tf.contrib.learn.DNNRegressor.get_params(deep=True) Get parameters for this estimator. Args: deep: boolean, optional If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns: params : mapping of string to any Parameter names mapped to their values.

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagWithSoftplusStDevTensor.input_dict

tf.contrib.distributions.Binomial.logits Log-odds.

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

tf.contrib.distributions.ExponentialWithSoftplusLam.alpha Shape parameter.

static bool tensorflow::TensorShapeUtils::IsMatrixOrHigher(const TensorShape &shape)

tf.contrib.distributions.MultivariateNormalFull.is_continuous

tf.contrib.learn.monitors.StepCounter.every_n_step_begin(step) Callback before every n'th step begins. Args: step: int, the current value of the global step. Returns: A list of tensors that will be evaluated at this step.

tf.contrib.distributions.Multinomial.is_reparameterized

tf.contrib.distributions.Poisson.parameters Dictionary of parameters used by this Distribution.