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