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安装说明
使用教程
应用实践
API 文档
- paddle
  - Overview
  - abs
  - acos
  - add
  - add_n
  - addmm
  - all
  - allclose
  - any
  - arange
  - argmax
  - argmin
  - argsort
  - asin
  - assign
  - atan
  - bernoulli
  - bmm
  - broadcast_shape
  - broadcast_to
  - cast
  - ceil
  - cholesky
  - chunk
  - clip
  - concat
  - conj
  - cos
  - cosh
  - CPUPlace
  - cross
  - CUDAPinnedPlace
  - CUDAPlace
  - cumsum
  - DataParallel
  - diag
  - disable_static
  - dist
  - divide
  - dot
  - empty
  - empty_like
  - enable_static
  - equal
  - equal_all
  - erf
  - exp
  - expand
  - expand_as
  - eye
  - flatten
  - flip
  - floor
  - floor_divide
  - flops
  - full
  - full_like
  - gather
  - gather_nd
  - get_cuda_rng_state
  - get_cudnn_version
  - get_default_dtype
  - get_device
  - grad
  - greater_equal
  - greater_than
  - histogram
  - imag
  - in_dynamic_mode
  - increment
  - index_sample
  - index_select
  - inverse
  - is_compiled_with_cuda
  - is_compiled_with_xpu
  - is_empty
  - is_tensor
  - isfinite
  - isinf
  - isnan
  - kron
  - less_equal
  - less_than
  - linspace
  - load
  - log
  - log10
  - log1p
  - log2
  - logical_and
  - logical_not
  - logical_or
  - logical_xor
  - logsumexp
  - masked_select
  - matmul
  - max
  - maximum
  - mean
  - median
  - meshgrid
  - min
  - minimum
  - mm
  - mod
  - Model
  - multinomial
  - multiplex
  - multiply
  - mv
  - no_grad
  - nonzero
  - norm
  - normal
  - not_equal
  - numel
  - ones
  - ones_like
  - ParamAttr
  - pow
  - prod
  - rand
  - randint
  - randn
  - randperm
  - rank
  - real
  - reciprocal
  - reshape
  - reshape_
  - roll
  - round
  - rsqrt
  - save
  - scale
  - scatter
  - scatter_
  - scatter_nd
  - scatter_nd_add
  - seed
  - set_cuda_rng_state
  - set_default_dtype
  - set_device
  - set_printoptions
  - shape
  - shard_index
  - sign
  - sin
  - sinh
  - slice
  - sort
  - split
  - sqrt
  - square
  - squeeze
  - squeeze_
  - stack
  - standard_normal
  - stanh
  - std
  - strided_slice
  - subtract
  - sum
  - summary
  - t
  - tanh
  - tanh_
  - Tensor
  - tile
  - to_tensor
  - topk
  - trace
  - transpose
  - tril
  - triu
  - unbind
  - uniform
  - unique
  - unsqueeze
  - unsqueeze_
  - unstack
  - var
  - where
  - XPUPlace
  - zeros
  - zeros_like
- paddle.amp
- paddle.callbacks
- paddle.compat
- paddle.distributed
  - Overview
  - all_gather
  - all_reduce
  - barrier
  - broadcast
  - fleet
  - get_rank
  - get_world_size
  - init_parallel_env
  - InMemoryDataset
  - ParallelEnv
  - QueueDataset
  - reduce
  - ReduceOp
  - scatter
  - spawn
  - split
- paddle.distribution
  - Overview
  - Categorical
  - Distribution
  - Normal
  - Uniform
- paddle.fluid
- paddle.io
- paddle.jit
- paddle.metric
  - Overview
  - Accuracy
  - accuracy
  - Auc
  - Metric
  - Precision
  - Recall
- paddle.nn
  - Overview
  - AdaptiveAvgPool1D
  - AdaptiveAvgPool2D
  - AdaptiveAvgPool3D
  - AdaptiveMaxPool1D
  - AdaptiveMaxPool2D
  - AdaptiveMaxPool3D
  - AlphaDropout
  - AvgPool1D
  - AvgPool2D
  - AvgPool3D
  - BatchNorm
  - BatchNorm1D
  - BatchNorm2D
  - BatchNorm3D
  - BCELoss
  - BCEWithLogitsLoss
  - BeamSearchDecoder
  - Bilinear
  - BiRNN
  - ClipGradByGlobalNorm
  - ClipGradByNorm
  - ClipGradByValue
  - Conv1D
  - Conv1DTranspose
  - Conv2D
  - Conv2DTranspose
  - Conv3D
  - Conv3DTranspose
  - CosineSimilarity
  - CrossEntropyLoss
  - CTCLoss
  - Dropout
  - Dropout2D
  - Dropout3D
  - dynamic_decode
  - ELU
  - Embedding
  - Flatten
  - functional
    - adaptive_avg_pool1d
    - adaptive_avg_pool2d
    - adaptive_avg_pool3d
    - adaptive_max_pool1d
    - adaptive_max_pool2d
    - adaptive_max_pool3d
    - affine_grid
    - alpha_dropout
    - avg_pool1d
    - avg_pool2d
    - avg_pool3d
    - batch_norm
    - bilinear
    - binary_cross_entropy
    - binary_cross_entropy_with_logits
    - conv1d
    - conv1d_transpose
    - conv2d
    - conv2d_transpose
