32 #include "aifes_config.h"
38 #define AILAYER_RESULT_LOWER_BOUND 0
39 #define AILAYER_RESULT_UPPER_BOUND 1
40 #define AILAYER_DELTAS_LOWER_BOUND 2
41 #define AILAYER_DELTAS_UPPER_BOUND 3
Type indicator of the layer.
Definition: aifes_core.h:79
void(* print_specs)(const ailayer_t *self)
Set a function to print specs of the layer (for example size, constants)
Definition: aifes_core.h:89
const char * name
Name of the layer type (for example "Dense")
Definition: aifes_core.h:80
Type indicator of the loss to check for the loss type.
Definition: aifes_core.h:118
void(* print_specs)(const ailoss_t *self)
Set a function to print specs of the loss.
Definition: aifes_core.h:128
const char * name
Name of the loss type (for example "Mean Squared Error")
Definition: aifes_core.h:119
Type indicator of the optimizer to check for the optimizer type.
Definition: aifes_core.h:157
void(* print_specs)(const aiopti_t *self)
Set a function to print specs of the optimizer.
Definition: aifes_core.h:167
const char * name
Name of the optimizer type (for example "ADAM")
Definition: aifes_core.h:158
AIfES layer interface.
Definition: aifes_core.h:249
void ** optimem
Array of memory pointers with length trainable_params_count.
Definition: aifes_core.h:314
aitensor_t result
The result of the forward function is stored here.
Definition: aifes_core.h:272
void(* set_paramem)(ailayer_t *self, void *memory_ptr)
Set and distribute the memory block internally.
Definition: aifes_core.h:332
void(* backward)(ailayer_t *self)
Calculate the backward pass and write the result to the deltas tensor.
Definition: aifes_core.h:321
const aicore_layertype_t * layer_type
Type of the layer (for example ailayer_dense_type)
Definition: aifes_core.h:250
void * layer_configuration
Layer specific configurations (back-link from abstract layer class to implementation)
Definition: aifes_core.h:251
uint8_t trainable_params_count
Number of trainable parameter tensors.
Definition: aifes_core.h:311
void(* set_trainmem)(ailayer_t *self, void *memory_ptr)
Set and distribute the memory block internally.
Definition: aifes_core.h:343
void(* forward)(ailayer_t *self)
Calculate the forward pass and write the result to the result tensor.
Definition: aifes_core.h:295
void(* calc_result_shape)(ailayer_t *self)
Calculate and write the shape to the result tensor.
Definition: aifes_core.h:280
aitensor_t deltas
The result of the backward function is stored here.
Definition: aifes_core.h:303
aitensor_t ** trainable_params
Array of tensor pointers with length trainable_params_count.
Definition: aifes_core.h:312
void(* calc_result_tensor_params)(ailayer_t *self)
If available, calculate and set the tensor_params of the result tensor.
Definition: aifes_core.h:289
uint32_t(* sizeof_paramem)(const ailayer_t *self)
Size of required memory (in bytes).
Definition: aifes_core.h:331
aitensor_t ** gradients
Array of tensor pointers with length trainable_params_count.
Definition: aifes_core.h:313
uint32_t(* sizeof_trainmem)(const ailayer_t *self)
Size of required memory (in bytes).
Definition: aifes_core.h:342
AIfES loss interface.
Definition: aifes_core.h:355
void * loss_configuration
Loss specific configurations (back-link from abstract loss class to implementation)
Definition: aifes_core.h:357
const aicore_losstype_t * loss_type
Type of the loss (for example ailoss_mse_type)
Definition: aifes_core.h:356
ailayer_t connection_layer
Dummy layer for docking to the layer structure.
Definition: aifes_core.h:359
void(* calc_delta)(ailoss_t *self, const aitensor_t *target_data)
Calculate the error on the target data and write it to the deltas tensor of connection layer.
Definition: aifes_core.h:374
void(* calc_loss)(ailoss_t *self, const aitensor_t *target_data, void *result)
Calculate the loss / cost for the model on the given targets.
Definition: aifes_core.h:367
Indicator for the used datatype.
Definition: aifes_math.h:44
AIfES artificial neural network model.
Definition: aifes_core.h:178
uint16_t trainable_params_count
Total number of trainable parameter tensors.
Definition: aifes_core.h:183
uint16_t layer_count
Total number of layers of the model (usually autogenerated).
Definition: aifes_core.h:182
ailayer_t * input_layer
Input layer of the model that gets the input data.
Definition: aifes_core.h:179
ailayer_t * output_layer
Output layer of the model.
Definition: aifes_core.h:180
ailoss_t * loss
The loss or cost function of the model (only for training).
Definition: aifes_core.h:185
AIfES optimizer interface.
Definition: aifes_core.h:408
void * optimizer_configuration
Optimizer specific configurations (back-link from abstract aiopti class to implementation)
Definition: aifes_core.h:410
void(* init_optimem)(aiopti_t *self, const aitensor_t *params, const aitensor_t *gradients, void *optimem)
Initialize the optimization memory for a trainable parameter tensor.
Definition: aifes_core.h:429
void(* begin_step)(aiopti_t *self)
Called in the beginning of every model optimization step for parameter initialization.
Definition: aifes_core.h:442
const aicore_optitype_t * optimizer_type
Type of the optimizer (for example aiopti_sgd_type)
Definition: aifes_core.h:409
void(* update_params)(aiopti_t *self, aitensor_t *params, const aitensor_t *gradients, void *optimem)
Performs an optimization step on the given tensor.
Definition: aifes_core.h:451
uint32_t(* sizeof_optimem)(aiopti_t *self, const aitensor_t *params)
Calculates the optimization memory size for a trainable parameter tensor.
Definition: aifes_core.h:420
void * learning_rate
The learning rate configures the training speed.
Definition: aifes_core.h:413
const aimath_dtype_t * dtype
The data-type of the parameter that the optimizer can optimize and the learning rate.
Definition: aifes_core.h:411
void(* zero_gradients)(aiopti_t *self, aitensor_t *gradients)
Set the gradient tensor to zero.
Definition: aifes_core.h:436
void(* end_step)(aiopti_t *self)
Called in the end of every model optimization step.
Definition: aifes_core.h:457
A tensor in AIfES.
Definition: aifes_math.h:89