利用Tensorflow讀取二進制CIFAR-10數據集

使用Tensorflow讀取CIFAR-10二進制數據集

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參考文獻 Tensorflow官方文檔 tf.transpose函數解析 tf.slice函數解析 CIFAR10/CIFAR100數據集介紹 tf.train.shuffle_batch函數解析 Python urllib urlretrieve函數解析

import os
import tarfile
import tensorflow as tf
from six.moves import urllib
from tensorflow.python.framework import ops

ops.reset_default_graph()

# 更改工做目錄
abspath = os.path.abspath(__file__)  # 獲取當前文件絕對地址
# E:\GitHub\TF_Cookbook\08_Convolutional_Neural_Networks\03_CNN_CIFAR10\ostest.py
dname = os.path.dirname(abspath)  # 獲取文件所在文件夾地址
# E:\GitHub\TF_Cookbook\08_Convolutional_Neural_Networks\03_CNN_CIFAR10
os.chdir(dname)  # 轉換目錄文件夾到上層

# Start a graph session
# 初始化Session
sess = tf.Session()

# 設置模型超參數
batch_size = 128  # 批處理數量
data_dir = 'temp'  # 數據目錄
output_every = 50  # 輸出訓練loss值
generations = 20000  # 迭代次數
eval_every = 500  # 輸出測試loss值
image_height = 32  # 圖片高度
image_width = 32  # 圖片寬度
crop_height = 24  # 裁剪後圖片高度
crop_width = 24  # 裁剪後圖片寬度
num_channels = 3  # 圖片通道數
num_targets = 10  # 標籤數
extract_folder = 'cifar-10-batches-bin'

# 指數學習速率衰減參數
learning_rate = 0.1  # 學習率
lr_decay = 0.1  # 學習率衰減速度
num_gens_to_wait = 250.  # 學習率更新週期

# 提取模型參數
image_vec_length = image_height*image_width*num_channels  # 將圖片轉化成向量所需大小
record_length = 1 + image_vec_length  # ( + 1 for the 0-9 label)

# 讀取數據
data_dir = 'temp'
if not os.path.exists(data_dir):  # 當前目錄下是否存在temp文件夾
    os.makedirs(data_dir)  # 若是當前文件目錄下不存在這個文件夾，建立一個temp文件夾
#  設定CIFAR10下載路徑
cifar10_url = 'http://www.cs.toronto.edu/~kriz/cifar-10-binary.tar.gz'

# 檢查這個文件是否存在，若是不存在下載這個文件
data_file = os.path.join(data_dir, 'cifar-10-binary.tar.gz')
# temp\cifar-10-binary.tar.gz
if os.path.isfile(data_file):
    pass
else:
    # 回調函數，當鏈接上服務器、以及相應的數據塊傳輸完畢時會觸發該回調，咱們能夠利用這個回調函數來顯示當前的下載進度。
    # block_num已經下載的數據塊數目，block_size數據塊大小，total_size下載文件總大小

    def progress(block_num, block_size, total_size):
        progress_info = [cifar10_url, float(block_num*block_size)/float(total_size)*100.0]
        print('\r Downloading {} - {:.2f}%'.format(*progress_info), end="")


    # urlretrieve(url, filename=None, reporthook=None, data=None)
    # 參數 finename 指定了保存本地路徑（若是參數未指定，urllib會生成一個臨時文件保存數據。）
    # 參數 reporthook 是一個回調函數，當鏈接上服務器、以及相應的數據塊傳輸完畢時會觸發該回調，咱們能夠利用這個回調函數來顯示當前的下載進度。
    # 參數 data 指 post 到服務器的數據，該方法返回一個包含兩個元素的(filename, headers)元組，filename 表示保存到本地的路徑，header 表示服務器的響應頭。
    # 此處 url=cifar10_url,filename=data_file,reporthook=progress

    filepath, _ = urllib.request.urlretrieve(cifar10_url, data_file, progress)
    # 解壓文件
    tarfile.open(filepath, 'r:gz').extractall(data_dir)


# Define CIFAR reader
# 定義CIFAR讀取器
def read_cifar_files(filename_queue, distort_images=True):
    reader = tf.FixedLengthRecordReader(record_bytes=record_length)
    # 返回固定長度的文件記錄 record_length函數參數爲一條圖片信息即1+32*32*3
    key, record_string = reader.read(filename_queue)
    # 此處調用tf.FixedLengthRecordReader.read函數返回鍵值對
    record_bytes = tf.decode_raw(record_string, tf.uint8)
    # 讀出來的原始文件是string類型，此處咱們須要用decode_raw函數將String類型轉換成uint8類型
    image_label = tf.cast(tf.slice(record_bytes, [0], [1]), tf.int32)
    # 見slice函數用法，取從0號索引開始的第一個元素。並將其轉化爲int32型數據。其中存儲的是圖片的標籤

