Question

我使用tf.Variable表示W（权重）和b（偏差），但是tf.placeholder表示X（输入批处理）和Y（此批处理的期望值）。一切正常。但今天我发现了这个主题： Tensorflow github issues 并引用：

Feed_dict从Python运行时到TensorFlow运行时执行单线程内容memcpy。如果GPU上需要数据，那么您将获得额外的CPU-＆gt; GPU传输。从feed_dict切换到本机TensorFlow（变量/队列）时，我习惯看到性能提高了10倍

现在我尝试找到如何使用tf.Variable或Queue作为输入数据而不是feed_dict，以提高速度，特别是对于批次。因为我需要逐个更改数据批量。当所有批次都完成 - 时代结束。而不是从开始，第二个时代等......

但很抱歉，我不明白我该怎么用。

Answer 1

以下是有关如何使用队列提供培训批次的自包含示例：

import tensorflow as tf

IMG_SIZE = [30, 30, 3]
BATCH_SIZE_TRAIN = 50

def get_training_batch(batch_size):
    ''' training data pipeline -- normally you would read data from files here using
    a TF reader of some kind. '''
    image = tf.random_uniform(shape=IMG_SIZE)
    label = tf.random_uniform(shape=[])

    min_after_dequeue = 100
    capacity = min_after_dequeue + 3 * batch_size
    images, labels = tf.train.shuffle_batch(
        [image, label], batch_size=batch_size, capacity=capacity,
        min_after_dequeue=min_after_dequeue)
    return images, labels

# define the graph
images_train, labels_train = get_training_batch(BATCH_SIZE_TRAIN)
'''inference, training and other ops generally are defined here too'''

# start a session
with tf.Session() as sess:
    sess.run(tf.initialize_all_variables())
    coord = tf.train.Coordinator()
    threads = tf.train.start_queue_runners(sess=sess, coord=coord)

    ''' do something interesting here -- training, validation, etc'''
    for _ in range(5):
        # typical training step where batch data are drawn from the training queue
        py_images, py_labels = sess.run([images_train, labels_train])
        print('\nData from queue:')
        print('\tImages shape, first element: ', py_images.shape, py_images[0][0, 0, 0])
        print('\tLabels shape, first element: ', py_labels.shape, py_labels[0])

    # close threads
    coord.request_stop()
    coord.join(threads)

对火车数据使用tensorflow tf.Variable而不是tf.placeholder

1 个答案: