tf.sparse_concat(concat_dim, sp_inputs, name=None, expand_nonconcat_dim=False) Concatenates a list of SparseTensor along the specified dimension. Concatenation is with respect to the dense versions of each sparse input. It is assumed that each inputs is a SparseTensor whose elements are ordered along increasing dimension number. If expand_nonconcat_dim is False, all inputs' shapes must match, except for the concat dimension. If expand_nonconcat_dim is True, then inputs' shapes are allowd to va

tf.assert_greater(x, y, data=None, summarize=None, message=None, name=None) Assert the condition x > y holds element-wise. Example of adding a dependency to an operation: with tf.control_dependencies([tf.assert_greater(x, y)]): output = tf.reduce_sum(x) Example of adding dependency to the tensor being checked: x = tf.with_dependencies([tf.assert_greater(x, y)], x) This condition holds if for every pair of (possibly broadcast) elements x[i], y[i], we have x[i] > y[i]. If both x and y

tf.contrib.graph_editor.get_walks_intersection_ops(forward_seed_ops, backward_seed_ops, forward_inclusive=True, backward_inclusive=True, within_ops=None, control_inputs=False, control_outputs=None, control_ios=None) Return the intersection of a foward and a backward walk. Args: forward_seed_ops: an iterable of operations from which the forward graph walk starts. If a list of tensors is given instead, the seed_ops are set to be the consumers of those tensors. backward_seed_ops: an iterable of

TTypes< T, NDIMS >::ConstTensor tensorflow::Tensor::bit_casted_shaped(gtl::ArraySlice< int64 > new_sizes) const Return the tensor data to an Eigen::Tensor with the new shape specified in new_sizes and cast to a new dtype T. Using a bitcast is useful for move and copy operations. The allowed bitcast is the only difference from shaped().

tf.contrib.bayesflow.stochastic_tensor.NormalTensor.clone(name=None, **dist_args)

string tensorflow::Tensor::SummarizeValue(int64 max_entries) const Render the first max_entries values in *this into a string.

Manages the partially known dimensions of a Tensor and their sizes. Member Details tensorflow::PartialTensorShape::PartialTensorShape() Construct an unknown PartialTensorShape. tensorflow::PartialTensorShape::PartialTensorShape(gtl::ArraySlice< int64 > dim_sizes) Construct a PartialTensorShape from the provided sizes. REQUIRES: dim_sizes[i] >= 0 tensorflow::PartialTensorShape::PartialTensorShape(std::initializer_list< int64 > dim_sizes) tensorflow::PartialTensorShape::PartialTens

tf.contrib.bayesflow.stochastic_tensor.TransformedDistributionTensor.name

tf.contrib.distributions.Exponential.pmf(value, name='pmf') Probability mass function. Args: value: float or double Tensor. name: The name to give this op. Returns: pmf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype. Raises: TypeError: if is_continuous.

tf.contrib.distributions.Categorical.log_pmf(value, name='log_pmf') Log probability mass function. Args: value: float or double Tensor. name: The name to give this op. Returns: log_pmf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype. Raises: TypeError: if is_continuous.