| Line | Branch | Exec | Source |
|---|---|---|---|
| 1 | module athena__loss | ||
| 2 | !! Module containing loss function implementations | ||
| 3 | !! | ||
| 4 | !! This module implements loss functions that quantify the difference between | ||
| 5 | !! model predictions and target values, guiding the optimisation process. | ||
| 6 | !! | ||
| 7 | !! Implemented loss functions: | ||
| 8 | !! | ||
| 9 | !! Mean Squared Error (MSE): | ||
| 10 | !! L = (1/N) Σ (y_pred - y_true)² | ||
| 11 | !! For regression, sensitive to outliers | ||
| 12 | !! | ||
| 13 | !! Mean Absolute Error (MAE): | ||
| 14 | !! L = (1/N) Σ |y_pred - y_true| | ||
| 15 | !! For regression, robust to outliers | ||
| 16 | !! | ||
| 17 | !! Binary Cross-Entropy: | ||
| 18 | !! L = -(1/N) Σ [y*log(ŷ) + (1-y)*log(1-ŷ)] | ||
| 19 | !! For binary classification (outputs in [0,1]) | ||
| 20 | !! | ||
| 21 | !! Categorical Cross-Entropy: | ||
| 22 | !! L = -(1/N) Σ_i Σ_c y_{i,c} * log(ŷ_{i,c}) | ||
| 23 | !! For multi-class classification with one-hot encoded targets | ||
| 24 | !! | ||
| 25 | !! Sparse Categorical Cross-Entropy: | ||
| 26 | !! L = -(1/N) Σ log(ŷ_{i,c_i}) | ||
| 27 | !! For multi-class with integer class labels | ||
| 28 | !! | ||
| 29 | !! Huber Loss: | ||
| 30 | !! L = (1/N) Σ { 0.5*(y-ŷ)² if |y-ŷ| ≤ δ | ||
| 31 | !! { δ*(|y-ŷ| - 0.5*δ) otherwise | ||
| 32 | !! Combines MSE and MAE, robust to outliers while smooth near zero | ||
| 33 | !! | ||
| 34 | !! where N is number of samples, y is true value, ŷ is prediction | ||
| 35 | use coreutils, only: real32 | ||
| 36 | use diffstruc, only: array_type, operator(+), operator(-), & | ||
| 37 | operator(*), operator(/), operator(**), mean, sum, log, abs, merge | ||
| 38 | use athena__diffstruc_extd, only: huber | ||
| 39 | implicit none | ||
| 40 | |||
| 41 | |||
| 42 | private | ||
| 43 | |||
| 44 | public :: base_loss_type | ||
| 45 | public :: bce_loss_type | ||
| 46 | public :: cce_loss_type | ||
| 47 | public :: mae_loss_type | ||
| 48 | public :: mse_loss_type | ||
| 49 | public :: nll_loss_type | ||
| 50 | public :: huber_loss_type | ||
| 51 | |||
| 52 | |||
| 53 | type, abstract :: base_loss_type | ||
| 54 | !! Abstract type for loss functions | ||
| 55 | character(len=:), allocatable :: name | ||
| 56 | !! Name of the loss function | ||
| 57 | real(real32) :: epsilon = 1.E-10_real32 | ||
| 58 | !! Small value to prevent log(0) | ||
| 59 | integer :: batch_index = 1 | ||
| 60 | !! Index of the batch to compute the loss for | ||
| 61 | integer :: sample_index = 1 | ||
| 62 | !! Index of the sample to compute the loss for | ||
| 63 | contains | ||
| 64 | procedure(compute_base), deferred, pass(this) :: compute | ||
| 65 | !! Compute the loss of a model | ||
| 66 | end type base_loss_type | ||
| 67 | |||
| 68 | interface | ||
| 69 | module function compute_base(this, predicted, expected) result(output) | ||
| 70 | !! Compute the loss of a model | ||
| 71 | class(base_loss_type), intent(in), target :: this | ||
| 72 | !! Instance of the physics-informed neural network loss function | ||
| 73 | type(array_type), dimension(:,:), intent(inout), target :: predicted | ||
| 74 | !! Predicted values | ||
| 75 | type(array_type), dimension(size(predicted,1),size(predicted,2)), intent(in) :: & | ||
| 76 | expected | ||
| 77 | !! Expected values | ||
| 78 | type(array_type), pointer :: output | ||
| 79 | !! Physics-informed neural network loss | ||
| 80 | end function compute_base | ||
| 81 | end interface | ||
| 82 | |||
| 83 | !------------------------------------------------------------------------------- | ||
| 84 | |||
| 85 | type, extends(base_loss_type) :: bce_loss_type | ||
| 86 | !! Binary cross entropy loss function | ||
| 87 | contains | ||
| 88 | procedure :: compute => compute_bce | ||
| 89 | !! Compute the loss of a model | ||
