From 557452e778da835ebf635e220e535308a40f5ac3 Mon Sep 17 00:00:00 2001
From: tensor-tang <tangjian03@baidu.com>
Date: Tue, 4 Jun 2019 19:23:28 +0800
Subject: [PATCH] update and polish hash op doc (#17809)

* update and polish hash op doc

test=develop

* update api spec

test=develop
---
 paddle/fluid/API.spec            |  2 +-
 python/paddle/fluid/layers/nn.py | 28 ++++++++++++++++++++++------
 2 files changed, 23 insertions(+), 7 deletions(-)

diff --git a/paddle/fluid/API.spec b/paddle/fluid/API.spec
index 8ce3e67cb43..6e7fdea742d 100644
--- a/paddle/fluid/API.spec
+++ b/paddle/fluid/API.spec
@@ -215,7 +215,7 @@ paddle.fluid.layers.affine_grid (ArgSpec(args=['theta', 'out_shape', 'name'], va
 paddle.fluid.layers.sequence_reverse (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '65c8362e48810b8226e311c5d046db51'))
 paddle.fluid.layers.affine_channel (ArgSpec(args=['x', 'scale', 'bias', 'data_layout', 'name', 'act'], varargs=None, keywords=None, defaults=(None, None, 'NCHW', None, None)), ('document', '9f303c67538e468a36c5904a0a3aa110'))
 paddle.fluid.layers.similarity_focus (ArgSpec(args=['input', 'axis', 'indexes', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '6f90d6ff76bf4f5e592332c1ef28494e'))
-paddle.fluid.layers.hash (ArgSpec(args=['input', 'hash_size', 'num_hash', 'name'], varargs=None, keywords=None, defaults=(1, None)), ('document', '97bf4353bb046a5629308a38f98ac204'))
+paddle.fluid.layers.hash (ArgSpec(args=['input', 'hash_size', 'num_hash', 'name'], varargs=None, keywords=None, defaults=(1, None)), ('document', 'da621ba1363e8f5fe7b702526bbae18f'))
 paddle.fluid.layers.grid_sampler (ArgSpec(args=['x', 'grid', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '5d16663e096d7f04954c70ce1cc5e195'))
 paddle.fluid.layers.log_loss (ArgSpec(args=['input', 'label', 'epsilon', 'name'], varargs=None, keywords=None, defaults=(0.0001, None)), ('document', 'af541e9263be61ce0e40df58d1b69294'))
 paddle.fluid.layers.add_position_encoding (ArgSpec(args=['input', 'alpha', 'beta', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'e399f9436fed5f7ff480d8532e42c937'))
diff --git a/python/paddle/fluid/layers/nn.py b/python/paddle/fluid/layers/nn.py
index 83ef37656b4..8d9343b9245 100644
--- a/python/paddle/fluid/layers/nn.py
+++ b/python/paddle/fluid/layers/nn.py
@@ -10594,8 +10594,8 @@ def hash(input, hash_size, num_hash=1, name=None):
 
         # shape [2, 2]
         input.data = [
-            [[1], [2]],
-            [[3], [4]],
+            [[1, 2],
+             [3, 4]],
         ]
 
         input.lod = [[0, 2]]
@@ -10612,8 +10612,8 @@ def hash(input, hash_size, num_hash=1, name=None):
 
         # shape [2, 4]
         output.data = [
-            [[9662], [9217], [1129], [8487]],
-            [[8310], [1327], [1654], [4567]],
+            [[9662, 9217, 1129, 8487],
+             [8310, 1327, 1654, 4567]],
         ]
 
         output.lod = [[0, 2]]
@@ -10632,8 +10632,24 @@ def hash(input, hash_size, num_hash=1, name=None):
     Examples:
        .. code-block:: python
 
-           x = fluid.layers.data(name="x", shape=[1], dtype='int32', lod_level=1)
-           out = fluid.layers.hash(input=x, num_hash=4, hash_size=1000)
+            import paddle.fluid as fluid
+            import paddle.fluid.layers as layers
+            import numpy as np
+
+            titles = fluid.layers.data(name='titles', shape=[1], dtype='int32', lod_level=1)
+            hash_r = fluid.layers.hash(name='hash_x', input=titles, num_hash=1, hash_size=1000)
+
+            place = fluid.core.CPUPlace()
+            exece = fluid.Executor(place)
+            exece.run(fluid.default_startup_program()) 
+
+            # Init Tensor
+            tensor = fluid.core.LoDTensor() 
+            tensor.set(np.random.randint(0, 10, (3, 1)).astype("int32"), place)
+            # Set LoD
+            tensor.set_recursive_sequence_lengths([[1, 1, 1]])
+
+            out = exece.run(feed={'titles': tensor}, fetch_list=[hash_r], return_numpy=False)
     """
     helper = LayerHelper('hash', **locals())
     out = helper.create_variable_for_type_inference(
-- 
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