Python NodeStepper.handle_node_names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 handle_node_names [as 别名]
  def testOverrideAndContToSameTensor(self):
    stepper = NodeStepper(self.sess, self.e)

    result = stepper.cont(self.c)
    self.assertAllClose(6.0, result)
    self.assertEqual({}, stepper.last_feed_types())
    self.assertEqual(["c:0"], stepper.handle_names())
    self.assertSetEqual({"c"}, stepper.handle_node_names())

    self.assertAllClose(6.0, stepper.cont(self.c))

    # The last cont() call should use the tensor handle directly.
    self.assertEqual({
        "c:0": NodeStepper.FEED_TYPE_HANDLE
    }, stepper.last_feed_types())

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

    # As a result of the override, the tensor handle should have been
    # invalidated.
    self.assertEqual([], stepper.handle_names())
    self.assertSetEqual(set(), stepper.handle_node_names())

    result = stepper.cont(self.c)
    self.assertAllClose(7.0, result)

    self.assertEqual({
        "c:0": NodeStepper.FEED_TYPE_OVERRIDE
    }, 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 handle_node_names [as 别名]
  def testOverrideValueTwice(self):
    stepper = NodeStepper(self.sess, self.e)

    # Override once.
    stepper.override_tensor("c:0", 7.0)
    self.assertAllClose(28.0, stepper.cont(self.e))
    self.assertEqual({
        "c:0": NodeStepper.FEED_TYPE_OVERRIDE
    }, stepper.last_feed_types())

    self.assertEqual(["e:0"], stepper.handle_names())
    self.assertSetEqual({"e"}, stepper.handle_node_names())
    self.assertEqual(["c:0"], stepper.override_names())

    # Calling cont(self.e) again. This time the cached tensor handle of e
    # should be used.
    self.assertEqual(28.0, stepper.cont(self.e))
    self.assertEqual({
        "e:0": NodeStepper.FEED_TYPE_HANDLE
    }, stepper.last_feed_types())

    # Override c again. This should have invalidated the cache for e.
    stepper.override_tensor("c:0", 8.0)

    self.assertEqual([], stepper.handle_names())
    self.assertEqual(set(), stepper.handle_node_names())
    self.assertEqual(["c:0"], stepper.override_names())

    self.assertAllClose(32.0, stepper.cont(self.e))
    self.assertEqual({
        "c:0": NodeStepper.FEED_TYPE_OVERRIDE
    }, 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 handle_node_names [as 别名]
  def testUsingNodesNotUsingIntermediateTensors(self):
    stepper = NodeStepper(self.sess, self.e)

    # There should be no handles before any cont() calls.
    self.assertEqual([], stepper.handle_names())
    self.assertSetEqual(set(), stepper.handle_node_names())

    # Before the cont() call, the stepper should not have access to the value
    # of c:0.
    with self.assertRaisesRegexp(
        ValueError,
        "This stepper instance does not have access to the value of tensor "
        "\"c:0\""):
      stepper.get_tensor_value("c:0")

    # Using the node/tensor itself, instead of the name str, should work on
    # cont().
    result = stepper.cont(self.c)
    self.assertAllClose(6.0, result)
    self.assertEqual({}, stepper.last_feed_types())

    self.assertEqual(["c:0"], stepper.handle_names())
    self.assertEqual({"c"}, stepper.handle_node_names())

    # After the cont() call, the stepper should have access to the value of c:0
    # via a tensor handle.
    self.assertAllClose(6.0, stepper.get_tensor_value("c:0"))

    result = stepper.cont(self.e)
    self.assertAllClose(24.0, result)
    self.assertEqual({
        "c: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 handle_node_names [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 handle_node_names [as 别名]
  def testContWithPlaceholders(self):
    stepper = NodeStepper(
        self.sess,
        self.y,
        feed_dict={
            self.ph0: [[1.0, 2.0], [-3.0, 5.0]],
            self.ph1: [[-1.0], [0.5]]
        })