| 90 | end type bce_loss_type | ||
| 91 | |||
| 92 | interface bce_loss_type | ||
| 93 | !! Interface for binary cross entropy loss function | ||
| 94 | module function setup_loss_bce() result(loss) | ||
| 95 | !! Set up binary cross entropy loss function | ||
| 96 | type(bce_loss_type) :: loss | ||
| 97 | !! Binary cross entropy loss function | ||
| 98 | end function setup_loss_bce | ||
| 99 | end interface bce_loss_type | ||
| 100 | |||
| 101 | !------------------------------------------------------------------------------- | ||
| 102 | |||
| 103 | type, extends(base_loss_type) :: cce_loss_type | ||
| 104 | !! Categorical cross entropy loss function | ||
| 105 | contains | ||
| 106 | procedure :: compute => compute_cce | ||
| 107 | !! Compute the loss of a model | ||
| 108 | end type cce_loss_type | ||
| 109 | |||
| 110 | interface cce_loss_type | ||
| 111 | !! Interface for categorical cross entropy loss function | ||
| 112 | module function setup_loss_cce() result(loss) | ||
| 113 | !! Set up categorical cross entropy loss function | ||
| 114 | type(cce_loss_type) :: loss | ||
| 115 | !! Categorical cross entropy loss function | ||
| 116 | end function setup_loss_cce | ||
| 117 | end interface cce_loss_type | ||
| 118 | |||
| 119 | !------------------------------------------------------------------------------- | ||
| 120 | |||
| 121 | type, extends(base_loss_type) :: mae_loss_type | ||
| 122 | !! Mean absolute error loss function | ||
| 123 | contains | ||
| 124 | procedure :: compute => compute_mae | ||
| 125 | !! Compute the loss of a model | ||
| 126 | end type mae_loss_type | ||
| 127 | |||
| 128 | interface mae_loss_type | ||
| 129 | !! Interface for mean absolute error loss function | ||
| 130 | module function setup_loss_mae() result(loss) | ||
| 131 | !! Set up mean absolute error loss function | ||
| 132 | type(mae_loss_type) :: loss | ||
| 133 | !! Mean absolute error loss function | ||
| 134 | end function setup_loss_mae | ||
| 135 | end interface mae_loss_type | ||
| 136 | |||
| 137 | !------------------------------------------------------------------------------- | ||
| 138 | |||
| 139 | type, extends(base_loss_type) :: mse_loss_type | ||
| 140 | !! Mean squared error loss function | ||
| 141 | contains | ||
| 142 | procedure :: compute => compute_mse | ||
| 143 | !! Compute the loss of a model | ||
| 144 | end type mse_loss_type | ||
| 145 | |||
| 146 | interface mse_loss_type | ||
| 147 | !! Interface for mean squared error loss function | ||
| 148 | module function setup_loss_mse() result(loss) | ||
| 149 | !! Set up mean squared error loss function | ||
| 150 | type(mse_loss_type) :: loss | ||
| 151 | !! Mean squared error loss function | ||
| 152 | end function setup_loss_mse | ||
| 153 | end interface mse_loss_type | ||
| 154 | |||
| 155 | !------------------------------------------------------------------------------- | ||
| 156 | |||
| 157 | type, extends(base_loss_type) :: nll_loss_type | ||
| 158 | !! Negative log likelihood loss function | ||
| 159 | contains | ||
| 160 | procedure :: compute => compute_nll | ||
| 161 | !! Compute the loss of a model | ||
| 162 | end type nll_loss_type | ||
| 163 | |||
| 164 | interface nll_loss_type | ||
| 165 | !! Interface for negative log likelihood loss function | ||
| 166 | module function setup_loss_nll() result(loss) | ||
| 167 | !! Set up negative log likelihood loss function | ||
| 168 | type(nll_loss_type) :: loss | ||
| 169 | !! Negative log likelihood loss function | ||
| 170 | end function setup_loss_nll | ||
| 171 | end interface nll_loss_type | ||
| 172 | |||
| 173 | !------------------------------------------------------------------------------- | ||
| 174 | |||
| 175 | type, extends(base_loss_type) :: huber_loss_type | ||
| 176 | !! Huber loss function | ||
