TensorFlow提供了一種TFRecords的格式來統一存儲數據。理論上，TFRecords可以存儲任何形式的數據 , TFRecords文件的是以tf.train.Example Protocol Buffer的格式存儲的。以下的代碼給出了tf.train.Example的數據結構：

message Example {Features features = 1;};message Features {map<string, Feature> feature = 1;};message Feature {oneof kind {BytesList bytes_list = 1;FloatList float_list = 2;Int64List int64_list = 3;}}; 首先介紹一下我接下來要展示給大家的工程結構（使用的IDE是 pycharm 2017 community）：

接下來代碼分三個文件， 分別是 加載數據 prepare_data.py ，制作tfrecords文件 make_data.py, 讀取tfrecords文件read_data.py。

1.prepare_data.py

下面代碼中數據增強部分我就略過了，可以參考tensorflow數據增強

# -*- coding: utf-8 -*- import os import cv2 import numpy as npdir = "imgs/" # 加載jpg， testShape = (333, 500, 3)def data_augmentation(data):"""數據增強處理:param data::return: """return datadef get_img_data(file_dir):"""獲取圖片數據， 返回類型是 list:param file_dir: 圖片所在目錄:return: 返回類型是 list"""files = [os.path.join('imgs', x) for x in os.listdir(file_dir)]raw_data = [cv2.imread(img) for img in files]raw_data = data_augmentation(raw_data)return raw_dataif __name__ == "__main__":get_img_data(dir)

make_data.py

# _*_ coding: utf-8 _*_import tensorflow as tf import numpy as npfrom prepare_data import get_img_data# tfrecords 支持的數據類型 # tf.train.Feature(int64_list = tf.train.Int64List(value=[int_scalar])) # tf.train.Feature(bytes_list = tf.train.BytesList(value=[array_string_or_byte])) # tf.train.Feature(bytes_list = tf.train.FloatList(value=[float_scalar]))# 創建tfrecords文件 file_nums = 2 instance_per_file = 5 dir = "imgs/"data = get_img_data(dir) # type(data) list for i in range(file_nums):tfrecords_filename = './tfrecords/train.tfrecords-%.5d-of-%.5d' % (i, file_nums)writer = tf.python_io.TFRecordWriter(tfrecords_filename) # 創建.tfrecord文件for j in range(instance_per_file):# type(data[i*instance_per_file+j]) numpy.ndarrayimg_raw = np.asarray(data[i*instance_per_file+j]).tostring()example = tf.train.Example(features=tf.train.Features(feature={'label': tf.train.Feature(int64_list=tf.train.Int64List(value=[j])),'img_raw': tf.train.Feature(bytes_list=tf.train.BytesList(value=[img_raw]))}))writer.write(example.SerializeToString())writer.close()

read_data.py

import tensorflow as tf import numpy as np import cv2 import matplotlib.pyplot as plt# 讀取tfrecords文件 # --------------hyperParams-------------------------- batch_size = 2 capacity = 1000 + 3*batch_size train_rounds = 3 num_epochs = 30 img_h = 333 img_w = 500 # ---------------------------------------------------tfrecord_files = tf.train.match_filenames_once('./tfrecords/train.tfrecords-*') queue = tf.train.string_input_producer(tfrecord_files, num_epochs=num_epochs, shuffle=True, capacity=10)reader = tf.TFRecordReader() # 從文件中讀出一個隊列， 也可以使用read_uo_to函數一次性讀取多個樣例 _, serialized_example = reader.read(queue)# 讀取多個對應tf.parse_example() # 讀取單個對應tf.parse_single_example()features = tf.parse_single_example(serialized_example, features={'label': tf.FixedLenFeature([], tf.int64),'img_raw': tf.FixedLenFeature([], tf.string),} )image = tf.decode_raw(features['img_raw'], tf.uint8) # image_shape = tf.stack([img_h, img_w, 3]) image = tf.reshape(image, [img_h, img_w, 3]) label = tf.cast(features['label'], tf.int64)# tf.train.shuffle_batch() to_train_batch, to_label_batch = tf.train.shuffle_batch([image, label], batch_size=batch_size, capacity=capacity,allow_smaller_final_batch=True, num_threads=1, min_after_dequeue=1 )with tf.Session() as sess:sess.run(tf.group(tf.local_variables_initializer(), tf.global_variables_initializer()))coord = tf.train.Coordinator()threads = tf.train.start_queue_runners(sess=sess, coord=coord)for i in range(train_rounds):train_batch, label_batch = sess.run([to_train_batch, to_label_batch])plt.subplot(121)plt.imshow(train_batch[0])plt.subplot(122)plt.imshow(train_batch[1])plt.show()coord.request_stop()coord.join(threads)print('finish all') # 下圖是read_data.py 讀取 tfrecords 的結果：

總結

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