juice 0.3.0

Machine Learning Framework for Hackers
Documentation 
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//! Computes the softmax of its input.
//!
//! For the logarithmic softmax see the `LogSoftmax` layer.

use crate::co::{IBackend, SharedTensor};
use crate::conn;
use crate::layer::*;
use crate::util::ArcLock;

#[derive(Debug, Clone)]
#[allow(missing_copy_implementations)]
/// Softmax Layer
pub struct Softmax;

impl<B: IBackend + conn::Softmax<f32>> ILayer<B> for Softmax {
    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>>>,
    ) {
        let input_desc = input_data[0].read().unwrap().desc().clone();
        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();
    }
}

impl<B: IBackend + conn::Softmax<f32>> ComputeOutput<f32, B> for Softmax {
    fn compute_output(
        &self,
        backend: &B,
        _weights: &[&SharedTensor<f32>],
        input_data: &[&SharedTensor<f32>],
        output_data: &mut [&mut SharedTensor<f32>],
    ) {
        backend.softmax(input_data[0], output_data[0]).unwrap();
    }
}

impl<B: IBackend + conn::Softmax<f32>> ComputeInputGradient<f32, B> for Softmax {
    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>],
    ) {
        backend
            .softmax_grad(output_data[0], output_gradients[0], input_gradients[0])
            .unwrap();
    }
}

impl<B: IBackend + conn::Softmax<f32>> ComputeParametersGradient<f32, B> for Softmax {}

impl ::std::default::Default for Softmax {
    fn default() -> Softmax {
        Softmax
    }
}