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162 lines
4.3 KiB
162 lines
4.3 KiB
# Copyright (c) 2018 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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from __future__ import print_function
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import os
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from .layer_function_generator import generate_layer_fn, generate_activation_fn
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from .. import core
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from ..framework import convert_np_dtype_to_dtype_
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__activations_noattr__ = [
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'sigmoid',
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'logsigmoid',
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'exp',
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'tanh',
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'tanh_shrink',
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'softshrink',
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'sqrt',
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'abs',
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'ceil',
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'floor',
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'cos',
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'sin',
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'round',
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'reciprocal',
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'square',
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'softplus',
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'softsign',
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]
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__all__ = []
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for _OP in set(__all__):
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globals()[_OP] = generate_layer_fn(_OP)
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# It is a hot fix in some unittest using:
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# fluid.layers.scale(x=x, scale=10.0, out=out_var)
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# e.g.: test_program_code.py, test_dist_train.py
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globals()['_scale'] = generate_layer_fn('scale')
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globals()['_elementwise_div'] = generate_layer_fn('elementwise_div')
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__all__ += __activations_noattr__
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for _OP in set(__activations_noattr__):
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globals()[_OP] = generate_activation_fn(_OP)
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__all__ += ["uniform_random"]
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_uniform_random_ = generate_layer_fn('uniform_random')
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def uniform_random(shape, dtype='float32', min=-1.0, max=1.0, seed=0):
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"""
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This operator initializes a variable with random values sampled from a
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uniform distribution. The random result is in set [min, max].
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Args:
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shape (list): The shape of output variable.
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dtype(np.dtype|core.VarDesc.VarType|str): The type of data, such as
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float32, float64 etc. Default: float32.
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min (float): Minimum value of uniform random. Default -1.0.
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max (float): Maximun value of uniform random. Default 1.0.
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seed (int): Random seed used for generating samples. 0 means use a
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seed generated by the system. Note that if seed is not 0, this
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operator will always generate the same random numbers every time.
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Default 0.
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Examples:
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.. code-block:: python
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result = fluid.layers.uniform_random(shape=[32, 784])
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"""
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locals_var = locals().keys()
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if not isinstance(dtype, core.VarDesc.VarType):
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dtype = convert_np_dtype_to_dtype_(dtype)
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kwargs = dict()
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for name in locals_var:
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val = locals()[name]
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if val is not None:
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kwargs[name] = val
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return _uniform_random_(**kwargs)
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__all__ += ['hard_shrink']
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_hard_shrink_ = generate_layer_fn('hard_shrink')
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def hard_shrink(x, threshold=None):
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locals_var = locals().keys()
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kwargs = dict()
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for name in locals_var:
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val = locals()[name]
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if val is not None:
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kwargs[name] = val
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return _hard_shrink_(**kwargs)
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hard_shrink.__doc__ = _hard_shrink_.__doc__ + """
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Examples:
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>>> data = fluid.layers.data(name="input", shape=[784])
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>>> result = fluid.layers.hard_shrink(x=data, threshold=0.3)
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"""
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__all__ += ['cumsum']
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_cum_sum_ = generate_layer_fn('cumsum')
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def cumsum(x, axis=None, exclusive=None, reverse=None):
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locals_var = locals().keys()
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kwargs = dict()
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for name in locals_var:
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val = locals()[name]
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if val is not None:
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kwargs[name] = val
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return _cum_sum_(**kwargs)
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cumsum.__doc__ = _cum_sum_.__doc__ + """
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Examples:
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>>> data = fluid.layers.data(name="input", shape=[32, 784])
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>>> result = fluid.layers.cumsum(data, axis=0)
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"""
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__all__ += ['thresholded_relu']
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_thresholded_relu_ = generate_layer_fn('thresholded_relu')
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def thresholded_relu(x, threshold=None):
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locals_var = locals().keys()
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kwargs = dict()
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for name in locals_var:
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val = locals()[name]
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if val is not None:
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kwargs[name] = val
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return _thresholded_relu_(**kwargs)
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thresholded_relu.__doc__ = _thresholded_relu_.__doc__ + """
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Examples:
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>>> data = fluid.layers.data(name="input", shape=[1])
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>>> result = fluid.layers.thresholded_relu(data, threshold=0.4)
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"""
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