    # 截取圖像
    image_extracted = tf.reshape(tf.slice(record_bytes, [1], [image_vec_length]),
                                 [num_channels, image_height, image_width])
    # 從1號索引開始提取圖片信息。這和此數據集存儲圖片信息的格式相關。
    # CIFAR-10數據集中
    """第一個字節是第一個圖像的標籤，它是一個0-9範圍內的數字。接下來的3072個字節是圖像像素的值。
       前1024個字節是紅色通道值，下1024個綠色，最後1024個藍色。值以行優先順序存儲，所以前32個字節是圖像第一行的紅色通道值。
       每一個文件都包含10000個這樣的3073字節的「行」圖像，但沒有任何分隔行的限制。所以每一個文件應該徹底是30730000字節長。"""

    # Reshape image
    image_uint8image = tf.transpose(image_extracted, [1, 2, 0])
    # 詳見tf.transpose函數，將[channel,image_height,image_width]轉化爲[image_height,image_width,channel]的數據格式。
    reshaped_image = tf.cast(image_uint8image, tf.float32)
    # 將圖片剪裁或填充至合適大小
    final_image = tf.image.resize_image_with_crop_or_pad(reshaped_image, crop_width, crop_height)

    if distort_images:
        # 將圖像水平隨機翻轉，改變亮度和對比度。
        final_image = tf.image.random_flip_left_right(final_image)
        final_image = tf.image.random_brightness(final_image, max_delta=63)
        final_image = tf.image.random_contrast(final_image, lower=0.2, upper=1.8)

    # 對圖片作標準化處理
    """Linearly scales `image` to have zero mean and unit norm.
    This op computes `(x - mean) / adjusted_stddev`, where `mean` is the average
    of all values in image, and `adjusted_stddev = max(stddev, 1.0/sqrt(image.NumElements()))`.
    `stddev` is the standard deviation of all values in `image`.
    It is capped away from zero to protect against division by 0 when handling uniform images."""
    final_image = tf.image.per_image_standardization(final_image)
    return (final_image, image_label)


# Create a CIFAR image pipeline from reader
# 從閱讀器中構造CIFAR圖片管道
def input_pipeline(batch_size, train_logical=False):
    # train_logical標誌用於區分讀取訓練和測試數據集
    if train_logical:
        files = [os.path.join(data_dir, extract_folder, 'data_batch_{}.bin'.format(i)) for i in range(1, 6)]
    #  data_dir=tmp
    # extract_folder=cifar-10-batches-bin
    else:
        files = [os.path.join(data_dir, extract_folder, 'test_batch.bin')]
    filename_queue = tf.train.string_input_producer(files)
    image, label = read_cifar_files(filename_queue)
    print(train_logical, 'after read_cifar_files ops image', sess.run(tf.shape(image)))
    print(train_logical, 'after read_cifar_files ops label', sess.run(tf.shape(label)))
    # min_after_dequeue defines how big a buffer we will randomly sample
    #   from -- bigger means better shuffling but slower start up and more
    #   memory used.
    # capacity must be larger than min_after_dequeue and the amount larger
    #   determines the maximum we will prefetch.  Recommendation:
    #   min_after_dequeue + (num_threads + a small safety margin) * batch_size
    min_after_dequeue = 5000
    capacity = min_after_dequeue + 3*batch_size
    # 批量讀取圖片數據
    example_batch, label_batch = tf.train.shuffle_batch([image, label],
                                                        batch_size=batch_size,
                                                        capacity=capacity,
                                                        min_after_dequeue=min_after_dequeue)
    print(train_logical, 'after shuffle_batch ops image', sess.run(tf.shape(image)))
    print(train_logical, 'after shuffle_batch ops example_batch', sess.run(tf.shape(example_batch)))
    print(train_logical, 'after shuffle_batch ops label', sess.run(tf.shape(label)))
    print(train_logical, 'after shuffle_batch ops label_batch', sess.run(tf.shape(label_batch)))


    return (example_batch, label_batch)


# 獲取數據
print('Getting/Transforming Data.')
# 初始化數據管道獲取訓練數據和對應標籤
images, targets = input_pipeline(batch_size, train_logical=True)
# 獲取測試數據和對應標籤
test_images, test_targets = input_pipeline(batch_size, train_logical=False)

sess.close()

# True after read_cifar_files ops image [24 24  3]
# True after read_cifar_files ops label [1]
# True after shuffle_batch ops image [24 24  3]
# True after shuffle_batch ops example_batch [128  24  24   3]
# True after shuffle_batch ops label [1]
# True after shuffle_batch ops label_batch [128   1]
# False after read_cifar_files ops image [24 24  3]
# False after read_cifar_files ops label [1]
# False after shuffle_batch ops image [24 24  3]
# False after shuffle_batch ops example_batch [128  24  24   3]
# False after shuffle_batch ops label [1]
# False after shuffle_batch ops label_batch [128   1]