tf.contrib.bayesflow.stochastic_tensor.NormalWithSoftplusSigmaTensor.value_type

tf.contrib.bayesflow.stochastic_tensor.MultinomialTensor.name

tf.contrib.learn.monitors.LoggingTrainable.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.MultivariateNormalDiagWithSoftplusStDev.get_event_shape() Shape of a single sample from a single batch as a TensorShape. Same meaning as event_shape. May be only partially defined. Returns: event_shape: TensorShape, possibly unknown.

tf.contrib.bayesflow.stochastic_tensor.LaplaceWithSoftplusScaleTensor.graph

tf.contrib.learn.TensorFlowRNNRegressor.get_variable_value(name) Returns value of the variable given by name. Args: name: string, name of the tensor. Returns: Numpy array - value of the tensor.

tf.contrib.distributions.Bernoulli.get_batch_shape() Shape of a single sample from a single event index as a TensorShape. Same meaning as batch_shape. May be only partially defined. Returns: batch_shape: TensorShape, possibly unknown.

tf.contrib.distributions.Laplace.is_reparameterized

tf.contrib.distributions.BetaWithSoftplusAB.pmf(value, name='pmf') Probability mass function. Args: value: float or double Tensor. name: The name to give this op. Returns: pmf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype. Raises: TypeError: if is_continuous.

virtual Status tensorflow::Env::GetSymbolFromLibrary(void *handle, const char *symbol_name, void **symbol)=0