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use ndarray::ArrayD;use serde::{Deserialize, Serialize};
#[derive(Serialize, Deserialize, Debug, Clone)]pub struct BackendConfig {    pub silent: Option<bool>,    pub size: Vec<usize>,    pub layers: Vec<Layer>,    pub cost: Cost,    pub optimizer: Optimizer,}
#[derive(Debug)]pub struct Dataset {    pub inputs: ArrayD<f32>,    pub outputs: ArrayD<f32>,}
#[derive(Serialize, Deserialize, Debug, Clone)]#[serde(tag = "type", content = "config")]#[serde(rename_all = "lowercase")]pub enum Layer {    Activation(ActivationLayer),    Dense(DenseLayer),    BatchNorm1D(BatchNormLayer),    BatchNorm2D(BatchNormLayer),    Conv2D(Conv2DLayer),    ConvTranspose2D(ConvTranspose2DLayer),    Pool2D(Pool2DLayer),    Flatten(FlattenLayer),    Dropout1D(DropoutLayer),    Dropout2D(DropoutLayer),    Softmax,}
#[derive(Serialize, Deserialize, Debug, Clone)]#[serde(rename_all = "lowercase")]pub enum Activation {    Elu,    LeakyRelu,    Linear,    Relu,    Relu6,    Selu,    Sigmoid,    Tanh,}
#[derive(Serialize, Deserialize, Debug, Clone)]pub struct JSTensor {    pub data: Vec<f32>,    pub shape: Vec<usize>,}
#[derive(Serialize, Deserialize, Debug, Clone)]pub struct DenseLayer {    pub size: Vec<usize>,    pub init: Option<Init>,}
#[derive(Serialize, Deserialize, Debug, Clone)]#[serde(rename_all = "camelCase")]pub struct Conv2DLayer {    pub init: Option<Init>,    pub kernel: Option<JSTensor>,    pub kernel_size: Vec<usize>,    pub padding: Option<Vec<usize>>,    pub strides: Option<Vec<usize>>,}
#[derive(Serialize, Deserialize, Debug, Clone)]#[serde(rename_all = "camelCase")]pub struct ConvTranspose2DLayer {    pub init: Option<Init>,    pub kernel: Option<JSTensor>,    pub kernel_size: Vec<usize>,    pub padding: Option<Vec<usize>>,    pub strides: Option<Vec<usize>>,}
#[derive(Serialize, Deserialize, Debug, Clone)]pub struct Pool2DLayer {    pub mode: usize, // 0 = avg, 1 = max    pub strides: Option<Vec<usize>>,}
#[derive(Serialize, Deserialize, Debug, Clone)]pub struct FlattenLayer {    pub size: Vec<usize>,}
#[derive(Serialize, Deserialize, Debug, Clone)]pub struct DropoutLayer {    pub probability: f32,    pub inplace: Option<bool>,}
#[derive(Serialize, Deserialize, Debug, Clone)]pub struct BatchNormLayer {    pub momentum: f32,    pub epsilon: f32,}
#[derive(Serialize, Deserialize, Debug, Clone)]pub struct ActivationLayer {    pub activation: Activation,}
#[derive(Serialize, Deserialize, Debug, Clone)]#[serde(rename_all = "lowercase")]pub enum Cost {    CrossEntropy,    Hinge,    MSE,    BinCrossEntropy,}
#[derive(Serialize, Deserialize, Debug, Clone)]#[serde(rename_all = "lowercase")]pub enum Init {    Uniform,    Xavier,    XavierN,    Kaiming,}
#[derive(Serialize, Deserialize, Debug, Clone)]#[serde(rename_all = "lowercase")]pub struct  AdamOptimizer {    pub beta1: f32,    pub beta2: f32,    pub epsilon: f32,}
#[derive(Serialize, Deserialize, Debug, Clone)]#[serde(tag = "type", content = "config")]#[serde(rename_all = "lowercase")]pub enum Optimizer {    SGD,    Adam(AdamOptimizer)}
#[derive(Serialize, Deserialize, Debug, Clone)]#[serde(rename_all = "camelCase")]pub struct TrainOptions {    pub datasets: usize,    pub input_shape: Vec<usize>,    pub output_shape: Vec<usize>,    pub epochs: usize,    pub batches: usize,    pub rate: f32,}
#[derive(Serialize, Deserialize, Debug, Clone)]#[serde(rename_all = "camelCase")]pub struct PredictOptions {    pub input_shape: Vec<usize>,    pub output_shape: Vec<usize>,}