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  • Transfer learning fine-tuning | TensorFlow Core
    Skipping registering GPU devices In general, all weights are trainable weights The only built-in layer that has non-trainable weights is the BatchNormalization layer It uses non-trainable weights to keep track of the mean and variance of its inputs during training
  • 転移学習とファインチューニング | TensorFlow Core
    trainable_weights は、トレーニング中の損失を最小限に抑えるために（勾配降下を介して）更新を意図した重みのリストです。
  • 전이 학습 및 미세 조정 | TensorFlow Core
    trainable = False 를 설정하여 기본 모델의 모든 레이어를 동결합니다 기본 모델에서 하나 이상의 레이어 출력 위에 새 모델을 만듭니다
  • hub. KerasLayer - TensorFlow
    See the migration guide for guidance on how to pick up trainable variables, losses and updates explicitly from Keras objects instead of relying on graph collections
  • Models and layers | TensorFlow. js
    Name and type of all layers in the model Output shape for each layer Number of weight parameters of each layer If the model has general topology (discussed below), the inputs each layer receives The total number of trainable and non-trainable parameters of the model For the model we defined above, we get the following output on the console:
  • Making new layers and models via subclassing - TensorFlow
    Besides trainable weights, you can add non-trainable weights to a layer as well Such weights are meant not to be taken into account during backpropagation, when you are training the layer
  • Writing a training loop from scratch - TensorFlow Core
    Calling a model inside a GradientTape scope enables you to retrieve the gradients of the trainable weights of the layer with respect to a loss value Using an optimizer instance, you can use these gradients to update these variables (which you can retrieve using model trainable_weights)
  • Custom layers - TensorFlow Core
    For example, you can inspect all variables # in a layer using `layer variables` and trainable variables using # `layer trainable_variables` In this case a fully-connected layer # will have variables for weights and biases layer variables
  • 그래디언트 및 자동 미분 소개 | TensorFlow Core
    대부분의 경우 모델의 훈련 가능한 변수에 대한 그래디언트를 계산하려고 합니다 tf Module 의 모든 서브 클래스는 Module trainable_variables 속성에서 변수를 집계하므로 몇 줄의 코드로 이러한 그래디언트를 계산할 수 있습니다





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