RCNNModelWithLSTM.py 文件源码

def train(self):
        learning_rate = tf.train.exponential_decay(self.learning_rate,
        self.global_step,self.decay_steps,
        self.decay_rate,staircase = True)

        #use grad_clip to hand exploding or vanishing gradients
        optimizer = tf.train.AdamOptimizer(learning_rate)
        grads_and_vars = optimizer.compute_gradients(self.loss_val)

        for idx ,(grad,var) in enumerate(grads_and_vars):
            if grad is not None:
                grads_and_vars[idx] = (tf.clip_by_norm(grad,self.grad_clip),var)

        train_op = optimizer.apply_gradients(grads_and_vars, global_step = self.global_step)

        return train_op