tf.contrib.bayesflow.stochastic_tensor.LaplaceWithSoftplusScaleTensor.clone(name=None, **dist_args)

tf.contrib.bayesflow.stochastic_tensor.NormalTensor.value(name='value')

tf.svd(tensor, compute_uv=True, full_matrices=False, name=None) Computes the singular value decompositions of one or more matrices. Computes the SVD of each inner matrix in tensor such that tensor[..., :, :] = u[..., :, :] * diag(s[..., :, :]) * transpose(v[..., :, :]) # a is a tensor. # s is a tensor of singular values. # u is a tensor of left singular vectors. # v is a tensor of right singular vectors. s, u, v = svd(a) s = svd(a, compute_uv=False) Args: matrix: Tensor of shape [..., M, N].

tf.contrib.distributions.Chi2WithAbsDf.is_continuous

tf.contrib.distributions.LaplaceWithSoftplusScale.sample(sample_shape=(), seed=None, name='sample') Generate samples of the specified shape. Note that a call to sample() without arguments will generate a single sample. Args: sample_shape: 0D or 1D int32 Tensor. Shape of the generated samples. seed: Python integer seed for RNG name: name to give to the op. Returns: samples: a Tensor with prepended dimensions sample_shape.

tf.contrib.learn.monitors.StepCounter.epoch_end(epoch) End epoch. Args: epoch: int, the epoch number. Raises: ValueError: if we've not begun an epoch, or epoch number does not match.

tf.contrib.learn.train(graph, output_dir, train_op, loss_op, global_step_tensor=None, init_op=None, init_feed_dict=None, init_fn=None, log_every_steps=10, supervisor_is_chief=True, supervisor_master='', supervisor_save_model_secs=600, keep_checkpoint_max=5, supervisor_save_summaries_steps=100, feed_fn=None, steps=None, fail_on_nan_loss=True, monitors=None, max_steps=None) Train a model. Given graph, a directory to write outputs to (output_dir), and some ops, run a training loop. The given trai

tf.contrib.bayesflow.stochastic_tensor.BinomialTensor.input_dict

tf.contrib.graph_editor.matcher.input_ops(*args) Add input matches.

class tf.contrib.bayesflow.variational_inference.ELBOForms Constants to control the elbo calculation. analytic_kl uses the analytic KL divergence between the variational distribution(s) and the prior(s). analytic_entropy uses the analytic entropy of the variational distribution(s). sample uses the sample KL or the sample entropy is the joint is provided. See elbo for what is used with default.