#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
#
# 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.
from layer_function_generator import generate_layer_fn

__activations__ = [
    'sigmoid',
    'logsigmoid',
    'exp',
    'relu',
    'tanh',
    'tanh_shrink',
    'softshrink',
    'sqrt',
    'abs',
    'ceil',
    'floor',
    'round',
    'reciprocal',
    'log',
    'square',
    'softplus',
    'softsign',
    'brelu',
    'leaky_relu',
    'soft_relu',
    'elu',
    'relu6',
    'pow',
    'stanh',
    'hard_shrink',
    'thresholded_relu',
    'hard_sigmoid',
    'swish',
]

__all__ = [
    'mean', 'mul', 'reshape', 'scale', 'transpose',
    'sigmoid_cross_entropy_with_logits', 'elementwise_add', 'elementwise_div',
    'elementwise_sub', 'elementwise_mul', 'elementwise_max', 'elementwise_min',
    'clip', 'clip_by_norm', 'sequence_softmax'
] + __activations__

for _OP in set(__all__):
    globals()[_OP] = generate_layer_fn(_OP)
