# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # TODO: define activation functions of neural network __all__ = [ 'ELU', 'GELU', 'Hardshrink', 'Hardswish', 'Tanh', 'Hardtanh', 'PReLU', 'ReLU', 'ReLU6', 'SELU', 'LeakyReLU', 'Sigmoid', 'Hardsigmoid', 'Softmax', 'Softplus', 'Softshrink', 'Softsign', 'Swish', 'Tanhshrink', 'ThresholdedReLU', 'LogSigmoid', 'LogSoftmax', 'Maxout', ] from ...fluid.dygraph import layers from ...fluid import core from ...fluid.framework import in_dygraph_mode from ...fluid.param_attr import ParamAttr from ...fluid.initializer import Constant from paddle.framework import get_default_dtype from .. import functional as F class ELU(layers.Layer): r""" ELU Activation. .. math:: ELU(x) = max(0, x) + min(0, \\alpha * (e^{x}-1)) Parameters: alpha (float, optional): The 'alpha' value of the ELU formulation. Default is 1.0. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Shape: - input: Tensor with any shape. - output: Tensor with the same shape as input. Examples: .. code-block:: python import paddle x = paddle.to_tensor([[-1. ,6.], [1., 15.6]]) m = paddle.nn.ELU(0.2) out = m(x) # [[-0.12642411 6. ] # [ 1. 15.6 ]] """ def __init__(self, alpha=1.0, name=None): super(ELU, self).__init__() self._alpha = alpha self._name = name def forward(self, x): return F.elu(x, self._alpha, self._name) class GELU(layers.Layer): r""" GELU Activation. If approximate is True .. math:: GELU(x) = 0.5 * x * (1 + tanh(\\sqrt{\\frac{2}{\\pi}} * (x + 0.044715x^{3}))) else .. math:: GELU(x) = 0.5 * x * (1 + erf(\\frac{x}{\\sqrt{2}})) Parameters: approximate (bool, optional): Wether to enable approximation. Default is False. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Shape: - input: Tensor with any shape. - output: Tensor with the same shape as input. Examples: .. code-block:: python import paddle import numpy as np x = paddle.to_tensor(np.array([[-1, 0.5],[1, 1.5]])) m = paddle.nn.GELU() out = m(x) # [-0.158655 0.345731 0.841345 1.39979] m = paddle.nn.GELU(True) out = m(x) # [-0.158808 0.345714 0.841192 1.39957] """ def __init__(self, approximate=False, name=None): super(GELU, self).__init__() self._approximate = approximate self._name = name def forward(self, x): return F.gelu(x, self._approximate, self._name) class Hardshrink(layers.Layer): r""" Hardshrink Activation .. math:: hardshrink(x)= \\left\\{ \\begin{aligned} &x, & & if \\ x > threshold \\\\ &x, & & if \\ x < -threshold \\\\ &0, & & if \\ others \\end{aligned} \\right. Parameters: threshold (float, optional): The value of threshold for hardthrink. Default is 0.5 name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Shape: - input: Tensor with any shape. - output: Tensor with the same shape as input. Examples: .. code-block:: python import paddle x = paddle.to_tensor([-1, 0.3, 2.5]) m = paddle.nn.Hardshrink() out = m(x) # [-1., 0., 2.5] """ def __init__(self, threshold=0.5, name=None): super(Hardshrink, self).__init__() self._threshold = threshold self._name = name def forward(self, x): return F.hardshrink(x, self._threshold, self._name) class Hardswish(layers.Layer): r""" Hardswish activation Hardswish is proposed in MobileNetV3, and performs better in computational stability and efficiency compared to swish function. For more details please refer to: https://arxiv.org/pdf/1905.02244.pdf .. math:: Hardswish(x)= \\left\\{ \\begin{aligned} &0, & & \\text{if } x \\leq -3 \\\\ &x, & & \\text{if } x \\geq 3 \\\\ &\\frac{x(x+3)}{6}, & & \\text{otherwise} \\end{aligned} \\right. Parameters: name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Shape: - input: Tensor with any shape. - output: Tensor with the same shape as input. Examples: .. code-block:: python import paddle x = paddle.to_tensor([-4., 5., 1.]) m = paddle.nn.Hardswish() out = m(x) # [0., 5., 0.666667] """ def __init__(self, name=None): super(Hardswish, self).