Python NodeStepper.remove_override方法代码示例

# 需要导入模块: from tensorflow.python.debug.stepper import NodeStepper [as 别名]
# 或者: from tensorflow.python.debug.stepper.NodeStepper import remove_override [as 别名]
  def testRemoveNonexistentOverrideValue(self):
    stepper = NodeStepper(self.sess, self.e)
    self.assertEqual([], stepper.override_names())

    with self.assertRaisesRegexp(
        ValueError, "No overriding value exists for tensor \"c:0\""):
      stepper.remove_override("c:0")

# 需要导入模块: from tensorflow.python.debug.stepper import NodeStepper [as 别名]
# 或者: from tensorflow.python.debug.stepper.NodeStepper import remove_override [as 别名]
  def testRemoveOverrideValue(self):
    stepper = NodeStepper(self.sess, self.e)

    result = stepper.cont(self.c)
    self.assertAllClose(6.0, result)
    self.assertEqual({}, stepper.last_feed_types())

    # The previous cont() step should have generated a cached tensor handle.
    self.assertEqual(["c:0"], stepper.handle_names())
    self.assertSetEqual({"c"}, stepper.handle_node_names())

    # Override c:0.
    stepper.override_tensor("c:0", 7.0)

    # The overriding should have invalidated the tensor handle.
    self.assertEqual([], stepper.handle_names())
    self.assertSetEqual(set(), stepper.handle_node_names())
    self.assertEqual(["c:0"], stepper.override_names())

    result = stepper.cont(self.e)
    self.assertAllClose(28.0, result)  # Should reflect the overriding value.
    self.assertEqual({
        "c:0": NodeStepper.FEED_TYPE_OVERRIDE
    }, stepper.last_feed_types())

    # The handle to tensor e:0 should have been cached, even though its
    # transitive closure contains an override.
    self.assertIn("e:0", stepper.handle_names())
    self.assertSetEqual({"e"}, stepper.handle_node_names())

    # Remove the override.
    stepper.remove_override("c:0")
    # c:0 should not be in the overrides anymore.
    self.assertEqual([], stepper.override_names())

    # Removing the override should have invalidated the tensor handle for c.
    self.assertNotIn("e:0", stepper.handle_names())
    self.assertNotIn("e", stepper.handle_node_names())

    # Should reflect the non-overriding value.
    self.assertAllClose(24.0, stepper.cont(self.e))

    # This time, the handle to tensor e:0 should have been cached again, even
    # thought its transitive closure contains an override.
    self.assertIn("e:0", stepper.handle_names())
    self.assertIn("e", stepper.handle_node_names())

    # Calling cont(self.e) again should have used the tensor handle to e:0.
    self.assertAllClose(24.0, stepper.cont(self.e))
    self.assertEqual({
        "e:0": NodeStepper.FEED_TYPE_HANDLE
    }, stepper.last_feed_types())

# 需要导入模块: from tensorflow.python.debug.stepper import NodeStepper [as 别名]
# 或者: from tensorflow.python.debug.stepper.NodeStepper import remove_override [as 别名]
  def testOverrideThenContToUpdate(self):
    """Test cont() to update nodes after overriding tensor values."""

    stepper = NodeStepper(self.sess, "optim")

    result = stepper.cont("d:0")
    self.assertAllClose(2.0, result)
    self.assertEqual({}, stepper.last_feed_types())
    self.assertEqual(set(), stepper.dirty_variables())
    self.assertEqual(["d:0"], stepper.handle_names())

    # Override the value from 1.0 to 10.0.
    stepper.override_tensor("a/read:0", 10.0)

    self.assertEqual(["a/read:0"], stepper.override_names())

    result = stepper.cont("optim/update_c/ApplyGradientDescent",
                          restore_variable_values=True)
    self.assertIsNone(result)

    # The last cont() call should have not used the tensor handle to d:0,
    # because the transitive closure of d:0 contains an override tensor.
    self.assertEqual({
        "a/read:0": NodeStepper.FEED_TYPE_OVERRIDE
    }, stepper.last_feed_types())

    # The tensor handle to d:0 should have been removed due to the dirty
    # transitive closure.
    self.assertEqual([], stepper.handle_names())

    # For this backprop on c, the overriding value of a/read:0 should have been
    # used:
    #   4.0 - learning_rate * a * b * b
    #     = 4.0 - 0.01 * 10.0 * 2.0 * 2.0 = 3.6.
    self.assertAllClose(3.6, self.sess.run(self.c))

    # Now remove the overriding value of a/read:0.
    stepper.remove_override("a/read:0")
    self.assertEqual([], stepper.override_names())

    # Obtain the tensor handle to d:0 again.
    result = stepper.cont("d:0")
    self.assertAllClose(2.0, result)
    self.assertEqual(["d:0"], stepper.handle_names())

    # Then call update_c again, without restoring c.
    result = stepper.cont(
        "optim/update_c/ApplyGradientDescent", restore_variable_values=False)
    self.assertIsNone(result)

    # This time, the d:0 tensor handle should have been used, because its
    # transitive closure is clean.
    self.assertEqual({
        "d:0": NodeStepper.FEED_TYPE_HANDLE
    }, stepper.last_feed_types())

    # For this backprop on c, the overriding value of a/read:0 should have been
    # used:
    #   3.6 - learning_rate * a * b * b
    #     = 3.6 - 0.01 * 1.0 * 2.0 * 2.0 = 3.56.
    self.assertAllClose(3.56, self.sess.run(self.c))