Function gradients

tensorflow/python/ops/gradients_impl.py:44–159

Constructs symbolic derivatives of sum of `ys` w.r.t. x in `xs`. `ys` and `xs` are each a `Tensor` or a list of tensors. `grad_ys` is a list of `Tensor`, holding the gradients received by the `ys`. The list must be the same length as `ys`. `gradients()` adds ops to the graph to output the

(ys,
              xs,
              grad_ys=None,
              name="gradients",
              colocate_gradients_with_ops=False,
              gate_gradients=False,
              aggregation_method=None,
              stop_gradients=None,
              unconnected_gradients=UnconnectedGradients.NONE)

@tf_export(v1=["gradients"])
def gradients(ys,
              xs,
              grad_ys=None,
              name="gradients",
              colocate_gradients_with_ops=False,
              gate_gradients=False,
              aggregation_method=None,
              stop_gradients=None,
              unconnected_gradients=UnconnectedGradients.NONE):
  """Constructs symbolic derivatives of sum of `ys` w.r.t. x in `xs`.

  `ys` and `xs` are each a `Tensor` or a list of tensors.  `grad_ys`
  is a list of `Tensor`, holding the gradients received by the
  `ys`. The list must be the same length as `ys`.

  `gradients()` adds ops to the graph to output the derivatives of `ys` with
  respect to `xs`.  It returns a list of `Tensor` of length `len(xs)` where
  each tensor is the `sum(dy/dx)` for y in `ys`.

  `grad_ys` is a list of tensors of the same length as `ys` that holds
  the initial gradients for each y in `ys`.  When `grad_ys` is None,
  we fill in a tensor of '1's of the shape of y for each y in `ys`.  A
  user can provide their own initial `grad_ys` to compute the
  derivatives using a different initial gradient for each y (e.g., if
  one wanted to weight the gradient differently for each value in
  each y).

  `stop_gradients` is a `Tensor` or a list of tensors to be considered constant
  with respect to all `xs`. These tensors will not be backpropagated through,
  as though they had been explicitly disconnected using `stop_gradient`.  Among
  other things, this allows computation of partial derivatives as opposed to
  total derivatives. For example:

  ```python
  a = tf.constant(0.)
  b = 2 * a
  g = tf.gradients(a + b, [a, b], stop_gradients=[a, b])
  ```

  Here the partial derivatives `g` evaluate to `[1.0, 1.0]`, compared to the
  total derivatives `tf.gradients(a + b, [a, b])`, which take into account the
  influence of `a` on `b` and evaluate to `[3.0, 1.0]`.  Note that the above is
  equivalent to:

  ```python
  a = tf.stop_gradient(tf.constant(0.))
  b = tf.stop_gradient(2 * a)
  g = tf.gradients(a + b, [a, b])
  ```

  `stop_gradients` provides a way of stopping gradient after the graph has
  already been constructed, as compared to `tf.stop_gradient` which is used
  during graph construction.  When the two approaches are combined,
  backpropagation stops at both `tf.stop_gradient` nodes and nodes in
  `stop_gradients`, whichever is encountered first.

  All integer tensors are considered constant with respect to all `xs`, as if
  they were included in `stop_gradients`.