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238 lines
8.8 KiB
238 lines
8.8 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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import warnings
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from ...fluid.layer_helper import LayerHelper
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from ...fluid.framework import in_dygraph_mode, convert_np_dtype_to_dtype_
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from ...fluid import core
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from ...fluid.data_feeder import check_variable_and_dtype
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# TODO: define activation functions of neural network
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__all__ = [
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# 'brelu',
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# 'elu',
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# 'erf',
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# 'gelu',
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# 'hard_shrink',
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# 'hard_sigmoid',
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# 'hard_swish',
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# 'hsigmoid',
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# 'leaky_relu',
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# 'logsigmoid',
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# 'maxout',
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# 'prelu',
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'relu',
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# 'relu6',
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# 'selu',
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'sigmoid',
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# 'soft_relu',
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# 'softmax',
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# 'softplus',
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# 'softshrink',
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# 'softsign',
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# 'swish',
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# 'tanh_shrink',
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# 'thresholded_relu',
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'log_softmax',
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]
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def relu(input, inplace=False, name=None):
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"""
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ReLU Activation.
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.. math:
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out = max(x, 0)
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Parameters:
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input (Variable): The input variable. A multi-dimension Tensor with type float16, float32, or float64.
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inplace (bool, optional): If inplace is True, the input and output of ``ReLU`` are the same variable.
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Otherwise, the input and output of ``ReLU`` are different variables. Default: False. Note that if x is
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more than one OPs' input, inplace must be False.
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name (str, optional): The default value is None. Normally there is no need for user to set this property.
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For more information, please refer to :ref:`api_guide_Name` .
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Returns:
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Output of relu operator, a Tensor with shape same as input
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Examples:
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.. code-block:: python
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import paddle.fluid as fluid
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import paddle.nn.functional as functional
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import numpy as np
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data = np.array([-2, 0, 1]).astype('float32')
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with fluid.dygraph.guard():
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data = fluid.dygraph.to_variable(data)
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res = functional.relu(data) # [0, 0, 1]
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"""
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if in_dygraph_mode():
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if inplace:
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warnings.warn(
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"Inplace on ReLU is not allowed and will be discarded in dygraph mode currently."
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)
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return core.ops.relu(input)
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helper = LayerHelper('relu', **locals())
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outs = input if inplace else helper.create_variable_for_type_inference(
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input.dtype)
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helper.append_op(type='relu', inputs={'X': [input]}, outputs={'Out': outs})
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return outs
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def sigmoid(input, inplace=False, name=None):
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"""
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Sigmoid Activation.
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.. math:
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output = \frac{1}{1 + e^{-input}}
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Parameters:
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input (Variable): The input variable. A multi-dimension Tensor with type float16, float32, or float64.
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inplace (bool, optional): If inplace is True, the input and output are the same variable.
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Otherwise, the input and output of are different variables. Default: False. Note that if x is
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more than one OPs' input, inplace must be False.
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name (str, optional): The default value is None. Normally there is no need for user to set this property.
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For more information, please refer to :ref:`api_guide_Name` .
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Returns:
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Output of sigmoid operator, a Tensor with shape same as input
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Examples:
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.. code-block:: python
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import paddle.fluid as fluid
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import paddle.nn.functional as functional
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import numpy as np
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# In the static graph mode
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input = fluid.data(name="input", shape=[None, 4])
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output = functional.sigmoid(input)
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place = fluid.CPUPlace()
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exe = fluid.Executor(place)
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exe.run(fluid.default_startup_program())
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input_data = np.array([1.0, 2.0, 3.0, 4.0]).astype('float32')
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output_data = exe.run(feed={"input": input_data},
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fetch_list=[output])
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print(output_data) # [0.7310586, 0.880797, 0.95257413, 0.98201376]
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# In the dynamic graph mode
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with fluid.dygraph.guard():
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input = fluid.dygraph.to_variable(input_data)
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output = functional.sigmoid(input)
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print(output) # [0.7310586, 0.880797, 0.95257413, 0.98201376]
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"""
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if in_dygraph_mode():
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if inplace:
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warnings.warn(
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"Inplace on sigmoid is not allowed and will be discarded in dygraph mode currently."
