if brick.name == 'encoder':
encoder = brick
if brick.name == 'decoder':
generator = brick
# Copy-pasted from https://github.com/fchollet/keras/blob/master/examples/variational_autoencoder.py
x_test, y_test = MNIST(("test", )).get_data(state=None,
request=range(10000))
a = T.matrix(u'features')
b = encoder.apply(a)
c = generator.apply(a)
encode = theano.function([a], [b])
generate = theano.function([a], [c])
# display a 2D plot of the digit classes in the latent space
x_test_encoded = encode(x_test.reshape((10000, 784)))[0]
y_test = y_test.reshape((10000, )).astype('uint8')
#import ipdb; ipdb.set_trace()
plt.figure(figsize=(6, 6))
plt.scatter(x_test_encoded[:, 0], x_test_encoded[:, 1], c=y_test)
plt.colorbar()
plt.show()
# display a 2D manifold of the digits
n = 15 # figure with 15x15 digits
digit_size = 28
figure = np.zeros((digit_size * n, digit_size * n))
# we will sample n points within [-15, 15] standard deviations
grid_x = np.linspace(-15, 15, n)
grid_y = np.linspace(-15, 15, n)