    self.assertEqual(4, len(stepper.sorted_nodes()))
    self.assertSetEqual({"ph0:0", "ph1:0", "x:0", "y:0"},
                        set(stepper.closure_elements()))

    result = stepper.cont(self.x)
    self.assertAllClose([[0.0], [5.5]], result)
    self.assertEqual({
        "ph0:0": NodeStepper.FEED_TYPE_CLIENT,
        "ph1:0": NodeStepper.FEED_TYPE_CLIENT,
    }, stepper.last_feed_types())

    self.assertEqual(["x:0"], stepper.handle_names())
    self.assertSetEqual({"x"}, stepper.handle_node_names())

    result = stepper.cont(self.y)
    self.assertAllClose([[-1.0], [6.0]], result)
    self.assertEqual({
        "x:0": NodeStepper.FEED_TYPE_HANDLE,
        "ph1:0": NodeStepper.FEED_TYPE_CLIENT,
    }, 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 handle_node_names [as 别名]
  def testContToNodeWithOutputTensors(self):
    """cont() to an op should cache its output tensors if appropriate."""

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

    # In the transitive closure of the stepper, look for an op of which the
    # output tensor also is in the transitive closure.
    # Do not assume a specific op, e.g., ""gradients/e_grad/Reshape_1",
    # because it may vary between builds.
    closure_elements = stepper.closure_elements()
    op_with_output_in_closure = None
    for element_name in closure_elements:
      if element_name + ":0" in closure_elements:
        op_with_output_in_closure = str(element_name)
        break

    self.assertEqual([0],
                     stepper.output_slots_in_closure(op_with_output_in_closure))

    self.assertIsNotNone(op_with_output_in_closure)
    output_tensor = op_with_output_in_closure + ":0"

    # The op "gradients/?_grad/Reshape_1" is in the transitive closure of the
    # stepper, because it is the control input to another o. However, its
    # output tensor "gradients/?_grad/Reshape_1:0" is also in the transitive
    # closure, because it is the (non-control) input of certain ops. Calling
    # cont() on the op should lead to the caching of the tensor handle for
    # the output tensor.
    stepper.cont(op_with_output_in_closure)

    self.assertEqual([output_tensor], stepper.handle_names())
    self.assertSetEqual({op_with_output_in_closure},
                        stepper.handle_node_names())

    # Do a cont() call that uses the cached tensor of
    # "gradients/?_grad/Reshape_1:0".
    stepper.cont(output_tensor)
    self.assertEqual({
        output_tensor: 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 handle_node_names [as 别名]
  def testOverrideValue(self):
    stepper = NodeStepper(self.sess, self.e)

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

    # There should be no overrides before any cont() calls.
    self.assertEqual([], stepper.override_names())

    # Calling cont() on c again should lead to use of the handle.
    result = stepper.cont(self.c)
    self.assertAllClose(6.0, result)
    self.assertEqual({
        "c:0": NodeStepper.FEED_TYPE_HANDLE
    }, stepper.last_feed_types())

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

    # After the overriding, calling get_tensor_value() on c:0 should yield the
    # overriding value.
    self.assertEqual(7.0, stepper.get_tensor_value("c:0"))

    # Now c:0 should have only an override value, but no cached handle, because
    # the handle should have been invalidated.
    self.assertEqual([], stepper.handle_names())
    self.assertSetEqual(set(), stepper.handle_node_names())
    self.assertEqual(["c:0"], stepper.override_names())

    # Run a downstream tensor after the value override.
    result = stepper.cont(self.e)
    self.assertAllClose(28.0, result)  # Should reflect the overriding value.

    # Should use override, instead of the handle.
    self.assertEqual({
        "c:0": NodeStepper.FEED_TYPE_OVERRIDE
    }, 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 handle_node_names [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())
    self.assertSetEqual({"d"}, stepper.handle_node_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())
    self.assertSetEqual(set(), stepper.handle_node_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())
    self.assertSetEqual({"d"}, stepper.handle_node_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))