SYMBOL INDEX (286 symbols across 11 files)

FILE: core/generative.py
  class UnsupervisedMmdLoss (line 17) | class UnsupervisedMmdLoss(ls.Loss):
    method __init__ (line 24) | def __init__(self, **kwargs):
    method load_target (line 28) | def load_target(self, target, **kwargs):
    method _make_s_mat (line 39) | def _make_s_mat(self, n_pred, n_target):
    method compute_not_weighted_loss_and_grad (line 49) | def compute_not_weighted_loss_and_grad(self, pred, compute_grad=False):
    method get_name (line 72) | def get_name(self):
    method get_id (line 75) | def get_id(self):
    method __repr__ (line 78) | def __repr__(self):
  class UnsupervisedMmdLossMultiScale (line 84) | class UnsupervisedMmdLossMultiScale(ls.Loss):
    method __init__ (line 91) | def __init__(self, sigma=[1.0], scale_weight=None, **kwargs):
    method load_target (line 102) | def load_target(self, target, **kwargs):
    method _make_s_mat (line 113) | def _make_s_mat(self, n_pred, n_target):
    method compute_not_weighted_loss_and_grad (line 123) | def compute_not_weighted_loss_and_grad(self, pred, compute_grad=False):
    method get_name (line 154) | def get_name(self):
    method get_id (line 157) | def get_id(self):
    method __repr__ (line 160) | def __repr__(self):
  class LinearTimeUnsupervisedMmdLoss (line 166) | class LinearTimeUnsupervisedMmdLoss(ls.Loss):
    method __init__ (line 175) | def __init__(self, **kwargs):
    method load_target (line 181) | def load_target(self, target, **kwargs):
    method compute_not_weighted_loss_and_grad (line 192) | def compute_not_weighted_loss_and_grad(self, pred, compute_grad=False):
    method get_name (line 242) | def get_name(self):
    method get_id (line 245) | def get_id(self):
    method __repr__ (line 248) | def __repr__(self):
  class LinearTimeMinibatchUnsupervisedMmdLoss (line 254) | class LinearTimeMinibatchUnsupervisedMmdLoss(ls.Loss):
    method __init__ (line 265) | def __init__(self, **kwargs):
    method load_target (line 270) | def load_target(self, target, **kwargs):
    method _make_s_mat (line 281) | def _make_s_mat(self, n_pred, n_target):
    method compute_not_weighted_loss_and_grad (line 291) | def compute_not_weighted_loss_and_grad(self, pred, compute_grad=False):
    method get_name (line 329) | def get_name(self):
    method get_id (line 332) | def get_id(self):
    method __repr__ (line 335) | def __repr__(self):
  class RandomFeatureMmdLoss (line 341) | class RandomFeatureMmdLoss(ls.Loss):
    method __init__ (line 351) | def __init__(self, sigma=[1.0], scale_weight=None, n_features=1024, **...
    method _generate_random_matrix (line 365) | def _generate_random_matrix(self, n_features, n_dims, sigma):
    method _generate_random_features (line 374) | def _generate_random_features(self, x, w):
    method load_target (line 378) | def load_target(self, target, **kwargs):
    method compute_not_weighted_loss_and_grad (line 396) | def compute_not_weighted_loss_and_grad(self, pred, compute_grad=False):
    method get_name (line 421) | def get_name(self):
    method get_id (line 424) | def get_id(self):
    method __repr__ (line 427) | def __repr__(self):
  class PairMmdLossMultiScale (line 434) | class PairMmdLossMultiScale(ls.Loss):
    method __init__ (line 444) | def __init__(self, sigma=[1.0], scale_weight=None, **kwargs):
    method load_target (line 455) | def load_target(self, target, **kwargs):
    method _make_s_mat (line 466) | def _make_s_mat(self, n_pred, n_target):
    method compute_not_weighted_loss_and_grad (line 475) | def compute_not_weighted_loss_and_grad(self, pred, compute_grad=False):
    method get_name (line 506) | def get_name(self):
    method get_id (line 509) | def get_id(self):
    method __repr__ (line 512) | def __repr__(self):
  class DifferentiableKernelMmdLoss (line 522) | class DifferentiableKernelMmdLoss(ls.Loss):
    method __init__ (line 526) | def __init__(self, **kwargs):
    method load_target (line 529) | def load_target(self, target, **kwargs):
    method _make_s_mat (line 536) | def _make_s_mat(self, n_pred, n_target):
    method compute_not_weighted_loss_and_grad (line 555) | def compute_not_weighted_loss_and_grad(self, pred, compute_grad=False):
  class MultiScaleDifferentiableKernelMmdLoss (line 561) | class MultiScaleDifferentiableKernelMmdLoss(DifferentiableKernelMmdLoss):
