How to visualize a neural network

Question

I want to draw a dynamic picture for a neural network to watch the weights changed and the activation of neurons during learning. How could I simulate the process in Python?

More precisely, if the network shape is: [1000, 300, 50], then I wish to draw a three layer NN which contains 1000, 300 and 50 neurons respectively. Further, I hope the picture could reflect the saturation of neurons on each layer during each epoch.

I've no idea about how to do it. Can someone shed some light on me?

Answer 1

I adapted some parts to the answer of Milo

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() 

Now the layers are also labeled, the axis are deleted and constructing the plot is easier. It's simply done by:

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

Here a net with the following structure is constructed:

• 2 Neurons in the input layer
• 8 Neurons in the 1st hidden layer
• 8 Neurons in the 2nd hidden layer
• 1 Neuron in the output layer