after comments

Author: kwinkunks

1808_Neural_Networks/utils.py

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+import numpy as np
+import matplotlib.pyplot as plt
+
+
+def draw_neural_net(ax, left, right, bottom, top, layer_sizes):
+    '''
+    Draw a neural network cartoon using matplotilb.
+    
+    :usage:
+        >>> fig = plt.figure(figsize=(12, 12))
+        >>> draw_neural_net(fig.gca(), .1, .9, .1, .9, [4, 7, 2])
+    
+    :parameters:
+        - ax : matplotlib.axes.AxesSubplot
+            The axes on which to plot the cartoon (get e.g. by plt.gca())
+        - left : float
+            The center of the leftmost node(s) will be placed here
+        - right : float
+            The center of the rightmost node(s) will be placed here
+        - bottom : float
+            The center of the bottommost node(s) will be placed here
+        - top : float
+            The center of the topmost node(s) will be placed here
+        - layer_sizes : list of int
+            List of layer sizes, including input and output dimensionality
+    '''
+    n_layers = len(layer_sizes)
+    v_spacing = (top - bottom)/float(max(layer_sizes))
+    h_spacing = (right - left)/float(len(layer_sizes) - 1)
+    # Nodes
+    for n, layer_size in enumerate(layer_sizes):
+        layer_top = v_spacing*(layer_size - 1)/2. + (top + bottom)/2.
+        for m in range(layer_size):
+            circle = plt.Circle((n*h_spacing + left, layer_top - m*v_spacing), v_spacing/4.,
+                                color='w', ec='k', zorder=4)
+            ax.add_artist(circle)
+    # Edges
+    for n, (layer_size_a, layer_size_b) in enumerate(zip(layer_sizes[:-1], layer_sizes[1:])):
+        layer_top_a = v_spacing*(layer_size_a - 1)/2. + (top + bottom)/2.
+        layer_top_b = v_spacing*(layer_size_b - 1)/2. + (top + bottom)/2.
+        for m in range(layer_size_a):
+            for o in range(layer_size_b):
+                line = plt.Line2D([n*h_spacing + left, (n + 1)*h_spacing + left],
+                                  [layer_top_a - m*v_spacing, layer_top_b - o*v_spacing], c='k')
+                ax.add_artist(line)
+
+    return ax
+