无法在 Python 中的指定相对路径中找到文件 [英] Couldn't find file at a specified relative path in Python

问题描述

通常在 Python 中，默认文件夹应该是我假设的当前工作目录，或者可能在默认用户目录中.但是，在运行以下代码后 来自在这里，我在之前的任何一个地方都找不到下载的数据.那么问题来了，/tmp/tensorflow/mnist/input_data的相对路径在哪里?谢谢！

Normally in Python, the default folder should be the current working directory I assume, or maybe in the default user directory. However, after running the following code from here, I couldn't find the downloaded data in either of the previous places. So the question is where is the relative path /tmp/tensorflow/mnist/input_data located? Thanks!

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import argparse
import sys

from tensorflow.examples.tutorials.mnist import input_data

import tensorflow as tf

FLAGS = None


def main(_):
  # Import data
  mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True)

  # Create the model
  x = tf.placeholder(tf.float32, [None, 784])
  W = tf.Variable(tf.zeros([784, 10]))
  b = tf.Variable(tf.zeros([10]))
  y = tf.matmul(x, W) + b

  # Define loss and optimizer
  y_ = tf.placeholder(tf.float32, [None, 10])

  # The raw formulation of cross-entropy,
  #
  #   tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(tf.nn.softmax(y)),
  #                                 reduction_indices=[1]))
  #
  # can be numerically unstable.
  #
  # So here we use tf.nn.softmax_cross_entropy_with_logits on the raw
  # outputs of 'y', and then average across the batch.
  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()
  # Train
  for _ in range(1000):
    batch_xs, batch_ys = mnist.train.next_batch(100)
    sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})

  # Test trained model
  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, feed_dict={x: mnist.test.images,
                                      y_: mnist.test.labels}))

if __name__ == '__main__':
  parser = argparse.ArgumentParser()
  parser.add_argument('--data_dir', type=str, default='/tmp/tensorflow/mnist/input_data',
                      help='Directory for storing input data')
  FLAGS, unparsed = parser.parse_known_args()
  tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)

推荐答案

我也运行了这些示例，并且我的路径存储在 c: 中.我正在使用窗户.

I've also run these examples and the path for me gets stored in c:. I'm using windows.

完整路径为:

C:\tmp\tensorflow\mnist\input_data

C:\tmp\tensorflow\mnist\input_data

如果您希望它相对于您的工作目录，请在代码中的路径前添加一个点(."):

If you want it to be relative to your working directory add a dot (".") before the path in your code:

parser.add_argument('--data_dir', type=str, default='./tmp/tensorflow/mnist/input_data',
                      help='Directory for storing input data')

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