__init__() self._name = name def forward(self, x): return F.hardswish(x, self._name) class Tanh(layers.Layer): r""" Tanh Activation. .. math:: Tanh(x) = \\frac{e^{x} - e^{-x}}{e^{x} + e^{-x}} Parameters: name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Shape: - input: Tensor with any shape. - output: Tensor with the same shape as input. Examples: .. code-block:: python import paddle import numpy as np x = paddle.to_tensor(np.array([-0.4, -0.2, 0.1, 0.3])) m = paddle.nn.Tanh() out = m(x) print(out) # [-0.37994896 -0.19737532 0.09966799 0.29131261] """ def __init__(self, name=None): super(Tanh, self).__init__() self._name = name def forward(self, x): return F.tanh(x, self._name) class Hardtanh(layers.Layer): r""" Hardtanh Activation .. math:: Hardtanh(x)= \\begin{cases} max, \\text{if } x > max \\\\ min, \\text{if } x < min \\\\ x, \\text{otherwise} \\end{cases} Parameters: min (float, optional): The value of min for Hardtanh. Default is -1. max (float, optional): The value of max for Hardtanh. Default is 1. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Shape: - input: Tensor with any shape. - output: Tensor with the same shape as input. Examples: .. code-block:: python import paddle x = paddle.to_tensor([-1.5, 0.3, 2.5]) m = paddle.nn.Hardtanh() out = m(x) # [-1., 0.3, 1.] """ def __init__(self, min=-1.0, max=1.0, name=None): super(Hardtanh, self).__init__() self._min = min self._max = max self._name = name def forward(self, x): return F.hardtanh(x, self._min, self._max, self._name) class PReLU(layers.Layer): """ PReLU Activation. .. math:: PReLU(x) = max(0, x) + weight * min(0, x) Parameters: num_parameters (int, optional): Number of `weight` to learn. The supported values are: 1 - a single parameter `alpha` is used for all input channels; Number of channels - a seperate `alpha` is used for each input channel. Default is 1. init (float, optional): Init value of learnable `weight`. Default is 0.25. weight_attr(ParamAttr, optional): The parameter attribute for the learnable `weight`. Default is None. For more information, please refer to :ref:`api_paddle_ParamAttr`. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Shape: - input: Tensor with any shape. Default dtype is float32. - output: Tensor with the same shape as input. Examples: .. code-block:: python import paddle import numpy as np paddle.set_default_dtype("float64") data = np.array([[[[-2.0, 3.0, -4.0, 5.0], [ 3.0, -4.0, 5.0, -6.0], [-7.0, -8.0, 8.0, 9.0]], [[ 1.0, -2.0, -3.0, 4.0], [-5.0, 6.0, 7.0, -8.0], [ 6.0, 7.0, 8.0, 9.0]]]], 'float64') x = paddle.to_tensor(data) m = paddle.nn.PReLU(1, 0.25) out = m(x) # [[[[-0.5 , 3. , -1. , 5. ], # [ 3. , -1. , 5. , -1.5 ], # [-1.75, -2. , 8. , 9. ]], # [[ 1. , -0.5 , -0.75, 4. ], # [-1.25, 6. , 7. , -2. ], # [ 6. , 7. , 8. , 9. ]]]] """ def __init__(self, num_parameters=1, init=0.25, weight_attr=None, name=None): super(PReLU, self).__init__() self._num_parameters = num_parameters self._init = init self._weight_attr = weight_attr self._name = name self._weight = self.create_parameter( attr=self._weight_attr, shape=[self._num_parameters], dtype=get_default_dtype(), is_bias=False, default_initializer=Constant(self._init)) def forward(self, x): return F.prelu(x, self._weight) class ReLU(layers.Layer): """ ReLU Activation. .. math:: ReLU(x) = max(x, 0) Parameters: name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Shape: - input: Tensor with any shape. - output: Tensor with the same shape as input. Examples: .. code-block:: python import paddle x = paddle.to_tensor([-2., 0., 1.]) m = paddle.nn.ReLU() out = m(x) # [0., 0., 1.] """ def __init__(self, name=None): super(ReLU, self).__init__() self._name = name def forward(self, x): return F.relu(x, self._name) class ReLU6(layers.Layer): """ ReLU6 Activation .. math:: ReLU6(x) = min(max(0,x), 6) Parameters: name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Shape: - input: Tensor with any shape. - output: Tensor with the same shape as input. Examples: .. code-block:: python import paddle import numpy as np x = paddle.to_tensor(np.array([-1, 0.3, 6.5])) m = paddle.nn.ReLU6() out = m(x) # [0, 0.3, 6] """ def __init__(self, name=None): super(ReLU6, self).