tf.cumprod(x, axis=0, exclusive=False, reverse=False, name=None) Compute the cumulative product of the tensor x along axis. By default, this op performs an inclusive cumprod, which means that the first element of the input is identical to the first element of the output: prettyprint tf.cumprod([a, b, c]) ==> [a, a * b, a * b * c] By setting the exclusive kwarg to True, an exclusive cumprod is performed instead: prettyprint tf.cumprod([a, b, c], exclusive=True) ==> [0, a, a * b] By settin

tf.cos(x, name=None) Computes cos of x element-wise. Args: x: A Tensor. Must be one of the following types: half, float32, float64, complex64, complex128. name: A name for the operation (optional). Returns: A Tensor. Has the same type as x.

tf.cross(a, b, name=None) Compute the pairwise cross product. a and b must be the same shape; they can either be simple 3-element vectors, or any shape where the innermost dimension is 3. In the latter case, each pair of corresponding 3-element vectors is cross-multiplied independently. Args: a: A Tensor. Must be one of the following types: float32, float64, int32, int64, uint8, int16, int8, uint16, half. A tensor containing 3-element vectors. b: A Tensor. Must have the same type as a. Anoth

tf.contrib.util.make_ndarray(tensor) Create a numpy ndarray from a tensor. Create a numpy ndarray with the same shape and data as the tensor. Args: tensor: A TensorProto. Returns: A numpy array with the tensor contents. Raises: TypeError: if tensor has unsupported type.

tf.contrib.util.stripped_op_list_for_graph(graph_def) Collect the stripped OpDefs for ops used by a graph. This function computes the stripped_op_list field of MetaGraphDef and similar protos. The result can be communicated from the producer to the consumer, which can then use the C++ function RemoveNewDefaultAttrsFromGraphDef to improve forwards compatibility. Args: graph_def: A GraphDef proto, as from graph.as_graph_def(). Returns: An OpList of ops used by the graph. Raises: ValueError:

tf.contrib.util.ops_used_by_graph_def(graph_def) Collect the list of ops used by a graph. Does not validate that the ops are all registered. Args: graph_def: A GraphDef proto, as from graph.as_graph_def(). Returns: A list of strings, each naming an op used by the graph.

tf.contrib.util.make_tensor_proto(values, dtype=None, shape=None) Create a TensorProto. Args: values: Values to put in the TensorProto. dtype: Optional tensor_pb2 DataType value. shape: List of integers representing the dimensions of tensor. Returns: A TensorProto. Depending on the type, it may contain data in the "tensor_content" attribute, which is not directly useful to Python programs. To access the values you should convert the proto back to a numpy ndarray with tensor_util.MakeNdarr

tf.contrib.util.constant_value(tensor) Returns the constant value of the given tensor, if efficiently calculable. This function attempts to partially evaluate the given tensor, and returns its value as a numpy ndarray if this succeeds. TODO(mrry): Consider whether this function should use a registration mechanism like gradients and ShapeFunctions, so that it is easily extensible. NOTE: If constant_value(tensor) returns a non-None result, it will no longer be possible to feed a different value

tf.contrib.training.stratified_sample(tensors, labels, target_probs, batch_size, init_probs=None, enqueue_many=False, queue_capacity=16, threads_per_queue=1, name=None) Stochastically creates batches based on per-class probabilities. This method discards examples. Internally, it creates one queue to amortize the cost of disk reads, and one queue to hold the properly-proportioned batch. See stratified_sample_unknown_dist for a function that performs stratified sampling with one queue per class

tf.contrib.training.stratified_sample_unknown_dist(tensors, labels, probs, batch_size, enqueue_many=False, queue_capacity=16, threads_per_queue=1, name=None) Stochastically creates batches based on per-class probabilities. NOTICE This sampler can be significantly slower than stratified_sample due to each thread discarding all examples not in its assigned class. This uses a number of threads proportional to the number of classes. See stratified_sample for an implementation that discards fewer e