Question

我很抱歉，但我仍然遇到一些处理张量流的问题，希望能在这里得到一些帮助....

我做了Sraw在Tensorflow does not terminate using batches建议的第一个解决方案，但我现在收到以下错误

OutOfRangeError (see above for traceback): RandomShuffleQueue '_1_shuffle_batch/random_shuffle_queue' is closed and has insufficient elements (requested 30, current size 0)
 [[Node: shuffle_batch = QueueDequeueManyV2[component_types=[DT_FLOAT, DT_FLOAT], timeout_ms=-1, _device="/job:localhost/replica:0/task:0/cpu:0"](shuffle_batch/random_shuffle_queue, shuffle_batch/n)]]

我已经尝试过使用

了

try:
    while not coord.should_stop():
        # Run training steps or whatever
        sess.run(train_op)

except tf.errors.OutOfRangeError:

但它不起作用。文件dataset.csv已存在。但我无法弄清楚是否有办法检查我的脚本是否正确读取了它。

孔文件看起来像

from __future__ import print_function

import numpy as np
import tensorflow as tf

from urllib.request import urlopen

num_attributes = 15
num_types = 7


def read_from_cvs(filename_queue):
    reader = tf.TextLineReader()
    key, value = reader.read(filename_queue)
    record_defaults = [[] for col in range((num_attributes+2))]  # no defaults
    attributes = tf.decode_csv(value, record_defaults=record_defaults)
    features = tf.stack(attributes[1:-1])  
    #labels = tf.stack(attributes[-1])  
    labels = tf.one_hot(tf.cast(tf.stack(attributes[-1]), tf.uint8), num_types)
    return features, labels

def input_pipeline(filename='dataset.csv', batch_size=30, num_epochs=None):
    filename_queue = tf.train.string_input_producer([filename], num_epochs=num_epochs, shuffle=True)
    features, labels = read_from_cvs(filename_queue)

    min_after_dequeue = batch_size
    capacity = min_after_dequeue + 3 * batch_size
    feature_batch, label_batch = tf.train.shuffle_batch(
        [features, labels], batch_size=batch_size, capacity=capacity,
        min_after_dequeue=min_after_dequeue)
    return feature_batch, label_batch


def tensorflow():
    x, y_ = input_pipeline()

    W = tf.Variable(tf.zeros([num_attributes, num_types]), name="weights")
    b = tf.Variable(tf.zeros([num_types]), name="bias")

    y = tf.nn.softmax(tf.matmul(x, W) + b)

    cross_entropy = tf.reduce_mean(
        tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y))
    train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)

    sess = tf.InteractiveSession()

    tf.global_variables_initializer().run()

    coord = tf.train.Coordinator()
    threads = tf.train.start_queue_runners(coord=coord)

    for _ in range(1200):
        sess.run(train_step)
    coord.request_stop()
    coord.join(threads)

    #correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
    #accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
    #print(sess.run(accuracy, ))

    sess.close()

def main():
    tensorflow()


if __name__ == '__main__':
    main()

我希望有些人可以提供一些帮助。

修改我发现问题是我的csv文件包含格式失败。

Tensorflow队列未填充

0 个答案: