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use co::{IBackend, SharedTensor};
use conn;
use layer::*;
use util::ArcLock;
#[derive(Debug, Clone)]
#[allow(missing_copy_implementations)]
pub struct TanH;
#[cfg(all(feature="cuda", not(feature="native")))]
impl<B: IBackend + conn::Tanh<f32> + conn::TanhPointwise<f32>> ILayer<B> for TanH {
impl_ilayer_activation!();
fn compute_in_place(&self) -> bool {
true
}
fn reshape(&mut self,
backend: ::std::rc::Rc<B>,
input_data: &mut Vec<ArcLock<SharedTensor<f32>>>,
input_gradient: &mut Vec<ArcLock<SharedTensor<f32>>>,
weights_data: &mut Vec<ArcLock<SharedTensor<f32>>>,
weights_gradient: &mut Vec<ArcLock<SharedTensor<f32>>>,
output_data: &mut Vec<ArcLock<SharedTensor<f32>>>,
output_gradient: &mut Vec<ArcLock<SharedTensor<f32>>>) {
if let Some(inp) = input_data.get(0) {
let read_inp = inp.read().unwrap();
let input_desc = read_inp.desc();
input_gradient[0].write().unwrap().resize(input_desc).unwrap();
output_data[0].write().unwrap().resize(input_desc).unwrap();
output_gradient[0].write().unwrap().resize(input_desc).unwrap();
}
}
}
#[cfg(all(feature="cuda", not(feature="native")))]
impl<B: IBackend + conn::Tanh<f32> + conn::TanhPointwise<f32>> ComputeOutput<f32, B> for TanH {
fn compute_output(&self,
backend: &B,
_weights: &[&SharedTensor<f32>],
input_data: &[&SharedTensor<f32>],
output_data: &mut [&mut SharedTensor<f32>]) {
match input_data.get(0) {
Some(input) => backend.tanh_plain(input, output_data[0]).unwrap(),
None => backend.tanh_pointwise_plain(output_data[0]).unwrap(),
}
}
}
#[cfg(all(feature="cuda", not(feature="native")))]
impl<B: IBackend + conn::Tanh<f32> + conn::TanhPointwise<f32>> ComputeInputGradient<f32, B> for TanH {
fn compute_input_gradient(&self,
backend: &B,
weights_data: &[&SharedTensor<f32>],
output_data: &[&SharedTensor<f32>],
output_gradients: &[&SharedTensor<f32>],
input_data: &[&SharedTensor<f32>],
input_gradients: &mut [&mut SharedTensor<f32>]) {
match output_data.get(0) {
Some(_) => backend.tanh_grad_plain(output_data[0], output_gradients[0], input_data[0], input_gradients[0]).unwrap(),
None => backend.tanh_pointwise_grad_plain(input_data[0], input_gradients[0]).unwrap(),
}
}
}
#[cfg(all(feature="cuda", not(feature="native")))]
impl<B: IBackend + conn::Tanh<f32> + conn::TanhPointwise<f32>> ComputeParametersGradient<f32, B> for TanH {}
#[cfg(feature="native")]
impl<B: IBackend + conn::Tanh<f32>> ILayer<B> for TanH {
impl_ilayer_activation!();
fn reshape(&mut self,
backend: ::std::rc::Rc<B>,
input_data: &mut Vec<ArcLock<SharedTensor<f32>>>,
input_gradient: &mut Vec<ArcLock<SharedTensor<f32>>>,
weights_data: &mut Vec<ArcLock<SharedTensor<f32>>>,
weights_gradient: &mut Vec<ArcLock<SharedTensor<f32>>>,
output_data: &mut Vec<ArcLock<SharedTensor<f32>>>,
output_gradient: &mut Vec<ArcLock<SharedTensor<f32>>>) {
if let Some(inp) = input_data.get(0) {
let read_inp = inp.read().unwrap();
let input_desc = read_inp.desc();
input_gradient[0].write().unwrap().resize(input_desc).unwrap();
output_data[0].write().unwrap().resize(input_desc).unwrap();
output_gradient[0].write().unwrap().resize(input_desc).unwrap();
}
}
}
#[cfg(feature="native")]
impl<B: IBackend + conn::Tanh<f32>> ComputeOutput<f32, B> for TanH {
fn compute_output(&self,
backend: &B,
_weights: &[&SharedTensor<f32>],
input_data: &[&SharedTensor<f32>],
output_data: &mut [&mut SharedTensor<f32>]) {
match input_data.get(0) {
Some(input) => backend.tanh_plain(input, output_data[0]).unwrap(),
None => panic!("No input provided for TanH layer."),
}
}
}
#[cfg(feature="native")]
impl<B: IBackend + conn::Tanh<f32>> ComputeInputGradient<f32, B> for TanH {
fn compute_input_gradient(&self,
backend: &B,
weights_data: &[&SharedTensor<f32>],
output_data: &[&SharedTensor<f32>],
output_gradients: &[&SharedTensor<f32>],
input_data: &[&SharedTensor<f32>],
input_gradients: &mut [&mut SharedTensor<f32>]) {
match output_data.get(0) {
Some(_) => backend.tanh_grad_plain(output_data[0], output_gradients[0], input_data[0], input_gradients[0]).unwrap(),
None => panic!("No output_data provided for TanH layer backward."),
}
}
}
#[cfg(feature="native")]
impl<B: IBackend + conn::Tanh<f32>> ComputeParametersGradient<f32, B> for TanH {}