hash

paddle.fluid.layers.hash(input, hash_size, num_hash=1, name=None)[source]

This OP hash the input to an integer less than the hash_size. The hash algorithm we used was xxHash - Extremely fast hash algorithm (https://github.com/Cyan4973/xxHash/tree/v0.6.5)

Parameters
  • input (Variable) – A Two-Dimensional LoDTensor with type int32, int64. Only support LoDTensor.

  • num_hash (int, optional) – The times of hash, default is 1.

  • name (str, optional) – The default value is None. Normally there is no need for user to set this property. For more information, please refer to Name.

Returns

A LoDTensor with the same data type as input.

Return type

Variable

Examples

import paddle.fluid as fluid
import numpy as np

place = fluid.core.CPUPlace()

x = fluid.data(name="x", shape=[1], dtype="int32", lod_level=1)
res = fluid.layers.hash(name="res",input=x, hash_size=1000, num_hash=4)

exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
in1 = np.array([[1,2],[3,4]]).astype("int32")
print(in1)
x_i = fluid.core.LoDTensor()
x_i.set(in1,place)
x_i.set_recursive_sequence_lengths([[0,2]])
res = exe.run(fluid.default_main_program(), feed={'x':x_i}, fetch_list=[res], return_numpy=False)
print(np.array(res[0]))
# [[[722]
#   [407]
#   [337]
#   [395]]
#  [[603]
#   [590]
#   [386]
#   [901]]]