DrawNN.py

#Code from https://gist.github.com/craffel/2d727968c3aaebd10359

import matplotlib.pyplot as plt

def draw_neural_net(ax, left, right, bottom, top, layer_sizes, bias=0, draw_edges=False):
    '''
    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
        - bias : Boolean
            Draw an extra bias node at each layer
        - draw_edges : Boolean
            If false, omit edge connections
    '''
    n_layers = len(layer_sizes)
    v_spacing = (top - bottom)/float(max(layer_sizes)+bias)
    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.
        bias_node = (bias if n<len(layer_sizes)-1 else 0)
        for m in range(layer_size+bias_node ):
            node_color = 'w' if m<layer_size else 'b'
            circle = plt.Circle((n*h_spacing + left, layer_top - m*v_spacing), v_spacing/8.,
                                color=node_color, ec='k', zorder=4)
            ax.add_artist(circle)
    # Edges
    if draw_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+bias):
                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)