    method __init__ (line 565) | def __init__(self, sigma=[1.0], scale_weight=None, **kwargs):
  class GaussianKernelMmdLoss (line 580) | class GaussianKernelMmdLoss(MultiScaleDifferentiableKernelMmdLoss):
    method __init__ (line 587) | def __init__(self, sigma=[1.0], scale_weight=None, **kwargs):
    method compute_not_weighted_loss_and_grad (line 591) | def compute_not_weighted_loss_and_grad(self, pred, compute_grad=False):
    method get_name (line 621) | def get_name(self):
    method get_id (line 624) | def get_id(self):
    method __repr__ (line 627) | def __repr__(self):
  class LaplacianKernelMmdLoss (line 633) | class LaplacianKernelMmdLoss(MultiScaleDifferentiableKernelMmdLoss):
    method __init__ (line 637) | def __init__(self, sigma=[1.0], scale_weight=None, **kwargs):
    method compute_not_weighted_loss_and_grad (line 641) | def compute_not_weighted_loss_and_grad(self, pred, compute_grad=False):
    method get_name (line 685) | def get_name(self):
    method get_id (line 688) | def get_id(self):
    method __repr__ (line 691) | def __repr__(self):
  class LaplacianL1KernelMmdLoss (line 697) | class LaplacianL1KernelMmdLoss(MultiScaleDifferentiableKernelMmdLoss):
    method __init__ (line 701) | def __init__(self, sigma=[1.0], scale_weight=None, **kwargs):
    method compute_not_weighted_loss_and_grad (line 705) | def compute_not_weighted_loss_and_grad(self, pred, compute_grad=False):
    method get_name (line 736) | def get_name(self):
    method get_id (line 739) | def get_id(self):
    method __repr__ (line 742) | def __repr__(self):
  class SqrtGaussianKernelMmdLoss (line 748) | class SqrtGaussianKernelMmdLoss(GaussianKernelMmdLoss):
    method __init__ (line 755) | def __init__(self, sigma=[1.0], scale_weight=None, **kwargs):
    method compute_not_weighted_loss_and_grad (line 759) | def compute_not_weighted_loss_and_grad(self, pred, compute_grad=False):
    method get_name (line 764) | def get_name(self):
    method get_id (line 767) | def get_id(self):
    method __repr__ (line 770) | def __repr__(self):
  class CpuDifferentiableKernelMmdLoss (line 776) | class CpuDifferentiableKernelMmdLoss(ls.Loss):
    method __init__ (line 780) | def __init__(self, **kwargs):
    method load_target (line 783) | def load_target(self, target, **kwargs):
    method _make_s_mat (line 790) | def _make_s_mat(self, n_pred, n_target):
    method compute_not_weighted_loss_and_grad (line 803) | def compute_not_weighted_loss_and_grad(self, pred, compute_grad=False):
  class CpuMultiScaleDifferentiableKernelMmdLoss (line 809) | class CpuMultiScaleDifferentiableKernelMmdLoss(CpuDifferentiableKernelMm...
    method __init__ (line 813) | def __init__(self, sigma=[1.0], scale_weight=None, **kwargs):
  class CpuGaussianKernelMmdLoss (line 828) | class CpuGaussianKernelMmdLoss(CpuMultiScaleDifferentiableKernelMmdLoss):
    method __init__ (line 835) | def __init__(self, sigma=[1.0], scale_weight=None, **kwargs):
    method compute_not_weighted_loss_and_grad (line 839) | def compute_not_weighted_loss_and_grad(self, pred, compute_grad=False):
    method get_name (line 870) | def get_name(self):
    method get_id (line 873) | def get_id(self):
    method __repr__ (line 876) | def __repr__(self):
  class CpuSqrtGaussianKernelMmdLoss (line 882) | class CpuSqrtGaussianKernelMmdLoss(CpuGaussianKernelMmdLoss):
    method __init__ (line 889) | def __init__(self, sigma=[1.0], scale_weight=None, **kwargs):
    method compute_not_weighted_loss_and_grad (line 893) | def compute_not_weighted_loss_and_grad(self, pred, compute_grad=False):
    method get_name (line 898) | def get_name(self):
    method get_id (line 901) | def get_id(self):
    method __repr__ (line 904) | def __repr__(self):
  class CpuPerExampleSqrtGaussianKernelMmdLoss (line 910) | class CpuPerExampleSqrtGaussianKernelMmdLoss(ls.Loss):
    method __init__ (line 914) | def __init__(self, sigma=[1.0], scale_weight=None, pred_per_example=1,...
    method load_target (line 919) | def load_target(self, target, **kwargs):
    method compute_not_weighted_loss_and_grad (line 928) | def compute_not_weighted_loss_and_grad(self, pred, compute_grad=False):
    method get_name (line 951) | def get_name(self):
    method get_id (line 954) | def get_id(self):
    method __repr__ (line 957) | def __repr__(self):
  class StochasticGenerativeNet (line 967) | class StochasticGenerativeNet(nn.NeuralNet):
