images = mnist.train_images()
images = images.reshape(images.shape[0],
images.shape[1] * images.shape[2]) / 255
labels = mnist.train_labels()
labels_oh = np.array([np.zeros(10) for a in labels])
for oh, idx in zip(labels_oh, labels):
np.put(oh, idx, 1)
# nn = NeuralNetwork((images.shape[1], 64, 32, 32, 32, 10))
nn = NeuralNetwork(file="nn3.npz")
sessions = 20
for i in range(sessions):
train(nn)
print("session {0}/{1} complete".format(i + 1, sessions))
nn.save("nn3")
test_images = mnist.test_images()
test_images = test_images.reshape(
test_images.shape[0], test_images.shape[1] * test_images.shape[2]) / 255
test_labels = mnist.test_labels()
test_labels_oh = np.array([np.zeros(10) for a in test_labels])
for oh, idx in zip(test_labels_oh, test_labels):
np.put(oh, idx, 1)
print("network guessing correct digit {0}% of the time".format(
nn.evaluate_network(test_images, test_labels_oh, 1000) * 100))
# '''
from nn import NeuralNetwork
import numpy as np
x = np.array([[0, 0, 1], [0, 1, 0], [1, 0, 0]])
y = np.array([[0, 1, 0], [1, 0, 0], [0, 0, 1]])
nn = NeuralNetwork((3, 3))
def train(nn):
for i in range(100):
nn.train_batch(x, y, 0.2)
# train(nn)
# nn.save("nn.npz")
nn.load("nn.npz")
print("{0}".format(nn.evaluate_network(x, y)))