| 177 | real(real32) :: gamma = 1._real32 | ||
| 178 | !! Gamma value for the huber loss function | ||
| 179 | contains | ||
| 180 | procedure :: compute => compute_huber | ||
| 181 | !! Compute the loss of a model | ||
| 182 | end type huber_loss_type | ||
| 183 | |||
| 184 | interface huber_loss_type | ||
| 185 | !! Interface for huber loss function | ||
| 186 | module function setup_loss_huber() result(loss) | ||
| 187 | !! Set up huber loss function | ||
| 188 | type(huber_loss_type) :: loss | ||
| 189 | !! Huber loss function | ||
| 190 | end function setup_loss_huber | ||
| 191 | end interface huber_loss_type | ||
| 192 | |||
| 193 | !------------------------------------------------------------------------------- | ||
| 194 | |||
| 195 | |||
| 196 | |||
| 197 | contains | ||
| 198 | !############################################################################### | ||
| 199 | − | module function setup_loss_bce() result(loss) | |
| 200 | !! Set up binary cross entropy loss function | ||
| 201 | implicit none | ||
| 202 | |||
| 203 | ! Local variables | ||
| 204 | type(bce_loss_type) :: loss | ||
| 205 | !! Binary cross entropy loss function | ||
| 206 | |||
| 207 | − | loss%name = 'bce' | |
| 208 | − | end function setup_loss_bce | |
| 209 | !------------------------------------------------------------------------------- | ||
| 210 | − | module function setup_loss_cce() result(loss) | |
| 211 | !! Set up categorical cross entropy loss function | ||
| 212 | implicit none | ||
| 213 | |||
| 214 | ! Local variables | ||
| 215 | type(cce_loss_type) :: loss | ||
| 216 | !! Categorical cross entropy loss function | ||
| 217 | |||
| 218 | − | loss%name = 'cce' | |
| 219 | − | end function setup_loss_cce | |
| 220 | !------------------------------------------------------------------------------- | ||
| 221 | − | module function setup_loss_mae() result(loss) | |
| 222 | !! Set up mean absolute error loss function | ||
| 223 | implicit none | ||
| 224 | |||
| 225 | ! Local variables | ||
| 226 | type(mae_loss_type) :: loss | ||
| 227 | !! Mean absolute error loss function | ||
| 228 | |||
| 229 | − | loss%name = 'mae' | |
| 230 | − | end function setup_loss_mae | |
| 231 | !------------------------------------------------------------------------------- | ||
| 232 | − | module function setup_loss_mse() result(loss) | |
| 233 | !! Set up mean squared error loss function | ||
| 234 | implicit none | ||
| 235 | |||
| 236 | ! Local variables | ||
| 237 | type(mse_loss_type) :: loss | ||
| 238 | !! Mean squared error loss function | ||
| 239 | |||
| 240 | − | loss%name = 'mse' | |
| 241 | − | end function setup_loss_mse | |
| 242 | !------------------------------------------------------------------------------- | ||
| 243 | − | module function setup_loss_nll() result(loss) | |
| 244 | !! Set up negative log likelihood loss function | ||
| 245 | implicit none | ||
| 246 | |||
| 247 | ! Local variables | ||
| 248 | type(nll_loss_type) :: loss | ||
| 249 | !! Negative log likelihood loss function | ||
| 250 | |||
| 251 | − | loss%name = 'nll' | |
| 252 | − | end function setup_loss_nll | |
| 253 | !------------------------------------------------------------------------------- | ||
| 254 | − | module function setup_loss_huber() result(loss) | |
| 255 | !! Set up huber loss function | ||
| 256 | implicit none | ||
| 257 | |||
| 258 | ! Local variables | ||
| 259 | type(huber_loss_type) :: loss | ||
| 260 | !! Huber loss function | ||
| 261 | |||
| 262 | − | loss%name = 'hub' | |
| 263 | − | end function setup_loss_huber | |
| 264 | !############################################################################### | ||
| 265 | |||
| 266 | |||
| 267 | !############################################################################### | ||
