我正在尝试加载此神经网络和深度学习示例的 mnist 数据集 (http://neuralnetworksanddeeplearning.com/chap1.html)，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/Python36-32\mnist_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/Python36-32\mnist_loader.py", line 42, in load_data
f = open('mnist.pkl', 'rb')
File "C:\Users\Joseph Gannon\AppData\Local\Programs\Python\Python36-32\lib\gzip.py", line 53, in open
binary_file = GzipFile(filename, gz_mode, compresslevel)
File "C:\Users\Joseph Gannon\AppData\Local\Programs\Python\Python36-32\lib\gzip.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
我该如何修复它？
我 99% 确定您提供了错误的文件路径以得到这样的错误。
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应该是当前文件夹上方的一个文件夹，并且位于
datamnist.pkl另请注意，Windows/Unix 路径方向的不同也可能会导致问题。
c:\ vs../data/面对这么多问题后，
示例
允许
temp="W:\项目"
temp="W:\Python 项目"
temp="C:\用户"
temp =“C：\程序文件”
不允许
temp="C:\Program Files (x86)"
temp="C:\Program Files (x86)\DocClass"
pickle.dump(train_tfidf_transformer,open(temp+" rain_TfidfTransformer.pkl","wb"))
我的结论是文件夹名称中没有两个空格，但如果您直接给出此路径，那么它就可以工作，因为当您传递像 temp 这样的字符串时，他们会检查该字符串是否为原始字符串？如果不转换为原始字符串，如果是，则不转换
.pkl 文件是在运行 .py 文件后创建的。指定目录中必须有 mnist.py 文件。运行“python mnist.py”