学习谷歌的深度学习终于有点眉目了，给大家分享我的Tensorflow学习历程。
tensorflow的官方中文文档比较生涩，数据集一直采用的MNIST二进制数据集。并没有过多讲述怎么构建自己的图片数据集tfrecords。
流程是：制作数据集—读取数据集—-加入队列
先贴完整的代码：
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#encoding=utf-8import osimport tensorflow as tffrom PIL import Imagecwd = os.getcwd()classes = {'test','test1','test2'}#制作二进制数据def create_record(): writer = tf.python_io.TFRecordWriter("train.tfrecords") for index, name in enumerate(classes): class_path = cwd +"/"+ name+"/" for img_name in os.listdir(class_path): img_path = class_path + img_name img = Image.open(img_path) img = img.resize((64, 64)) img_raw = img.tobytes() #将图片转化为原生bytes print index,img_raw example = tf.train.Example( features=tf.train.Features(feature={ "label": tf.train.Feature(int64_list=tf.train.Int64List(value=[index])), 'img_raw': tf.train.Feature(bytes_list=tf.train.BytesList(value=[img_raw])) })) writer.write(example.SerializeToString()) writer.close()data = create_record()#读取二进制数据def read_and_decode(filename): # 创建文件队列,不限读取的数量 filename_queue = tf.train.string_input_producer([filename]) # create a reader from file queue reader = tf.TFRecordReader() # reader从文件队列中读入一个序列化的样本 _, serialized_example = reader.read(filename_queue) # get feature from serialized example # 解析符号化的样本 features = tf.parse_single_example( serialized_example, features={ 'label': tf.FixedLenFeature([], tf.int64), 'img_raw': tf.FixedLenFeature([], tf.string) } ) label = features['label'] img = features['img_raw'] img = tf.decode_raw(img, tf.uint8) img = tf.reshape(img, [64, 64, 3]) img = tf.cast(img, tf.float32) * (1. / 255) - 0.5 label = tf.cast(label, tf.int32) return img, labelif __name__ == '__main__': if 0: data = create_record("train.tfrecords") else: img, label = read_and_decode("train.tfrecords") print "tengxing",img,label #使用shuffle_batch可以随机打乱输入 next_batch挨着往下取 # shuffle_batch才能实现[img,label]的同步,也即特征和label的同步,不然可能输入的特征和label不匹配 # 比如只有这样使用,才能使img和label一一对应,每次提取一个image和对应的label # shuffle_batch返回的值就是RandomShuffleQueue.dequeue_many()的结果 # Shuffle_batch构建了一个RandomShuffleQueue，并不断地把单个的[img,label],送入队列中 img_batch, label_batch = tf.train.shuffle_batch([img, label], batch_size=4, capacity=2000, min_after_dequeue=1000) # 初始化所有的op init = tf.initialize_all_variables() with tf.Session() as sess: sess.run(init) # 启动队列 threads = tf.train.start_queue_runners(sess=sess) for i in range(5): print img_batch.shape,label_batch val, l = sess.run([img_batch, label_batch]) # l = to_categorical(l, 12) print(val.shape, l) |
制作数据集
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#制作二进制数据def create_record(): cwd = os.getcwd() classes = {'1','2','3'} writer = tf.python_io.TFRecordWriter("train.tfrecords") for index, name in enumerate(classes): class_path = cwd +"/"+ name+"/" for img_name in os.listdir(class_path): img_path = class_path + img_name img = Image.open(img_path) img = img.resize((28, 28)) img_raw = img.tobytes() #将图片转化为原生bytes #print index,img_raw example = tf.train.Example( features=tf.train.Features( feature={ "label": tf.train.Feature(int64_list=tf.train.Int64List(value=[index])), 'img_raw': tf.train.Feature(bytes_list=tf.train.BytesList(value=[img_raw])) } ) ) writer.write(example.SerializeToString()) writer.close() |
TFRecords文件包含了tf.train.Example 协议内存块(protocol buffer)(协议内存块包含了字段 Features)。我们可以写一段代码获取你的数据， 将数据填入到Example协议内存块(protocol buffer)，将协议内存块序列化为一个字符串， 并且通过tf.python_io.TFRecordWriter 写入到TFRecords文件。
读取数据集
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#读取二进制数据def read_and_decode(filename): # 创建文件队列,不限读取的数量 filename_queue = tf.train.string_input_producer([filename]) # create a reader from file queue reader = tf.TFRecordReader() # reader从文件队列中读入一个序列化的样本 _, serialized_example = reader.read(filename_queue) # get feature from serialized example # 解析符号化的样本 features = tf.parse_single_example( serialized_example, features={ 'label': tf.FixedLenFeature([], tf.int64), 'img_raw': tf.FixedLenFeature([], tf.string) } ) label = features['label'] img = features['img_raw'] img = tf.decode_raw(img, tf.uint8) img = tf.reshape(img, [64, 64, 3]) img = tf.cast(img, tf.float32) * (1. / 255) - 0.5 label = tf.cast(label, tf.int32) return img, label |
一个Example中包含Features，Features里包含Feature（这里没s）的字典。最后，Feature里包含有一个 FloatList， 或者ByteList，或者Int64List
加入队列
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with tf.Session() as sess: sess.run(init) # 启动队列 threads = tf.train.start_queue_runners(sess=sess) for i in range(5): print img_batch.shape,label_batch val, l = sess.run([img_batch, label_batch]) # l = to_categorical(l, 12) print(val.shape, l) |
这样就可以的到和tensorflow官方的二进制数据集了，
注意：
- 启动队列那条code不要忘记，不然卡死
- 使用的时候记得使用val和l，不然会报类型错误：TypeError: The value of a feed cannot be a tf.Tensor object. Acceptable feed values include Python scalars, strings, lists, or numpy ndarrays.
