Backpropagation (tf.custom_gradient) で self を使用したい (tensorflow) - Qiita

import tensorflow as tf optimizer = tf.keras.optimizers.Adam(learning_rate=0.1) class MyModel(tf.keras.Model): def __init__(self): super(MyModel, self).__init__() self.alpha = self.add_weight(name="alpha", initializer=tf.keras.initializers.Ones()) @tf.custom_gradient def forward(self, x): y = self.alpha * x tf.print("forward") tf.print(" y: ", y) def backward(w, variables=None): z = self.alpha * w