Same as tf.maximum, but with helpful gradient for inputs < bound.

This function behaves just like tf.maximum, but the behavior of the gradient with respect to inputs for input values that hit the bound depends on gradient:

If set to 'disconnected', the returned gradient is zero for values that hit the bound. This is identical to the behavior of tf.maximum.

If set to 'identity', the gradient is unconditionally replaced with the identity function (i.e., pretending this function does not exist).

If set to 'identity_if_towards', the gradient is replaced with the identity function, but only if applying gradient descent would push the values of inputs towards the bound. For gradient values that push away from the bound, the returned gradient is still zero.

inputs Input tensor.
bound Lower bound for the input tensor.
gradient 'disconnected', 'identity', or 'identity_if_towards' (default).
name Name for this op.

tf.maximum(inputs, bound)

ValueError for invalid value of gradient.