| 268 | − | function compute_bce(this, predicted, expected) result(output) | |
| 269 | !! Compute the binary cross entropy loss of a model | ||
| 270 | implicit none | ||
| 271 | |||
| 272 | ! Arguments | ||
| 273 | class(bce_loss_type), intent(in), target :: this | ||
| 274 | !! Instance of the physics-informed neural network loss function | ||
| 275 | type(array_type), dimension(:,:), intent(inout), target :: predicted | ||
| 276 | !! Predicted values | ||
| 277 | − | type(array_type), dimension(size(predicted,1),size(predicted,2)), intent(in) :: & | |
| 278 | expected | ||
| 279 | !! Expected values | ||
| 280 | type(array_type), pointer :: output | ||
| 281 | !! Binary cross entropy loss | ||
| 282 | |||
| 283 | ! Local variables | ||
| 284 | integer :: s, i | ||
| 285 | !! Loop indices | ||
| 286 | type(array_type), pointer :: ptr | ||
| 287 | !! Temporary pointer for calculations | ||
| 288 | |||
| 289 | − | output => mean(-expected(1,1) * log(predicted(1,1) + this%epsilon), dim=2) | |
| 290 | − | if(any(shape(predicted).gt.1))then | |
| 291 | − | do s = 1, size(predicted,2) | |
| 292 | − | do i = 1, size(predicted,1) | |
| 293 | − | if(.not.predicted(i,s)%allocated .or. & | |
| 294 | − | .not.expected(i,s)%allocated) cycle | |
| 295 | − | ptr => mean(-expected(i,s) * log(predicted(i,s) + this%epsilon), dim=2) | |
| 296 | |||
| 297 | − | output => output + ptr | |
| 298 | end do | ||
| 299 | end do | ||
| 300 | end if | ||
| 301 | |||
| 302 | − | end function compute_bce | |
| 303 | !############################################################################### | ||
| 304 | |||
| 305 | |||
| 306 | !############################################################################### | ||
| 307 | − | function compute_cce(this, predicted, expected) result(output) | |
| 308 | !! Compute the categorical cross entropy loss of a model | ||
| 309 | implicit none | ||
| 310 | |||
| 311 | ! Arguments | ||
| 312 | class(cce_loss_type), intent(in), target :: this | ||
| 313 | !! Instance of the physics-informed neural network loss function | ||
| 314 | type(array_type), dimension(:,:), intent(inout), target :: predicted | ||
| 315 | !! Predicted values | ||
| 316 | − | type(array_type), dimension(size(predicted,1),size(predicted,2)), intent(in) :: & | |
| 317 | expected | ||
| 318 | !! Expected values | ||
| 319 | type(array_type), pointer :: output | ||
| 320 | !! Categorical cross entropy loss | ||
| 321 | |||
| 322 | ! Local variables | ||
| 323 | integer :: s, i | ||
| 324 | !! Loop indices | ||
| 325 | type(array_type), pointer :: ptr | ||
| 326 | !! Temporary pointer for calculations | ||
| 327 | |||
| 328 | output => -mean( sum( & | ||
| 329 | − | expected(1,1) * log(predicted(1,1) + this%epsilon), & | |
| 330 | − | dim=1 ), dim=2) | |
| 331 | − | if(any(shape(predicted).gt.1))then | |
| 332 | − | do s = 1, size(predicted,2) | |
| 333 | − | do i = 1, size(predicted,1) | |
| 334 | − | if(.not.predicted(i,s)%allocated .or. & | |
| 335 | − | .not.expected(i,s)%allocated) cycle | |
| 336 | ptr => mean( sum( & | ||
| 337 | − | expected(i,s) * log(predicted(i,s) + this%epsilon), & | |
| 338 | − | dim=1 ), dim=2) | |
| 339 | |||
| 340 | − | output => output - ptr | |
| 341 | end do | ||
| 342 | end do | ||
| 343 | end if | ||
| 344 | |||
| 345 | − | end function compute_cce | |
| 346 | !############################################################################### | ||
| 347 | |||
| 348 | |||
| 349 | !############################################################################### | ||
| 350 | − | function compute_mae(this, predicted, expected) result(output) | |
| 351 | !! Compute the mean absolute error of a model | ||
| 352 | implicit none | ||
| 353 | |||
| 354 | ! Arguments | ||
| 355 | class(mae_loss_type), intent(in), target :: this | ||
