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class?AE(keras.Model):????????def?__init__(self):??????????super(AE,?self).__init__()????????????#?Encoders??????????self.encoder?=?Sequential([??????????????layers.Dense(256,?activation=tf.nn.relu),??????????????layers.Dense(128,?activation=tf.nn.relu),??????????????layers.Dense(h_dim)??????????])????????????#?Decoders??????????self.decoder?=?Sequential([??????????????layers.Dense(128,?activation=tf.nn.relu),??????????????layers.Dense(256,?activation=tf.nn.relu),??????????????layers.Dense(784)??????????])??????????def?call(self,?inputs,?training=None):??????????#?[b,?784]?=>?[b,?10]??????????h?=?self.encoder(inputs)??????????#?[b,?10]?=>?[b,?784]??????????x_hat?=?self.decoder(h)????????????return?x_hat??

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model?=?AE()?model.build(input_shape=(None,?784))?model.summary()??optimizer?=?tf.optimizers.Adam(lr=lr)??for?epoch?in?range(100):??????for?step,?x?in?enumerate(train_db):??????????#[b,?28,?28]?=>?[b,?784]?????????x?=?tf.reshape(x,?[-1,?784])??????????with?tf.GradientTape()?as?tape:?????????????x_rec_logits?=?model(x)??????????????rec_loss?=?tf.losses.binary_crossentropy(x,?x_rec_logits,?from_logits=True)?????????????rec_loss?=?tf.reduce_mean(rec_loss)??????????grads?=?tape.gradient(rec_loss,?model.trainable_variables)?????????optimizer.apply_gradients(zip(grads,?model.trainable_variables))???????????if?step?%?100?==0:?????????????print(epoch,?step,?float(rec_loss))?

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