Added updated Python script files.

diff --git a/code/example1.py b/code/example1.py
new file mode 100644
index 0000000..4399910
--- /dev/null
+++ b/code/example1.py
@@ -0,0 +1,52 @@
+#
+# Introduction to Neural Networks.
+# Given at SciNet, September 25 2017, by Erik Spence.
+#
+# This file, example1.py, contains a routine to generate the data for
+# the first example.
+#
+
+#######################################################################
+
+
+"""
+example1.py contains a routine for generating the data for the class'
+first example.
+
+"""
+
+
+#######################################################################
+
+
+import sklearn.datasets as skd
+import sklearn.model_selection as skms
+
+
+#######################################################################
+
+
+def get_data(n):
+
+    """
+    This functon generates n data points generated using
+    scikit-learn's make_blobs function.  The data is then split into
+    training and testing data sets, and returned.
+
+    Inputs:
+    - n: int, the number of points to return.
+
+    Outputs: two arrays of size 0.8 * n, 0.2 * n, randomly generated
+    using scikit-learn's make_blobs routine.
+
+    """
+
+    # Generate the data.
+    pos, value = skd.make_blobs(n, centers = 2,
+                                center_box = (-3, 3))
+
+    # Split the data into training and testing data sets, and return.
+    return skms.train_test_split(pos, value,
+                                test_size = 0.2)
+
+#######################################################################
diff --git a/code/example2.py b/code/example2.py
new file mode 100644
index 0000000..24f8ec7
--- /dev/null
+++ b/code/example2.py
@@ -0,0 +1,51 @@
+#
+# Introduction to Neural Networks.
+# Given at SciNet, September 25 2017, by Erik Spence.
+#
+# This file, example2.py, contains a routine to generate the data
+# for the second example.
+#
+
+#######################################################################
+
+
+"""
+example2.py contains a routine for generating the data for the class'
+second example.
+
+"""
+
+
+#######################################################################
+
+
+import sklearn.datasets as skd
+import sklearn.model_selection as skms
+
+
+#######################################################################
+
+
+def get_data(n):
+
+    """This functon generates n data points generated using
+    scikit-learn's make_circles function.  The data is then split into
+    training and testing data sets, and returned.
+
+    Inputs:
+    - n: int, the number of points to return.
+
+    Outputs: two arrays of size 0.8 * n, 0.2 * n, randomly generated
+    using scikit-learn's make_circles routine.
+
+    """
+
+    # Generate the data.
+    pos, value = skd.make_circles(n, noise = 0.1)
+
+    # Split the data into training and testing data sets, and return.
+    return skms.train_test_split(pos, value,
+                                test_size = 0.2)
+
+
+#######################################################################
diff --git a/code/first_network.py b/code/first_network.py
index 9a06572..619390c 100644
--- a/code/first_network.py
+++ b/code/first_network.py
@@ -1,6 +1,6 @@
 #
 # Introduction to Neural Networks.
-# Given at SciNet, May 30 2017, by Erik Spence.
+# Given at SciNet, September 25 2017, by Erik Spence.
 #
 # This file, first_network.py, contains the implementation of our
 # first neural network.
diff --git a/code/mnist_loader.py b/code/mnist_loader.py
index 59d3e71..572a3c9 100644
--- a/code/mnist_loader.py
+++ b/code/mnist_loader.py
@@ -1,6 +1,6 @@
 #
 # Introduction to Neural Networks.
-# Given at SciNet, May 30 2017, by Erik Spence.
+# Given at SciNet, September 25 2017, by Erik Spence.
 #
 # This file, mnist_loader.py, contains the code needed to load the
 # MNIST dataset.  The code borrows heavily from
@@ -28,16 +28,17 @@ except:

 import gzip
 import numpy as np
+import keras.utils as ku


 #######################################################################


-def load_mnist_1D(filename = '../data/mnist.pkl.gz'):
+def load_mnist_1D_large(filename = '../data/mnist.pkl.gz'):

     """
-    Returns the MNIST data as a tuple containing the training data,
-    the validation data, and the test data.
+    Returns the full MNIST data set as a tuple containing the training
+    data, the validation data, and the test data.

     Inputs:

@@ -53,8 +54,8 @@ def load_mnist_1D(filename = '../data/mnist.pkl.gz'):
             - 2D array of floats of shape (50000, 784), containing the
               pixel values for each image.

-            - integer vector of length 50000, containing the value of
-              the number in the image.
+            - a one-hot-encoded array of shape (50000, 10), containing
+              the value of the number in the image.

