neural-networks-and-deep-learning mnist_average_darkness.py

就是根据每一幅图片的平均sum来计算。。正确率很低

"""mnist_average_darkness~~~~~~~~~~~~~~~~~~~~~~A naive classifier for recognizing handwritten digits from the MNISTdata set.  The program classifies digits based on how dark they are--- the idea is that digits like "1" tend to be less dark than digitslike "8", simply because the latter has a more complex shape.  Whenshown an image the classifier returns whichever digit in the trainingdata had the closest average darkness.The program works in two steps: first it trains the classifier, andthen it applies the classifier to the MNIST test data to see how manydigits are correctly classified.Needless to say, this isn't a very good way of recognizing handwrittendigits!  Still, it's useful to show what sort of performance we getfrom naive ideas."""#### Libraries# Standard libraryfrom collections import defaultdict# My librariesimport mnist_loaderdef main():    training_data, validation_data, test_data = mnist_loader.load_data()    # training phase: compute the average darknesses for each digit,    # based on the training data    avgs = avg_darknesses(training_data)    # testing phase: see how many of the test images are classified    # correctly    num_correct = sum(int(guess_digit(image, avgs) == digit)                      for image, digit in zip(test_data[0], test_data[1]))    print "Baseline classifier using average darkness of image."    print "%s of %s values correct." % (num_correct, len(test_data[1]))def avg_darknesses(training_data):    """ Return a defaultdict whose keys are the digits 0 through 9.    For each digit we compute a value which is the average darkness of    training images containing that digit.  The darkness for any    particular image is just the sum of the darknesses for each pixel."""    digit_counts = defaultdict(int)    darknesses = defaultdict(float)    for image, digit in zip(training_data[0], training_data[1]):        digit_counts[digit] += 1        darknesses[digit] += sum(image)    avgs = defaultdict(float)    for digit, n in digit_counts.iteritems():        avgs[digit] = darknesses[digit] / n    return avgsdef guess_digit(image, avgs):    """Return the digit whose average darkness in the training data is    closest to the darkness of ``image``.  Note that ``avgs`` is    assumed to be a defaultdict whose keys are 0...9, and whose values    are the corresponding average darknesses across the training data."""    darkness = sum(image)    distances = {k: abs(v-darkness) for k, v in avgs.iteritems()}    return min(distances, key=distances.get)if __name__ == "__main__":    main()