Method set_weights

tensorflow/python/keras/optimizer_v2/adam.py:177–185

(self, weights)

    ))

  def set_weights(self, weights):
    params = self.weights
    # If the weights are generated by Keras V1 optimizer, it includes vhats
    # even without amsgrad, i.e, V1 optimizer has 3x + 1 variables, while V2
    # optimizer has 2x + 1 variables. Filter vhats out for compatibility.
    num_vars = int((len(params) - 1) / 2)
    if len(weights) == 3 * num_vars + 1:
      weights = weights[:len(params)]
    super(Adam, self).set_weights(weights)

  def _resource_apply_dense(self, grad, var, apply_state=None):
    var_device, var_dtype = var.device, var.dtype.base_dtype