    - conv3d
    - conv3d_transpose
    - cosine_similarity
    - cross_entropy
    - ctc_loss
    - diag_embed
    - dice_loss
    - dropout
    - dropout2d
    - dropout3d
    - elu
    - elu_
    - embedding
    - gather_tree
    - gelu
    - grid_sample
    - hardshrink
    - hardsigmoid
    - hardswish
    - hardtanh
    - hsigmoid_loss
    - instance_norm
    - interpolate
    - kl_div
    - l1_loss
    - label_smooth
    - layer_norm
    - leaky_relu
    - linear
    - local_response_norm
    - log_loss
    - log_sigmoid
    - log_softmax
    - margin_ranking_loss
    - max_pool1d
    - max_pool2d
    - max_pool3d
    - maxout
    - mse_loss
    - nll_loss
    - normalize
    - npair_loss
    - one_hot
    - pad
    - pixel_shuffle
    - prelu
    - relu
    - relu6
    - relu_
    - selu
    - sigmoid
    - sigmoid_focal_loss
    - smooth_l1_loss
    - softmax
    - softmax_
    - softmax_with_cross_entropy
    - softplus
    - softshrink
    - softsign
    - square_error_cost
    - swish
    - tanhshrink
    - temporal_shift
    - thresholded_relu
    - unfold
    - upsample
  - GELU
  - GroupNorm
  - GRU
  - GRUCell
  - Hardshrink
  - Hardsigmoid
  - Hardswish
  - Hardtanh
  - HSigmoidLoss
  - initializer
  - InstanceNorm1D
  - InstanceNorm2D
  - InstanceNorm3D
  - KLDivLoss
  - L1Loss
  - Layer
  - LayerList
  - LayerNorm
  - LeakyReLU
  - Linear
  - LocalResponseNorm
  - LogSigmoid
  - LogSoftmax
  - LSTM
  - LSTMCell
  - MarginRankingLoss
  - Maxout
  - MaxPool1D
  - MaxPool2D
  - MaxPool3D
  - MSELoss
  - MultiHeadAttention
  - NLLLoss
  - Pad1D
  - Pad2D
  - Pad3D
  - PairwiseDistance
  - ParameterList
  - PixelShuffle
  - PReLU
  - ReLU
  - ReLU6
  - RNN
  - RNNCellBase
  - SELU
  - Sequential
  - Sigmoid
  - SimpleRNN
  - SimpleRNNCell
  - SmoothL1Loss
  - Softmax
  - Softplus
  - Softshrink
  - Softsign
  - SpectralNorm
  - Swish
  - SyncBatchNorm
  - Tanh
  - Tanhshrink
  - ThresholdedReLU
  - Transformer
  - TransformerDecoder
  - TransformerDecoderLayer
  - TransformerEncoder
  - TransformerEncoderLayer
  - Upsample
  - UpsamplingBilinear2D
  - UpsamplingNearest2D
  - utils
    - remove_weight_norm
    - weight_norm
- paddle.onnx
  - export
- paddle.optimizer
  - Overview
  - Adadelta
  - Adagrad
  - Adam
  - Adamax
  - AdamW
  - Lamb
  - lr
  - Momentum
  - Optimizer
  - RMSProp
  - SGD
- paddle.regularizer
  - L1Decay
  - L2Decay
- paddle.static
- paddle.sysconfig
  - get_include
  - get_lib
- paddle.text
  - Overview
  - datasets
    - Conll05st
    - Imdb
    - Imikolov
    - Movielens
    - UCIHousing
    - WMT14
    - WMT16
- paddle.utils
  - Overview
  - cpp_extension
  - deprecated
  - download
    - get_weights_path_from_url
  - run_check
  - unique_name
    - generate
    - guard
    - switch
- paddle.vision
  - Overview
  - datasets
    - Cifar10
    - Cifar100
    - DatasetFolder
    - FashionMNIST
    - Flowers
    - ImageFolder
    - MNIST
    - VOC2012
  - get_image_backend
  - image_load
  - models
    - LeNet
    - mobilenet_v1
    - mobilenet_v2
    - MobileNetV1
    - MobileNetV2
    - ResNet
    - resnet101
    - resnet152
    - resnet18
    - resnet34
    - resnet50
    - VGG
    - vgg11
    - vgg13
    - vgg16
    - vgg19
  - ops
  - set_image_backend
  - transforms
    - adjust_brightness
    - adjust_contrast
    - adjust_hue
    - BaseTransform
    - BrightnessTransform
    - center_crop
    - CenterCrop
    - ColorJitter
    - Compose
    - ContrastTransform
    - crop
    - Grayscale
    - hflip
    - HueTransform
    - Normalize
    - normalize
    - Pad
    - pad
    - RandomCrop
    - RandomHorizontalFli
    - RandomResizedCrop
    - RandomRotation
    - RandomVerticalFlip
    - Resize
    - resize
    - rotate
    - SaturationTransform
    - to_grayscale
    - to_tensor
    - ToTensor
    - Transpose
    - vflip
常见问题与解答
Release Note

神经网络层

»
神经网络层
在 GitHub 上修改

神经网络层¶

卷积
池化
图像检测
序列
数学操作
激活函数
损失函数
数据输入输出
控制流
稀疏更新
使用DataFeeder传入训练/预测数据
学习率调度器
张量