60 lines
1.5 KiB
Rust
60 lines
1.5 KiB
Rust
//! Functional API for neural network operations
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use crate::Result;
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use rtx_tensor::Tensor;
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/// Linear transformation: y = xW^T + b
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pub fn linear(input: &Tensor, weight: &Tensor, bias: Option<&Tensor>) -> Result<Tensor> {
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let output = input.matmul(&weight.transpose(0, 1)?)?;
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if let Some(bias) = bias {
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output.add(bias).map_err(Into::into)
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} else {
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Ok(output)
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}
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}
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/// ReLU activation function
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pub fn relu(input: &Tensor) -> Result<Tensor> {
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input
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.maximum(&Tensor::zeros_like(input)?)
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.map_err(Into::into)
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}
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/// GELU activation function (simplified)
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pub fn gelu(input: &Tensor) -> Result<Tensor> {
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// Simplified: just return input for now
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Ok(input.clone())
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}
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/// Softmax function
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pub fn softmax(input: &Tensor, dim: i32) -> Result<Tensor> {
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Ok(input.softmax(dim)?)
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}
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/// 2D convolution operation (placeholder)
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pub fn conv2d(
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input: &Tensor,
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weight: &Tensor,
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bias: Option<&Tensor>,
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_stride: (usize, usize),
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_padding: (usize, usize),
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) -> Result<Tensor> {
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// Placeholder: just do matrix multiplication
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let output = input.matmul(weight)?;
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if let Some(bias) = bias {
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output.add(bias).map_err(Into::into)
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} else {
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Ok(output)
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}
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}
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/// Dropout function (placeholder)
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pub fn dropout(input: &Tensor, _p: f32, training: bool) -> Result<Tensor> {
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if training {
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// Placeholder: just return input for now
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Ok(input.clone())
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} else {
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Ok(input.clone())
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}
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}
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