Python NodeStepper.sorted_transitive_closure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 sorted_transitive_closure [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(["ph0:0", "ph1:0", "x:0", "y:0"],
                     stepper.sorted_transitive_closure())

    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())

    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 sorted_transitive_closure [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 = stepper.sorted_transitive_closure()
    op_with_output_in_closure = None
    for element_name in closure:
      if element_name + ":0" in closure:
        op_with_output_in_closure = str(element_name)
        break

    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())

    # 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 sorted_transitive_closure [as 别名]
  def testAttemptToContToFetchNotInTransitiveClosure(self):
    stepper = NodeStepper(self.sess, "e:0")

    self.assertEqual(
        ["a:0", "b:0", "b/read:0", "a/read:0", "c:0", "d:0", "e:0"],
        stepper.sorted_transitive_closure())

    with self.assertRaisesRegexp(
        ValueError,
        "Target \"f:0\" is not in the transitive closure for the fetch of the "
        "stepper: \"e:0\""):
      stepper.cont("f:0")