new data loader thingy

.gitignore

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

RUN.py

@@ -3,15 +3,13 @@
 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([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([262144, 30, 10])                                         #Testé : 94,56%
+net.SGD(dataset_loader.loadTrainingSet("training"), 30, 10, 3.0, test_data=dataset_loader.loadTestSet("test"))
 
-# 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
@@ -19,3 +17,4 @@ import network
 
 # net = network.Network([784, 30, 10])                                         #Marche pas du tout apparemment
 # net.SGD(training_data, 30, 10, 100.0, test_data=test_data)
+

dataset_loader.py

@@ -0,0 +1,90 @@
+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

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