mikecvet

  /// Runs forward propagation in this layer of the network. Flattens input, computes probabilities of 
  /// outputs based on the dot product with this layer's weights and softmax outputs.
  pub fn 
  forward_propagation<'a> (&mut self, input: &'a Array3<f64>, ctx: &mut SoftmaxContext<'a>) -> Array1<f64> 
  {
    let flattened: Array1<f64> = input.to_owned().into_shape((input.len(),)).unwrap();
    let dot_result = flattened.dot(&self.weights).add(&self.bias);
    let probabilities = softmax(&dot_result);

    ctx.input = Some(input);

mikecvet

    let patches = self.patches(input);

    for p in patches.iter() {
      let depth = p.data.dim().2;
      let v: Vec<f64> = (0..depth).map(|i| {
        p.data
          .slice(s![.., .., i])
          .fold(f64::NEG_INFINITY, |acc, &v| acc.max(v))
      }).collect();

mikecvet

  /// A patch is a concept from image processing which captures subregions of the image pixels, 
  /// used to capture groups of spatial features from the underlying image.
  pub fn
  patches (&mut self, image: &Array2<f64>) -> Vec<Patch>
  {
    let mut data: Vec<Patch> = Vec::new();

    for x in 0..(image.shape()[0] - self.rows + 1) {
      for y in 0..(image.shape()[1] - self.cols + 1) {
        let p = Patch {

mikecvet

  /// A patch is a concept from image processing which captures subregions of the image pixels, 
  /// used to capture groups of spatial features from the underlying image.
  pub fn
  patches (&mut self, image: &Array2<f64>) -> Vec<Patch>
  {
    let mut data: Vec<Patch> = Vec::new();

    for x in 0..(image.shape()[0] - self.rows + 1) {
      for y in 0..(image.shape()[1] - self.cols + 1) {
        let p = Patch {