    method __init__ (line 976) | def __init__(self, in_dim=0, out_dim=0):
    method sample_hiddens (line 979) | def sample_hiddens(self, n_samples):
    method generate_samples (line 986) | def generate_samples(self, z=None, n_samples=100, sample_batch_size=10...
  class StochasticGenerativeNetWithAutoencoder (line 1009) | class StochasticGenerativeNetWithAutoencoder(StochasticGenerativeNet):
    method __init__ (line 1014) | def __init__(self, in_dim=0, out_dim=0, autoencoder=None):
    method _generate_code_samples (line 1018) | def _generate_code_samples(self, z=None, n_samples=100, sample_batch_s...
    method generate_samples (line 1022) | def generate_samples(self, z=None, n_samples=100, sample_batch_size=10...
    method load_target (line 1026) | def load_target(self, target, *args, **kwargs):
  class StochasticGenerativeNetWithAutoencoderContainer (line 1033) | class StochasticGenerativeNetWithAutoencoderContainer(object):
    method __init__ (line 1038) | def __init__(self, net, autoencoder):
    method generate_samples (line 1042) | def generate_samples(self, z=None, n_samples=100, sample_batch_size=10...
  class SampleFilter (line 1046) | class SampleFilter(object):
    method __init__ (line 1050) | def __init__(self):
    method filter (line 1053) | def filter(self, x):
  class BlankSampleFilter (line 1062) | class BlankSampleFilter(SampleFilter):
    method filter (line 1066) | def filter(self, x):
  class ClassifierSampleFilter (line 1069) | class ClassifierSampleFilter(SampleFilter):
    method __init__ (line 1073) | def __init__(self, classifier, threshold, prev=None):
    method filter (line 1086) | def filter(self, x):
  class ClassifierSampleStochasticFilter (line 1099) | class ClassifierSampleStochasticFilter(SampleFilter):
    method __init__ (line 1104) | def __init__(self, classifier, prev=None):
    method filter (line 1111) | def filter(self, x):
  class StochasticGenerativeNetWithFilter (line 1128) | class StochasticGenerativeNetWithFilter(object):
    method __init__ (line 1135) | def __init__(self, net, sample_filter):
    method generate_samples (line 1143) | def generate_samples(self, z=None, n_samples=100):
  class StochasticGenerativeNetLearner (line 1163) | class StochasticGenerativeNetLearner(learner.Learner):
    method __init__ (line 1167) | def __init__(self, net):
    method load_data (line 1175) | def load_data(self, x_train):
    method load_train_target (line 1178) | def load_train_target(self):
    method sample_hiddens (line 1181) | def sample_hiddens(self):
    method f_and_fprime (line 1184) | def f_and_fprime(self, w):
    method create_minibatch_generator (line 1196) | def create_minibatch_generator(self, minibatch_size):
    method f_and_fprime_minibatch (line 1200) | def f_and_fprime_minibatch(self, w):
    method train_stochastic_lbfgs (line 1219) | def train_stochastic_lbfgs(self, **kwargs):
    method f_info (line 1234) | def f_info(self, w):
    method _process_options (line 1270) | def _process_options(self, kwargs):
    method f_post_training (line 1293) | def f_post_training(self):
    method save_model (line 1298) | def save_model(self):
    method save_checkpoint (line 1302) | def save_checkpoint(self, label):
  class StochasticGenerativeNetLearnerAutoScale (line 1305) | class StochasticGenerativeNetLearnerAutoScale(learner.Learner):
    method __init__ (line 1310) | def __init__(self, net):
    method load_data (line 1322) | def load_data(self, x_train):
    method load_train_target (line 1325) | def load_train_target(self):
    method sample_hiddens (line 1328) | def sample_hiddens(self):
    method update_loss_scale (line 1331) | def update_loss_scale(self):
    method f_and_fprime (line 1355) | def f_and_fprime(self, w):
    method create_minibatch_generator (line 1376) | def create_minibatch_generator(self, minibatch_size):
    method f_and_fprime_minibatch (line 1380) | def f_and_fprime_minibatch(self, w):
    method train_stochastic_lbfgs (line 1404) | def train_stochastic_lbfgs(self, **kwargs):
    method f_info (line 1419) | def f_info(self, w):
    method _process_options (line 1455) | def _process_options(self, kwargs):
    method f_post_training (line 1490) | def f_post_training(self):
    method save_model (line 1495) | def save_model(self):
    method save_checkpoint (line 1499) | def save_checkpoint(self, label):