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)
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return core.ops.sigmoid(input)
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check_variable_and_dtype(input, 'X', ['float16', 'float32', 'float64'],
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'sigmoid')
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helper = LayerHelper("sigmoid", **locals())
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outputs = helper.create_variable_for_type_inference(input.dtype)
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helper.append_op(
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type='sigmoid', inputs={'X': [input]}, outputs={'Out': outputs})
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return outputs
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def log_softmax(input, axis=None, dtype=None, name=None):
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"""
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This operator implements the log_softmax layer. The calculation process is as follows:
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.. math::
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Out[i, j] = log(softmax(x))
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= log(\\frac{\exp(X[i, j])}{\sum_j(exp(X[i, j])})
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Parameters:
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input (Variable): The input variable. A multi-dimension Tensor with type float32, or float64.
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axis (int, optional): The index of dimension to perform softmax calculations, it should be in
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range :math:`[-1, rank-1]`, while :math:`rank` is the rank of input variable. Default: None.
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None and -1 means the last dimension.
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dtype (np.dtype|core.VarDesc.VarType|str): The desired data type of returned tensor. If specified,
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the input tensor is casted to dtype before the operation is performed. This is useful for
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preventing data type overflows. Default: None. Supported dtype: float32 or float64
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name (str, optional): The default value is None. Normally there is no need for user to set this property.
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For more information, please refer to :ref:`api_guide_Name` .
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Returns:
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Variable: ``Tensor`` indicates the output of softmax. The data type and shape are the same as ``input``.
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Examples:
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.. code-block:: python
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import paddle.fluid as fluid
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import paddle.nn.functional as F
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import numpy as np
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data = np.array([[[-2.0, 3.0, -4.0, 5.0],
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[3.0, -4.0, 5.0, -6.0],
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[-7.0, -8.0, 8.0, 9.0]],
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[[1.0, -2.0, -3.0, 4.0],
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[-5.0, 6.0, 7.0, -8.0],
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[6.0, 7.0, 8.0, 9.0]]]).astype('float32')
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with fluid.dygraph.guard():
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data = fluid.dygraph.to_variable(data)
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res = F.log_softmax(data, -1)
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# [[[ -7.1278396 -2.1278396 -9.127839 -0.12783948]
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# [ -2.1270514 -9.127051 -0.12705144 -11.127051 ]
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# [-16.313261 -17.313261 -1.3132617 -0.31326184]]
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# [[ -3.0518122 -6.051812 -7.051812 -0.051812 ]
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# [-12.313267 -1.3132664 -0.3132665 -15.313267 ]
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# [ -3.4401896 -2.4401896 -1.4401896 -0.44018966]]]
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"""
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axis = -1 if axis is None else axis
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dtype = convert_np_dtype_to_dtype_(dtype) if dtype is not None else dtype
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if in_dygraph_mode():
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outs_cast = input if dtype is None \
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else core.ops.cast(input, 'in_dtype', input.dtype, 'out_dtype', dtype)
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outs_softmax = core.ops.softmax(outs_cast, 'axis', axis, 'use_cudnn',
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False)
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return core.ops.log(outs_softmax)
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helper = LayerHelper("log_softmax", **locals())
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outs_cast = input
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if dtype is not None:
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outs_cast = helper.create_variable_for_type_inference(dtype)
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helper.append_op(
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type='cast',
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inputs={'X': input},
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outputs={'Out': outs_cast},
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attrs={'in_dtype': input.dtype,
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'out_dtype': dtype})
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outs_softmax = helper.create_variable_for_type_inference(outs_cast.dtype)
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helper.append_op(
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type='softmax',
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inputs={'X': outs_cast},
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outputs={'Out': outs_softmax},
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attrs={'axis': axis,
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'use_cudnn': False})
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outs_log = helper.create_variable_for_type_inference(outs_softmax.dtype)
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helper.append_op(
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type='log', inputs={'X': outs_softmax}, outputs={'Out': outs_log})
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return outs_log
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