- 算交叉熵时候：cross_entropy=tf.nn.sparse_softmax_cross_entropy_with_logits(logits,labels)算交叉熵
- 最后评估的时候用tf.nn.in_top_k(logits,labels,1)选logits最大的数的索引和label比较
- cross_entropy = -tf.reduce_sum(y_*tf.log(y_conv))算交叉熵，所以label必须转成one-hot向量
实例2：将图片文件夹下的图片转存tfrecords的数据集。
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############################################################################################ #!/usr/bin/python2.7 # -*- coding: utf-8 -*- #Author : zhaoqinghui #Date : 2016.5.10 #Function: image convert to tfrecords ############################################################################################# import tensorflow as tf import numpy as np import cv2 import os import os.path from PIL import Image #参数设置 ############################################################################################### train_file = 'train.txt' #训练图片 name='train' #生成train.tfrecords output_directory='./tfrecords'resize_height=32 #存储图片高度 resize_width=32 #存储图片宽度 ############################################################################################### def _int64_feature(value): return tf.train.Feature(int64_list=tf.train.Int64List(value=[value])) def _bytes_feature(value): return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value])) def load_file(examples_list_file): lines = np.genfromtxt(examples_list_file, delimiter=" ", dtype=[('col1', 'S120'), ('col2', 'i8')]) examples = [] labels = [] for example, label in lines: examples.append(example) labels.append(label) return np.asarray(examples), np.asarray(labels), len(lines) def extract_image(filename, resize_height, resize_width): image = cv2.imread(filename) image = cv2.resize(image, (resize_height, resize_width)) b,g,r = cv2.split(image) rgb_image = cv2.merge([r,g,b]) return rgb_image def transform2tfrecord(train_file, name, output_directory, resize_height, resize_width): if not os.path.exists(output_directory) or os.path.isfile(output_directory): os.makedirs(output_directory) _examples, _labels, examples_num = load_file(train_file) filename = output_directory + "/" + name + '.tfrecords' writer = tf.python_io.TFRecordWriter(filename) for i, [example, label] in enumerate(zip(_examples, _labels)): print('No.%d' % (i)) image = extract_image(example, resize_height, resize_width) print('shape: %d, %d, %d, label: %d' % (image.shape[0], image.shape[1], image.shape[2], label)) image_raw = image.tostring() example = tf.train.Example(features=tf.train.Features(feature={ 'image_raw': _bytes_feature(image_raw), 'height': _int64_feature(image.shape[0]), 'width': _int64_feature(image.shape[1]), 'depth': _int64_feature(image.shape[2]), 'label': _int64_feature(label) })) writer.write(example.SerializeToString()) writer.close() def disp_tfrecords(tfrecord_list_file): filename_queue = tf.train.string_input_producer([tfrecord_list_file]) reader = tf.TFRecordReader() _, serialized_example = reader.read(filename_queue) features = tf.parse_single_example( serialized_example, features={ 'image_raw': tf.FixedLenFeature([], tf.string), 'height': tf.FixedLenFeature([], tf.int64), 'width': tf.FixedLenFeature([], tf.int64), 'depth': tf.FixedLenFeature([], tf.int64), 'label': tf.FixedLenFeature([], tf.int64) } ) image = tf.decode_raw(features['image_raw'], tf.uint8) #print(repr(image)) height = features['height'] width = features['width'] depth = features['depth'] label = tf.cast(features['label'], tf.int32) init_op = tf.initialize_all_variables() resultImg=[] resultLabel=[] with tf.Session() as sess: sess.run(init_op) coord = tf.train.Coordinator() threads = tf.train.start_queue_runners(sess=sess, coord=coord) for i in range(21): image_eval = image.eval() resultLabel.append(label.eval()) image_eval_reshape = image_eval.reshape([height.eval(), width.eval(), depth.eval()]) resultImg.append(image_eval_reshape) pilimg = Image.fromarray(np.asarray(image_eval_reshape)) pilimg.show() coord.request_stop() coord.join(threads) sess.close() return resultImg,resultLabel def read_tfrecord(filename_queuetemp): filename_queue = tf.train.string_input_producer([filename_queuetemp]) reader = tf.TFRecordReader() _, serialized_example = reader.read(filename_queue) features = tf.parse_single_example( serialized_example, features={ 'image_raw': tf.FixedLenFeature([], tf.string), 'width': tf.FixedLenFeature([], tf.int64), 'depth': tf.FixedLenFeature([], tf.int64), 'label': tf.FixedLenFeature([], tf.int64) } ) image = tf.decode_raw(features['image_raw'], tf.uint8) # image tf.reshape(image, [256, 256, 3]) # normalize image = tf.cast(image, tf.float32) * (1. /255) - 0.5 # label label = tf.cast(features['label'], tf.int32) return image, label def test(): transform2tfrecord(train_file, name , output_directory, resize_height, resize_width) #转化函数 img,label=disp_tfrecords(output_directory+'/'+name+'.tfrecords') #显示函数 img,label=read_tfrecord(output_directory+'/'+name+'.tfrecords') #读取函数 print label if __name__ == '__main__': test() |
这样就可以得到自己专属的数据集.tfrecords了 ，它可以直接用于tensorflow的数据集。
以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持服务器之家。
原文链接：http://blog.csdn.net/tengxing007/article/details/56847828