FILE: core/kernels.py
  function safe_diag (line 10) | def safe_diag(x):
  class Kernel (line 21) | class Kernel(object):
    method __init__ (line 22) | def __init__(self):
    method compute_kernel_matrix (line 25) | def compute_kernel_matrix(self, x):
    method compute_kernel_transformation (line 33) | def compute_kernel_transformation(self, x_base, x_new):
    method get_name (line 44) | def get_name(self):
  class GaussianKernel (line 47) | class GaussianKernel(Kernel):
    method __init__ (line 48) | def __init__(self, sigma):
    method compute_kernel_matrix (line 51) | def compute_kernel_matrix(self, x):
    method compute_kernel_transformation (line 58) | def compute_kernel_transformation(self, x_base, x_new):
    method get_name (line 67) | def get_name(self):
  class EuclideanKernel (line 70) | class EuclideanKernel(Kernel):
    method __init__ (line 71) | def __init__(self):
    method compute_kernel_matrix (line 74) | def compute_kernel_matrix(self, x):
    method compute_kernel_transformation (line 81) | def compute_kernel_transformation(self, x_base, x_new):
  class CPUGaussianKernel (line 91) | class CPUGaussianKernel(Kernel):
    method __init__ (line 92) | def __init__(self, sigma):
    method compute_kernel_matrix (line 95) | def compute_kernel_matrix(self, x):
  class LinearKernel (line 98) | class LinearKernel(Kernel):
    method compute_kernel_matrix (line 99) | def compute_kernel_matrix(self, x):
    method compute_kernel_transformation (line 103) | def compute_kernel_transformation(self, x_base, x_new):
    method get_name (line 109) | def get_name(self):
  class CosineKernel (line 112) | class CosineKernel(Kernel):
    method compute_kernel_matrix (line 113) | def compute_kernel_matrix(self, x):
    method compute_kernel_transformation (line 120) | def compute_kernel_transformation(self, x_base, x_new):

