123 lines
3.7 KiB
Python
123 lines
3.7 KiB
Python
"""more_data
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~~~~~~~~~~~~
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Plot graphs to illustrate the performance of MNIST when different size
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training sets are used.
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"""
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# Standard library
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import json
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import random
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import sys
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# My library
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sys.path.append('../src/')
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import mnist_loader
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import network2
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# Third-party libraries
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import matplotlib.pyplot as plt
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import numpy as np
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from sklearn import svm
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# The sizes to use for the different training sets
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SIZES = [100, 200, 500, 1000, 2000, 5000, 10000, 20000, 50000]
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def main():
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run_networks()
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run_svms()
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make_plots()
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def run_networks():
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# Make results more easily reproducible
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random.seed(12345678)
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np.random.seed(12345678)
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training_data, validation_data, test_data = mnist_loader.load_data_wrapper()
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net = network2.Network([784, 30, 10], cost=network2.CrossEntropyCost())
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accuracies = []
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for size in SIZES:
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print "\n\nTraining network with data set size %s" % size
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net.large_weight_initializer()
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num_epochs = 1500000 / size
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net.SGD(training_data[:size], num_epochs, 10, 0.5, lmbda = size*0.0001)
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accuracy = net.accuracy(validation_data) / 100.0
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print "Accuracy was %s percent" % accuracy
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accuracies.append(accuracy)
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f = open("more_data.json", "w")
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json.dump(accuracies, f)
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f.close()
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def run_svms():
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svm_training_data, svm_validation_data, svm_test_data \
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= mnist_loader.load_data()
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accuracies = []
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for size in SIZES:
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print "\n\nTraining SVM with data set size %s" % size
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clf = svm.SVC()
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clf.fit(svm_training_data[0][:size], svm_training_data[1][:size])
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predictions = [int(a) for a in clf.predict(svm_validation_data[0])]
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accuracy = sum(int(a == y) for a, y in
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zip(predictions, svm_validation_data[1])) / 100.0
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print "Accuracy was %s percent" % accuracy
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accuracies.append(accuracy)
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f = open("more_data_svm.json", "w")
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json.dump(accuracies, f)
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f.close()
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def make_plots():
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f = open("more_data.json", "r")
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accuracies = json.load(f)
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f.close()
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f = open("more_data_svm.json", "r")
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svm_accuracies = json.load(f)
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f.close()
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make_linear_plot(accuracies)
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make_log_plot(accuracies)
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make_combined_plot(accuracies, svm_accuracies)
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def make_linear_plot(accuracies):
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fig = plt.figure()
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ax = fig.add_subplot(111)
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ax.plot(SIZES, accuracies, color='#2A6EA6')
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ax.plot(SIZES, accuracies, "o", color='#FFA933')
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ax.set_xlim(0, 50000)
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ax.set_ylim(60, 100)
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ax.grid(True)
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ax.set_xlabel('Training set size')
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ax.set_title('Accuracy (%) on the validation data')
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plt.show()
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def make_log_plot(accuracies):
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fig = plt.figure()
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ax = fig.add_subplot(111)
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ax.plot(SIZES, accuracies, color='#2A6EA6')
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ax.plot(SIZES, accuracies, "o", color='#FFA933')
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ax.set_xlim(100, 50000)
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ax.set_ylim(60, 100)
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ax.set_xscale('log')
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ax.grid(True)
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ax.set_xlabel('Training set size')
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ax.set_title('Accuracy (%) on the validation data')
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plt.show()
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def make_combined_plot(accuracies, svm_accuracies):
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fig = plt.figure()
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ax = fig.add_subplot(111)
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ax.plot(SIZES, accuracies, color='#2A6EA6')
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ax.plot(SIZES, accuracies, "o", color='#2A6EA6',
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label='Neural network accuracy (%)')
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ax.plot(SIZES, svm_accuracies, color='#FFA933')
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ax.plot(SIZES, svm_accuracies, "o", color='#FFA933',
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label='SVM accuracy (%)')
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ax.set_xlim(100, 50000)
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ax.set_ylim(25, 100)
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ax.set_xscale('log')
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ax.grid(True)
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ax.set_xlabel('Training set size')
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plt.legend(loc="lower right")
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plt.show()
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if __name__ == "__main__":
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main()
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