neuronika 0.2.0

Tensors and dynamic neural networks.
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mod arithmetic;
mod concatenate;
mod convolution;
mod linalg;
mod loss;
mod stack;

use super::{
    broadcasted_zeros, expect_tensor, expect_tensor_mut, push_gradient, push_mat_mat_gradient,
    push_mat_vec_gradient, push_vec_mat_gradient, push_vec_vec_gradient, reduce, Backward,
    BroadTensor, Broadcasted, Data, DotDim, Forward, Gradient, Overwrite, Tensor,
};

#[cfg(test)]
use super::{assert_almost_equals, new_backward_input, new_input, new_tensor};

pub(crate) use arithmetic::*;
pub(crate) use concatenate::*;
pub(crate) use linalg::*;
pub(crate) use loss::*;
pub(crate) use stack::*;

pub use convolution::{
    Constant, Convolve, ConvolveWithGroups, PaddingMode, Reflective, Replicative, Zero,
};