Code files added

Chapter 9/mnist_loader.py → Chapter09/mnist_loader.py

@@ -1,85 +1,85 @@
-"""
-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
-
-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)
-
-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
+"""
+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
+
+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)
+
+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