__init__() self._name = name def forward(self, x): return F.relu6(x, self._name) class SELU(layers.Layer): r""" SELU Activation .. math:: SELU(x)= scale * \\begin{cases} x, \\text{if } x > 0 \\\\ alpha * e^{x} - alpha, \\text{if } x <= 0 \\end{cases} Parameters: scale (float, optional): The value of scale(must be greater than 1.0) for SELU. Default is 1.0507009873554804934193349852946 alpha (float, optional): The value of alpha(must be no less than zero) for SELU. Default is 1.6732632423543772848170429916717 name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Shape: - input: Tensor with any shape. - output: Tensor with the same shape as input. Examples: .. code-block:: python import paddle import numpy as np x = paddle.to_tensor(np.array([[0.0, 1.0],[2.0, 3.0]])) m = paddle.nn.SELU() out = m(x) # [[0, 1.050701],[2.101402, 3.152103]] """ def __init__(self, scale=1.0507009873554804934193349852946, alpha=1.6732632423543772848170429916717, name=None): super(SELU, self).__init__() self._scale = scale self._alpha = alpha self._name = name def forward(self, x): return F.selu(x, self._scale, self._alpha, self._name) class LeakyReLU(layers.Layer): r""" Leaky ReLU Activation. .. math:: LeakyReLU(x)= \\left\\{ \\begin{aligned} &x, & & if \\ x >= 0 \\\\ &negative\_slope * x, & & otherwise \\\\ \\end{aligned} \\right. \\\\ Parameters: negative_slope (float, optional): Slope of the activation function at :math:`x < 0` . Default is 0.01. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Shape: - input: Tensor with any shape. - output: Tensor with the same shape as input. Examples: .. code-block:: python import paddle import numpy as np paddle.disable_static() m = paddle.nn.LeakyReLU() x = paddle.to_tensor(np.array([-2, 0, 1], 'float32')) out = m(x) # [-0.02, 0., 1.] """ def __init__(self, negative_slope=0.01, name=None): super(LeakyReLU, self).__init__() self._negative_slope = negative_slope self._name = name def forward(self, x): return F.leaky_relu(x, self._negative_slope, self._name) class Sigmoid(layers.Layer): """ this interface is used to construct a callable object of the ``Sigmoid`` class. This layer calcluate the `sigmoid` of input x. .. math:: Sigmoid(x) = \frac{1}{1 + e^{-x}} Parameters: name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Shape: x: N-D tensor, available dtype is float16, float32, float64. Returns: A callable object of Sigmoid. Examples: .. code-block:: python import numpy as np import paddle paddle.disable_static() input_data = np.array([1.0, 2.0, 3.0, 4.0]).astype('float32') m = paddle.nn.Sigmoid() x = paddle.to_tensor(input_data) output = m(x) print(output.numpy()) # [0.7310586, 0.880797, 0.95257413, 0.98201376] """ def __init__(self, name=None): super(Sigmoid, self).__init__() self.name = name def forward(self, x): return F.sigmoid(x, self.name) class Hardsigmoid(layers.Layer): r""" This interface is used to construct a callable object of the ``Hardsigmoid`` class. This layer calcluate the `hardsigmoid` of input x. A 3-part piecewise linear approximation of sigmoid(https://arxiv.org/abs/1603.00391), which is much faster than sigmoid. .. math:: Hardsigmoid(x)= \\left\\{ \\begin{aligned} &0, & & \\text{if } x \\leq -3 \\\\ &1, & & \\text{if } x \\geq 3 \\\\ &x/6 + 1/2, & & \\text{otherwise} \\end{aligned} \\right. Parameters: name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Shape: x: N-D tensor, available dtype is float32, float64. Returns: A callable object of Hardsigmoid. Examples: .. code-block:: python import paddle m = paddle.nn.Hardsigmoid() x = paddle.to_tensor([-4., 5., 1.]) out = m(x) # [0., 1, 0.666667] """ def __init__(self, name=None): super(Hardsigmoid, self).