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我試圖讓我的NN在20x20的畫布中手工繪製出「快樂」和「悲傷」的表情。我使用了400個輸入圖層單元，100個隱藏圖層單元和2個輸出單元（開心/傷心）。隱藏的&輸出圖層的激活功能是sigmoid。NN輸出傾向於最新的訓練示例
我正在通過手動繪製人臉，選擇是否快樂或難過，並將訓練樣本送入神經網絡來逐步訓練神經網絡。另外，當我繪製時，程序會動態顯示NN輸出。
問題是，只要我連續添加一堆快樂的面孔，「happy」的輸出就會提高到接近1.但是，如果我開始添加悲傷面孔，輸出會變爲happy = 0.0241 ... sad = 0.9988 ...
有時，兩個輸出都會上升到接近1（happy = 0.99/sad = 0.99）。
看來它應該以這種方式工作，但爲了測試神經網絡，我開始在畫布的上半部分繪製HAPPY面部，並在下半部分繪製SAD面部。同樣的事情發生了（輸出傾向於最新的樣本），但是我增加了50個訓練樣例，輸出似乎不會對上/下半部分的繪畫做出反應。
我不明白，如果我正確地構建NN或正確提供數據（它只是一個400（或0.01（白色）或0.99（黑色）大小的數組）。
如何防止此行爲？
神經網絡類：
import scipy.special
import numpy
class NeuralNetwork:
def __init__(self, inputnodes, hiddennodes, outputnodes, learningrate):
# set number of nodes in each input, hidden, output layer
self.inodes = inputnodes
self.hnodes = hiddennodes
self.onodes = outputnodes
# learning rate
self.lr = learningrate
# activation function
self.activation_function = lambda x: scipy.special.expit(x)
# link weight matrices, wih and who
self.wih = numpy.random.normal(0.0, pow(self.hnodes, -0.5), (self.hnodes, self.inodes))
self.who = numpy.random.normal(0.0, pow(self.onodes, -0.5), (self.onodes, self.hnodes))
pass
# train the neural network
def train(self, inputs_list, targets_list):
# convert inputs list to 2d array
inputs = numpy.array(inputs_list, ndmin=2).T
targets = numpy.array(targets_list, ndmin=2).T
# calculate signals into hidden layer
hidden_inputs = numpy.dot(self.wih, inputs)
# calculate the signals emerging from hidden layer
hidden_outputs = self.activation_function(hidden_inputs)
# calculate signals into final output layer
final_inputs = numpy.dot(self.who, hidden_outputs)
# calculate signals emerging from final output layer
final_outputs = self.activation_function(final_inputs)
# error is the (target - actual)
output_errors = targets - final_outputs
# hidden layer error is the output_errors, split by weights, recombined at hidden nodes
hidden_errors = numpy.dot(self.who.T, output_errors)
# update the weights for the links between the hidden and output layers
self.who += self.lr * numpy.dot((output_errors * final_outputs * (1.0 - final_outputs)),
numpy.transpose(hidden_outputs))
# update the weights for the links between the input and hidden layers
self.wih += self.lr * numpy.dot((hidden_errors * hidden_outputs * (1.0 - hidden_outputs)),
numpy.transpose(inputs))
pass
def query(self, inputs_list):
# convert inputs list to 2d array
inputs = numpy.array(inputs_list, ndmin=2).T
# calculate signals into hidden layer
hidden_inputs = numpy.dot(self.wih, inputs)
# calculate the signals emerging from the hidden layer
hidden_outputs = self.activation_function(hidden_inputs)
# calculate signals into final output layer
final_inputs = numpy.dot(self.who, hidden_outputs)
# calculate the signals emerging from final output layer
final_outputs = self.activation_function(final_inputs)
return final_outputs
主要代碼：
from tkinter import *
import numpy
# scipy.special for the sigmoid function expit()
import scipy.special
# library for plotting arrays
import matplotlib.pyplot
