神经网络的可视化|matplotlib

2016-10-25

用programming based on stackoverflow 找了一段可视化神经网络的脚本。。。

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
from matplotlib import pyplot
from math import cos, sin, atan
class Neuron():
def __init__(self, x, y):
self.x = x
self.y = y
def draw(self, neuron_radius):
circle = pyplot.Circle((self.x, self.y), radius=neuron_radius, fill=False)
pyplot.gca().add_patch(circle)
class Layer():
def __init__(self, network, number_of_neurons, number_of_neurons_in_widest_layer):
self.vertical_distance_between_layers = 6
self.horizontal_distance_between_neurons = 2
self.neuron_radius = 0.5
self.number_of_neurons_in_widest_layer = number_of_neurons_in_widest_layer
self.previous_layer = self.__get_previous_layer(network)
self.y = self.__calculate_layer_y_position()
self.neurons = self.__intialise_neurons(number_of_neurons)
def __intialise_neurons(self, number_of_neurons):
neurons = []
x = self.__calculate_left_margin_so_layer_is_centered(number_of_neurons)
for iteration in xrange(number_of_neurons):
neuron = Neuron(x, self.y)
neurons.append(neuron)
x += self.horizontal_distance_between_neurons
return neurons
def __calculate_left_margin_so_layer_is_centered(self, number_of_neurons):
return self.horizontal_distance_between_neurons * (self.number_of_neurons_in_widest_layer - number_of_neurons) / 2
def __calculate_layer_y_position(self):
if self.previous_layer:
return self.previous_layer.y + self.vertical_distance_between_layers
else:
return 0
def __get_previous_layer(self, network):
if len(network.layers) > 0:
return network.layers[-1]
else:
return None
def __line_between_two_neurons(self, neuron1, neuron2):
angle = atan((neuron2.x - neuron1.x) / float(neuron2.y - neuron1.y))
x_adjustment = self.neuron_radius * sin(angle)
y_adjustment = self.neuron_radius * cos(angle)
line = pyplot.Line2D((neuron1.x - x_adjustment, neuron2.x + x_adjustment), (neuron1.y - y_adjustment, neuron2.y + y_adjustment))
pyplot.gca().add_line(line)
def draw(self, layerType=0):
for neuron in self.neurons:
neuron.draw( self.neuron_radius )
if self.previous_layer:
for previous_layer_neuron in self.previous_layer.neurons:
self.__line_between_two_neurons(neuron, previous_layer_neuron)
# write Text
x_text = self.number_of_neurons_in_widest_layer * self.horizontal_distance_between_neurons
if layerType == 0:
pyplot.text(x_text, self.y, 'Input Layer', fontsize = 12)
elif layerType == -1:
pyplot.text(x_text, self.y, 'Output Layer', fontsize = 12)
else:
pyplot.text(x_text, self.y, 'Hidden Layer '+str(layerType), fontsize = 12)
class NeuralNetwork():
def __init__(self, number_of_neurons_in_widest_layer):
self.number_of_neurons_in_widest_layer = number_of_neurons_in_widest_layer
self.layers = []
self.layertype = 0
def add_layer(self, number_of_neurons ):
layer = Layer(self, number_of_neurons, self.number_of_neurons_in_widest_layer)
self.layers.append(layer)
def draw(self):
pyplot.figure()
for i in range( len(self.layers) ):
layer = self.layers[i]
if i == len(self.layers)-1:
i = -1
layer.draw( i )
pyplot.axis('scaled')
pyplot.axis('off')
pyplot.title( 'Neural Network architecture', fontsize=15 )
pyplot.show()
class DrawNN():
def __init__( self, neural_network ):
self.neural_network = neural_network
def draw( self ):
widest_layer = max( self.neural_network )
network = NeuralNetwork( widest_layer )
for l in self.neural_network:
network.add_layer(l)
network.draw()

使用方法

1
2
network = plotNN.DrawNN( [2,8,8,1] )
network.draw()