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custom loss function eras

VVY answered on April 7, 2023 Popularity 5/10 Helpfulness 7/10

answer custom loss function eras

custom loss function eras

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class Loss(keras.losses.Loss): # usefull when saving hyperparameters
  def __init__(self, hyperparameter=1., **kwargs):
    self.hyperparameter = hyperparameter
    super().__init__(**kwargs)

  def call(self, y_true, y_pred):
    pass # depends on the particular loss function you need

  def get_config(self):
    base_config = super().get_config() # get_config() method returns a dictionary mapping each name to its value
    return {**base_config, "hyperparameter": self.hyperparameter} # adds new hyperparameters to this dictionary
  
model.compile(loss=Loss(2.), optimizers="sgd")

# When you save the model, the threshold will be saved along with it
# When you want to load the model:
model = keras.models.load_model("model.h5", custom_objects={"Loss": Loss})

Popularity 5/10 Helpfulness 7/10 Language python

Source: Grepper

Tags: loss-function python

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Contributed on Apr 07 2023

VVY

0 Answers Avg Quality 2/10

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