回答編集履歴
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```Python
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class CategoricalCrossentropy1(keras.losses.Loss):
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def __init__(self, steps_per_epoch=None, end_epoch=None, name="example"):
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super().__init__(name=name)
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self.steps_per_epoch, self.end_epoch = steps_per_epoch, end_epoch
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self.step = tf.Variable(0.)
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def call(self, y_true, y_pred):
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epoch = self.step/self.steps_per_epoch
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if epoch < self.end_epoch:
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weight=1.0
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else:
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weight=-1.0 #Note that extreme number was put here to check how this code work
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loss = -tf.reduce_sum(weight*y_true*tf.math.log(y_pred)) #impose weight on CategoricalCrossentropy
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self.step.assign(self.step + 1) # add to step
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return loss
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```
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どうでしょうか
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```Python
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loss_weight =
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loss_weight = tf.Variable(1.0)
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model.compile(loss=CategoricalCrossentropy1(steps_per_epoch, end_epoch),optimizer=keras.optimizers.SGD(),loss_weights=(loss_weight,))
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loss_weight.assign(1.5)#値の更新方法
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```
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loss_weightsにテンソルを渡して、それを更新するという方法もあるかと思います。
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値の更新はkeras.callbacks.Callbackを継承したクラスに更新処理を書いてmodel.fit時に渡す。または、model.train_on_batchで書き換えて、iter処理中に書くかという方法があるかと思います。
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