model.py 文件源码

def Train(self,
            loss,
            learning_rate,
            clip_value_min,
            clip_value_max,
            name='training'):
    tf.scalar_summary(':'.join([name, loss.op.name]), loss)
    optimizer = tf.train.AdagradOptimizer(learning_rate)
    grads_and_vars = optimizer.compute_gradients(loss)

    clipped_grads_and_vars = [
        (tf.clip_by_value(g, clip_value_min, clip_value_max), v)
        for g, v in grads_and_vars
    ]

    for g, v in clipped_grads_and_vars:
      _ = tf.histogram_summary(':'.join([name, v.name]), v)
      _ = tf.histogram_summary('%s: gradient for %s' % (name, v.name), g)

    train_op = optimizer.apply_gradients(clipped_grads_and_vars)

    return train_op