blusson/PT21-22-Reseau-Neurones
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

Jalon 2 complete

Browse Source
This commit is contained in:
eynard 2021-12-22 21:35:06 +01:00
parent 83e220282c
commit 7733de01d2
7 changed files with 280 additions and 68 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

BIN
MNIST30epoch Normal file
View File

Binary file not shown.

41
MNISTDrawingPrediction.py Normal file
View File

@ -0,0 +1,41 @@
import tkinter
from PIL import Image, ImageDraw
from sobek.network import network
import numpy as np
class Sketchpad(tkinter.Canvas):
def __init__(self, parent, predictionLabel, **kwargs, ):
super().__init__(parent, **kwargs)
self.bind("<Button-3>", self.test)
self.bind("<B1-Motion>", self.add_line)
self.PILImage = Image.new("F", (560, 560), 100)
self.draw = ImageDraw.Draw(self.PILImage)
self.MNISTNN = network.networkFromFile("MNIST30epoch")
self.predictionLabel = predictionLabel
def add_line(self, event):
self.create_oval((event.x+32, event.y+32, event.x-32, event.y-32), fill="black")
self.draw.ellipse([event.x-32, event.y-32, event.x+32, event.y+32], fill="black")
smallerImage = self.PILImage.reduce(20)
imageAsArray = np.array(smallerImage.getdata())
imageAsArray = (100 - imageAsArray)/100
self.predictionLabel['text'] = ( "Predicted number : " + str(np.argmax(self.MNISTNN.process(imageAsArray))))
def test(self, event):
self.PILImage = Image.new("F", (560, 560), 100)
self.draw = ImageDraw.Draw(self.PILImage)
self.delete("all")
window = tkinter.Tk()
window.title("Number guesser")
window.resizable(False, False)
window.columnconfigure(0, weight=1)
window.rowconfigure(0, weight=1)
predictionLabel = tkinter.Label(window, text="Predicted number :")
sketch = Sketchpad(window, predictionLabel, width=560, height=560)
sketch.grid(column=0, row=0, sticky=(tkinter.N, tkinter.W, tkinter.E, tkinter.S))
predictionLabel.grid(column=0, row=1)
window.mainloop()

57
MNISTLearning.py Normal file
View File

@ -0,0 +1,57 @@
import numpy as np
from sobek.network import network
import gzip
import time
print("--- Data loading ---")
def getData(fileName):
with open(fileName, 'rb') as f:
data = f.read()
return np.frombuffer(gzip.decompress(data), dtype=np.uint8).copy()
tempTrainImages = getData("./MNIST/train-images-idx3-ubyte.gz")[0x10:].reshape((-1, 784)).tolist()
trainImages = []
for image in tempTrainImages:
for pixel in range(784):
if image[pixel] !=0:
image[pixel] = image[pixel]/256
trainImages.append(np.array(image, dtype=np.float64))
tempTrainLabels = getData("./MNIST/train-labels-idx1-ubyte.gz")[8:]
trainLabels = []
for label in tempTrainLabels:
trainLabels.append(np.zeros(10))
trainLabels[-1][label] = 1.0
myNetwork = network(784, 30, 10)
learningRate = 3.0
print("--- Learning ---")
startTime = time.perf_counter()
"""
for i in range(1):
print("Epoch: " + str(i))
batchEnd = 10
while batchEnd < 1000:
batchImages = trainImages[:batchEnd]
batchLabels = trainLabels[:batchEnd]
myNetwork.train(batchImages, batchLabels, learningRate)
batchEnd += 10
if (batchEnd%100) == 0:
print(batchEnd)
"""
myNetwork.train(trainImages, trainLabels, learningRate, 10, 30)
endTime = time.perf_counter()
print("Learning time : " + str(endTime - startTime))
print(trainLabels[121])
print(myNetwork.process(trainImages[121]))
myNetwork.saveToFile("MNIST30epoch")

