tf.contrib.bayesflow.stochastic_tensor.WishartCholeskyTensor.input_dict

tf.contrib.learn.monitors.ValidationMonitor.end(session=None)

tf.sparse_to_dense(sparse_indices, output_shape, sparse_values, default_value=0, validate_indices=True, name=None) Converts a sparse representation into a dense tensor. Builds an array dense with shape output_shape such that # If sparse_indices is scalar dense[i] = (i == sparse_indices ? sparse_values : default_value) # If sparse_indices is a vector, then for each i dense[sparse_indices[i]] = sparse_values[i] # If sparse_indices is an n by d matrix, then for each i in [0, n) dense[sparse_ind

tf.contrib.learn.monitors.StopAtStep.__init__(num_steps=None, last_step=None) Create a StopAtStep monitor. This monitor requests stop after either a number of steps have been executed or a last step has been reached. Only of the two options can be specified. if num_steps is specified, it indicates the number of steps to execute after begin() is called. If instead last_step is specified, it indicates the last step we want to execute, as passed to the step_begin() call. Args: num_steps: Number

tf.sparse_maximum(sp_a, sp_b, name=None) Returns the element-wise max of two SparseTensors. Assumes the two SparseTensors have the same shape, i.e., no broadcasting. Example: sp_zero = ops.SparseTensor([[0]], [0], [7]) sp_one = ops.SparseTensor([[1]], [1], [7]) res = tf.sparse_maximum(sp_zero, sp_one).eval() # "res" should be equal to SparseTensor([[0], [1]], [0, 1], [7]). Args: sp_a: a SparseTensor operand whose dtype is real, and indices lexicographically ordered. sp_b: the other SparseTe

tf.segment_sum(data, segment_ids, name=None) Computes the sum along segments of a tensor. Read the section on Segmentation for an explanation of segments. Computes a tensor such that \(output_i = \sum_j data_j\) where sum is over j such that segment_ids[j] == i. Args: data: A Tensor. Must be one of the following types: float32, float64, int64, int32, uint8, uint16, int16, int8, complex64, complex128, qint8, quint8, qint32, half. segment_ids: A Tensor. Must be one of the following types: in

static Status tensorflow::TensorShapeUtils::MakeShape(const int32 *dims, int64 n, TensorShape *out) Returns a TensorShape whose dimensions are dims[0], dims[1], ..., dims[n-1].

tf.QueueBase.__init__(dtypes, shapes, names, queue_ref) Constructs a queue object from a queue reference. The two optional lists, shapes and names, must be of the same length as dtypes if provided. The values at a given index i indicate the shape and name to use for the corresponding queue component in dtypes. Args: dtypes: A list of types. The length of dtypes must equal the number of tensors in each element. shapes: Constraints on the shapes of tensors in an element: A list of shape tuples

tf.contrib.distributions.MultivariateNormalFull.parameters Dictionary of parameters used by this Distribution.

tf.contrib.bayesflow.stochastic_tensor.TransformedDistributionTensor.name