variables_initializer()) # restore weight if FLAGS.continue_training: saver.restore(sess, FLAGS.checkpoints) # begin training coord = tf.train.Coordinator() threads = tf.train.start_queue_runners(sess=sess, coord=coord) epoch = 0 try: while not coord.should_stop(): # begin training train_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 += 1 if 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.checkpoints saver.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% 文章来源: tensorflow随笔――VGG网络