Constructs symbolic derivatives of sum of ys w.r.t. x in xs.

# Aliases:

• tf.compat.v2.gradients

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

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:

 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 . Note th`a`t the `above is equiva`lent to:

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

tf.stop_gradientstop_gradients provides a way of stopping gradient after the graph has already been constructed, as compared to which is used during graph construction. When the two approaches are combined, backpropagation stops at both 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. unconnected_gradients determines the value returned for each x in xs if it is unconnected in the graph to ys. By default this is None to safeguard against errors. Mathematically these gradients are zero which can be requested using the 'zero' option. tf.UnconnectedGradients provides the following options and behaviors:

 a = tf.ones([1, 2])
b = tf.ones([3, 1])
g1 = tf.gradients([b], [a], unnconnected_gradients='none')
sess.run(g1)  # [None]

g2 = tf.gradients([b], [a], unconnected_gradients='zero')
sess.run(g2)  # [array([[0., 0.]], dtype=float32)]

# Args:

• ys: A Tensor or list of tensors to be differentiated.
• xs: A Tensor or list of tensors to be used for differentiation.
• grad_ys: Optional. A Tensor or list of tensors the same size as ys and holding the gradients computed for each y in ys.
• name: Optional name to use for grouping all the gradient ops together. defaults to 'gradients'.
• gate_gradients: If True, add a tuple around the gradients returned for an operations. This avoids some race conditions.
• aggregation_method: Specifies the method used to combine gradient terms. Accepted values are constants defined in the class AggregationMethod.
• stop_gradients: Optional. A Tensor or list of tensors not to differentiate through.
• unconnected_gradients: Optional. Specifies the gradient value returned when the given input tensors are unconnected. Accepted values are constants defined in the class tf.UnconnectedGradients and the default value is none.

# Returns:

A list of sum(dy/dx) for each x in ``.

# Raises:

• LookupError: if one of the operations between x and y does not have a registered gradient function.
• ValueError: if the arguments are invalid.
• RuntimeError: if called in Eager mode.