Python 3 - FileNotFoundError： [Errno 2] 没有这样的文件或目录： '../data/mnist.pkl'

Question

我正在尝试为这个神经网络和深度学习示例(http://neuralnetworksanddeeplearning.com/chap1.html(加载mnist数据集，但我不断收到此错误。

>>> import mnist_loader
>>> training_data, validation_data, test_data = mnist_loader.load_data_wrapper()
Traceback (most recent call last):
  File "<pyshell#34>", line 1, in <module>
    training_data, validation_data, test_data = mnist_loader.load_data_wrapper()
  File "C:/Users/Joseph Gannon/AppData/Local/Programs/Python/Python36-32mnist_loader.py", line 68, in load_data_wrapper
    tr_d, va_d, te_d = load_data()
  File "C:/Users/Joseph Gannon/AppData/Local/Programs/Python/Python36-32mnist_loader.py", line 42, in load_data
    f = open('mnist.pkl', 'rb')
  File "C:UsersJoseph GannonAppDataLocalProgramsPythonPython36-32libgzip.py", line 53, in open
    binary_file = GzipFile(filename, gz_mode, compresslevel)
  File "C:UsersJoseph GannonAppDataLocalProgramsPythonPython36-32libgzip.py", line 163, in __init__
    fileobj = self.myfileobj = builtins.open(filename, mode or 'rb')
FileNotFoundError: [Errno 2] No such file or directory: '../data/mnist.pkl'

这是我正在使用的代码：

"""
mnist_loader
~~~~~~~~~~~~
A library to load the MNIST image data.  For details of the data
structures that are returned, see the doc strings for ``load_data``
and ``load_data_wrapper``.  In practice, ``load_data_wrapper`` is the
function usually called by our neural network code.
"""
#### Libraries
# Standard library
import _pickle as cPickle
import gzip
# Third-party libraries
import numpy as np
def load_data():
    """Return the MNIST data as a tuple containing the training data,
    the validation data, and the test data.
    The ``training_data`` is returned as a tuple with two entries.
    The first entry contains the actual training images.  This is a
    numpy ndarray with 50,000 entries.  Each entry is, in turn, a
    numpy ndarray with 784 values, representing the 28 * 28 = 784
    pixels in a single MNIST image.
    The second entry in the ``training_data`` tuple is a numpy ndarray
    containing 50,000 entries.  Those entries are just the digit
    values (0...9) for the corresponding images contained in the first
    entry of the tuple.
    The ``validation_data`` and ``test_data`` are similar, except
    each contains only 10,000 images.
    This is a nice data format, but for use in neural networks it's
    helpful to modify the format of the ``training_data`` a little.
    That's done in the wrapper function ``load_data_wrapper()``, see
    below.
    """
    f = gzip.open('mnist.pkl.gz', 'rb')
    training_data, validation_data, test_data = cPickle.load(f)
    f.close()
    return (training_data, validation_data, test_data)
def load_data_wrapper():
    """Return a tuple containing ``(training_data, validation_data,
    test_data)``. Based on ``load_data``, but the format is more
    convenient for use in our implementation of neural networks.
    In particular, ``training_data`` is a list containing 50,000
    2-tuples ``(x, y)``.  ``x`` is a 784-dimensional numpy.ndarray
    containing the input image.  ``y`` is a 10-dimensional
    numpy.ndarray representing the unit vector corresponding to the
    correct digit for ``x``.
    ``validation_data`` and ``test_data`` are lists containing 10,000
    2-tuples ``(x, y)``.  In each case, ``x`` is a 784-dimensional
    numpy.ndarry containing the input image, and ``y`` is the
    corresponding classification, i.e., the digit values (integers)
    corresponding to ``x``.
    Obviously, this means we're using slightly different formats for
    the training data and the validation / test data.  These formats
    turn out to be the most convenient for use in our neural network
    code."""
    tr_d, va_d, te_d = load_data()
    training_inputs = [np.reshape(x, (784, 1)) for x in tr_d[0]]
    training_results = [vectorized_result(y) for y in tr_d[1]]
    training_data = zip(training_inputs, training_results)
    validation_inputs = [np.reshape(x, (784, 1)) for x in va_d[0]]
    validation_data = zip(validation_inputs, va_d[1])
    test_inputs = [np.reshape(x, (784, 1)) for x in te_d[0]]
    test_data = zip(test_inputs, te_d[1])
    return (training_data, validation_data, test_data)
def vectorized_result(j):
    """Return a 10-dimensional unit vector with a 1.0 in the jth
    position and zeroes elsewhere.  This is used to convert a digit
    (0...9) into a corresponding desired output from the neural
    network."""
    e = np.zeros((10, 1))
    e[j] = 1.0
    return e

我该如何解决它？

Answer 1

我就像 99% 确定您为文件提供了错误的路径以获得这样的错误。

Answer 2

您面临的错误是：

FileNotFoundError: [Errno 2] No such file or directory: '../data/mnist.pkl'

这意味着python找不到输入数据文件mnist.pkl

代码正在以下位置查找文件../data/mnist.pkl

它应该是当前文件夹上方和data文件夹内的一个文件夹。

• 文件mnist.pkl是否存在？

另请注意，窗口/Unix 路径方向之间的差异也可能导致问题。 c: vs ../data/ (

Answer 3

在面临这么多问题之后，

例

允许

temp="W：\Projects">

temp="W：\Python project">

temp="C：\users">

temp="C：\Program Files">

不允许

temp="C：\Program Files (x86(">

temp="C：\Program Files (x86(\DocClass">

pickle.dump(train_tfidf_transformer，open(temp+"\train_TfidfTransformer.pkl"，"wb"((

我的结论是文件夹名称中没有两个空格，但是如果您直接提供此路径，那么它就可以工作，因为当您传递像 temp 这样的字符串时，他们会检查字符串是否是原始字符串？ 如果不转换为原始字符串，如果是，则没有转换

Answer 4

.

pkl 文件是在运行.py文件后创建的。您必须在指定的目录中具有 mnist.py 文件。运行"Python mnist.py">