neuralnetwork.h

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#pragma once

#include <cmath>
#include <ctime>
#include "compute/op_utilities.h"
#include "model/model.h"
#include "layer/layers.h"
#include "optimizer/optimizers.h"
#include "math/argmax.h"
#include "dataloader/dataloaders.h"

namespace magmadnn {
namespace model {

struct nn_params_t {
    unsigned int n_epochs;  
    unsigned int batch_size;    
    double learning_rate;   
};

template <typename T>
class NeuralNetwork : public Model<T> {
public:
    NeuralNetwork(std::vector<layer::Layer<T> *> layers, optimizer::loss_t loss_func, optimizer::optimizer_t optimizer, nn_params_t params);

    NeuralNetwork(std::vector<layer::Layer<T> *> layers, optimizer::loss_t loss_func, optimizer::Optimizer<T> *optim, nn_params_t params);

    virtual ~NeuralNetwork();

    virtual magmadnn_error_t fit(Tensor<T> *x, Tensor<T> *y, metric_t& metric_out, bool verbose=false);
    virtual Tensor<T> *predict(Tensor<T> *sample);
    virtual unsigned int predict_class(Tensor<T> *sample);


    virtual std::vector<layer::Layer<T> *> get_layers() { return this->layers; }

protected:
    typename std::vector<layer::Layer<T> *> layers;
    optimizer::loss_t loss_func;
    optimizer::optimizer_t optimizer;
    nn_params_t model_params;

    /* network inputs/output */
    op::Operation<T> *network_input_op_ptr, *network_output_op_ptr;
    Tensor<T> *network_input_tensor_ptr, *network_output_tensor_ptr;

    /* ground truth */
    op::Operation<T> *ground_truth_op_ptr;
    Tensor<T> *ground_truth_tensor_ptr;

    T default_learning_rate = (T) 0.05;     /* assumed learning rate if one is not given */
    std::vector<op::Operation<T> *> _vars;  /* network weights */
    op::Operation<T> *_obj;             /* objective function to optimize -- i.e. the loss function */
    Tensor<T> *_obj_tensor_ptr;         /* pointer to objective function's tensor */
    optimizer::Optimizer<T> *optim;     /* network optimizer */
};

}   // namespace model
}   // namespace magmadnn