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160 lines
3.5 KiB
160 lines
3.5 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 .layer_function_generator import generate_layer_fn
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__activations__ = [
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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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'brelu',
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'leaky_relu',
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'soft_relu',
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'elu',
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'relu6',
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'pow',
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'stanh',
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'hard_sigmoid',
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'swish',
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]
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__all__ = [
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'mean',
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'mul',
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'scale',
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'sigmoid_cross_entropy_with_logits',
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'elementwise_add',
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'elementwise_div',
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'elementwise_sub',
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'elementwise_mul',
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'elementwise_max',
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'elementwise_min',
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'elementwise_pow',
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'clip',
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'clip_by_norm',
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'logical_and',
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'logical_or',
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'logical_xor',
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'logical_not',
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'uniform_random_batch_size_like',
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'gaussian_random',
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'gaussian_random_batch_size_like',
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'scatter',
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'sum',
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'slice',
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'shape',
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'maxout',
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] + __activations__
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for _OP in set(__all__):
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globals()[_OP] = generate_layer_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=None, min=None, max=None, seed=None):
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kwargs = dict()
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for name in locals():
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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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uniform_random.__doc__ = _uniform_random_.__doc__ + """
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Examples:
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>>> result = fluid.layers.uniform_random(shape=[32, 784])
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"""
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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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kwargs = dict()
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for name in locals():
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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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kwargs = dict()
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for name in locals():
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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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kwargs = dict()
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for name in locals():
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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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_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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