ParameterizedTruncatedNormal

tensorflow C++ API

tensorflow::ops::ParameterizedTruncatedNormal

Outputs random values from a normal distribution.

Summary

The parameters may each be a

scalar which applies to the entire output, or a vector of length shape[0] which stores the parameters for each batch.

Arguments:

• scope: A Scope object
• shape: The shape of the output tensor. Batches are indexed by the 0th dimension.
• means: The mean parameter of each batch.
• stdevs: The standard deviation parameter of each batch. Must be greater than 0.
• minvals: The minimum cutoff. May be -infinity.
• maxvals: The maximum cutoff. May be +infinity, and must be more than the minval for each batch.

Optional attributes (see Attrs):

• seed: If either seed or seed2 are set to be non-zero, the random number generator is seeded by the given seed. Otherwise, it is seeded by a random seed.
• seed2: A second seed to avoid seed collision.

Returns:

• Output

  A matrix of shape num_batches x samples_per_batch, filled with random truncated normal values using the parameters for each row.

ParameterizedTruncatedNormal block

Source link : https://github.com/EXPNUNI/enuSpaceTensorflow/blob/master/enuSpaceTensorflow/tf_random.cpp

Argument:

• Scope scope : A Scope object (A scope is generated automatically each page. A scope is not connected.)
• Input shape: connect Input node.
• Input means : connect Input node.
• Input stdevs: connect Input node.
• Input means : connect Input node.
• Input minvals: connect Input node
• Input maxvals: connect Input node.
• ParameterizedTruncatedNormal ::Attrs attrs : Input attrs in value. ex) seed_ = 0;seed2_ = 0;

Return:

• Output output : Output object of ParameterizedTruncatedNormal class object.

Result:

• std::vector(Tensor) product_result : Returned object of executed result by calling session.

Using Method