前馈神经网络

Question

我在python中建立了一个基本的神经网络。其思想是，神经网络可以有任何你想要的结构，而不仅仅是标准层，每个神经元都连接到下一层的每一个神经元。

from numpy import exp

class Feed_forward_network:
    """
    Feed_forward_network
    inputs: the number of inputs, int
    outputs: the number of outputs, int
    neuron_data: the neuron data, list of tuples|None

    the first inputs of neuron_data needs to be None
    each item in neuron_data is data about the neuron
    tuple[0]: the activation function of the neuron, function(float) -> float
    tuple[1]: the bias of the neuron, float
    tuple[2]: the connections of the neuron, list of tuples

    each item in connections is data about the connection
    tuple[0]: the neuron the connection is to
    tuple[1]: the weight of the connection
    """
    def __init__(self, inputs: int, outputs: int, neuron_data):
        if outputs > len(neuron_data):
            raise RuntimeError("outputs < len(neuron_data)")
        self.inputs = inputs
        self.outputs = outputs
        self.neuron_data = neuron_data
        self.neuron_values = [None]*(len(neuron_data))

    def activate(self, inputs):
        if self.inputs != len(inputs):
            raise RuntimeError("self.inputs != len(inputs)")
        
        self.neuron_values = [None]*len(self.neuron_values)
        
        for i in range(len(inputs)):
            self.neuron_values[i] = inputs[i]
        
        return tuple([self.calculate_neuron(i+self.inputs) for i in range(self.outputs)])
    
    def calculate_neuron(self, neuron):
        if neuron < self.inputs:
            return self.neuron_values[neuron]
        
        neuron_value = self.neuron_values[neuron]

        if neuron_value == None:
            neuron_data = self.neuron_data[neuron]

            self.neuron_values[neuron] = 0 # avoid RecursionError

            value = neuron_data[0](sum([self.calculate_neuron(conn)*weight for conn, weight in neuron_data[2]]) * neuron_data[1])
            
            self.neuron_values[neuron] = value
            return value

        return neuron_value
        

def sigmoid(x: float) -> float:
    return 1 / (1 + exp(-(x)))

if __name__ == "__main__":
    ffn = Feed_forward_network(2, 1, [None, None, (sigmoid, 1.0, [(0, 1.0), (1, 1.0), (3, 1.0)]), (sigmoid, 1.0, [(0, 1.0), (1, 1.0), (3, 1.0)])])
    print(ffn.activate([1, 1]))
    print(ffn.neuron_values)

我主要是在寻找性能改进，所以下面是分析时的输出(我做了一个for循环，重复if __name__ == "__main__"位100次，因为它们都是0.000次)：

ncalls  tottime  percall  cumtime  percall filename:lineno(function)
        1    0.000    0.000    0.000    0.000 <string>:1(__new__)
    100    0.001    0.000    0.001    0.000 <string>:20(__init__)
    100    0.001    0.000    0.009    0.000 <string>:28(activate)
        1    0.000    0.000    0.000    0.000 <string>:3(Feed_forward_network)
    100    0.000    0.000    0.008    0.000 <string>:37(<listcomp>)
700/100    0.002    0.000    0.008    0.000 <string>:39(calculate_neuron)
200/100    0.001    0.000    0.005    0.000 <string>:50(<listcomp>)
    200    0.005    0.000    0.005    0.000 <string>:58(sigmoid)

Answer 1

Nit：

在calculate_neuron()中，有一个由if neuron_value == None: (使用is/not None (PEP 8))控制的块。很好，但后面是一个无条件的return <value>：

使用否定的条件提前退出，并保存一个缩进级别--“精神上的”。

    if neuron_value is not None:
        return neuron_value

(修订版：

`if neuron_value is not None or neuron < self.num_inputs:`)