30
MNISTLoadTest.py Normal file
View File

@ -0,0 +1,30 @@
import numpy as np
from sobek.network import network
import gzip
print("--- Data loading ---")
def getData(fileName):
with open(fileName, 'rb') as f:
data = f.read()
return np.frombuffer(gzip.decompress(data), dtype=np.uint8).copy()
tempTrainImages = getData("./MNIST/t10k-images-idx3-ubyte.gz")[0x10:].reshape((-1, 784)).tolist()
trainImages = []
for image in tempTrainImages:
for pixel in range(784):
if image[pixel] !=0:
image[pixel] = image[pixel]/256
trainImages.append(np.array(image, dtype=np.float64))
tempTrainLabels = getData("./MNIST/t10k-labels-idx1-ubyte.gz")[8:]
trainLabels = []
for label in tempTrainLabels:
trainLabels.append(np.zeros(10))
trainLabels[-1][label] = 1.0
print("--- Testing ---")
myNetwork = network.networkFromFile("MNIST30epoch")
print(myNetwork.accuracy(trainImages, trainLabels))

112
sobek/network.py
View File

@ -1,4 +1,8 @@
import random
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
import pickle
class network:
@ -26,9 +30,9 @@ class network:
def process(self, _input, __storeValues=False):
if type(_input) != np.ndarray:
raise TypeError("The input must be a vector!")
raise TypeError("The input must be a numpy array!")
if _input.size != self.__inputLayerSize:
raise ValueError("The input vector has the wrong size!")
raise ValueError("The input vector has the wrong size! " + str(_input.size) + " != " + str(self.__inputLayerSize))
if _input.dtype != np.float64:
print(_input.dtype)
raise TypeError("The input vector must contain floats!")
@ -59,11 +63,48 @@ class network:
def train(self, inputs, desiredOutputs, learningRate):
def train(self, inputs, desiredOutputs, learningRate, batchSize, epochs=1, visualize=False):
if (type(inputs) != list or type(desiredOutputs) != list):
raise TypeError("The inputs and desired outputs must be lists of numpy arrays !")
if (len(inputs) != len(desiredOutputs)):
raise ValueError("The inputs and desired outputs vectors must have the same amount of data !")
raise ValueError("The inputs and desired outputs lists must have the same amount of data ! " + str(len(inputs)) + " != " + str(len(desiredOutputs)))
if (len(inputs) == 0):
raise ValueError("The list is empty !")
if (visualize == False):
if (self.__inputLayerSize != 2):
raise ValueError("Visualization is only possible for 2 inputs networks")
if (len(self.weights[-1]) != 1):
raise ValueError("Visualization is only possible for 1 output networks")
for _input, desiredOutput in zip(inputs, desiredOutputs):
errorSumsWeights = []
errorSumsBiases = []
if (visualize):
vizualisationData = []
fig, graph = plt.subplots()
for epoch in range(epochs):
randomState = random.getstate()
random.shuffle(inputs)
random.setstate(randomState)
random.shuffle(desiredOutputs)
if (visualize and epoch%10 == 0):
vizualisationFrame = np.empty((30, 30))
for x in range(30):
for y in range(30):
vizualisationFrame[x][y] = self.process(np.array([float(x), float(y)]))
vizualisationData.append([graph.imshow(vizualisationFrame, animated=True)])
inputBatches = [inputs[j:j+batchSize] for j in range(0, len(inputs), batchSize)]
desiredOutputsBatches = [desiredOutputs[j:j+batchSize] for j in range(0, len(inputs), batchSize)]
for inputBatch, desiredOutputsBatch in zip(inputBatches, desiredOutputsBatches):
for _input, desiredOutput in zip(inputBatch, desiredOutputsBatch):
errorSumsWeights = [np.zeros(layer.shape) for layer in self.weights]
errorSumsBiases = [np.zeros(layer.shape) for layer in self.biases]
@ -73,44 +114,49 @@ class network:
self.process(_input, True)
self.__desiredOutput = desiredOutput
#Somme de matrice ?
#A optimiser
for layerNumber in range(len(errorSumsWeights)-1, -1, -1):
for neuronNumber in range(len(errorSumsWeights[layerNumber])):
errorSumsBiases[layerNumber][neuronNumber] += self.__Error(layerNumber, neuronNumber)
for weightNumber in range(len(errorSumsWeights[layerNumber][neuronNumber])):
#for weightNumber in range(len(errorSumsWeights[layerNumber][neuronNumber])):
#print("layer : " + str(layerNumber) + " neuron : " + str(neuronNumber) + " weight : " + str(weightNumber))
errorSumsWeights[layerNumber][neuronNumber][weightNumber] += self.__PartialDerivative(layerNumber, neuronNumber, weightNumber)
