7.3 TensorFlow笔记(基础篇):加载数据之从队列中读取

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前言

整体步骤

在TensorFlow中进行模型训练时,在官网给出的三种读取方式,中最好的文件读取方式就是将利用队列进行文件读取,而且步骤有两步:
1. 把样本数据写入TFRecords二进制文件
2. 从队列中读取数据

读取TFRecords文件步骤

使用队列读取数TFRecords 文件数据的步骤
1. 创建张量,从二进制文件读取一个样本数据
2. 创建张量,从二进制文件随机读取一个mini-batch
3. 把每一批张量传入网络作为输入点

TensorFlow使用TFRecords文件训练样本的步骤

在生成文件名的序列中,设定epoch数量
训练时,设定为无穷循环
在读取数据时,如果捕捉到错误,终止

source code

tensorflow-master\tensorflow\examples\how_tos\reading_data\fully_connected_reader.py(1.2.1)

CODE

代码与解析

解析主要在注释中,最后一个模块if __name__ == '__main__':的运行,建议参考’http://blog.csdn.net/fontthrone/article/details/76735591’

import tensorflow as tfimport os# from tensorflow.contrib.learn.python.learn.datasets import mnist# 注意上面的这个mnist 与 example 中的 mnist 是不同的,本文件中请使用下面的那个 mnistos.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'import argparseimport os.pathimport sysimport timefrom tensorflow.examples.tutorials.mnist import mnist# Basic model parameters as external flags.FLAGS = None# This part of the code is added by FontTian,which comes from the source code of tensorflow.examples.tutorials.mnist# The MNIST images are always 28x28 pixels.# IMAGE_SIZE = 28# IMAGE_PIXELS = IMAGE_SIZE * IMAGE_SIZE# Constants used for dealing with the files, matches convert_to_records.TRAIN_FILE = 'train.tfrecords'VALIDATION_FILE = 'validation.tfrecords'def read_and_decode(filename_queue):    reader = tf.TFRecordReader()    _, serialized_example = reader.read(filename_queue)    features = tf.parse_single_example(        serialized_example,        # Defaults are not specified since both keys are required.        # 必须写明faetures 中的 key 的名称        features={            'image_raw': tf.FixedLenFeature([], tf.string),            'label': tf.FixedLenFeature([], tf.int64),        })    # Convert from a scalar string tensor (whose single string has    # length mnist.IMAGE_PIXELS) to a uint8 tensor with shape    # [mnist.IMAGE_PIXELS].    # 将一个标量字符串张量(其单个字符串的长度是mnist.image像素) # 0 维的Tensor    # 转换为一个带有形状mnist.图像像素的uint8张量。 # 一维的Tensor    image = tf.decode_raw(features['image_raw'], tf.uint8)    # print(tf.shape(image)) # Tensor("input/Shape:0", shape=(1,), dtype=int32)    image.set_shape([mnist.IMAGE_PIXELS])    # print(tf.shape(image)) # Tensor("input/Shape_1:0", shape=(1,), dtype=int32)    # OPTIONAL: Could reshape into a 28x28 image and apply distortions    # here.  Since we are not applying any distortions in this    # example, and the next step expects the image to be flattened    # into a vector, we don't bother.    # Convert from [0, 255] -> [-0.5, 0.5] floats.    image = tf.cast(image, tf.float32) * (1. / 255) - 0.5    # print(tf.shape(image)) # Tensor("input/Shape_2:0", shape=(1,), dtype=int32)    # Convert label from a scalar uint8 tensor to an int32 scalar.    label = tf.cast(features['label'], tf.int32)    # print(tf.shape(label)) # Tensor("input/Shape_3:0", shape=(0,), dtype=int32)    return image, label# 使用 tf.train.shuffle_batch 将前面生成的样本随机化,获得一个最小批次的张量def inputs(train, batch_size, num_epochs):    """Reads input data num_epochs times.    Args:      train: Selects between the training (True) and validation (False) data.      batch_size: Number of examples per returned batch.      num_epochs: Number of times to read the input data, or 0/None to         train forever.    Returns:      A tuple (images, labels), where:      * images is a float tensor with shape [batch_size, mnist.IMAGE_PIXELS]        in the range [-0.5, 0.5].      * labels is an int32 tensor with shape [batch_size] with the true label,        a number in the range [0, mnist.NUM_CLASSES).      Note that an tf.train.QueueRunner is added to the graph, which      must be run using e.g. tf.train.start_queue_runners().    输入参数:      train: Selects between the training (True) and validation (False) data.      batch_size: 训练的每一批有多少个样本      num_epochs: 读取输入数据的次数, or 0/None 表示永远训练下去    返回结果:      A tuple (images, labels), where:      * images is a float tensor with shape [batch_size, mnist.IMAGE_PIXELS]        范围: [-0.5, 0.5].      * labels is an int32 tensor with shape [batch_size] with the true label,        范围: [0, mnist.NUM_CLASSES).      注意 :  tf.train.QueueRunner 被添加进 graph, 它必须用 tf.train.start_queue_runners() 来启动线程.    """    if not num_epochs: num_epochs = None    filename = os.path.join(FLAGS.train_dir,                            TRAIN_FILE if train else VALIDATION_FILE)    with tf.name_scope('input'):        # tf.train.string_input_producer 返回一个 QueueRunner,里面有一个 FIFQueue        filename_queue = tf.train.string_input_producer(            [filename], num_epochs=num_epochs)        # 如果样本数据很大,可以分成若干文件,把文件名列表传入        # Even when reading in multiple threads, share the filename queue.        