| 356 | type(array_type), dimension(:,:), intent(inout), target :: predicted | ||
| 357 | !! Predicted values | ||
| 358 | − | type(array_type), dimension(size(predicted,1),size(predicted,2)), intent(in) :: & | |
| 359 | expected | ||
| 360 | !! Expected values | ||
| 361 | type(array_type), pointer :: output | ||
| 362 | !! Mean absolute error | ||
| 363 | |||
| 364 | ! Local variables | ||
| 365 | integer :: s, i | ||
| 366 | !! Loop indices | ||
| 367 | type(array_type), pointer :: ptr | ||
| 368 | !! Temporary pointer for calculations | ||
| 369 | |||
| 370 | − | output => mean( abs( predicted(1,1) - expected(1,1) ) ) / & | |
| 371 | − | 2._real32 | |
| 372 | − | if(any(shape(predicted).gt.1))then | |
| 373 | − | do s = 1, size(predicted,2) | |
| 374 | − | do i = 1, size(predicted,1) | |
| 375 | − | if(.not.predicted(i,s)%allocated .or. & | |
| 376 | − | .not.expected(i,s)%allocated) cycle | |
| 377 | − | ptr => mean( abs( predicted(i,s) - expected(i,s) ) ) / & | |
| 378 | − | 2._real32 | |
| 379 | |||
| 380 | − | output => output + ptr | |
| 381 | end do | ||
| 382 | end do | ||
| 383 | end if | ||
| 384 | |||
| 385 | − | end function compute_mae | |
| 386 | !############################################################################### | ||
| 387 | |||
| 388 | |||
| 389 | !############################################################################### | ||
| 390 | − | function compute_mse(this, predicted, expected) result(output) | |
| 391 | !! Compute the mean squared error of a model | ||
| 392 | implicit none | ||
| 393 | |||
| 394 | ! Arguments | ||
| 395 | class(mse_loss_type), intent(in), target :: this | ||
| 396 | !! Instance of the mean squared error loss function | ||
| 397 | type(array_type), dimension(:,:), intent(inout), target :: predicted | ||
| 398 | !! Predicted values | ||
| 399 | − | type(array_type), dimension(size(predicted,1),size(predicted,2)), intent(in) :: & | |
| 400 | expected | ||
| 401 | !! Expected values | ||
| 402 | type(array_type), pointer :: output | ||
| 403 | !! Mean squared error loss | ||
| 404 | |||
| 405 | ! Local variables | ||
| 406 | integer :: s, i | ||
| 407 | !! Loop indices | ||
| 408 | type(array_type), pointer :: ptr | ||
| 409 | !! Temporary pointer for calculations | ||
| 410 | |||
| 411 | − | output => mean( ( predicted(1,1) - expected(1,1) ) ** 2._real32 ) / & | |
| 412 | − | 2._real32 | |
| 413 | − | if(any(shape(predicted).gt.1))then | |
| 414 | − | do s = 1, size(predicted,2) | |
| 415 | − | do i = 1, size(predicted,1) | |
| 416 | − | if(.not.predicted(i,s)%allocated .or. & | |
| 417 | − | .not.expected(i,s)%allocated) cycle | |
| 418 | − | ptr => mean( ( predicted(i,s) - expected(i,s) ) ** 2._real32 ) / & | |
| 419 | − | 2._real32 | |
| 420 | |||
| 421 | − | output => output + ptr | |
| 422 | end do | ||
| 423 | end do | ||
| 424 | end if | ||
| 425 | |||
| 426 | − | end function compute_mse | |
| 427 | !############################################################################### | ||
| 428 | |||
| 429 | |||
| 430 | !############################################################################### | ||
| 431 | − | function compute_nll(this, predicted, expected) result(output) | |
| 432 | !! Compute the negative log likelihood of a model | ||
| 433 | implicit none | ||
| 434 | |||
| 435 | ! Arguments | ||
| 436 | class(nll_loss_type), intent(in), target :: this | ||
| 437 | !! Instance of the physics-informed neural network loss function | ||
| 438 | type(array_type), dimension(:,:), intent(inout), target :: predicted | ||
| 439 | !! Predicted values | ||
| 440 | − | type(array_type), dimension(size(predicted,1),size(predicted,2)), intent(in) :: & | |
| 441 | expected | ||
| 442 | !! Expected values | ||
| 443 | type(array_type), pointer :: output | ||
| 444 | !! Negative log likelihood loss | ||