#errorSumsWeights[layerNumber][neuronNumber][weightNumber] += self.__PartialDerivative(layerNumber, neuronNumber, weightNumber)
#errorSumsWeights[layerNumber][neuronNumber][weightNumber] = errorSumsBiases[layerNumber][neuronNumber] * self.outputs[layerNumber][weightNumber]
errorSumsWeights[layerNumber][neuronNumber] = np.dot(errorSumsBiases[layerNumber][neuronNumber],self.outputs[layerNumber])
total = 0
for layerNumber in range(len(errorSumsWeights)):
errorSumsWeights[layerNumber] = np.multiply(errorSumsWeights[layerNumber], -(learningRate/len(inputBatch)))
self.weights[layerNumber] = np.add(self.weights[layerNumber], errorSumsWeights[layerNumber])
errorSumsWeights = np.multiply(errorSumsWeights, -(learningRate/len(inputs)))
self.weights = np.add(self.weights, errorSumsWeights)
errorSumsBiases = np.multiply(errorSumsBiases, -(learningRate/len(inputs)))
self.biases = np.add(self.biases, errorSumsBiases)
errorSumsBiases[layerNumber] = np.multiply(errorSumsBiases[layerNumber], -(learningRate/len(inputBatch)))
self.biases[layerNumber] = np.add(self.biases[layerNumber], errorSumsBiases[layerNumber])
#print(self.__biases)
"""
for layerNumber in range(len(errorSumsWeights)):
for neuronNumber in range(len(errorSumsWeights[layerNumber])):
errorSumsBiases[layerNumber][neuronNumber] = errorSumsBiases[layerNumber][neuronNumber] / len(inputs)
errorSumsBiases[layerNumber][neuronNumber] = errorSumsBiases[layerNumber][neuronNumber] / len(inputBatch)
total += errorSumsBiases[layerNumber][neuronNumber]
self.biases[layerNumber][neuronNumber] -= learningRate * errorSumsBiases[layerNumber][neuronNumber]
for weightNumber in range(len(errorSumsWeights[layerNumber][neuronNumber])):
#Probablement faisable avec une multiplication de matrices
errorSumsWeights[layerNumber][neuronNumber][weightNumber] = errorSumsWeights[layerNumber][neuronNumber][weightNumber] / len(inputs)
errorSumsWeights[layerNumber][neuronNumber][weightNumber] = errorSumsWeights[layerNumber][neuronNumber][weightNumber] / len(inputBatch)
total += errorSumsWeights[layerNumber][neuronNumber][weightNumber]
#total += errorSumsWeights[layerNumber][neuronNumber][weightNumber]
#Probablement faisable avec une somme de matrices
self.weights[layerNumber][neuronNumber][weightNumber] -= learningRate * errorSumsWeights[layerNumber][neuronNumber][weightNumber]
#print("Error : " + str(total))"""
if (visualize):
ani = animation.ArtistAnimation(fig, vizualisationData, interval=100)
plt.show()
def __Error(self, layer, neuron):
if (self.__errors[layer][neuron] == 0 ):
@ -122,20 +168,38 @@ class network:
def __ErrorHiddenLayer(self, layer, neuron):
upperLayerLinksSum = 0
#Probablement faisable avec une multiplication de matrices
for upperLayerNeuron in range(len(self.weights[layer+1])):
upperLayerLinksSum += self.weights[layer+1][upperLayerNeuron][neuron] * self.__errors[layer+1][upperLayerNeuron]
return network.__sigmoid(self.activations[layer][neuron], derivative=True) * upperLayerLinksSum
def __PartialDerivative(self, layer, neuron, weight):
return self.__Error(layer, neuron) * self.outputs[layer][weight]
#def __PartialDerivative(self, layer, neuron, weight):
# return self.__Error(layer, neuron) * self.outputs[layer][weight]
def accuracy(self, inputs, desiredOutputs):
if (type(inputs) != list or type(desiredOutputs) != list):
raise TypeError("The inputs and desired outputs must be lists of numpy arrays !")
if (len(inputs) != len(desiredOutputs)):
raise ValueError("The inputs and desired outputs lists must have the same amount of data !")
if (len(inputs) == 0):
raise ValueError("The list is empty !")
sum = 0
for i in range(len(desiredOutputs)):
if (np.argmax(desiredOutputs[i]) == np.argmax(self.process(inputs[i]))):
sum += 1
return sum/len(desiredOutputs)
def saveToFile(self, fileName):
np.savez(fileName, biases=self.biases, weights=self.weights)
with open(fileName, "wb") as file:
pickle.dump(self, file)
def loadFromFile(self, fileName):
data = np.load(fileName)
self.biases = data['biases']
self.weights = data['weights']
with open(fileName, "rb") as file:
fromNetwork = pickle.load(file)
self.weights = fromNetwork.weights
self.biases = fromNetwork.biases
self.__inputLayerSize = fromNetwork.__inputLayerSize
def networkFromFile(fileName):
with open(fileName, "rb") as file:
return pickle.load(file)