image, label = read_and_decode(filename_queue)        # Shuffle the examples and collect them into batch_size batches.        # (Internally uses a RandomShuffleQueue.)        # We run this in two threads to avoid being a bottleneck.        images, sparse_labels = tf.train.shuffle_batch(            [image, label], batch_size=batch_size, num_threads=2,            capacity=1000 + 3 * batch_size,            # Ensures a minimum amount of shuffling of examples.            # 留下一部分队列,来保证每次有足够的数据做随机打乱            min_after_dequeue=1000)        return images, sparse_labelsdef run_training():    """Train MNIST for a number of steps."""    # Tell TensorFlow that the model will be built into the default Graph.    with tf.Graph().as_default():        # Input images and labels.        images, labels = inputs(train=True, batch_size=FLAGS.batch_size,                                num_epochs=FLAGS.num_epochs)        # 构建一个从推理模型来预测数据的图        logits = mnist.inference(images,                                 FLAGS.hidden1,                                 FLAGS.hidden2)        # Add to the Graph the loss calculation.        # 定义损失函数        loss = mnist.loss(logits, labels)        # 将模型添加到图操作中        train_op = mnist.training(loss, FLAGS.learning_rate)        # 初始化变量的操作        init_op = tf.group(tf.global_variables_initializer(),                           tf.local_variables_initializer())        # Create a session for running operations in the Graph.        # 在图中创建一个用于运行操作的会话        sess = tf.Session()        # 初始化变量,注意:string_input_product 内部创建了一个epoch计数器        sess.run(init_op)        # Start input enqueue threads.        coord = tf.train.Coordinator()        threads = tf.train.start_queue_runners(sess=sess, coord=coord)        try:            step = 0            while not coord.should_stop():                start_time = time.time()                # Run one step of the model.  The return values are                # the activations from the `train_op` (which is                # discarded) and the `loss` op.  To inspect the values                # of your ops or variables, you may include them in                # the list passed to sess.run() and the value tensors                # will be returned in the tuple from the call.                _, loss_value = sess.run([train_op, loss])                duration = time.time() - start_time                # Print an overview fairly often.                if step % 100 == 0:                    print('Step %d: loss = %.2f (%.3f sec)' % (step, loss_value,                                                               duration))                step += 1        except tf.errors.OutOfRangeError:            print('Done training for %d epochs, %d steps.' % (FLAGS.num_epochs, step))        finally:            # 通知其他线程关闭            coord.request_stop()        # Wait for threads to finish.        coord.join(threads)        sess.close()def main(_):    run_training()if __name__ == '__main__':    parser = argparse.ArgumentParser()    parser.add_argument(        '--learning_rate',        type=float,        default=0.01,        help='Initial learning rate.'    )    parser.add_argument(        '--num_epochs',        type=int,        default=2,        help='Number of epochs to run trainer.'    )    parser.add_argument(        '--hidden1',        type=int,        default=128,        help='Number of units in hidden layer 1.'    )    parser.add_argument(        '--hidden2',        type=int,        default=32,        help='Number of units in hidden layer 2.'    )    parser.add_argument(        '--batch_size',        type=int,        default=100,        help='Batch size.'    )    parser.add_argument(        '--train_dir',        type=str,        default='/tmp/data',        help='Directory with the training data.'    )    FLAGS, unparsed = parser.parse_known_args()    tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)

运行结果

Step 0: loss = 2.31 (0.106 sec)Step 100: loss = 2.14 (0.016 sec)Step 200: loss = 1.91 (0.016 sec)Step 300: loss = 1.69 (0.016 sec)Step 400: loss = 1.28 (0.016 sec)Step 500: loss = 1.02 (0.016 sec)Step 600: loss = 0.70 (0.016 sec)Step 700: loss = 0.71 (0.016 sec)Step 800: loss = 0.71 (0.016 sec)Step 900: loss = 0.49 (0.016 sec)Step 1000: loss = 0.58 (0.016 sec)Done training for 2 epochs, 1100 steps.