ops.py 2.7 KB
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#   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");
# you may not use this file except in compliance with the License.
# 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
# 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.
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from layer_function_generator import generate_layer_fn
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__activations__ = [
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    'sigmoid',
    'logsigmoid',
    'exp',
    'relu',
    'tanh',
    'tanh_shrink',
    'softshrink',
    'sqrt',
    'abs',
    'ceil',
    'floor',
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    'cos',
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    'sin',
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    'round',
    'reciprocal',
    'log',
    'square',
    'softplus',
    'softsign',
    'brelu',
    'leaky_relu',
    'soft_relu',
    'elu',
    'relu6',
    'pow',
    'stanh',
    'thresholded_relu',
    'hard_sigmoid',
    'swish',
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]

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__all__ = [
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    'mean',
    'mul',
    'scale',
    'sigmoid_cross_entropy_with_logits',
    'elementwise_add',
    'elementwise_div',
    'elementwise_sub',
    'elementwise_mul',
    'elementwise_max',
    'elementwise_min',
    'elementwise_pow',
    'clip',
    'clip_by_norm',
    'logical_and',
    'logical_or',
    'logical_xor',
    'logical_not',
    'uniform_random_batch_size_like',
    'gaussian_random',
    'gaussian_random_batch_size_like',
    'cumsum',
    'scatter',
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    'sum',
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    'slice',
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    'polygon_box_transform',
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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"]

_uniform_random_ = generate_layer_fn('uniform_random')


def uniform_random(shape, dtype=None, min=None, max=None, seed=None):
    kwargs = dict()
    for name in locals():
        val = locals()[name]
        if val is not None:
            kwargs[name] = val
    return _uniform_random_(**kwargs)

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uniform_random.__doc__ = _uniform_random_.__doc__ + "\n" \
                         + """
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Examples:

    >>> result = fluid.layers.uniform_random(shape=[32, 784])
"""
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__all__ += ['hard_shrink']

_hard_shrink_ = generate_layer_fn('hard_shrink')


def hard_shrink(x, threshold=None):
    kwargs = dict()
    for name in locals():
        val = locals()[name]
        if val is not None:
            kwargs[name] = val
    return _hard_shrink_(**kwargs)


hard_shrink.__doc__ = _hard_shrink_.__doc__ + "\n" \
                      + """
Examples:

    >>> data = fluid.layers.data(name="input", shape=[784])
    >>> result = fluid.layers.hard_shrink(x=data, threshold=0.3)
"""