11
testLearning.py
View File

@ -8,7 +8,7 @@ myNetwork = network(10, 10)
learningRate = 3
for j in range(10000):
for j in range(1000):
rand = []
inputs = []
desiredOutputs = []
@ -36,4 +36,13 @@ test[0][1] = 1.0
test[1][5] = 1.0
print(test[0])
print(myNetwork.process(test[0]))
print(test[1])
print(myNetwork.process(test[1]))
print("Save and load test :")
myNetwork.saveToFile("test")
myNetwork2 = network.networkFromFile("test")
print(myNetwork.process(test[0]).all() == myNetwork2.process(test[0]).all())

41
testLearningNAND.py
View File

@ -1,10 +1,11 @@
import numpy as np
import random
from sobek.network import network
import time
random.seed()
myNetwork = network(2, 1)
myNetwork = network(2, 2, 1)
learningRate = 3
@ -23,23 +24,29 @@ result.append(np.ones(1))
result.append(np.ones(1))
result.append(np.zeros(1))
for j in range(10000):
inputs = []
desiredOutputs = []
learningTime = 0
if (j%1000 == 0):
print(j)
nbRep = 1
random.shuffle(test)
for i in range(nbRep):
if (i%(nbRep/10) == 0): print(i)
for i in range(4):
if (test[i][0] == 1.0) and (test[i][1] == 1.0):
result[i][0] = 0.0
else:
result[i][0] = 1.0
startTime = time.perf_counter()
myNetwork.train(test, result, learningRate)
#for j in range(10000):
# inputs = []
# desiredOutputs = []
#if (j%1000 == 0):
# print(j)
# myNetwork.train(test, result, learningRate)
myNetwork.train(test, result, learningRate, len(test), 10000, visualize=False)
endTime = time.perf_counter()
learningTime += endTime - startTime
learningTime = learningTime / nbRep
test = []
result = []
test.append(np.zeros(2))
@ -55,9 +62,13 @@ result.append(np.ones(1))
result.append(np.ones(1))
result.append(np.zeros(1))
print(myNetwork.weights)
print(myNetwork.biases)
#print(myNetwork.weights)
#print(myNetwork.biases)
print("0 0 : " + str(myNetwork.process(test[0])) + " == 1 ?")
print("0 1 : " + str(myNetwork.process(test[1])) + " == 1 ?")
print("1 0 : " + str(myNetwork.process(test[2])) + " == 1 ?")
print("1 1 : " + str(myNetwork.process(test[3])) + " == 0 ?")
myNetwork.saveToFile("NAND")
print("Learning time : " + str(endTime - startTime))