├── .gitignore
├── README.md
├── LICENSE.md
└── main.py


/.gitignore:
--------------------------------------------------------------------------------
1 | **/*.pyc
2 | .idea\
3 | .idea/
4 | 


--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
 1 | # Visualise Neural Network
 2 | Generates a static diagram of a neural network, where each neuron is connected to every neuron in the previous layer.
 3 | 
 4 | For example, this code:
 5 | 
 6 |     network = NeuralNetwork()
 7 |     network.add_layer(3)
 8 |     network.add_layer(4)
 9 |     network.add_layer(1)
10 |     network.draw()
11 | 
12 | Will generate this diagram:
13 | 
14 | ![Diagram of a neural network with 3 neurons in the first layer, 4 neurons in the second layer and 1 neuron in the 3rd layer](http://i.stack.imgur.com/8oxCO.png)
15 | 


--------------------------------------------------------------------------------
/LICENSE.md:
--------------------------------------------------------------------------------
 1 | The MIT License (MIT)
 2 | 
 3 | Copyright (c) 2015 Milo Spencer-Harper
 4 | 
 5 | Permission is hereby granted, free of charge, to any person obtaining a copy
 6 | of this software and associated documentation files (the "Software"), to deal
 7 | in the Software without restriction, including without limitation the rights
 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 9 | copies of the Software, and to permit persons to whom the Software is
10 | furnished to do so, subject to the following conditions:
11 | 
12 | The above copyright notice and this permission notice shall be included in all
13 | copies or substantial portions of the Software.
14 | 
15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21 | SOFTWARE.


--------------------------------------------------------------------------------
/main.py:
--------------------------------------------------------------------------------
 1 | from matplotlib import pyplot
 2 | from math import cos, sin, atan
 3 | 
 4 | 
 5 | class Neuron():
 6 |     def __init__(self, x, y):
 7 |         self.x = x
 8 |         self.y = y
 9 | 
10 |     def draw(self):
11 |         circle = pyplot.Circle((self.x, self.y), radius=neuron_radius, fill=False)
12 |         pyplot.gca().add_patch(circle)
13 | 
14 | 
15 | class Layer():
16 |     def __init__(self, network, number_of_neurons):
17 |         self.previous_layer = self.__get_previous_layer(network)
18 |         self.y = self.__calculate_layer_y_position()
19 |         self.neurons = self.__intialise_neurons(number_of_neurons)
20 | 
21 |     def __intialise_neurons(self, number_of_neurons):
22 |         neurons = []
23 |         x = self.__calculate_left_margin_so_layer_is_centered(number_of_neurons)
24 |         for iteration in xrange(number_of_neurons):
25 |             neuron = Neuron(x, self.y)
26 |             neurons.append(neuron)
27 |             x += horizontal_distance_between_neurons
28 |         return neurons
29 | 
30 |     def __calculate_left_margin_so_layer_is_centered(self, number_of_neurons):
31 |         return horizontal_distance_between_neurons * (number_of_neurons_in_widest_layer - number_of_neurons) / 2
32 | 
33 |     def __calculate_layer_y_position(self):
34 |         if self.previous_layer:
35 |             return self.previous_layer.y + vertical_distance_between_layers
36 |         else:
37 |             return 0
38 | 
39 |     def __get_previous_layer(self, network):
40 |         if len(network.layers) > 0:
41 |             return network.layers[-1]
42 |         else:
43 |             return None
44 | 
45 |     def __line_between_two_neurons(self, neuron1, neuron2):
46 |         angle = atan((neuron2.x - neuron1.x) / float(neuron2.y - neuron1.y))
47 |         x_adjustment = neuron_radius * sin(angle)
48 |         y_adjustment = neuron_radius * cos(angle)
49 |         line = pyplot.Line2D((neuron1.x - x_adjustment, neuron2.x + x_adjustment), (neuron1.y - y_adjustment, neuron2.y + y_adjustment))
50 |         pyplot.gca().add_line(line)
51 | 
52 |     def draw(self):
53 |         for neuron in self.neurons:
54 |             neuron.draw()
55 |             if self.previous_layer:
56 |                 for previous_layer_neuron in self.previous_layer.neurons:
57 |                     self.__line_between_two_neurons(neuron, previous_layer_neuron)
58 | 
59 | 
60 | class NeuralNetwork():
61 |     def __init__(self):
62 |         self.layers = []
63 | 
64 |     def add_layer(self, number_of_neurons):
65 |         layer = Layer(self, number_of_neurons)
66 |         self.layers.append(layer)
67 | 
68 |     def draw(self):
69 |         for layer in self.layers:
70 |             layer.draw()
71 |         pyplot.axis('scaled')
72 |         pyplot.show()
73 | 
74 | if __name__ == "__main__":
75 |     vertical_distance_between_layers = 6
76 |     horizontal_distance_between_neurons = 2
77 |     neuron_radius = 0.5
78 |     number_of_neurons_in_widest_layer = 4
79 |     network = NeuralNetwork()
80 |     network.add_layer(3)
81 |     network.add_layer(4)
82 |     network.add_layer(1)
83 |     network.draw()
84 | 


--------------------------------------------------------------------------------