__init__() self.name = name def forward(self, x): return F.hardsigmoid(x, self.name) class Softplus(layers.Layer): r""" Softplus Activation .. math:: Softplus(x) = \\frac{1}{beta} * \\log(1 + e^{beta * x}) \\\\ \\text{For numerical stability, the implementation reverts to the linear function when: beta * x > threshold.} Parameters: beta (float, optional): The value of beta for Softplus. Default is 1 threshold (float, optional): The value of threshold for Softplus. Default is 20 name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Shape: - input: Tensor with any shape. - output: Tensor with the same shape as input. Examples: .. code-block:: python import paddle import numpy as np x = paddle.to_tensor(np.array([-0.4, -0.2, 0.1, 0.3])) m = paddle.nn.Softplus() out = m(x) # [0.513015, 0.598139, 0.744397, 0.854355] """ def __init__(self, beta=1, threshold=20, name=None): super(Softplus, self).__init__() self._beta = beta self._threshold = threshold self._name = name def forward(self, x): return F.softplus(x, self._beta, self._threshold, self._name) class Softshrink(layers.Layer): r""" Softshrink Activation .. math:: Softshrink(x)= \\begin{cases} x - threshold, \\text{if } x > threshold \\\\ x + threshold, \\text{if } x < -threshold \\\\ 0, \\text{otherwise} \\end{cases} Parameters: threshold (float, optional): The value of threshold(must be no less than zero) for softplus. Default is 0.5 name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Shape: - input: Tensor with any shape. - output: Tensor with the same shape as input. Examples: .. code-block:: python import paddle import numpy as np x = paddle.to_tensor(np.array([-0.9, -0.2, 0.1, 0.8])) m = paddle.nn.Softshrink() out = m(x) # [-0.4, 0, 0, 0.3] """ def __init__(self, threshold=0.5, name=None): super(Softshrink, self).__init__() self._threshold = threshold self._name = name def forward(self, x): return F.softshrink(x, self._threshold, self._name) class Softsign(layers.Layer): r""" Softsign Activation .. math:: Softsign(x) = \\frac{x}{1 + |x|} Parameters: name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Shape: - input: Tensor with any shape. - output: Tensor with the same shape as input. Examples: .. code-block:: python import paddle import numpy as np x = paddle.to_tensor(np.array([-0.4, -0.2, 0.1, 0.3])) m = paddle.nn.Softsign() out = m(x) # [-0.285714, -0.166667, 0.0909091, 0.230769] """ def __init__(self, name=None): super(Softsign, self).__init__() self._name = name def forward(self, x): return F.softsign(x, self._name) class Swish(layers.Layer): r""" Swish Activation. .. math:: Swish(x) = \\frac{x}{1 + e^{-x}} Parameters: name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Shape: - input: Tensor with any shape. - output: Tensor with the same shape as input. Examples: .. code-block:: python import paddle import numpy as np x = paddle.to_tensor(np.array([-2., 0., 1.])) m = paddle.nn.Swish() out = m(x) # [-0.238406, 0., 0.731059] """ def __init__(self, name=None): super(Swish, self).__init__() self._name = name def forward(self, x): return F.swish(x, self._name) class Tanhshrink(layers.Layer): """ Tanhshrink Activation .. math:: Tanhshrink(x) = x - tanh(x) Parameters: name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Shape: - input: Tensor with any shape. - output: Tensor with the same shape as input. Examples: .. code-block:: python import paddle import numpy as np x = paddle.to_tensor(np.array([-0.4, -0.2, 0.1, 0.3])) m = paddle.nn.Tanhshrink() out = m(x) # [-0.020051, -0.00262468, 0.000332005, 0.00868739] """ def __init__(self, name=None): super(Tanhshrink, self).__init__() self._name = name def forward(self, x): return F.tanhshrink(x, self._name) class ThresholdedReLU(layers.Layer): r""" Thresholded ReLU Activation .. math:: ThresholdedReLU(x) = \\begin{cases} x, \\text{if } x > threshold \\\\ 0, \\text{otherwise} \\end{cases} Parameters: threshold (float, optional): The value of threshold for ThresholdedReLU. Default is 1.0 name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Shape: - input: Tensor with any shape. - output: Tensor with the same shape as input. Examples: .. code-block:: python import paddle import numpy as np x = paddle.to_tensor(np.array([2., 0., 1.])) m = paddle.nn.ThresholdedReLU() out = m(x) # [2., 0., 0.] """ def __init__(self, threshold=1.0, name=None): super(ThresholdedReLU, self).