         - validation_data: same as training_data, except with length
           10000
@@ -74,9 +75,58 @@ def load_mnist_1D(filename = '../data/mnist.pkl.gz'):
     f.close()

     # Return the values.
-    return training_data[0], training_data[1], \
-        validation_data[0], validation_data[1], \
-        test_data[0], test_data[1]
+    return training_data[0], ku.to_categorical(training_data[1]), \
+        validation_data[0], ku.to_categorical(validation_data[1]), \
+        test_data[0], ku.to_categorical(test_data[1])
+
+
+#######################################################################
+
+
+def load_mnist_1D_small(filename = '../data/mnist.pkl.gz'):
+
+    """
+    Returns a small version of the MNIST data as a tuple containing
+    the training data, the validation data, and the test data.
+
+    Inputs:
+
+    - filename: string, name of the file containing the data.
+
+    Outputs:
+
+    - tuple, containing the training, validation and test data.  These
+      take the form:
+
+        - training_data: tuple, consisting of:
+
+            - 2D array of floats of shape (500, 784), containing the
+              pixel values for each image.
+
+            - integer vector of length 500, containing the value of
+              the number in the image.
+
+        - validation_data: same as training_data, except with length
+          200
+
+        - test_data: same as training_data, except with length
+          100
+
+    """
+
+    # Open the file.
+    f = gzip.open(filename, 'rb')
+
+    # Load the data.
+    training_data, validation_data, test_data = cPickle.load(f)
+
+    # Close the file.
+    f.close()
+
+    # Return the values.
+    return training_data[0][0:500, :], training_data[1][0:500], \
+        validation_data[0][0:200, :], validation_data[1][0:200], \
+        test_data[0][0:100, :], test_data[1][0:100]


 #######################################################################
@@ -114,7 +164,8 @@ def load_mnist_2D(filename = ''):
     """

     # Get the data.
-    tr_d, tr_v, va_d, va_v, te_d, te_v = load_mnist_1D(filename = filename)
+    tr_d, tr_v, va_d, va_v, te_d, te_v = \
+            load_mnist_1D(filename = filename)

     # Reshape the data.
     training_inputs = np.array([x.reshape(28, 28, 1) for x in tr_d])
@@ -122,6 +173,7 @@ def load_mnist_2D(filename = ''):
     test_inputs = np.array([x.reshape(28, 28, 1) for x in te_d])

     # Return the data.
-    return training_inputs, tr_v, validation_inputs, va_v, \
-        test_inputs, te_v
+    return training_inputs, ku.to_categorical(tr_v, 10), \
+        validation_inputs, ku.to_categorical(va_v, 10), \
+        test_inputs, ku.to_categorical(te_v, 10)