FILE: core/util.py
  function to_garray (line 10) | def to_garray(x):
  function to_nparray (line 13) | def to_nparray(x):
  function to_one_of_K (line 16) | def to_one_of_K(t, K=None):
  function to_plus_minus_of_K (line 27) | def to_plus_minus_of_K(t, K=None):

FILE: dataio/mnist.py
  function load_raw_data (line 20) | def load_raw_data():
  function load_data (line 29) | def load_data():
  function load_labeled_data (line 54) | def load_labeled_data(n_val=5000):

FILE: dataio/tfd.py
  function _load_raw_data (line 13) | def _load_raw_data(image_size=48):
  function get_fixed_rand_permutation (line 17) | def get_fixed_rand_permutation(size, seed=1):
  class TFD (line 25) | class TFD(object):
    method __init__ (line 26) | def __init__(self, image_size=48):
    method get_fold (line 33) | def get_fold(self, fold, set_name, center=False, scale=False):
    method get_proper_fold (line 62) | def get_proper_fold(self, fold, set_name, center=False, scale=False):
  function load_fold (line 100) | def load_fold(fold, set_name, center=False, scale=False, image_size=48):
  function load_proper_fold (line 109) | def load_proper_fold(fold, set_name, center=False, scale=False, image_si...

FILE: eval_mmd_generative_model.py
  function load_tfd_fold (line 14) | def load_tfd_fold(fold=0):
  function linear_classifier_discrimination (line 30) | def linear_classifier_discrimination(model, data, C_range=[1], verbose=T...
  function eval_filter_thresholds (line 68) | def eval_filter_thresholds(model, data, thres_range=np.arange(0, 0.9, 0....
  function get_filtered_model (line 93) | def get_filtered_model(net, data):
  function test_single_filter_old (line 99) | def test_single_filter_old(net, data, base_samples, base_classifier, thr...
  function test_single_filter (line 125) | def test_single_filter(net, data, threshold, base_samples=None, base_cla...
  function log_exp_sum_1d (line 128) | def log_exp_sum_1d(x):
  function log_exp_sum (line 140) | def log_exp_sum(x, axis=1):
  class KDE (line 147) | class KDE(object):
    method __init__ (line 151) | def __init__(self, data, sigma):
    method _log_likelihood (line 160) | def _log_likelihood(self, data):
    method log_likelihood (line 163) | def log_likelihood(self, data, batch_size=1000):
    method likelihood (line 178) | def likelihood(self, data):
    method average_likelihood (line 184) | def average_likelihood(self, data):
    method average_log_likelihood (line 187) | def average_log_likelihood(self, data, batch_size=1000):
    method average_std_log_likelihood (line 190) | def average_std_log_likelihood(self, data, batch_size=1000):
    method average_se_log_likelihood (line 194) | def average_se_log_likelihood(self, data, batch_size=1000):
  class AlternativeKDE (line 198) | class AlternativeKDE(object):
    method __init__ (line 202) | def __init__(self, data, sigma):
    method _compute_log_prob (line 208) | def _compute_log_prob(self, data, batch_size=1000):
    method likelihood (line 222) | def likelihood(self, data):
    method average_likelihood (line 228) | def average_likelihood(self, data):
    method log_likelihood (line 231) | def log_likelihood(self, data):
    method average_log_likelihood (line 235) | def average_log_likelihood(self, data):
  function kde_evaluation (line 239) | def kde_evaluation(test_data, samples, sigma_range=np.arange(0.1, 0.3, 0...
  function kde_evaluation_tfd (line 254) | def kde_evaluation_tfd(test_data, samples, sigma_range=np.arange(0.05, 0...
  function kde_evaluation_all_folds (line 257) | def kde_evaluation_all_folds(test_data, samples, sigma_range=np.arange(0...
  function generate_fold_samples (line 274) | def generate_fold_samples(net, fold_model_format, ae=None, fold_ae_forma...
  function get_fold_data (line 285) | def get_fold_data(set_name, n_folds=5):
  function kde_eval_mnist (line 297) | def kde_eval_mnist(net, test_data, n_samples=10000, sigma_range=np.arang...
  function kde_eval_tfd (line 317) | def kde_eval_tfd(net, test_data_all_folds, n_samples=10000, sigma_range=...

FILE: generate_sample_figures.py
  function get_mnist_input_space_model (line 28) | def get_mnist_input_space_model():
  function get_mnist_code_space_model (line 33) | def get_mnist_code_space_model():
  function get_tfd_input_space_model (line 41) | def get_tfd_input_space_model():
  function get_tfd_code_space_model (line 46) | def get_tfd_code_space_model():
  function get_model (line 54) | def get_model(dataset='mnist', mode='input_space'):
  function generate_samples (line 66) | def generate_samples(dataset='mnist', mode='input_space'):
  function generate_all_samples (line 75) | def generate_all_samples():
  function load_train_data (line 81) | def load_train_data(dataset='mnist'):
  function get_nearest_neighbor (line 90) | def get_nearest_neighbor(dataset='mnist', mode='input_space'):
  function get_all_nearest_neighbors (line 100) | def get_all_nearest_neighbors():
  function get_morphing_figure (line 106) | def get_morphing_figure(dataset='mnist', mode='input_space'):
  function get_all_morphing_figures (line 117) | def get_all_morphing_figures():

