SciTech-BigDataAIML-Tensorflow-Keras API-Layers层的API：inputs输入 + outputs产出 + states(weights权重)

https://keras.io/api/layers/

How to Use Word Embedding Layers for Deep Learning with Keras

Layer层 是Keras的 NN(神经网络)的 必要模块; 一个Layer由：

• Layer.call() 对外API调用接口
• tensor-in function张量输入函数
• tensor-out function张量产出函数
• states(Layer层的)状态(由Layer.weights属性即TensorFlow变量存储);

Keras layers API

Layers are the basic building blocks of neural networks in Keras. A layer consists of a tensor-in tensor-out computation function (the layer's call method) and some state, held in TensorFlow variables (the layer's weights).

一个Layer Class类的 Instance示例是可调用的(通过Python Class的通用类方法__call__())

A Layer instance is callable, much like a function:

import keras
from keras import layers

layer = layers.Dense(32, activation='relu')
inputs = keras.random.uniform(shape=(10, 20))
outputs = layer(inputs)
# Unlike a function, though, layers maintain a state, updated when the layer receives data during training, and stored in layer.weights:

>>> layer.weights
[,
 ]
Creating custom layers

While Keras offers a wide range of built-in layers, they don't cover ever possible use case. Creating custom layers is very common, and very easy.

See the guide Making new layers and models via subclassing for an extensive overview, and refer to the documentation for the base Layer class.

Layers API overview

• The base Layer class
• Layer class
• weights property
• trainable_weights property
• non_trainable_weights property
  
  add_weight method
  
  trainable property
  
  get_weights method
  
  set_weights method
  
  get_config method
  
  add_loss method
  
  losses property
  
  Layer activations
  
  relu function
  
  sigmoid function
  
  softmax function
  
  softplus function
  
  softsign function
  
  tanh function
  
  selu function
  
  elu function
  
  exponential function
  
  leaky_relu function
  
  relu6 function
  
  silu function
  
  hard_silu function
  
  gelu function
  
  hard_sigmoid function
  
  linear function
  
  mish function
  
  log_softmax function
  
  Layer weight initializers
  
  RandomNormal class
  
  RandomUniform class
  
  TruncatedNormal class
  
  Zeros class
  
  Ones class
  
  GlorotNormal class
  
  GlorotUniform class
  
  HeNormal class
  
  HeUniform class
  
  Orthogonal class
  
  Constant class
  
  VarianceScaling class
  
  LecunNormal class
  
  LecunUniform class
  
  IdentityInitializer class
  
  Layer weight regularizers
  
  Regularizer class
  
  L1 class
  
  L2 class
  
  L1L2 class
  
  OrthogonalRegularizer class
  
  Layer weight constraints
  
  Constraint class
  
  MaxNorm class
  
  MinMaxNorm class
  
  NonNeg class
  
  UnitNorm class
  
  Core layers
  
  Input object
  
  InputSpec object
  
  Dense layer
  
  EinsumDense layer
  
  Activation layer
  
  Embedding layer
  
  Masking layer
  
  Lambda layer
  
  Identity layer
  
  Convolution layers
  
  Conv1D layer
  
  Conv2D layer
  
  Conv3D layer
  
  SeparableConv1D layer
  
  SeparableConv2D layer
  
  DepthwiseConv1D layer
  
  DepthwiseConv2D layer
  
  Conv1DTranspose layer
  
  Conv2DTranspose layer
  
  Conv3DTranspose layer
  
  Pooling layers
  
  MaxPooling1D layer
  
  MaxPooling2D layer
  
  MaxPooling3D layer
  
  AveragePooling1D layer
  
  AveragePooling2D layer
  
  AveragePooling3D layer
  
  GlobalMaxPooling1D layer
  
  GlobalMaxPooling2D layer
  
  GlobalMaxPooling3D layer
  
  GlobalAveragePooling1D layer
  
  GlobalAveragePooling2D layer
  
  GlobalAveragePooling3D layer
  
  Recurrent layers
  
  LSTM layer
  
  LSTM cell layer
  
  GRU layer
  
  GRU Cell layer
  
  SimpleRNN layer
  
  TimeDistributed layer
  
  Bidirectional layer
  
  ConvLSTM1D layer
  
  ConvLSTM2D layer
  
  ConvLSTM3D layer
  
  Base RNN layer
  
  Simple RNN cell layer
  
  Stacked RNN cell layer
  
  Preprocessing layers
  
  Text preprocessing
  
  Numerical features preprocessing layers
  
  Categorical features preprocessing layers
  
  Image preprocessing layers
  
  Image augmentation layers
  
  Normalization layers
  
  BatchNormalization layer
  
  LayerNormalization layer
  
  UnitNormalization layer
  
  GroupNormalization layer
  
  Regularization layers
  
  Dropout layer
  
  SpatialDropout1D layer
  
  SpatialDropout2D layer
  
  SpatialDropout3D layer
  
  GaussianDropout layer
  
  AlphaDropout layer
  
  GaussianNoise layer
  
  ActivityRegularization layer
  
  Attention layers
  
  GroupQueryAttention
  
  MultiHeadAttention layer
  
  Attention layer
  
  AdditiveAttention layer
  
  Reshaping layers
  
  Reshape layer
  
  Flatten layer
  
  RepeatVector layer
  
  Permute layer
  
  Cropping1D layer
  
  Cropping2D layer
  
  Cropping3D layer
  
  UpSampling1D layer
  
  UpSampling2D layer
  
  UpSampling3D layer
  
  ZeroPadding1D layer
  
  ZeroPadding2D layer
  
  ZeroPadding3D layer
  
  Merging layers
  
  Concatenate layer
  
  Average layer
  
  Maximum layer
  
  Minimum layer
  
  Add layer
  
  Subtract layer
  
  Multiply layer
  
  Dot layer
  
  Activation layers
  
  ReLU layer
  
  Softmax layer
  
  LeakyReLU layer
  
  PReLU layer
  
  ELU layer
  
  Backend-specific layers
  
  TorchModuleWrapper layer
  
  Tensorflow SavedModel layer
  
  JaxLayer
  
  FlaxLayer