MurmurHash3
Description
The underlying implementation is MurmurHash3_x86_32 generating low latency 32bits hash suitable for implementing lookup tables, Bloom filters, count min sketch or feature hashing.
Documentation illustration (illustration unavailable in the archive).
Input parameters
specified_outputs_name : array, this parameter lets you manually assign custom names to the output tensors of a node.
X (heterogeneous) – T1 : object, an input tensor to hash.
Parameters : cluster,
positive : enum, output type.
Default value “uint32”.
seed : integer, seed for the hashing algorithm, unsigned 32-bit integer.
Default value “0”.
training? : boolean, whether the layer is in training mode (can store data for backward).
Default value “True”.
lda coeff : float, defines the coefficient by which the loss derivative will be multiplied before being sent to the previous layer (since during the backward run we go backwards).
Default value “1”.
name (optional) : string, name of the node.
Documentation illustration (illustration unavailable in the archive).
Output parameters
Y (heterogeneous) – T2 : object, 32-bit hash value.
Type Constraints
T1 in (tensor(uint32), tensor(int32), tensor(uint64), tensor(int64), tensor(float), tensor(double), tensor(string)) : Constrain input type to unsigned or signed 32-bit integer tensor, or string tensor. It should be utf-8 encoded if using unicode.
T2 in (tensor(uint32), tensor(int32)) : Constrain output type to unsigned and signed 32-bit integer tensor.
Example
All these exemples are snippets PNG, you can drop these Snippet onto the block diagram and get the depicted code added to your VI (Do not forget to install Deep Learning library to run it).