FILE: test.py
  function good_colored_str (line 28) | def good_colored_str(txt):
  function bad_colored_str (line 31) | def bad_colored_str(txt):
  function vec_str (line 34) | def vec_str(v):
  function test_vec_pair (line 41) | def test_vec_pair(v1, msg1, v2, msg2, error_thres=_GRAD_CHECK_EPS):
  function finite_difference_gradient (line 55) | def finite_difference_gradient(f, x):
  function fdiff_grad_generator (line 68) | def fdiff_grad_generator(net, x, t, add_noise=False, seed=None):
  function test_net_io (line 85) | def test_net_io(f_create, f_create_void):
  function test_databias_loss (line 104) | def test_databias_loss(loss_type, **kwargs):
  function create_databias_net (line 129) | def create_databias_net(dropout_rate):
  function test_databias_loss_with_net (line 135) | def test_databias_loss_with_net(add_noise, loss_type, **kwargs):
  function test_generative_mmd_loss (line 170) | def test_generative_mmd_loss(sigma=1):
  function test_generative_multi_scale_mmd_loss (line 193) | def test_generative_multi_scale_mmd_loss(sigma=[1, 10], scale_weight=None):
  function test_linear_time_mmd_loss (line 216) | def test_linear_time_mmd_loss(sigma=1.0, use_modified_loss=False, use_ab...
  function test_linear_time_minibatch_mmd_loss (line 240) | def test_linear_time_minibatch_mmd_loss(sigma=1.0, minibatch_size=100):
  function test_random_feature_mmd_loss (line 265) | def test_random_feature_mmd_loss(sigma=[1,10], scale_weight=[0.5, 1], n_...
  function test_random_feature_mmd_loss_approximation (line 290) | def test_random_feature_mmd_loss_approximation(sigma=[1,10], scale_weigh...
  function test_pair_mmd_loss_multiscale (line 321) | def test_pair_mmd_loss_multiscale(sigma=[1, 10], scale_weight=None):
  function test_diff_kernel_mmd_loss (line 345) | def test_diff_kernel_mmd_loss(sigma=[1], scale_weight=[1], loss_name=None):
  function test_diff_kernel_per_example_mmd_loss (line 372) | def test_diff_kernel_per_example_mmd_loss(sigma=[1], scale_weight=[1], p...
  function test_all_diff_kernel_per_example_mmd_loss (line 404) | def test_all_diff_kernel_per_example_mmd_loss():
  function test_all_diff_kernel_mmd_loss (line 439) | def test_all_diff_kernel_mmd_loss():
  function test_all_generative_mmd_loss (line 470) | def test_all_generative_mmd_loss():
  function run_all_tests (line 535) | def run_all_tests():

FILE: train.py
  function write_config (line 26) | def write_config(file_name, config):
  function cat_list (line 35) | def cat_list(lst):
  function load_tfd_fold (line 39) | def load_tfd_fold(fold=0):
  function load_tfd_all_folds (line 54) | def load_tfd_all_folds(set_name='val', n_folds=5):
  function mnist_mmd_input_space (line 62) | def mnist_mmd_input_space(n_hids=[10,64,256,256,1024], sigma=[2,5,10,20,...
  function mnist_mmd_code_space (line 165) | def mnist_mmd_code_space(
  function tfd_mmd_input_space (line 322) | def tfd_mmd_input_space(n_hids=[10,64,256,256,1024], sigma=[5,10,20,40,8...
  function tfd_mmd_code_space (line 425) | def tfd_mmd_code_space(

FILE: vistools.py
  function bwpatchview (line 9) | def bwpatchview(data, imsz, nrows, gridwidth=1, gridintensity=0, rowmajo...
  function cpatchview (line 56) | def cpatchview(data, imsz, nrows, gridwidth=1, gridintensity=0, rowmajor...
  function listpatchview (line 111) | def listpatchview(data, nrows, gridwidth=1, gridintensity=0, ax=None):
  function plot2dgaussian (line 165) | def plot2dgaussian(mu, sigma, npoints=100, linespec=None, linewidth=1, a...
  function intarray_to_rgb (line 190) | def intarray_to_rgb(x, cmap):
  function pil_png_cmap_to_dict (line 208) | def pil_png_cmap_to_dict(pil_palette):

FILE: visualize.py
  function nn_search (line 15) | def nn_search(samples, database, top_k=1, imsz=[28,28], orientation='hor...
  function view_checkpoints (line 53) | def view_checkpoints(model_dir, sigma, imsz=[28,28], figid=101):
  function generation_on_a_line (line 76) | def generation_on_a_line(net, n_points=100, imsz=[28,28], nrows=10, h_se...
  function generate_morphing_video (line 98) | def generate_morphing_video(net, h_seeds, n_points=100, imsz=[28,28], ou...
  function plot_dataset (line 121) | def plot_dataset(x, t, ax=None):
  function plot_decision_boundary (line 139) | def plot_decision_boundary(f, x_range, y_range, density, ax=None, **kwar...