Tensorflow:故障排除tf。估计器。输入。numpy\u input\u fn函数

numpy_input_fn documentation 我认为底层实现的行为(挂起)取决于队列运行器。队列运行程序未启动时会发生挂起。尝试根据以下内容修改会话运行脚本: this guide

with tf.Session() as sess:
    coord = tf.train.Coordinator()
    threads = tf.train.start_queue_runners(coord=coord)
    try:
        for step in xrange(1000000):
            if coord.should_stop():
                break
            features_data = sess.run(features)
            print(features_data)

    except Exception, e:
        # Report exceptions to the coordinator.
        coord.request_stop(e)
    finally:
        # Terminate as usual. It is safe to call `coord.request_stop()` twice.
        coord.request_stop()
        coord.join(threads)

或者,我鼓励您查看 tf.data.Dataset 接口(可能 tf.contrib.data.Dataset Dataset.from_tensor_slices . 创建要稍微复杂一些,但接口要灵活得多,并且实现不使用队列运行器,这意味着会话运行要简单得多。

import tensorflow as tf
import numpy as np

x_data = np.random.random((100000, 2))
y_data = np.random.random((100000,))

batch_size = 2
buff = 100


def input_fn():
    # possible tf.contrib.data.Dataset.from... in tf 1.3 or earlier
    dataset = tf.data.Dataset.from_tensor_slices((x_data, y_data))
    dataset = dataset.repeat().shuffle(buff).batch(batch_size)
    x, y = dataset.make_one_shot_iterator().get_next()
    return x, y


x, y = input_fn()
with tf.Session() as sess:
    print(sess.run([x, y]))