from nn import *
root = Tk()
frame = Frame(root)
w = Canvas(frame, width=400, height=400, background="green")
w.pack()
canvasRectangles = []
for i in range(20):
ls = []
for k in range(20):
x = 20*i
y = 20*k
ls.append(w.create_rectangle(x,y,x+20,y+20,fill="white"))
canvasRectangles.append(ls)
label = Label(frame,text='Number pressed: N/A')
label.pack()
hdnLayer1Label = Label(frame,text="Hidden Layer 1",justify=LEFT,wraplength=300)
hdnLayer1Label.pack()
outLayerLabel = Label(frame,text="Output Layer",justify=LEFT,wraplength=300)
outLayerLabel.pack()
def clearCanvas():
for i in range(20):
for k in range(20):
w.itemconfig(canvasRectangles[i][k],fill="white")
def key(event):
if event.char is 'r':
clearCanvas()
else:
if event.char is '1':
label.config(text="SMILE: Happy")
if event.char is '2':
label.config(text="SMILE: Sad")
global number
number = event.char
def initNN():
input_nodes = 400
hidden_nodes = 100
output_nodes = 2
learning_rate = 0.3
global n
n = NeuralNetwork(input_nodes, hidden_nodes, output_nodes, learning_rate)
def queryNN(rectangles):
unscaledInputs = getRectangleValues(rectangles)
arr = numpy.array(unscaledInputs)
scaledInputs = scaleInputs(arr)
res = n.query(scaledInputs)
return res
def trainNN(rectangles, desiredOuts):
inputs = getRectangleValues(rectangles)
arr = numpy.array(inputs)
# Scale the inputs
scaledInputs = scaleInputs(arr)
global n
n.train(scaledInputs, desiredOuts)
pass
def getRectangleValues(rectangles):
rectvals = []
for col in range(20):
for row in range(20):
if w.itemcget(rectangles[col][row], "fill") == "black":
rectvals.append(0)
else:
rectvals.append(1)
return rectvals
def tab(event):
desiredOuts = [0, 0]
if number is '1':
desiredOuts[0] = 1
print("desiredSmile= HAPPY")
if number is '2':
desiredOuts[1] = 1
print("desiredSmile= SAD")
print(desiredOuts)
trainNN(canvasRectangles, desiredOuts)
clearCanvas()
def draw(event):
x = event.x
y = event.y
if x <= 400 and y <= 400 and x >= 0 and y >= 0:
squarex = int(translate(x, 0, 400, 0, 20))
squarey = int(translate(y, 0, 400, 0, 20))
if not squarex is 20 or not squarey is 20:
w.itemconfig(canvasRectangles[squarex][squarey], fill="black")
outLayerLabel.config(text="Output: " + repr(queryNN(canvasRectangles)))
def translate(value, leftMin, leftMax, rightMin, rightMax):
# Figure out how 'wide' each range is
leftSpan = leftMax - leftMin
rightSpan = rightMax - rightMin
# Convert the left range into a 0-1 range (float)
valueScaled = float(value - leftMin)/float(leftSpan)
# Convert the 0-1 range into a value in the right range.
return rightMin + (valueScaled * rightSpan)
def scaleInputs(unscaledins):
return (numpy.asfarray(unscaledins)/1.0 * 0.99) + 0.01
initNN()
w.bind("<Button-1>", draw)
w.bind("<B1-Motion>", draw)
frame.bind("1",key)
frame.bind("2",key)
frame.bind("r", key)
frame.bind("<Tab>", tab)
frame.pack()
frame.focus_set()
'root.after(0,doWork)'
root.mainloop()
這與您昨天或前一天所做的問題有何不同？此外，修復縮進並放置完整的代碼（導入缺失） –
爲什麼您關心網絡在學習階段的表現？訓練結束後，是否將面部分類錯誤？ –
@IgnacioVergaraKausel我提出了問題並離開了，所以我無法添加源代碼。 –