neural-networks-and-deep-learning mnist-loader

最近发现笔记本里还有这么个文件夹中的代码没看，感觉需要看一看的。

这是写的第二个代码分析了。第一个是gibbsLDA++，感觉还挺有意思的。

那就来分析看看吧，先从最简单的开始吧。

cPickle和gzip是很方便的压缩存储的工具。cPickle是进行序列化的工具，而gzip是压缩的工具。

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('../data/mnist.pkl.gz', 'rb')
training_data, validation_data, test_data = cPickle.load(f)
f.close()
return (training_data, validation_data, test_data)

其实最原始的load_data就是将序列化的东西摘取出来。

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)

其实这个函数load_data_wrapper主要的工作其实是，对于train_data的数据进行zip操作，而且将标签进行向量化。

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