diff --git a/code/model1.py b/code/model1.py
new file mode 100644
index 0000000..e54c6e8
--- /dev/null
+++ b/code/model1.py
@@ -0,0 +1,63 @@
+#
+# Introduction to Neural Networks.
+# Given at SciNet, September 25 2017, by Erik Spence.
+#
+# This file, model1.py, contains a routine to generate the model for
+# the first Keras example.
+#
+
+#######################################################################
+
+
+"""
+model1.py contains a routine which generates the model for the class'
+first Keras example.
+
+"""
+
+
+#######################################################################
+
+
+import keras.models as km
+import keras.layers as kl
+
+
+#######################################################################
+
+
+def get_model(numnodes, input_size = 784, output_size = 10):
+
+    """
+    This function returns a simple Keras model, consisting of a
+    re-implementation of the second_network.py neural network, with
+    numnodes in the hidden layer.
+
+    Inputs:
+    - numnodes: int, the number of nodes in the hidden layer.
+
+    - intput_size: int, the size of the input data, default = 784.
+
+    - output_size: int, the number of nodes in the output layer,
+      default = 10.
+
+    Output: the constructed Keras model.
+
+    """
+
+    # Initialize the model.
+    model = km.Sequential()
+
+    # Add a hidden fully-connected layer.
+    model.add(kl.Dense(numnodes,
+                       input_dim = input_size,
+                       activation = 'sigmoid'))
+
+    # Add the output layer.
+    model.add(kl.Dense(output_size, activation = 'sigmoid'))
+
+    # Return the model.
+    return model
+
+
+#######################################################################
diff --git a/code/model2.py b/code/model2.py
new file mode 100644
index 0000000..bc2a5be
--- /dev/null
+++ b/code/model2.py
@@ -0,0 +1,64 @@
+#
+# Introduction to Neural Networks.
+# Given at SciNet, September 25 2017, by Erik Spence.
+#
+# This file, model2.py, contains a routine to generate the model for
+# the second Keras example.
+#
+
+#######################################################################
+
+
+"""
+model2.py contains a routine which generates the model for the class'
+second Keras example.
+
+"""
+
+
+#######################################################################
+
+
+import keras.models as km
+import keras.layers as kl
+
+
+#######################################################################
+
+
+def get_model(numnodes, input_size = 784, output_size = 10):
+
+    """
+    This function returns a simple Keras model, consisting of a
+    re-implementation of the second_network.py neural network, with
+    numnodes in the hidden layer.  It is the same as model1.py, except
+    for the activation functions.
+
+    Inputs:
+    - numnodes: int, the number of nodes in the hidden layer.
+
+    - intput_size: int, the size of the input data, default = 784.
+
+    - output_size: int, the number of nodes in the output layer,
+      default = 10.
+
+    Output: the constructed Keras model.
+
+    """
+
+    # Initialize the model.
+    model = km.Sequential()
+
+    # Add a hidden fully-connected layer.
+    model.add(kl.Dense(numnodes,
+                       input_dim = input_size,
+                       activation = 'tanh'))
+
+    # Add the output layer.
+    model.add(kl.Dense(output_size, activation = 'softmax'))
+
+    # Return the model.
+    return model
+
+
+#######################################################################
diff --git a/code/model3.py b/code/model3.py
new file mode 100644
index 0000000..e4299a3
--- /dev/null
+++ b/code/model3.py
@@ -0,0 +1,78 @@
+#
+# Introduction to Neural Networks.
+# Given at SciNet, September 25 2017, by Erik Spence.
+#
+# This file, model3.py, contains a routine to generate the model for
+# the third Keras example.
+#
+
+#######################################################################
+
+
+"""
+model3.py contains a routine which generates the model for the class'
+third Keras example.
+
+"""
+
+
+#######################################################################
+
+
+import keras.models as km
+import keras.layers as kl
+
+
+#######################################################################
+
+
+def get_model(numfm, numnodes, input_shape = (28, 28, 1),
+              output_size = 10):
+
+    """
+    This function returns a convolution neural network Keras model,
+    with numfm feature maps and numnodes neurons in the
+    fully-connected layer.
+
+    Inputs:
+    - numfm: int, the number of feature maps in the convolution layer.
+
+    - numnodes: int, the number of nodes in the fully-connected layer.
+
+    - intput_shape: tuple, the shape of the input data,
+    default = (28, 28, 1).
+
+    - output_size: int, the number of nodes in the output layer,
+      default = 10.
+
+    Output: the constructed Keras model.
+
+    """
+
+    # Initialize the model.
+    model = km.Sequential()
+
+    # Add a 2D convolution layer, with numfm feature maps.
+    model.add(kl.Conv2D(numfm, kernel_size = (5, 5),
+                        input_shape = input_shape,
+                        activation = 'relu'))
+
+    # Add a max pooling layer.
+    model.add(kl.MaxPooling2D(pool_size = (2, 2),
+                              strides = (2, 2)))
+
+    # Convert the network from 2D to 1D.
+    model.add(kl.Flatten())
+
+    # Add a fully-connected layer.
+    model.add(kl.Dense(numnodes,
+                       activation = 'tanh'))
+
+    # Add the output layer.
+    model.add(kl.Dense(10, activation = 'softmax'))
+
+    # Return the model.
+    return model
+
+
+#######################################################################
diff --git a/code/plotting_routines.py b/code/plotting_routines.py
index a9b4ad3..122f5ec 100644
--- a/code/plotting_routines.py
+++ b/code/plotting_routines.py
@@ -1,6 +1,6 @@
 #
 # Introduction to Neural Networks.
-# Given at SciNet, May 30 2017, by Erik Spence.
+# Given at SciNet, September 25 2017, by Erik Spence.
 #
 # This file, plotting_routines.py, contains some simple plotting
 # routines.
@@ -11,7 +11,7 @@

 """
 plotting_routines.py contains two routines for plotting the class'
-data.
+data, for the first two examples.

 """

@@ -135,3 +135,6 @@ def plot_decision_boundary(x, v, model, predict_function, **kwargs):
     plot_dots(x, v)

     plt.show()
+
+
+#######################################################################
diff --git a/code/second_network.py b/code/second_network.py
index f685537..55d1121 100644
--- a/code/second_network.py
+++ b/code/second_network.py
@@ -1,6 +1,6 @@
 #
 # Introduction to Neural Networks.
-# Given at SciNet, May 30 2017, by Erik Spence.
+# Given at SciNet, September 25 2017, by Erik Spence.
 #
 # This file, second_network.py, contains the implementation of our
 # second neural network.