def visualize_weights(self, layer, imsize, layout):
"""
Displays the weights of a specified layer as images.
:param layer: the layer whose weights to display
:param imsize: the image size
:param layout: number of rows and columns for each page
:return: none
"""
util.disp_imdata(self.Ws[layer].get_value().T, imsize, layout)
plt.show(block=False)
def show_images(self, split):
"""
Displays the images in a given split.
:param split: string
"""
# get split
data_split = getattr(self, split, None)
if data_split is None:
raise ValueError('Invalid data split')
# display images
util.disp_imdata(data_split.x, self.image_size, [6, 10])
plt.show()
def show_images(self, split):
"""
Displays the images in a given split.
:param split: string
"""
# get split
data = getattr(self, split, None)
if data is None:
raise ValueError('Invalid data split')
# display images
x = np.stack([data.r, data.g, data.b], axis=2)
util.disp_imdata(x, self.image_size, [6, 10])
plt.show()
def show_images(self, split):
"""
Displays the images in a given split.
:param split: string
"""
# get split
data_split = getattr(self, split, None)
if data_split is None:
raise ValueError('Invalid data split')
# add a pixel at the bottom right
last_pixel = -np.sum(data_split.x, axis=1)
images = np.hstack([data_split.x, last_pixel[:, np.newaxis]])
# display images
util.disp_imdata(images, self.image_size, [6, 10])
plt.show()
def evaluate(model, split, n_samples=None):
"""
Evaluate a trained model.
:param model: the model to evaluate. Can be any made, maf, or real nvp
:param split: string, the data split to evaluate on. Must be 'trn', 'val' or 'tst'
:param n_samples: number of samples to generate from the model, or None for no samples
"""
assert is_data_loaded(), 'Dataset hasn\'t been loaded'
# choose which data split to evaluate on
data_split = getattr(data, split, None)
if data_split is None:
raise ValueError('Invalid data split')
if is_conditional(model):
# calculate log probability
logprobs = model.eval([data_split.y, data_split.x])
print('logprob(x|y) = {0:.2f} +/- {1:.2f}').format(
logprobs.mean(), 2 * logprobs.std() / np.sqrt(data_split.N))
# classify test set
logprobs = np.empty([data_split.N, data.n_labels])
for i in xrange(data.n_labels):
y = np.zeros([data_split.N, data.n_labels])
y[:, i] = 1
logprobs[:, i] = model.eval([y, data_split.x])
predict_label = np.argmax(logprobs, axis=1)
accuracy = (predict_label == data_split.labels).astype(float)
logprobs = scipy.misc.logsumexp(logprobs, axis=1) - np.log(
logprobs.shape[1])
print('logprob(x) = {0:.2f} +/- {1:.2f}'.format(
logprobs.mean(), 2 * logprobs.std() / np.sqrt(data_split.N)))
print('classification accuracy = {0:.2%} +/- {1:.2%}'.format(
accuracy.mean(), 2 * accuracy.std() / np.sqrt(data_split.N)))
# generate data conditioned on label
if n_samples is not None:
for i in xrange(data.n_labels):
# generate samples and sort according to log prob
y = np.zeros(data.n_labels)
y[i] = 1
samples = model.gen(y, n_samples)
lp_samples = model.eval([np.tile(y, [n_samples, 1]), samples])
lp_samples = lp_samples[np.logical_not(np.isnan(lp_samples))]
idx = np.argsort(lp_samples)
samples = samples[idx][::-1]
if data_name == 'mnist':
samples = (util.logistic(samples) -
data.alpha) / (1 - 2 * data.alpha)
elif data_name == 'bsds300':
samples = np.hstack(
[samples, -np.sum(samples, axis=1)[:, np.newaxis]])
elif data_name == 'cifar10':
samples = (util.logistic(samples) -
data.alpha) / (1 - 2 * data.alpha)
D = int(data.n_dims / 3)
r = samples[:, :D]
g = samples[:, D:2 * D]
b = samples[:, 2 * D:]
samples = np.stack([r, g, b], axis=2)
else:
raise ValueError('non-image dataset')
util.disp_imdata(samples, data.image_size, [5, 8])
else:
# calculate average log probability
logprobs = model.eval(data_split.x)
print('logprob(x) = {0:.2f} +/- {1:.2f}'.format(
logprobs.mean(), 2 * logprobs.std() / np.sqrt(data_split.N)))
# generate data
if n_samples is not None:
# generate samples and sort according to log prob
samples = model.gen(n_samples)
lp_samples = model.eval(samples)
lp_samples = lp_samples[np.logical_not(np.isnan(lp_samples))]
idx = np.argsort(lp_samples)
samples = samples[idx][::-1]
if data_name == 'mnist':
samples = (util.logistic(samples) -
data.alpha) / (1 - 2 * data.alpha)
elif data_name == 'bsds300':
samples = np.hstack(
[samples, -np.sum(samples, axis=1)[:, np.newaxis]])
elif data_name == 'cifar10':
samples = (util.logistic(samples) -
data.alpha) / (1 - 2 * data.alpha)
D = int(data.n_dims / 3)
r = samples[:, :D]
g = samples[:, D:2 * D]
b = samples[:, 2 * D:]
samples = np.stack([r, g, b], axis=2)
else:
raise ValueError('non-image dataset')
util.disp_imdata(samples, data.image_size, [5, 8])
plt.show()