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.distributions.Bernoulli.mean(name='mean') Mean.

tf.contrib.bayesflow.stochastic_tensor.ExponentialWithSoftplusLamTensor.distribution

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.

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalCholeskyTensor.name

tf.contrib.learn.LinearClassifier.__init__(feature_columns, model_dir=None, n_classes=2, weight_column_name=None, optimizer=None, gradient_clip_norm=None, enable_centered_bias=None, _joint_weight=False, config=None) Construct a LinearClassifier estimator object. Args: feature_columns: An iterable containing all the feature columns used by the model. All items in the set should be instances of classes derived from FeatureColumn. model_dir: Directory to save model parameters, graph and etc. Th

tf.contrib.learn.monitors.ExportMonitor.last_export_dir Returns the directory containing the last completed export. Returns: The string path to the exported directory. NB: this functionality was added on 2016/09/25; clients that depend on the return value may need to handle the case where this function returns None because the estimator being fitted does not yet return a value during export.

tf.contrib.learn.monitors.SummarySaver.epoch_begin(epoch) Begin epoch. Args: epoch: int, the epoch number. Raises: ValueError: if we've already begun an epoch, or epoch < 0.

tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.df Degrees of freedom in these Student's t distribution(s).

tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.__init__(mu, diag_large, v, diag_small=None, validate_args=False, allow_nan_stats=True, name='MultivariateNormalDiagPlusVDVT') Multivariate Normal distributions on R^k. For every batch member, this distribution represents k random variables (X_1,...,X_k), with mean E[X_i] = mu[i], and covariance matrix C_{ij} := E[(X_i - mu[i])(X_j - mu[j])] The user initializes this class by providing the mean mu, and a lightweight definition of C: C = S