neuronika 0.2.0

Tensors and dynamic neural networks.
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mod chunk;
mod dropout;
mod exp;
mod leaky_relu;
mod logn;
mod logsoftmax;
mod mean;
mod negation;
mod power;
mod relu;
mod sigmoid;
mod softmax;
mod softplus;
mod sqrt;
mod sum;
mod tanh;
mod transpose;
mod unsqueeze;

use super::{
    expect_tensor, expect_tensor_mut, push_gradient, Backward, Data, Eval, Forward, Gradient,
    Overwrite, Tensor,
};

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

pub(crate) use chunk::{Chunk, ChunkBackward};
pub(crate) use dropout::{Dropout, DropoutBackward};
pub(crate) use exp::{Exp, ExpBackward};
pub(crate) use leaky_relu::{LeakyReLU, LeakyReLUBackward};
pub(crate) use logn::{Logn, LognBackward};
pub(crate) use logsoftmax::{LogSoftmax, LogSoftmaxBackward};
pub(crate) use mean::{Mean, MeanBackward};
pub(crate) use negation::{Negation, NegationBackward};
pub(crate) use power::{Power, PowerBackward};
pub(crate) use relu::{ReLU, ReLUBackward};
pub(crate) use sigmoid::{Sigmoid, SigmoidBackward};
pub(crate) use softmax::{Softmax, SoftmaxBackward};
pub(crate) use softplus::{SoftPlus, SoftPlusBackward};
pub(crate) use sqrt::{Sqrt, SqrtBackward};
pub(crate) use sum::{Sum, SumBackward};
pub(crate) use tanh::{TanH, TanHBackward};
pub(crate) use transpose::{Transpose, TransposeBackward};
pub(crate) use unsqueeze::{Unsqueeze, UnsqueezeBackward};