def _SoftplusGradGrad(op, grad):
# Let:
# y = tf.nn.softplus(x)
# dx = gen_nn_ops.softplus_grad(dy, x) = dy / (1 + exp(-x))
# This op computes (ddy, d2x) from op.inputs == [dy, x] and grad == ddx.
dy, x = op.inputs
with ops.control_dependencies([grad]):
ddy = gen_nn_ops.softplus_grad(grad, x)
d2x = grad * dy / (math_ops.exp(-x) + 2.0 + math_ops.exp(x))
return (ddy, d2x)
def _SoftplusGrad(op, grad): return gen_nn_ops.softplus_grad(grad, op.inputs[0])
def _SoftplusGrad(op, grad):
return gen_nn_ops.softplus_grad(grad, op.inputs[0])