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mnist_loader.py
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mnist_loader.py
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# The code below was initially copied from the public git repository
# referenced in the ebook above. Authorship of the original code
# is by Michael Nielson http://michaelnielsen.org/.
# I have adapted that code to stand alone.
# simply call the get_mnist_function for gettting the data...
"""
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 cPickle
import gzip
# Third-party libraries
import numpy as np
import numpy # Yes, it's poor practise to import into two different namespaces.
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
def reshape_matrix(data):
""" Rehsape the data into two distinct matrices of
features and labels...
"""
x, y = zip(*data)
x = numpy.array(x)
x = x.reshape(-1, 784)
y = numpy.array(y).squeeze()
return x,y
def get_mnist_data():
"""
Returns three tuples for training, validation and testing
where each tuple contains two numpy arrays, one for pixels
information, other for the labels...
"""
training_data, validation_data, test_data = load_data_wrapper()
train=reshape_matrix(training_data)
validation=reshape_matrix(validation_data)
test=reshape_matrix(test_data)
return train[0],np.argmax(train[1],axis=1),validation[0],np.int32(validation[1].squeeze()),test[0],np.int32(test[1].squeeze())
def show_mean_image(dataset):
"""
Display the mean image of the mnist dataset:
input: dataset a matrix of dimensions (m,n) (m examples, n features)
"""
import matplotlib.pyplot as plt
m=np.mean(dataset)
plt.imshow(m.reshape(28,-1),cmap='gray')
plt.axis('off')