Ajout d'un print des resultats sans passer par le test à chaque epoch

.gitignore

@@ -1,3 +0,0 @@
-__pycache__/
-set/
-

RUN.py

@@ -3,18 +3,19 @@
 import mnist_loader
 training_data, validation_data, test_data = mnist_loader.load_data_wrapper()
 
+print(list(training_data)[0][1])
+
 import network
-import dataset_loader
 
-net = network.Network([262144, 30, 10])                                         #Testé : 94,56%
+#net = network.Network([784, 30, 10])                                         #Testé : 94,56%
-net.SGD(dataset_loader.loadTrainingSet("training"), 30, 10, 3.0, test_data=dataset_loader.loadTestSet("test"))
+#net.SGD(training_data, 10, 10, 3.0, test_data=None)
+#print("Results : {} / 10000".format(net.evaluate(test_data)))
 
-# net = network.Network([784, 100, 10])                                          #Marche mieux apparemment
+# net = network.Network([784, 100, 10])                                          #Marche mieux apparemment
 # net.SGD(training_data, 30, 10, 3.0, test_data=test_data)
 
 # net = network.Network([784, 100, 10])                                        #Marche pas bien apparemment
 # net.SGD(training_data, 30, 10, 0.001, test_data=test_data)
 
 # net = network.Network([784, 30, 10])                                         #Marche pas du tout apparemment
-# net.SGD(training_data, 30, 10, 100.0, test_data=test_data)
+# net.SGD(training_data, 30, 10, 100.0, test_data=test_data)
-

dataset_loader.py

@@ -1,90 +0,0 @@
-from mnist_loader import load_data
-import numpy as np
-import os
-from PIL import Image
-import resource
-
-
-def vectorized_result(j):
-    """Return a 10-dimensional unit vector with a 1.0 in the jth
-    position and zeroes elsewhere.  This is used to convert a digit
-    (0...9) into a corresponding desired output from the neural
-    network."""
-    e = np.zeros((10, 1))
-    e[j] = 1.0
-    return e
-
-def loadSet(path):
-
-	filelist = []
-
-	for root, dirs, files in os.walk(path):
-		for file in files:
-			filelist.append(os.path.join(root,file))
-
-	i = 0
-	pixels = []
-	result = []
-
-
-	for name in filelist:
-
-		if i >= 100:
-
-			break
-		
-		if ".png" in name:
-
-			with Image.open(path + "/" + name.split("/")[-1]) as im:
-
-				pix = im.load()
-				temparray = []
-
-				result.append(name.split("/")[-1][0])
-				
-				for x in range(im.size[0]):
-
-					for y in range(im.size[1]):
-
-						temparray.append(pix[x, y] / 255)
-
-				pixels.append(temparray)
-				print(temparray)
-				print(str("%.2f" % round(i / (len(filelist) if len(filelist) < 100 else 100) * 100, 2)) + "% Done, ram usage: " + str("%.2f" % round(resource.getrusage(resource.RUSAGE_SELF).ru_maxrss / (1024*1024), 2)) + "Go", end = '\r')
-				i += 1
-
-	print("max ram usage: " + str(resource.getrusage(resource.RUSAGE_SELF).ru_maxrss / (1024*1024)) + "Go")
-
-	return (pixels, result)
-
-
-def loadTrainingSet(path):
-
-	print("importing training set...")
-
-	set = loadSet(path)
-
-	training_inputs = [np.reshape(x, (262144, 1)) for x in set[0]]
-	training_results = [vectorized_result(int(y)) for y in set[1]]
-	training_data = zip(training_inputs, training_results)
-
-	return training_data
-
-def loadTestSet(path):
-
-	print("importing test set...")
-
-	set = loadSet(path)
-
-	test_inputs = [np.reshape(x, (262144, 1)) for x in set[0]]
-	test_data = zip(test_inputs, set[1])
-
-	return test_data
-
-
-
-
-
-if __name__ == "__main__":
-
-	print(loadSet("set")[0])

network.py

@@ -147,6 +147,3 @@ def sigmoid(z):
 def sigmoid_prime(z):
     """Derivative of the sigmoid function."""
     return sigmoid(z)*(1-sigmoid(z))
-
-
-