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fix doc, updates API documents of uniform_random and uniform_random_batch_size_like (#20316)

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revert-20712-fix_depthwise_conv
silingtong123 6 years ago committed by Tao Luo
parent b521992041
commit d5aa2dd818
2 changed files with 64 additions and 29 deletions
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4
paddle/fluid/API.spec
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@ -253,7 +253,7 @@ paddle.fluid.layers.elementwise_min (ArgSpec(args=['x', 'y', 'axis', 'act', 'nam
paddle.fluid.layers.elementwise_pow (ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None)), ('document', '6fc5d7492830d60c7fa61b3bc8f0d7e7'))
paddle.fluid.layers.elementwise_mod (ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None)), ('document', '4101ee1f9280f00dce54054ccc434890'))
paddle.fluid.layers.elementwise_floordiv (ArgSpec(args=['x', 'y', 'axis', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, None, None)), ('document', '67e6101c31314d4082621e8e443cfb68'))
paddle.fluid.layers.uniform_random_batch_size_like (ArgSpec(args=['input', 'shape', 'dtype', 'input_dim_idx', 'output_dim_idx', 'min', 'max', 'seed'], varargs=None, keywords=None, defaults=('float32', 0, 0, -1.0, 1.0, 0)), ('document', 'cfa120e583cd4a5bfa120c8a26f98a28'))
paddle.fluid.layers.uniform_random_batch_size_like (ArgSpec(args=['input', 'shape', 'dtype', 'input_dim_idx', 'output_dim_idx', 'min', 'max', 'seed'], varargs=None, keywords=None, defaults=('float32', 0, 0, -1.0, 1.0, 0)), ('document', '571c963b9b49f1a323d2ea2343f10dd2'))
paddle.fluid.layers.gaussian_random (ArgSpec(args=['shape', 'mean', 'std', 'seed', 'dtype'], varargs=None, keywords=None, defaults=(0.0, 1.0, 0, 'float32')), ('document', 'dd4ddb66c78a2564e5d1e0e345d8286f'))
paddle.fluid.layers.sampling_id (ArgSpec(args=['x', 'min', 'max', 'seed', 'dtype'], varargs=None, keywords=None, defaults=(0.0, 1.0, 0, 'float32')), ('document', '9ac9bdc45be94494d8543b8cec5c26e0'))
paddle.fluid.layers.gaussian_random_batch_size_like (ArgSpec(args=['input', 'shape', 'input_dim_idx', 'output_dim_idx', 'mean', 'std', 'seed', 'dtype'], varargs=None, keywords=None, defaults=(0, 0, 0.0, 1.0, 0, 'float32')), ('document', '2aed0f546f220364fb1da724a3176f74'))
@ -307,7 +307,7 @@ paddle.fluid.layers.filter_by_instag (ArgSpec(args=['ins', 'ins_tag', 'filter_ta
paddle.fluid.layers.shard_index (ArgSpec(args=['input', 'index_num', 'nshards', 'shard_id', 'ignore_value'], varargs=None, keywords=None, defaults=(-1,)), ('document', '3c6b30e9cd57b38d4a5fa1ade887f779'))
paddle.fluid.layers.hard_swish (ArgSpec(args=['x', 'threshold', 'scale', 'offset', 'name'], varargs=None, keywords=None, defaults=(6.0, 6.0, 3.0, None)), ('document', 'bd763b9ca99239d624c3cb4626e3627a'))
paddle.fluid.layers.mse_loss (ArgSpec(args=['input', 'label'], varargs=None, keywords=None, defaults=None), ('document', '88b967ef5132567396062d5d654b3064'))
paddle.fluid.layers.uniform_random (ArgSpec(args=['shape', 'dtype', 'min', 'max', 'seed'], varargs=None, keywords=None, defaults=('float32', -1.0, 1.0, 0)), ('document', '126ede8ce0e751244b1b54cd359c89d7'))
paddle.fluid.layers.uniform_random (ArgSpec(args=['shape', 'dtype', 'min', 'max', 'seed'], varargs=None, keywords=None, defaults=('float32', -1.0, 1.0, 0)), ('document', '34e7c1ff0263baf9551000b6bb3bc47e'))
paddle.fluid.layers.data (ArgSpec(args=['name', 'shape', 'append_batch_size', 'dtype', 'lod_level', 'type', 'stop_gradient'], varargs=None, keywords=None, defaults=(True, 'float32', 0, VarType.LOD_TENSOR, True)), ('document', '9d7806e31bdf727c1a23b8782a09b545'))
paddle.fluid.layers.read_file (ArgSpec(args=['reader'], varargs=None, keywords=None, defaults=None), ('document', 'd5b41c7b2df1b064fbd42dcf435268cd'))
paddle.fluid.layers.double_buffer (ArgSpec(args=['reader', 'place', 'name'], varargs=None, keywords=None, defaults=(None, None)), ('document', '556fa82daf62cbb0fb393f4125daba77'))

