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/
__init__.py
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/
__init__.py
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
# TODO: import all neural network related api under this directory,
# including layers, linear, conv, rnn etc.
from .activation import celu # noqa: F401
from .activation import elu # noqa: F401
from .activation import elu_ # noqa: F401
from .activation import gelu # noqa: F401
from .activation import hardshrink # noqa: F401
from .activation import hardtanh # noqa: F401
from .activation import hardsigmoid # noqa: F401
from .activation import hardswish # noqa: F401
from .activation import leaky_relu # noqa: F401
from .activation import log_sigmoid # noqa: F401
from .activation import maxout # noqa: F401
from .activation import prelu # noqa: F401
from .activation import relu # noqa: F401
from .activation import relu_ # noqa: F401
from .activation import relu6 # noqa: F401
from .activation import selu # noqa: F401
from .activation import sigmoid # noqa: F401
from .activation import silu # noqa: F401
from .activation import softmax # noqa: F401
from .activation import softmax_ # noqa: F401
from .activation import softplus # noqa: F401
from .activation import softshrink # noqa: F401
from .activation import softsign # noqa: F401
from .activation import swish # noqa: F401
from .activation import mish # noqa: F401
from .activation import tanh # noqa: F401
from .activation import tanh_ # noqa: F401
from .activation import tanhshrink # noqa: F401
from .activation import thresholded_relu # noqa: F401
from .activation import log_softmax # noqa: F401
from .activation import glu # noqa: F401
from .activation import gumbel_softmax # noqa: F401
from .common import dropout # noqa: F401
from .common import dropout2d # noqa: F401
from .common import dropout3d # noqa: F401
from .common import alpha_dropout # noqa: F401
from .common import label_smooth # noqa: F401
from .common import pad # noqa: F401
from .common import zeropad2d # noqa: F401
from .common import cosine_similarity # noqa: F401
from .common import unfold # noqa: F401
from .common import fold
from .common import interpolate # noqa: F401
from .common import upsample # noqa: F401
from .common import bilinear # noqa: F401
from .common import class_center_sample # noqa: F401
from .conv import conv1d # noqa: F401
from .conv import conv1d_transpose # noqa: F401
from .common import linear # noqa: F401
from .conv import conv2d # noqa: F401
from .conv import conv2d_transpose # noqa: F401
from .conv import conv3d # noqa: F401
from .conv import conv3d_transpose # noqa: F401
from .extension import diag_embed # noqa: F401
from .extension import sequence_mask
from .loss import binary_cross_entropy # noqa: F401
from .loss import binary_cross_entropy_with_logits # noqa: F401
from .loss import cross_entropy # noqa: F401
from .loss import dice_loss # noqa: F401
from .loss import hsigmoid_loss # noqa: F401
from .loss import kl_div # noqa: F401
from .loss import l1_loss # noqa: F401
from .loss import log_loss # noqa: F401
from .loss import margin_ranking_loss # noqa: F401
from .loss import mse_loss # noqa: F401
from .loss import nll_loss # noqa: F401
from .loss import npair_loss # noqa: F401
from .loss import sigmoid_focal_loss # noqa: F401
from .loss import smooth_l1_loss # noqa: F401
from .loss import softmax_with_cross_entropy # noqa: F401
from .loss import margin_cross_entropy # noqa: F401
from .loss import square_error_cost # noqa: F401
from .loss import ctc_loss # noqa: F401
from .loss import hinge_embedding_loss # noqa: F401
from .loss import triplet_margin_with_distance_loss
from .norm import batch_norm # noqa: F401
from .norm import instance_norm # noqa: F401
from .norm import layer_norm # noqa: F401
from .norm import local_response_norm # noqa: F401
from .norm import normalize # noqa: F401
from .pooling import avg_pool1d # noqa: F401
from .pooling import avg_pool2d # noqa: F401
from .pooling import avg_pool3d # noqa: F401
from .pooling import max_pool1d # noqa: F401
from .pooling import max_pool2d # noqa: F401
from .pooling import max_pool3d # noqa: F401
from .pooling import adaptive_max_pool1d # noqa: F401
from .pooling import adaptive_max_pool2d # noqa: F401
from .pooling import adaptive_max_pool3d # noqa: F401
from .pooling import adaptive_avg_pool1d # noqa: F401
from .pooling import adaptive_avg_pool2d # noqa: F401
from .pooling import adaptive_avg_pool3d # noqa: F401
from .pooling import max_unpool1d # noqa: F401
from .pooling import max_unpool2d # noqa: F401
from .pooling import max_unpool3d # noqa: F401
from .vision import affine_grid # noqa: F401
from .vision import grid_sample # noqa: F401
from .vision import pixel_shuffle # noqa: F401
from .vision import pixel_unshuffle # noqa: F401
from .vision import channel_shuffle # noqa: F401
from .input import one_hot # noqa: F401
from .input import embedding # noqa: F401
from ...fluid.layers import gather_tree # noqa: F401
from ...fluid.layers import temporal_shift # noqa: F401
from .sparse_attention import sparse_attention
__all__ = [ #noqa
'celu',
'conv1d',
'conv1d_transpose',
'conv2d',
'conv2d_transpose',
'conv3d',
'conv3d_transpose',
'elu',
'elu_',
'gelu',
'hardshrink',
'hardtanh',
'hardsigmoid',
'hardswish',
'leaky_relu',
'log_sigmoid',
'maxout',
'prelu',
'relu',
'relu_',
'relu6',
'selu',
'softmax',
'softmax_',
'softplus',
'softshrink',
'softsign',
'sigmoid',
'silu',
'swish',
'mish',
'tanh',
'tanh_',
'tanhshrink',
'thresholded_relu',
'log_softmax',
'glu',
'gumbel_softmax',
'diag_embed',
'sequence_mask',
'dropout',
'dropout2d',
'dropout3d',
'alpha_dropout',
'label_smooth',
'linear',
'pad',
'zeropad2d',
'unfold',
'interpolate',
'upsample',
'bilinear',
'cosine_similarity',
'avg_pool1d',
'avg_pool2d',
'avg_pool3d',
'max_pool1d',
'max_pool2d',
'max_pool3d',
'max_unpool1d',
'max_unpool2d',
'max_unpool3d',
'adaptive_avg_pool1d',
'adaptive_avg_pool2d',
'adaptive_avg_pool3d',
'adaptive_max_pool1d',
'adaptive_max_pool2d',
'adaptive_max_pool3d',
'binary_cross_entropy',
'binary_cross_entropy_with_logits',
'cross_entropy',
'dice_loss',
'hsigmoid_loss',
'kl_div',
'l1_loss',
'log_loss',
'mse_loss',
'margin_ranking_loss',
'nll_loss',
'npair_loss',
'sigmoid_focal_loss',
'smooth_l1_loss',
'softmax_with_cross_entropy',
'margin_cross_entropy',
'square_error_cost',
'ctc_loss',
'hinge_embedding_loss',
'affine_grid',
'grid_sample',
'local_response_norm',
'pixel_shuffle',
'pixel_unshuffle',
'channel_shuffle',
'embedding',
'gather_tree',
'one_hot',
'normalize',
'temporal_shift',
'batch_norm',
'layer_norm',
'instance_norm',
'class_center_sample',
'sparse_attention',
'fold',
'triplet_margin_with_distance_loss',
]