89 lines
1.8 KiB
Python
89 lines
1.8 KiB
Python
from mnist_loader import load_data
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import numpy as np
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import os
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from PIL import Image
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import resource
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def vectorized_result(j):
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"""Return a 10-dimensional unit vector with a 1.0 in the jth
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position and zeroes elsewhere. This is used to convert a digit
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(0...9) into a corresponding desired output from the neural
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network."""
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e = np.zeros((10, 1))
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e[j] = 1.0
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return e
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def loadSet(path):
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filelist = []
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for root, dirs, files in os.walk(path):
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for file in files:
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filelist.append(os.path.join(root,file))
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i = 0
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pixels = []
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result = []
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for name in filelist:
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if i >= 500:
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break
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if ".png" in name:
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with Image.open(path + "/" + name.split("/")[-1]) as im:
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pix = im.load()
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temparray = []
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result.append(int(name.split("/")[-1][0]))
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for x in range(im.size[0]):
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for y in range(im.size[1]):
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temparray.append(pix[x, y] / 255)
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pixels.append(temparray)
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print(str("%.2f" % round(i / (len(filelist) if len(filelist) < 500 else 500) * 100, 2)) + "% Done, ram usage: " + str("%.2f" % round(resource.getrusage(resource.RUSAGE_SELF).ru_maxrss / (1024*1024), 2)) + "Go", end = '\r')
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i += 1
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print("max ram usage: " + str(resource.getrusage(resource.RUSAGE_SELF).ru_maxrss / (1024*1024)) + "Go")
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return (pixels, result)
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def loadTrainingSet(path):
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print("importing training set...")
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set = loadSet(path)
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training_inputs = [np.reshape(x, (784, 1)) for x in set[0]]
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training_results = [vectorized_result(int(y)) for y in set[1]]
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training_data = zip(training_inputs, training_results)
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return training_data
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def loadTestSet(path):
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print("importing test set...")
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set = loadSet(path)
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test_inputs = [np.reshape(x, (784, 1)) for x in set[0]]
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test_data = zip(test_inputs, set[1])
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return test_data
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if __name__ == "__main__":
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print(loadSet("set")[0]) |