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165 lines
7.4 KiB
165 lines
7.4 KiB
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# TODO: define statistical functions of a tensor
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from ..fluid.layers import mean #DEFINE_ALIAS
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from ..fluid.layers import reduce_mean #DEFINE_ALIAS
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__all__ = ['mean', 'reduce_mean', 'std', 'var']
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import numpy as np
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from ..fluid.layer_helper import LayerHelper
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from ..fluid.framework import in_dygraph_mode
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from ..fluid import layers
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from .search import where
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from ..fluid.data_feeder import convert_dtype, check_variable_and_dtype, check_type, check_dtype
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def var(input, axis=None, keepdim=False, unbiased=True, out=None, name=None):
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"""
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:alias_main: paddle.var
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:alias: paddle.var,paddle.tensor.var,paddle.tensor.stat.var
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Computes the variance of the input Variable's elements along the specified
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axis.
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Args:
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input (Variable): The input Variable to be computed variance, with data
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type float32 and float64 supported.
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axis (list|int, optional): The axis along which the variance is computed.
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If `None`, compute the variance over all elements of :attr:`input`
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and return a Variable with a single element, otherwise it must be in
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the range :math:`[-rank(input), rank(input))`. If :math:`axis[i] < 0`,
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the axis to compute is :math:`rank(input) + axis[i]`.
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keepdim (bool, optional): Whether to reserve the reduced dimensions in
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the output Variable. The dimensions in :attr:`axis` will be squeezed
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and the result Variable will have :attr:`len(axis)` fewer dimensions
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than the :attr:`input` unless :attr:`keepdim` is true, default False.
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unbiased (bool, optional): Whether to compute variance via the unbiased
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estimator, in which the divisor used in the computation is
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:math:`N - 1`, where :math:`N` represents the number of elements
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along :attr:`axis`, otherwise the divisor is :math:`N`. Default True.
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out (Variable, optional): Alternate output Variable to store the result
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variance. Default None.
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name (str, optional): The name for this layer. Normally there is no
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need for user to set this property. For more information, please
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refer to :ref:`api_guide_Name`. Default None.
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Returns:
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Variable: The result variance with the same dtype as :attr:`input`.
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If :attr:`out = None`, returns a new Variable containing the
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variance, otherwise returns a reference to the output Variable.
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Examples:
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.. code-block:: python
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import numpy as np
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import paddle
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import paddle.fluid.dygraph as dg
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a = np.array([[1.0, 2.0], [3.0, 4.0]]).astype("float32")
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with dg.guard():
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data = dg.to_variable(a)
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variance = paddle.var(data, axis=[1])
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print(variance.numpy())
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# [0.5 0.5]
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"""
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dtype = convert_dtype(input.dtype)
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if dtype not in ["float32", "float64"]:
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raise ValueError("Layer tensor.var() only supports floating-point "
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"dtypes, but received {}.".format(dtype))
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rank = len(input.shape)
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axes = axis if axis != None and axis != [] else range(rank)
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axes = [e if e >= 0 else e + rank for e in axes]
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inp_shape = input.shape if in_dygraph_mode() else layers.shape(input)
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mean = layers.reduce_mean(input, dim=axis, keep_dim=True, name=name)
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tmp = layers.reduce_mean(
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(input - mean)**2, dim=axis, keep_dim=keepdim, name=name)
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if unbiased:
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n = 1
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for i in axes:
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n *= inp_shape[i]
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if not in_dygraph_mode():
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n = layers.cast(n, dtype)
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zero_const = layers.fill_constant(shape=[1], dtype=dtype, value=0.0)
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factor = where(n > 1.0, n / (n - 1.0), zero_const)
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else:
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factor = n / (n - 1.0) if n > 1.0 else 0.0
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tmp *= factor
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if out:
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layers.assign(input=tmp, output=out)
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return out
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else:
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return tmp
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def std(input, axis=None, keepdim=False, unbiased=True, out=None, name=None):
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"""
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:alias_main: paddle.std
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:alias: paddle.std,paddle.tensor.std,paddle.tensor.stat.std
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Computes the standard-deviation of the input Variable's elements along the specified
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axis.
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Args:
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input (Variable): The input Variable to be computed standard-deviation, with data
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type float32 and float64 supported.
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axis (list|int, optional): The axis along which the standard-deviation is computed.
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If `None`, compute the standard-deviation over all elements of :attr:`input`
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and return a Variable with a single element, otherwise it must be in
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the range :math:`[-rank(input), rank(input))`. If :math:`axis[i] < 0`,
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the axis to compute is :math:`rank(input) + axis[i]`.
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keepdim (bool, optional): Whether to reserve the reduced dimensions in
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the output Variable. The dimensions in :attr:`axis` will be squeezed
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and the result Variable will have :attr:`len(axis)` fewer dimensions
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than the :attr:`input` unless :attr:`keepdim` is true, default False.
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unbiased (bool, optional): Whether to compute standard-deviation via the unbiased
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estimator, in which the divisor used in the computation is
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:math:`N - 1`, where :math:`N` represents the number of elements
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along :attr:`axis`, otherwise the divisor is :math:`N`. Default True.
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out (Variable, optional): Alternate output Variable to store the result
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standard-deviation . Default None.
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name (str, optional): The name for this layer. Normally there is no
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need for user to set this property. For more information, please
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refer to :ref:`api_guide_Name`. Default None.
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Returns:
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Variable: The result standard-deviation with the same dtype as :attr:`input`.
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If :attr:`out = None`, returns a new Variable containing the
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standard-deviation , otherwise returns a reference to the output Variable.
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Examples:
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.. code-block:: python
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import paddle
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import paddle.fluid as fluid
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# x is a Tensor variable with following elements:
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# [[0.2, 0.3, 0.5, 0.9]
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# [0.1, 0.2, 0.6, 0.7]]
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# Each example is followed by the corresponding output tensor.
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x = fluid.data(name='x', shape=[2, 4], dtype='float32')
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paddle.std(x) # [0.28252685]
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paddle.std(x, axis=[0]) # [0.0707107, 0.07071075, 0.07071064, 0.1414217]
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paddle.std(x, axis=[-1]) # [0.30956957, 0.29439208]
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"""
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check_variable_and_dtype(input, 'input', ['float32', 'float64'], 'std')
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tmp = var(input, axis=axis, keepdim=keepdim, unbiased=unbiased, name=name)
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tmp = layers.sqrt(tmp)
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if out is not None:
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layers.assign(input=tmp, output=out)
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return out
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else:
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return tmp
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