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mnistprogram.py
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49 lines (34 loc) · 1.64 KB
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import numpy as np
import neuralnetwork as nn
import matplotlib.pyplot as plt
with np.load('mnist.npz') as data:
training_images = data['training_images']
training_labels = data['training_labels']
plt.imshow(training_images[0].reshape(28,28),cmap = 'gray')
plt.show()
# specify the layers, first layer should be 784 and output layer should be 10 for this example
layer_sizes = (784, 16, 16, 10)
# number of images used for training, the rest is used for testing the performance
training_set_size = 5000
#get training images
training_set_images = training_images[:training_set_size]
training_set_labels = training_labels[:training_set_size]
training_set_images = training_images[:training_set_size]
training_set_labels = training_labels[:training_set_size]
test_set_images = training_images[training_set_size:]
test_set_labels = training_labels[training_set_size:]
# initialize neural network
net = nn.NeuralNetwork(layer_sizes)
# evaluate performance without training
net.print_accuracy(test_set_images, test_set_labels)
net.calculate_average_cost(test_set_images, test_set_labels)
# first training session
net.train_network(training_set_images, training_set_labels, 4, 10, 4.0)
# evaluate performance after first training session
net.print_accuracy(test_set_images, test_set_labels)
net.calculate_average_cost(test_set_images, test_set_labels)
# second training session
net.train_network(training_set_images, training_set_labels, 8, 20, 2.0)
# evaluate performance after second training session
net.print_accuracy(test_set_images, test_set_labels)
net.calculate_average_cost(test_set_images, test_set_labels)