From eb23a8146ae5b09bfaa8cedc23d18ce2afe91bb3 Mon Sep 17 00:00:00 2001 From: silingtong123 <35439432+silingtong123@users.noreply.github.com> Date: Fri, 1 Nov 2019 19:13:30 +0800 Subject: [PATCH] modify the uniform_random_cn.rst (#1580) Add the description and example of shape tensor supporting int32 --- doc/fluid/api_cn/layers_cn/uniform_random_cn.rst | 16 +++++++++++----- 1 file changed, 11 insertions(+), 5 deletions(-) diff --git a/doc/fluid/api_cn/layers_cn/uniform_random_cn.rst b/doc/fluid/api_cn/layers_cn/uniform_random_cn.rst index db5269a9b..20f19d1c8 100644 --- a/doc/fluid/api_cn/layers_cn/uniform_random_cn.rst +++ b/doc/fluid/api_cn/layers_cn/uniform_random_cn.rst @@ -16,7 +16,7 @@ uniform_random result=[[0.8505902, 0.8397286]] 参数： - - **shape** (list|tuple|Variable)-输出Tensor的维度，shape类型支持list，tuple，Variable。如果shape类型是list或者tuple，它的元素可以是整数或者形状为[1]的Tensor，其中整数的数据类型为int，Tensor的数据类型为int64。如果shape的类型是Variable，则是1D的Tensor，Tensor的数据类型为int64。 + - **shape** (list|tuple|Variable)-输出Tensor的维度，shape类型支持list，tuple，Variable。如果shape类型是list或者tuple，它的元素可以是整数或者形状为[1]的Tensor，其中整数的数据类型为int，Tensor的数据类型为int32或int64。如果shape的类型是Variable，则是1D的Tensor，Tensor的数据类型为int32或int64。 - **dtype** (np.dtype|core.VarDesc.VarType|str，可选) – 输出Tensor的数据类型，支持float32（默认）， float64。 - **min** (float，可选)-要生成的随机值范围的下限，min包含在范围中。支持的数据类型：float。默认值为-1.0。 - **max** (float，可选)-要生成的随机值范围的上限，max不包含在范围中。支持的数据类型：float。默认值为1.0。 @@ -46,16 +46,22 @@ uniform_random # example 2: # attr shape is a list which contains tensor Variable. dim_1 = fluid.layers.fill_constant([1],"int64",3) - result_2 = fluid.layers.uniform_random(shape=[dim_1, 5]) + dim_2 = fluid.layers.fill_constant([1],"int32",5) + result_2 = fluid.layers.uniform_random(shape=[dim_1, dim_2]) # example 3: - # attr shape is a Variable, the data type must be int64 - var_shape = fluid.data(name='var_shape', shape=[2]) + # attr shape is a Variable, the data type must be int32 or int64 + var_shape = fluid.data(name='var_shape', shape=[2], dtype="int64") result_3 = fluid.layers.uniform_random(var_shape) + var_shape_int32 = fluid.data(name='var_shape_int32', shape=[2], dtype="int32") + result_4 = fluid.layers.uniform_random(var_shape_int32) + shape_1 = np.array([3,4]).astype("int64") + shape_2 = np.array([3,4]).astype("int32") exe = fluid.Executor(fluid.CPUPlace()) exe.run(startup_program) - outs = exe.run(train_program, feed = {'var_shape':np.array([3,4])}, fetch_list=[result_1, result_2, result_3]) + outs = exe.run(train_program, feed = {'var_shape':shape_1, 'var_shape_int32':shape_2}, + fetch_list=[result_1, result_2, result_3, result_4]) -- GitLab