89
python/paddle/fluid/layers/nn.py
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@ -12602,28 +12602,64 @@ def uniform_random_batch_size_like(input,
max=1.0,
seed=0):
"""
${comment}
This OP initializes a variable with random values sampled from a
uniform distribution in the range [min, max). The input_dim_idx used to get the input dimension value which will be used to resize the output dimension.
.. code-block:: text
*Case 1:
Given:
input =[[0.946741 , 0.1357001 , 0.38086128]] # input.shape=[1,3]
shape=[2,4]
result.shape[output_dim_idx] = input.shape[input_dim_idx],
output_dim_idx = 0,
input_dim_idx = 0,
result.shape[0] = input.shape[0],
then:
result=[[ 0.3443427 , -0.23056602, 0.3477049 , 0.06139076]] # result.shape=[1,4]
*Case 2:
Given:
input =[[0.946741 , 0.1357001 , 0.38086128]] # input.shape=[1,3]
shape=[2,4]
input_dim_idx=1
output_dim_idx=1
result.shape[output_dim_idx] = input.shape[input_dim_idx],
output_dim_idx = 1,
input_dim_idx = 1,
result.shape[1] = input.shape[1],
then:
result=[[-0.23133647, -0.84195036, 0.21441269],
[-0.08774924, 0.25605237, -0.09403259]] # result.shape=[2,3]
Args:
input (Variable): ${input_comment}
shape (tuple|list): ${shape_comment}
input_dim_idx (Int): ${input_dim_idx_comment}
output_dim_idx (Int): ${output_dim_idx_comment}
min (Float): ${min_comment}
max (Float): ${max_comment}
seed (Int): ${seed_comment}
dtype(np.dtype|core.VarDesc.VarType|str): The type of data : float32, float_16, int etc
input (Variable): A Tensor. Supported data types: float32, float64.
shape (tuple|list): A python list or python tuple. The shape of the output Tensor, the data type is int.
input_dim_idx (int, optional): An index used to get the input dimension value which will be used to resize the output dimension. Default 0.
output_dim_idx (int, optional): An index used to indicate the specific dimension that will be replaced by corresponding input dimension value. Default 0.
min (float, optional): The lower bound on the range of random values to generate, the min is included in the range. Default -1.0.
max (float, optional): The upper bound on the range of random values to generate, the max is excluded in the range. Default 1.0.
seed (int, optional): Random seed used for generating samples. 0 means use a seed generated by the system.Note that if seed is not 0, this operator will always generate the same random numbers every time.
dtype(np.dtype|core.VarDesc.VarType|str, optional): The data type of output Tensor. Supported data types: float32, float64. Default float32.
Returns:
out (Variable): ${out_comment}
Variable: A Tensor of the specified shape filled with uniform_random values. The shape of the Tensor is determined by the shape parameter and the specified dimension of the input Tensor.
Examples:
.. code-block:: python
import paddle.fluid as fluid
import paddle.fluid.layers as layers
# example 1:
input = fluid.data(name="input", shape=[1, 3], dtype='float32')
out_1 = fluid.layers.uniform_random_batch_size_like(input, [2, 4]) # out_1.shape=[1, 4]
input = layers.data(name="input", shape=[13, 11], dtype='float32')
out = layers.uniform_random_batch_size_like(input, [-1, 11])
# example 2:
out_2 = fluid.layers.uniform_random_batch_size_like(input, [2, 4], input_dim_idx=1, output_dim_idx=1) # out_2.shape=[2, 3]
"""
helper = LayerHelper('uniform_random_batch_size_like', **locals())
@ -16982,8 +17018,8 @@ def mse_loss(input, label):
@templatedoc()
def uniform_random(shape, dtype='float32', min=-1.0, max=1.0, seed=0):
"""
This operator initializes a variable with random values sampled from a
uniform distribution. The random result is in set [min, max).
This OP initializes a variable with random values sampled from a
uniform distribution in the range [min, max).
Examples:
::
@ -16995,24 +17031,23 @@ def uniform_random(shape, dtype='float32', min=-1.0, max=1.0, seed=0):
result=[[0.8505902, 0.8397286]]
Args:
shape (list|tuple|Variable): The shape of the output tensor, the data type of the integer is int,
and if the shape type is list or tuple, its elements can be an integer
or a tensor with the shape [1], the data type of the tensor is int64.
If the shape type is Variable,it ia a 1D tensor, the data type of the tensor is int64.
dtype(np.dtype|core.VarDesc.VarType|str, optional): The data type of the output tensor, such as float32, float64.
shape (list|tuple|Variable): The shape of the output Tensor, if the shape is a list or tuple,
its elements can be an integer
or a Tensor with the shape [1], and the type of the Tensor is int64.
If the shape is a Variable, it is a 1-D Tensor, and the type of the Tensor is int64.
dtype(np.dtype|core.VarDesc.VarType|str, optional): The type of the output Tensor. Supported data types: float32, float64.
Default: float32.
min (float, optional): Minimum value of uniform random, It's a closed interval. Default -1.0.
max (float, optional): Maximun value of uniform random, It's an open interval. Default 1.0.
min (float, optional): The lower bound on the range of random values to generate, the min is included in the range. Default -1.0.
max (float, optional): The upper bound on the range of random values to generate, the max is excluded in the range. Default 1.0.
seed (int, optional): Random seed used for generating samples. 0 means use a
seed generated by the system. Note that if seed is not 0, this
operator will always generate the same random numbers every time.
Default 0.
Returns: a Tensor with randomly initialized results whose data type is determined by the dtype parameter
and whose dimension is determined by the shape parameter.
Return type: Variable
Returns:
Variable: A Tensor of the specified shape filled with uniform_random values.
Throw exception:
Raises:
TypeError: The shape type should be list or tupple or variable.
Examples:
@ -17031,7 +17066,7 @@ def uniform_random(shape, dtype='float32', min=-1.0, max=1.0, seed=0):
# example 3:
# attr shape is a Variable, the data type must be int64
var_shape = fluid.layers.data(name='var_shape',shape=[2],append_batch_size=False)
var_shape = fluid.data(name='var_shape', shape=[2], dtype="int64")
result_3 = fluid.layers.uniform_random(var_shape)
"""

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