tf.contrib.bayesflow.stochastic_tensor.SampleValue.pushed_above(unused_value_type)

class tf.contrib.bayesflow.stochastic_tensor.LaplaceWithSoftplusScaleTensor LaplaceWithSoftplusScaleTensor is a StochasticTensor backed by the distribution LaplaceWithSoftplusScale.

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

tf.contrib.bayesflow.stochastic_tensor.ExponentialWithSoftplusLamTensor.distribution

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.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

tf.contrib.distributions.Poisson.name Name prepended to all ops created by this Distribution.

tf.contrib.distributions.TransformedDistribution.mean(name='mean') Mean.