Ajout de deux trois trucs

RUN.py

@@ -3,13 +3,13 @@
 import mnist_loader
 training_data, validation_data, test_data = mnist_loader.load_data_wrapper()
 
-print(list(training_data)[0][1])
+#print(list(training_data)[0][1])
 
 import network
 
-#net = network.Network([784, 30, 10])                                         #Testé : 94,56%
-#net.SGD(training_data, 10, 10, 3.0, test_data=None)
-#print("Results : {} / 10000".format(net.evaluate(test_data)))
+net = network.Network([784, 30, 10])                                         #Testé : 94,56% / 94,87%
+net.SGD(training_data, 30, 10, 3.0, test_data=test_data)
+print("Results : {} / 10000".format(net.evaluate(test_data)))
 
 # net = network.Network([784, 100, 10])                                          #Marche mieux apparemment
 # net.SGD(training_data, 30, 10, 3.0, test_data=test_data)

network mais annotations changées.py

@@ -0,0 +1,106 @@
+import random
+import numpy as np
+
+class Network(object):
+
+    def __init__(self, sizes):
+        """sizes : [nb neurones input, nb de neurones couche 1, ..., nb de neurones couche n, nb de neurones output]
+           biases : seuils générés aléatoirement
+           weights : poids générés aléatoirement"""
+        self.num_layers = len(sizes)
+        self.sizes = sizes
+        self.biases = [np.random.randn(y, 1) for y in sizes[1:]]
+        self.weights = [np.random.randn(y, x)
+                        for x, y in zip(sizes[:-1], sizes[1:])]
+
+    def feedforward(self, a):
+        for b, w in zip(self.biases, self.weights):
+            a = sigmoid(np.dot(w, a)+b)
+        return a
+
+    def SGD(self, training_data, epochs, mini_batch_size, eta,
+            test_data=None):
+        """epochs : iterations
+           eta : taux d'apprentissage
+           test_data : s'il y en a pas le programme ne s'arrêtera pas à chaque iterations pour se tester"""
+
+        training_data = list(training_data)
+        n = len(training_data)
+
+        if test_data:
+            test_data = list(test_data)
+            n_test = len(test_data)
+
+        for j in range(epochs):
+            random.shuffle(training_data)
+            mini_batches = [
+                training_data[k:k+mini_batch_size]
+                for k in range(0, n, mini_batch_size)]
+            for mini_batch in mini_batches:
+                self.update_mini_batch(mini_batch, eta)
+            if test_data:
+                print("Epoch {} : {} / {}".format(j,self.evaluate(test_data),n_test))
+            else:
+                print("Epoch {} complete".format(j))
+
+    def update_mini_batch(self, mini_batch, eta):
+        """Met à jour les poids et seuils grâce aux gradient descendant"""
+        nabla_b = [np.zeros(b.shape) for b in self.biases]
+        nabla_w = [np.zeros(w.shape) for w in self.weights]
+        for x, y in mini_batch:
+            delta_nabla_b, delta_nabla_w = self.backprop(x, y)
+            nabla_b = [nb+dnb for nb, dnb in zip(nabla_b, delta_nabla_b)]
+            nabla_w = [nw+dnw for nw, dnw in zip(nabla_w, delta_nabla_w)]
+        self.weights = [w-(eta/len(mini_batch))*nw
+                        for w, nw in zip(self.weights, nabla_w)]
+        self.biases = [b-(eta/len(mini_batch))*nb
+                       for b, nb in zip(self.biases, nabla_b)]
+
+    def backprop(self, x, y):
+        """Calcul du gradient descendant"""
+        nabla_b = [np.zeros(b.shape) for b in self.biases]
+        nabla_w = [np.zeros(w.shape) for w in self.weights]
+        # feedforward
+        activation = x
+        activations = [x] # list to store all the activations, layer by layer
+        zs = [] # list to store all the z vectors, layer by layer
+        for b, w in zip(self.biases, self.weights):
+            z = np.dot(w, activation)+b
+            zs.append(z)
+            activation = sigmoid(z)
+            activations.append(activation)
+        # backward pass
+        delta = self.cost_derivative(activations[-1], y) * \
+            sigmoid_prime(zs[-1])
+        nabla_b[-1] = delta
+        nabla_w[-1] = np.dot(delta, activations[-2].transpose())
+        # Note that the variable l in the loop below is used a little
+        # differently to the notation in Chapter 2 of the book.  Here,
+        # l = 1 means the last layer of neurons, l = 2 is the
+        # second-last layer, and so on.  It's a renumbering of the
+        # scheme in the book, used here to take advantage of the fact
+        # that Python can use negative indices in lists.
+        for l in range(2, self.num_layers):
+            z = zs[-l]
+            sp = sigmoid_prime(z)
+            delta = np.dot(self.weights[-l+1].transpose(), delta) * sp
+            nabla_b[-l] = delta
+            nabla_w[-l] = np.dot(delta, activations[-l-1].transpose())
+        return (nabla_b, nabla_w)
+
+    def evaluate(self, test_data):
+        """Teste le programme avec le dataset fourni"""
+        test_results = [(np.argmax(self.feedforward(x)), y)
+                        for (x, y) in test_data]
+        return sum(int(x == y) for (x, y) in test_results)
+
+    def cost_derivative(self, output_activations, y):
+        """Return the vector of partial derivatives \partial C_x /
+        \partial a for the output activations."""
+        return (output_activations-y)
+
+def sigmoid(z):
+    return 1.0/(1.0+np.exp(-z))
+
+def sigmoid_prime(z):
+    return sigmoid(z)*(1-sigmoid(z))