tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.mode(name='mode') Mode.

tf.contrib.learn.DNNRegressor.__repr__()

tf.random_crop(value, size, seed=None, name=None) Randomly crops a tensor to a given size. Slices a shape size portion out of value at a uniformly chosen offset. Requires value.shape >= size. If a dimension should not be cropped, pass the full size of that dimension. For example, RGB images can be cropped with size = [crop_height, crop_width, 3]. Args: value: Input tensor to crop. size: 1-D tensor with size the rank of value. seed: Python integer. Used to create a random seed. See set_ra

tf.contrib.bayesflow.stochastic_tensor.DirichletTensor.dtype

tf.contrib.distributions.DirichletMultinomial.log_cdf(value, name='log_cdf') Log cumulative distribution function. Given random variable X, the cumulative distribution function cdf is: log_cdf(x) := Log[ P[X <= x] ] Often, a numerical approximation can be used for log_cdf(x) that yields a more accurate answer than simply taking the logarithm of the cdf when x << -1. Args: value: float or double Tensor. name: The name to give this op. Returns: logcdf: a Tensor of shape sample_sha

class tf.contrib.bayesflow.stochastic_tensor.PoissonTensor PoissonTensor is a StochasticTensor backed by the distribution Poisson.

tf.contrib.learn.monitors.StopAtStep.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.

class tf.contrib.bayesflow.stochastic_tensor.StudentTWithAbsDfSoftplusSigmaTensor StudentTWithAbsDfSoftplusSigmaTensor is a StochasticTensor backed by the distribution StudentTWithAbsDfSoftplusSigma.

tf.contrib.bayesflow.stochastic_tensor.DirichletTensor.input_dict

tf.contrib.graph_editor.reroute_a2b_inputs(sgv0, sgv1) Re-route all the inputs of sgv0 to sgv1 (see reroute_inputs).