问题描述
这是我第一次使用python编码神经网络，也是我第一次编码梯度下降。我正在尝试创建一个神经网络来分析股票并预测接下来会发生什么。因为我很懒，所以我没有估算真实的导数，而是估算它，因为我太懒了。
问题是，当我运行它时，神经网络的输出成本之间的差异以权重的微小差异运行，并且运行相同但没有微小差异的情况下，其差异要大得多
这是我的代码：
import csv
import numpy as np
input = []
inputEnd = 199 #start at 0
inputLength = 200
metric = 4 #date,open,high,low,close,volume (start at 0)
output = []
predictionLength = 50
outputGoal = []
weights = []
biases = []
#hiddenLayersNum: int = [16,16,16]
hiddenLayersNum = [16]
hiddenLayers = []
#get inputs from file
with open('MacroTrends_Data_Download_IBM.csv',newline = '') as f:
reader = csv.reader(f)
i = 0
for row in reader:
if i >= inputEnd - inputLength and i <= inputEnd:
input.append(float(row[metric]))
i += 1
#set weights and biases to random from normal distribution
i = 0
while i < predictionLength:
output.append(0.0)
i += 1
weightsLength = len(output) * len(input)
biasesLength = len(output)
for i in hiddenLayersNum:
weightsLength *= i
biasesLength += i
i = 0
while i <= weightsLength:
weights.append(np.random.normal(0,1.0))
i += 1
i = 0
while i <= biasesLength:
biases.append(np.random.normal(0,1.0))
i += 1
for i in hiddenLayersNum:
x = 0
while x <= i:
hiddenLayers.append(0.0)
x += 1
def Run(inputs = [],weight = [],bias = []):
#set hidden layers
i = 0
while i < len(hiddenLayers):
neuron = 0.0
if i < hiddenLayersNum[0]:#first layer
x = 0
while x < len(inputs):
neuron += inputs[x] * weight[int(i * len(inputs) + x)]#add up input neurons times weights
x += 1
neuron += bias[int(i)]#add bias
else:
start = 0
end = hiddenLayersNum[0]
for x in hiddenLayersNum:# other layers
end += x
start += x
if i < end and i >= start:
y = 0
while y < end - start:
neuron += hiddenLayers[int(start - x + y)] * weight[int(len(inputs) * hiddenLayersNum[0] + (start - hiddenLayersNum[0]) * hiddenLayersNum[0] + y)]#add up neurons from prevIoUs layer multiplied by weights
y += 1
x += 1
neuron += bias[int(start + i)]# add bias
hiddenLayers[int(i)] = neuron
i += 1
#set output
i = 0
while i < predictionLength:
neuron = 0.0
x = 0
while x < hiddenLayersNum[-1]:
neuron += hiddenLayers[int(len(hiddenLayers) - hiddenLayersNum[-1] + x)] * weight[int(len(weight) - (len(output) * hiddenLayersNum[-1]) + x)]#add up neurons from last hidden layer * weights
x += 1
neuron += biases[int(len(hiddenLayers) + i)]#add bias
output[int(i)] = neuron
i += 1
return output
with open('MacroTrends_Data_Download_IBM.csv',newline = '') as f:
reader = csv.reader(f)
i = 0
for row in reader:
if i > inputEnd and i <= inputEnd + predictionLength:
outputGoal.append(float(row[metric]))
i += 1
def Cost(inputs = []):#add up all of the (outputs - correct outputs)^2
cost = 0.0
x = 0
for i in inputs:
cost += (i - outputGoal[x])**2
x += 1
return cost
def GradientDescent(learningRate = 0.01,derivativeDifference = 0.0001):
i = 0
origWeights = weights[:]
while i < weightsLength:#loop through each weight and change it to make the outputs closer to what they should be
weightsPlus = origWeights[:]
weightsPlus[i] += derivativeDifference#make the weight I am testing be a little bit more
weights[i] -= learningRate * (Cost(Run(input,weightsPlus,biases)) - Cost(Run(input,weights,biases)) / derivativeDifference)
#find an approximation of the derivative,and then subtract that times the learning rate from the weight I am testing
print(Cost(Run(input,biases)))
i+=1
GradientDescent()
我感觉这是一个语法错误，因为我曾经使用过c＃和Unity，但找不到任何东西。
请帮助！ 预先感谢。
解决方法
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