【发布时间】：2019-07-12 09:28:42
【问题描述】：
我已经为神经网络编写了这段代码，但不确定为什么我得到的输出是不正确的。
我创建了一个包含两个 1x1 层或神经元的网络。输入是 1 到 0 之间的随机数，并将其设置为网络所需的输出。这些是输入（左）和接收（右）值的示例：
[0.11631148733527708] [0.52613976]
[0.19471305546308992] [0.54367643]
[0.38620499751234083] [0.58595699]
[0.507207377588539] [0.61203927]
[0.9552623183688456] [0.70232115]
这是我的代码：
main.py
from NeuralNetwork import NeuralNetwork
from random import random
net = NeuralNetwork((1, 1))
net.learning_rate = 0.01
while True:
v1 = [random() for i in range(0, 1)]
actual = v1
net.input(v1)
net.actual(actual)
net.calculate()
net.backpropagate()
print(f"{v1} {net.output()}")
神经网络.py
import numpy as np
from math import e
def sigmoid(x):
sig_x = 1 / (1 + e**-x)
return sig_x
def d_sigmoid(x):
sig_x = 1 / (1 + e**-x)
d_sig_x = np.dot(sig_x.transpose(), (1 - sig_x))
return d_sig_x
class NeuralNetwork():
def __init__(self, sizes):
self.activations = [np.zeros((size, 1)) for size in sizes]
self.values = [np.zeros((size, 1)) for size in sizes[1:]]
self.biases = [np.zeros((size, 1)) for size in sizes[1:]]
self.weights = [np.zeros((sizes[i + 1], sizes[i])) for i in range(0, len(sizes) - 1)]
self.activation_functions = [(sigmoid, d_sigmoid) for i in range(0, len(sizes) - 1)]
self.last_layer_actual = np.zeros((sizes[-1], 1))
self.learning_rate = 0.01
def calculate(self):
for i, activations in enumerate(self.activations[:-1]):
activation_function = self.activation_functions[i][0]
self.values[i] = np.dot(self.weights[i], activations) + self.biases[i]
self.activations[i + 1] = activation_function(self.values[i])
def backpropagate(self):
current = 2 * (self.activations[-1] - self.last_layer_actual)
last_weights = 1
for i, weights in enumerate(self.weights[::-1]):
d_activation_func = self.activation_functions[-i - 1][1]
current = np.dot(last_weights, current)
current = np.dot(current, d_activation_func(self.values[-i - 1]))
weights_change = np.dot(current, self.activations[-i - 2].transpose())
weights -= weights_change * self.learning_rate
self.biases[-i - 1] -= current * self.learning_rate
last_weights = weights.transpose()
def input(self, network_input):
self.activations[0] = np.array(network_input).reshape(-1, 1)
def output(self):
return self.activations[-1].ravel()
def actual(self, last_layer_actual):
self.last_layer_actual = np.array(last_layer_actual).reshape(-1, 1)
【问题讨论】：
标签： python deep-learning recurrent-neural-network backpropagation