tf.contrib.framework.assert_scalar_int(tensor) Assert tensor is 0-D, of type tf.int32 or tf.int64. Args: tensor: Tensor to test. Returns: tensor, for chaining. Raises: ValueError: if tensor is not 0-D, of type tf.int32 or tf.int64.

tf.contrib.bayesflow.stochastic_tensor.InverseGammaWithSoftplusAlphaBetaTensor.graph

tf.contrib.distributions.Multinomial.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.

class tf.contrib.bayesflow.stochastic_tensor.BernoulliWithSigmoidPTensor BernoulliWithSigmoidPTensor is a StochasticTensor backed by the distribution BernoulliWithSigmoidP.

tf.contrib.bayesflow.stochastic_tensor.ExponentialWithSoftplusLamTensor.entropy(name='entropy')

Status tensorflow::EnvWrapper::GetRegisteredFileSystemSchemes(std::vector< string > *schemes) override Returns the file system schemes registered for this Env .

tf.contrib.bayesflow.stochastic_tensor.WishartCholeskyTensor.loss(final_loss, name='Loss')

tf.floor(x, name=None) Returns element-wise largest integer not greater than x. Args: x: A Tensor. Must be one of the following types: half, float32, float64. name: A name for the operation (optional). Returns: A Tensor. Has the same type as x.

tf.contrib.distributions.DirichletMultinomial.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.training.SequenceQueueingStateSaver.batch_size