tf.contrib.graph_editor.bypass(sgv) Bypass the given subgraph by connecting its inputs to its outputs. Args: sgv: the subgraph view to be bypassed. This argument is converted to a subgraph using the same rules than the function subgraph.make_view. Note that sgv is modified in place. Returns: A tuple (sgv, detached_inputs) where: sgv is a new subgraph view of the bypassed subgraph; detached_inputs is a list of the created input placeholders. Raises: StandardError: if sgv cannot be converted

tf.contrib.learn.DNNClassifier.bias_ DEPRECATED FUNCTION THIS FUNCTION IS DEPRECATED. It will be removed after 2016-10-13. Instructions for updating: This method inspects the private state of the object, and should not be used

tf.contrib.distributions.BernoulliWithSigmoidP.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_sh

tf.contrib.distributions.Chi2.param_static_shapes(cls, sample_shape) param_shapes with static (i.e. TensorShape) shapes. Args: sample_shape: TensorShape or python list/tuple. Desired shape of a call to sample(). Returns: dict of parameter name to TensorShape. Raises: ValueError: if sample_shape is a TensorShape and is not fully defined.

tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.batch_shape(name='batch_shape') Shape of a single sample from a single event index as a 1-D Tensor. The product of the dimensions of the batch_shape is the number of independent distributions of this kind the instance represents. Args: name: name to give to the op Returns: batch_shape: Tensor.

tf.contrib.bayesflow.stochastic_tensor.ExponentialWithSoftplusLamTensor.distribution

class tf.contrib.bayesflow.variational_inference.ELBOForms Constants to control the elbo calculation. analytic_kl uses the analytic KL divergence between the variational distribution(s) and the prior(s). analytic_entropy uses the analytic entropy of the variational distribution(s). sample uses the sample KL or the sample entropy is the joint is provided. See elbo for what is used with default.

int tensorflow::Tensor::dims() const Convenience accessor for the tensor shape. For all shape accessors, see comments for relevant methods of TensorShape in tensor_shape.h.

tf.contrib.learn.monitors.BaseMonitor.step_begin(step) Callback before training step begins. You may use this callback to request evaluation of additional tensors in the graph. Args: step: int, the current value of the global step. Returns: List of Tensor objects or string tensor names to be run. Raises: ValueError: if we've already begun a step, or step < 0, or step > max_steps.

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