| 445 | |||
| 446 | ! Local variables | ||
| 447 | integer :: s, i | ||
| 448 | !! Loop indices | ||
| 449 | type(array_type), pointer :: ptr | ||
| 450 | !! Temporary pointer for calculations | ||
| 451 | |||
| 452 | − | output => mean(-log(expected(1,1) - predicted(1,1) + this%epsilon) ) | |
| 453 | − | if(any(shape(predicted).gt.1))then | |
| 454 | − | do s = 1, size(predicted,2) | |
| 455 | − | do i = 1, size(predicted,1) | |
| 456 | − | if(.not.predicted(i,s)%allocated .or. & | |
| 457 | − | .not.expected(i,s)%allocated) cycle | |
| 458 | − | ptr => mean(-log(expected(i,s) - predicted(i,s) + this%epsilon) ) | |
| 459 | |||
| 460 | − | output => output + ptr | |
| 461 | end do | ||
| 462 | end do | ||
| 463 | end if | ||
| 464 | |||
| 465 | − | end function compute_nll | |
| 466 | !############################################################################### | ||
| 467 | |||
| 468 | |||
| 469 | !############################################################################### | ||
| 470 | − | function compute_huber(this, predicted, expected) result(output) | |
| 471 | !! Compute the huber loss of a model | ||
| 472 | implicit none | ||
| 473 | |||
| 474 | ! Arguments | ||
| 475 | class(huber_loss_type), intent(in), target :: this | ||
| 476 | !! Instance of the huber loss function | ||
| 477 | type(array_type), dimension(:,:), intent(inout), target :: predicted | ||
| 478 | !! Predicted values | ||
| 479 | − | type(array_type), dimension(size(predicted,1),size(predicted,2)), intent(in) :: & | |
| 480 | expected | ||
| 481 | !! Expected values | ||
| 482 | type(array_type), pointer :: output | ||
| 483 | !! Huber loss | ||
| 484 | |||
| 485 | ! Local variables | ||
| 486 | integer :: s, i | ||
| 487 | !! Loop indices | ||
| 488 | type(array_type), pointer :: ptr | ||
| 489 | !! Temporary pointer for calculations | ||
| 490 | |||
| 491 | − | ptr => predicted(1,1) - expected(1,1) | |
| 492 | − | output => mean( huber(predicted(1,1) - expected(1,1), this%gamma) ) | |
| 493 | − | if(any(shape(predicted).gt.1))then | |
| 494 | − | do s = 1, size(predicted,2) | |
| 495 | − | do i = 1, size(predicted,1) | |
| 496 | − | if(.not.predicted(i,s)%allocated .or. & | |
| 497 | − | .not.expected(i,s)%allocated) cycle | |
| 498 | − | ptr => predicted(i,s) - expected(i,s) | |
| 499 | |||
| 500 | − | output => output + mean( huber(ptr, this%gamma) ) | |
| 501 | end do | ||
| 502 | end do | ||
| 503 | end if | ||
| 504 | |||
| 505 | ! output => merge( & | ||
| 506 | ! 0.5_real32 * (ptr)**2._real32, & | ||
| 507 | ! this%gamma * (abs(ptr) - 0.5_real32 * this%gamma), & | ||
| 508 | ! abs(ptr) .le. this%gamma & | ||
| 509 | ! ) | ||
| 510 | |||
| 511 | − | end function compute_huber | |
| 512 | !############################################################################### | ||
| 513 | |||
| 514 | |||
| 515 | !############################################################################### | ||
| 516 | − | module function compute_base(this, predicted, expected) result(output) | |
| 517 | !! Placeholder for compute function in base_loss_type | ||
| 518 | implicit none | ||
| 519 | |||
| 520 | ! Arguments | ||
| 521 | class(base_loss_type), intent(in), target :: this | ||
| 522 | !! Instance of the base loss function | ||
| 523 | type(array_type), dimension(:,:), intent(inout), target :: predicted | ||
| 524 | !! Predicted values | ||
| 525 | − | type(array_type), dimension(size(predicted,1),size(predicted,2)), intent(in) :: & | |
| 526 | expected | ||
| 527 | !! Expected values | ||
| 528 | type(array_type), pointer :: output | ||
| 529 | !! Loss value | ||
| 530 | |||
| 531 | − | end function compute_base | |
| 532 | !############################################################################### | ||
| 533 | |||
| 534 | − | end module athena__loss | |
| 535 |