tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagWithSoftplusStDevTensor.value_type

tf.TextLineReader.serialize_state(name=None) Produce a string tensor that encodes the state of a reader. Not all Readers support being serialized, so this can produce an Unimplemented error. Args: name: A name for the operation (optional). Returns: A string Tensor.

tf.contrib.learn.monitors.ValidationMonitor.post_step(step, session)

tf.contrib.learn.monitors.PrintTensor.set_estimator(estimator) A setter called automatically by the target estimator. If the estimator is locked, this method does nothing. Args: estimator: the estimator that this monitor monitors. Raises: ValueError: if the estimator is None.

tf.contrib.distributions.Distribution.is_reparameterized

tf.contrib.bayesflow.stochastic_tensor.ExponentialWithSoftplusLamTensor.loss(final_loss, name='Loss')

tf.contrib.distributions.InverseGamma.log_pdf(value, name='log_pdf') Log probability density function. Args: value: float or double Tensor. name: The name to give this op. Returns: log_prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype. Raises: TypeError: if not is_continuous.

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

tf.contrib.learn.RunConfig.__init__(master=None, task=None, num_ps_replicas=None, num_cores=0, log_device_placement=False, gpu_memory_fraction=1, cluster_spec=None, tf_random_seed=None, save_summary_steps=100, save_checkpoints_secs=600, keep_checkpoint_max=5, keep_checkpoint_every_n_hours=10000, job_name=None, is_chief=None, evaluation_master='') Constructor. If set to None, master, task, num_ps_replicas, cluster_spec, job_name, and is_chief are set based on the TF_CONFIG environment variable,

tf.contrib.distributions.Mixture.validate_args Python boolean indicated possibly expensive checks are enabled.