tf.contrib.learn.Estimator.__repr__()

tf.contrib.graph_editor.SubGraphView.__bool__() Allows for implicit boolean conversion.

tf.TensorArray.__init__(dtype, size=None, dynamic_size=None, clear_after_read=None, tensor_array_name=None, handle=None, flow=None, infer_shape=True, name=None) Construct a new TensorArray or wrap an existing TensorArray handle. A note about the parameter name: The name of the TensorArray (even if passed in) is uniquified: each time a new TensorArray is created at runtime it is assigned its own name for the duration of the run. This avoids name collisions if a TensorArray is created within a w

tf.contrib.distributions.WishartFull.cholesky_input_output_matrices Boolean indicating if Tensor input/outputs are Cholesky factorized.

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagWithSoftplusStDevTensor.mean(name='mean')

class tf.contrib.bayesflow.stochastic_tensor.StochasticTensor StochasticTensor is a BaseStochasticTensor backed by a distribution.

class tf.errors.UnknownError Unknown error. An example of where this error may be returned is if a Status value received from another address space belongs to an error-space that is not known to this address space. Also errors raised by APIs that do not return enough error information may be converted to this error.

tf.contrib.distributions.Categorical.cdf(value, name='cdf') Cumulative distribution function. Given random variable X, the cumulative distribution function cdf is: cdf(x) := P[X <= x] Args: value: float or double Tensor. name: The name to give this op. Returns: cdf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

tf.contrib.distributions.Categorical.param_shapes(cls, sample_shape, name='DistributionParamShapes') Shapes of parameters given the desired shape of a call to sample(). Subclasses should override static method _param_shapes. Args: sample_shape: Tensor or python list/tuple. Desired shape of a call to sample(). name: name to prepend ops with. Returns: dict of parameter name to Tensor shapes.

bool tensorflow::PartialTensorShape::IsCompatibleWith(const PartialTensorShape &shape) const Return true iff the ranks match, and if the dimensions all either match or one is unknown.