ugrnn.py 文件源码

def add_training_ops(self):
        def apply_gradient_clipping(gradient):
            if gradient is not None:
                return tf.mul(tf.clip_by_value(tf.abs(grad), 0.1, 1.),
                              tf.sign(grad))
            else:
                return None

        # optimizer = tf.train.GradientDescentOptimizer(learning_rate=self.learning_rate)
        optimizer = tf.train.AdamOptimizer(learning_rate=self.learning_rate,
                                           beta1=0.9, beta2=0.999,
                                           epsilon=1e-08,
                                           use_locking=False, name='Adam')

        loss_op = self.loss_op + config.weight_decay_factor * tf.add_n(
            [tf.nn.l2_loss(v) for v in tf.get_collection('weights_decay')])

        gvs = optimizer.compute_gradients(loss_op)

        if self._clip_gradients:
            gvs = [(apply_gradient_clipping(grad), var) for grad, var in gvs]

        train_op = optimizer.apply_gradients(gvs)

        return train_op