__init__() self._threshold = threshold self._name = name def forward(self, x): return F.thresholded_relu(x, self._threshold, self._name) class LogSigmoid(layers.Layer): r""" LogSigmoid Activation. .. math:: LogSigmoid(x) = log \\frac{1}{1 + e^{-x}} Parameters: x (Tensor): The input Tensor with data type float32, or float64. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Shape: - input: Tensor with any shape. - output: Tensor with the same shape as input. Examples: .. code-block:: python import paddle x = paddle.to_tensor([1.0, 2.0, 3.0, 4.0]) m = paddle.nn.LogSigmoid() out = m(x) # [-0.313262 -0.126928 -0.0485874 -0.0181499] """ def __init__(self, name=None): super(LogSigmoid, self).__init__() self._name = name def forward(self, x): return F.log_sigmoid(x, self._name) class Softmax(layers.Layer): r""" Softmax Activation. This operator implements the softmax layer. The calculation process is as follows: 1. The dimension :attr:`axis` of ``x`` will be permuted to the last. 2. Then ``x`` will be logically flattened to a 2-D matrix. The matrix's second dimension(row length) is the same as the dimension :attr:`axis` of ``x``, and the first dimension(column length) is the product of all other dimensions of ``x``. For each row of the matrix, the softmax operator squashes the K-dimensional(K is the width of the matrix, which is also the size of ``x``'s dimension :attr:`axis`) vector of arbitrary real values to a K-dimensional vector of real values in the range [0, 1] that add up to 1. 3. After the softmax operation is completed, the inverse operations of steps 1 and 2 are performed to restore the two-dimensional matrix to the same dimension as the ``x`` . It computes the exponential of the given dimension and the sum of exponential values of all the other dimensions in the K-dimensional vector input. Then the ratio of the exponential of the given dimension and the sum of exponential values of all the other dimensions is the output of the softmax operator. For each row :math:`i` and each column :math:`j` in the matrix, we have: .. math:: Softmax[i, j] = \\frac{\\exp(x[i, j])}{\\sum_j(exp(x[i, j])} Example: .. code-block:: text Case 1: Input: x.shape = [2, 3, 4] x.data = [[[2.0, 3.0, 4.0, 5.0], [3.0, 4.0, 5.0, 6.0], [7.0, 8.0, 8.0, 9.0]], [[1.0, 2.0, 3.0, 4.0], [5.0, 6.0, 7.0, 8.0], [6.0, 7.0, 8.0, 9.0]]] Attrs: axis = -1 Output: out.shape = [2, 3, 4] out.data = [[[0.0320586 , 0.08714432, 0.23688282, 0.64391426], [0.0320586 , 0.08714432, 0.23688282, 0.64391426], [0.07232949, 0.19661193, 0.19661193, 0.53444665]], [[0.0320586 , 0.08714432, 0.23688282, 0.64391426], [0.0320586 , 0.08714432, 0.23688282, 0.64391426], [0.0320586 , 0.08714432, 0.23688282, 0.64391426]]] Case 2: Input: x.shape = [2, 3, 4] x.data = [[[2.0, 3.0, 4.0, 5.0], [3.0, 4.0, 5.0, 6.0], [7.0, 8.0, 8.0, 9.0]], [[1.0, 2.0, 3.0, 4.0], [5.0, 6.0, 7.0, 8.0], [6.0, 7.0, 8.0, 9.0]]] Attrs: axis = 1 Output: out.shape = [2, 3, 4] out.data = [[[0.00657326, 0.00657326, 0.01714783, 0.01714783], [0.01786798, 0.01786798, 0.04661262, 0.04661262], [0.97555875, 0.97555875, 0.93623955, 0.93623955]], [[0.00490169, 0.00490169, 0.00490169, 0.00490169], [0.26762315, 0.26762315, 0.26762315, 0.26762315], [0.72747516, 0.72747516, 0.72747516, 0.72747516]]] Parameters: axis (int, optional): The axis along which to perform log_softmax calculations. It should be in range [-D, D), where D is the dimensions of ``x`` . If ``axis`` < 0, it works the same way as :math:`axis + D` . Default is -1. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Shape: - input: Tensor with any shape. - output: Tensor with the same shape as input. Examples: .. code-block:: python import paddle import numpy as np x = np.array([[[2.0, 3.0, 4.0, 5.0], [3.0, 4.0, 5.0, 6.0], [7.0, 8.0, 8.0, 9.0]], [[1.0, 2.0, 3.0, 4.0], [5.0, 6.0, 7.0, 8.0], [6.0, 7.0, 8.0, 9.0]]], 'float32') x = paddle.to_tensor(x) m = paddle.nn.Softmax() out = m(x) # [[[0.0320586 , 0.08714432, 0.23688282, 0.64391426], # [0.0320586 , 0.08714432, 0.23688282, 0.64391426], # [0.07232949, 0.19661193, 0.19661193, 0.53444665]], # [[0.0320586 , 0.08714432, 0.23688282, 0.64391426], # [0.0320586 , 0.08714432, 0.23688282, 0.64391426], # [0.0320586 , 0.08714432, 0.23688282, 0.64391426]]] """ def __init__(self, axis=-1, name=None): super(Softmax, self).__init__() self._axis = axis self._dtype = None self._name = name def forward(self, x): return F.softmax(x, self._axis, self._dtype, self._name) class LogSoftmax(layers.Layer): r""" This operator implements the log_softmax layer. The calculation process is as follows: .. math:: \\begin{aligned} Out[i, j] &= log(softmax(x)) \\\\ &= log(\\frac{\\exp(X[i, j])}{\\sum_j(\\exp(X[i, j])}) \\end{aligned} Parameters: axis (int, optional): The axis along which to perform log_softmax calculations. It should be in range [-D, D), where D is the dimensions of the input Tensor . If ``axis`` < 0, it works the same way as :math:`axis + D` . Default is -1. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Shape: - input: Tensor with any shape. - output: Tensor with the same shape as input. Examples: .. code-block:: python import paddle x = [[[-2.0, 3.0, -4.0, 5.0], [3.0, -4.0, 5.0, -6.0], [-7.0, -8.0, 8.0, 9.0]], [[1.0, -2.0, -3.0, 4.0], [-5.0, 6.0, 7.0, -8.0], [6.0, 7.0, 8.0, 9.0]]] m = paddle.nn.LogSoftmax() x = paddle.to_tensor(x) out = m(x) # [[[ -7.1278396 -2.1278396 -9.127839 -0.12783948] # [ -2.1270514 -9.127051 -0.12705144 -11.127051 ] # [-16.313261 -17.313261 -1.3132617 -0.31326184]] # [[ -3.0518122 -6.051812 -7.051812 -0.051812 ] # [-12.313267 -1.3132664 -0.3132665 -15.313267 ] # [ -3.4401896 -2.4401896 -1.4401896 -0.44018966]]] """ def __init__(self, axis=-1, name=None): super(LogSoftmax, self).__init__() self._axis = axis self._name = name def forward(self, x): return F.log_softmax(x, self._axis) class Maxout(layers.Layer): r""" Maxout Activation. Assumed the input shape is (N, Ci, H, W). The output shape is (N, Co, H, W). Then Co = Ci/groups and the operator formula is as follows: .. math:: &out_{si+j} = \max_{k} x_{gsi + sk + j} \\\\ &g = groups \\\\ &s = \\frac{input.size}{num\\_channels} \\\\ &0 \\le i < \\frac{num\\_channels}{groups} \\\\ &0 \\le j < s \\\\ &0 \\le k < groups Parameters: groups (int, optional): The groups number of maxout. `groups` specifies the index of channel dimension where maxout will be performed. This must be a factor of number of features. Default is 1. axis (int, optional): The axis along which to perform maxout calculations. It should be 1 when data format is NCHW, be -1 or 3 when data format is NHWC. If ``axis`` < 0, it works the same way as :math:`axis + D` , where D is the dimensions of ``x`` . Default is 1. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Shape: - input: :math:`(N, C_{in}, H_{in}, W_{in})` - output: :math:`(N, C_{out}, H_{out}, W_{out})` Examples: .. code-block:: python import paddle x = paddle.rand([1, 2, 3, 4]) # [[[[0.5002636 0.22272532 0.17402348 0.2874594 ] # [0.95313174 0.6228939 0.7129065 0.7087491 ] # [0.02879342 0.88725346 0.61093384 0.38833922]] # [[0.5231306 0.03807496 0.91661984 0.15602879] # [0.666127 0.616567 0.30741522 0.24044901] # [0.7142536 0.7351477 0.31588817 0.23782359]]]] m = paddle.nn.Maxout(groups=2) out = m(x) # [[[[0.5231306 0.22272532 0.91661984 0.2874594 ] # [0.95313174 0.6228939 0.7129065 0.7087491 ] # [0.7142536 0.88725346 0.61093384 0.38833922]]]] """ def __init__(self, groups, axis=1, name=None): super(Maxout, self).__init__() self._groups = groups self._axis = axis self._name = name def forward(self, x): return F.maxout(x, self._groups, self._axis, self._name)