optimizers_test.py 文件源码

def testAdaptiveGradientClip(self):
    with self.test_session() as session:
      x, var, loss, global_step = _setup_model()
      clip_gradients = tf.contrib.layers.adaptive_clipping_fn()
      train = tf.contrib.layers.optimize_loss(loss,
                                              global_step,
                                              learning_rate=0.1,
                                              optimizer="SGD",
                                              clip_gradients=clip_gradients)
      tf.global_variables_initializer().run()
      session.run(train, feed_dict={x: 5})
      var_value, global_step_value = session.run([var, global_step])
      self.assertAlmostEqual(var_value, 9.8916, 4)
      self.assertEqual(global_step_value, 1)
      var_count = 0
      for var in tf.all_variables():
        if var.name.startswith("OptimizeLoss/AdaptiveMaxNorm"):
          var_count += 1
      self.assertEqual(2, var_count)