debut nouveau model

README.md

@@ -1,10 +1,2 @@
 # PT21-22-Reseau-Neurones
 
-Cette librairie python permet de créer simplement un modèle de perceptron multicouche en choisissant les hyperparamètres suivants:
- - Le nombre de couches
- - Le nombre de neurones pour chaque couche
- - Le learning rate
- - Le batch size
- - Le nombre d’époques
-
- Il est également possible de donner un jeu de données supplémentaire afin de mesurer la précision du modèle à la fin de l'entraînement. Lorsqu’il n'y a que deux neurones d'entrées, il y a une option pour visualiser l’entraînement.

sobek/activationFunctions.py

@@ -0,0 +1,23 @@
+class activationFunction:
+    def applyTo(value):
+        pass
+
+    def applyDerivateTo(value):
+        pass
+
+class sigmoid(activationFunction):
+    def applyTo(value):
+        return 1.0/(1.0+np.exp(-value))
+
+    def applyDerivateTo(value):
+        return sigmoid.applyTo(value) * (1 - sigmoid.applyTo(value))
+
+class reLu(activationFunction):
+    def applyTo(value):
+        return max(0, value)
+
+    def applyDerivateTo(value):
+        return 0 if (value < 0) else 1
+
+class softMax(activationFunction):
+    pass

sobek/layers.py

@@ -0,0 +1,30 @@
+import numpy as np
+
+class layer:
+    def __init__(self, neurons, activationFunction)
+        self.neurons = neurons
+        self.activationFunction = activationFunction
+
+    def process(_input, __storeValues=False)
+
+class dense(layer):
+    def process(_input, __storeValues=False):
+        
+        _input = np.dot(layerWeights, _input)
+        _input = np.add(_input, layerBias)
+
+        if (__storeValues):
+            self.activation = _input
+
+        _input = self.activationFunction.applyTo(_input)
+
+        if (__storeValues):
+            self.output = _input
+
+        return _input
+
+class convolution(layer):
+    pass
+
+class flatten(layer):
+    pass

sobek/network.py

@@ -1,6 +1,5 @@
 import random
 import numpy as np
-import matplotlib
 import matplotlib.pyplot as plt
 import matplotlib.animation as animation
 import pickle
@@ -94,7 +93,7 @@ class network:
                 vizualisationFrame = np.empty((30, 30))
                 for x in range(30):
                     for y in range(30):
-                        vizualisationFrame[x][y] = self.process(np.array([float(x)/30, float(y)/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)]
@@ -116,6 +115,7 @@ class network:
                     for layerNumber in range(len(errorSumsWeights)-1, -1, -1):
                         for neuronNumber in range(len(errorSumsWeights[layerNumber])):
                             errorSumsBiases[layerNumber][neuronNumber] += self.__Error(layerNumber, neuronNumber)
+                            #eventuellemtn sortir de boucle
                             errorSumsWeights[layerNumber][neuronNumber] = np.dot(errorSumsBiases[layerNumber][neuronNumber],self.outputs[layerNumber])
 
                 total = 0
@@ -136,7 +136,7 @@ class network:
                 print(self.accuracy(accuracyInputs, accuracyDesiredOutputs))
 
         if (visualize):
-            ani = animation.ArtistAnimation(fig, vizualisationData, interval=100, repeat_delay=1000)
+            ani = animation.ArtistAnimation(fig, vizualisationData, interval=100)
             plt.show()
 
     def __Error(self, layer, neuron):
@@ -181,3 +181,17 @@ class network:
     def networkFromFile(fileName):
         with open(fileName, "rb") as file:
             return pickle.load(file)
+
+
+
+
+class model:
+
+    def __init__(self, inputWidth, inputHeight, inputChannels):
+        self.inputWidth = inputWidth
+        self.inputHeight = inputHeight
+        self.inputChannels = inputChannels
+        self.layers = []
+
+    def add(layerType, activation):
+        self.layers.add()

tests/MNISTDrawingPrediction.py

@@ -14,7 +14,7 @@ class Sketchpad(tkinter.Canvas):
         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.MNISTNN = network.networkFromFile("MNISTtest2")
         self.predictionLabel = predictionLabel
 
     def add_line(self, event):

tests/flowerGardenLearningVisualization.py

@@ -1,17 +0,0 @@
-#!/bin/python3
-from sys import path
-path.insert(1, "..")
-from sobek.network import network
-import pickle
-
-with open("flowerGardenData", "rb") as file:
-    data = pickle.load(file)
-
-trainPoints = data[0]
-trainLabels = data[1]
-
-myNetwork = network(2, 16, 1)
-
-learningRate = 3.0
-
-myNetwork.train(trainPoints, trainLabels, learningRate, batchSize=100, epochs=3000, visualize=True)

tests/generateSobekFlowerGarden.py

@@ -1,34 +0,0 @@
-#!/bin/python3
-import random
-import numpy as np
-import math
-import pickle
-
-trainPoints = []
-trainLabels = []
-
-random.seed(1216513)
-
-for i in range(1000):
-    x = random.randint(-50, 50)
-    y = random.randint(-50, 50)
-
-    distance = math.sqrt(x**2 + y**2)
-
-    if (distance < 10 or 20 < distance < 30):
-        trainLabels.append(np.ones(1))
-    else :
-        trainLabels.append(np.zeros(1))
-
-    x = (x+50)/100
-    y = (y+50)/100
-
-    trainPoints.append(np.array([x, y]))
-
-print(trainPoints[1])
-print(trainLabels[1])
-
-data = [trainPoints, trainLabels]
-
-with open("flowerGardenData", "wb") as file:
-    pickle.dump(data, file)