我正在使用“人工智能：现代方法”中的反向传播算法来研究NeuralNetwork类。我使用调试器逐步运行了train函数，一切似乎都正常运行，但是运行该错误并没有解决。有人可以发现我在做什么吗？
import math, random
import numpy as np
CLOSE = 0.2
class Perceptron:
'''A single perceptron using sigmoid activation'''
def __init__(self, inputs):
'''Set up the perceptron with the given number of inputs'''
self.weights = np.empty(inputs)
for i in range(inputs):
self.weights[i] = random.random()
self.bias = random.random()
def getOutput(self, inputs):
'''Calculates, stores, and returns the output'''
assert len(inputs) == len(self.weights)
inj = np.sum(inputs * self.weights) + self.bias # Sum inputs
g = 1.0 / (1.0 + math.exp(-inj)) # Sigmoid activation
self.aj = g
return g
def adjust(self, delta):
'''Adjusts the weights and bias'''
self.bias += self.aj * delta
for i in range(len(self.weights)):
self.weights[i] += self.aj * delta
class Layer:
'''Creates a single layer in a single feed-forward neural network'''
def __init__(self, width, inputSize, prevLayer=False):
'''Create a new layer'''
self.prevLayer = prevLayer
self.nextLayer = False
self.nodes = []
for _ in range(width):
self.nodes.append(Perceptron(inputSize))
def setNext(self, nextLayer):
'''Set the next layer in the network'''
self.nextLayer = nextLayer
def getOutput(self, inputs):
'''Get an array of the output of the network'''
output = np.empty(len(self.nodes))
for i in range(len(self.nodes)):
output[i] = self.nodes[i].getOutput(inputs)
if isinstance(self.nextLayer, Layer):
# If this isn't the output layer, recurse to the next layer down
return self.nextLayer.getOutput(output)
return output
def backProp(self, deltas):
'''Back-propogate error through all the layers'''
if isinstance(self.prevLayer, Layer):
# If this isn't the input layer, calculate deltas for the next layer up
crossprod = np.empty((len(deltas), len(self.nodes[0].weights)))
for j in range(len(deltas)):
crossprod[j][:] = self.nodes[j].weights * deltas[j]
nextDeltas = crossprod.sum(axis=0)
for i in range(len(nextDeltas)):
# multiply by g'
nextDeltas[i] *= self.prevLayer.nodes[i].aj * (1.0 - self.prevLayer.nodes[i].aj)
# Recurse upwards
self.prevLayer.backProp(nextDeltas)
# Adjust the weights of neurons in this layer
for i in range(len(self.nodes)):
self.nodes[i].adjust(deltas[i])
class NeuralNetwork:
def __init__(self, layerSizes=np.array(0), filename=""):
'''Creates a neural network with the given layer sizes.'''
prev = False
inputLayer = False
for i in range(len(layerSizes)-1):
inputSize = layerSizes[i]
outputSize = layerSizes[i+1]
layer = Layer(outputSize, inputSize, prev)
if isinstance(prev, Layer):
prev.setNext(layer)
if not isinstance(inputLayer, Layer):
inputLayer = layer
prev = layer
self.inputLayer = inputLayer
self.outputLayer = prev
def train(self, inputs, outputs):
'''Train the network on the given sample'''
pred = self.inputLayer.getOutput(inputs)
# calculate error of output layer
error = outputs - pred
deltas = error * pred * (1.0 - pred)
# back-propogate the error
self.outputLayer.backProp(deltas)
# return error
return np.max(abs(error))
def test(self, inputs, outputs):
'''Test the network on the given sample'''
pred = self.inputLayer.getOutput(inputs)
correct = True
for i in range(len(pred)):
if abs(pred[i] - outputs[i]) > CLOSE:
correct = False
return correct
答案 0 :(得分：0)
您可以尝试以下任何一种措施：