| Line | Branch | Exec | Source |
|---|---|---|---|
| 1 | !!!############################################################################# | ||
| 2 | !!! Code written by Ned Thaddeus Taylor | ||
| 3 | !!! Code part of the ATHENA library - a feedforward neural network library | ||
| 4 | !!!############################################################################# | ||
| 5 | !!! module contains implementation of 0D and 1D batch normalisation layers | ||
| 6 | !!!############################################################################# | ||
| 7 | module batchnorm1d_layer | ||
| 8 | use constants, only: real12 | ||
| 9 | use base_layer, only: batch_layer_type, learnable_layer_type | ||
| 10 | use custom_types, only: initialiser_type | ||
| 11 | implicit none | ||
| 12 | |||
| 13 | |||
| 14 | type, extends(batch_layer_type) :: batchnorm1d_layer_type | ||
| 15 | integer :: num_inputs = 1 | ||
| 16 | real(real12), allocatable, dimension(:,:,:) :: output | ||
| 17 | real(real12), allocatable, dimension(:,:,:) :: di ! gradient of input (i.e. delta) | ||
| 18 | contains | ||
| 19 | procedure, pass(this) :: get_output => get_output_batchnorm1d | ||
| 20 | procedure, pass(this) :: init => init_batchnorm1d | ||
| 21 | procedure, pass(this) :: set_batch_size => set_batch_size_batchnorm1d | ||
| 22 | procedure, pass(this) :: print => print_batchnorm1d | ||
| 23 | |||
| 24 | procedure, pass(this) :: forward => forward_rank | ||
| 25 | procedure, pass(this) :: backward => backward_rank | ||
| 26 | procedure, private, pass(this) :: forward_3d | ||
| 27 | procedure, private, pass(this) :: backward_3d | ||
| 28 | |||
| 29 | procedure, pass(this) :: reduce => layer_reduction | ||
| 30 | procedure, pass(this) :: merge => layer_merge | ||
| 31 | procedure :: add_t_t => layer_add !t = type, r = real, i = int | ||
| 32 | generic :: operator(+) => add_t_t !, public | ||
| 33 | end type batchnorm1d_layer_type | ||
| 34 | |||
| 35 | |||
| 36 | interface batchnorm1d_layer_type | ||
| 37 | module function layer_setup( & | ||
| 38 | input_shape, batch_size, & | ||
| 39 | num_channels, num_inputs, & | ||
| 40 | momentum, epsilon, & | ||
| 41 | gamma_init_mean, gamma_init_std, & | ||
| 42 | beta_init_mean, beta_init_std, & | ||
| 43 | kernel_initialiser, bias_initialiser, & | ||
| 44 | moving_mean_initialiser, moving_variance_initialiser & | ||
| 45 | ) result(layer) | ||
| 46 | integer, dimension(:), optional, intent(in) :: input_shape | ||
| 47 | integer, optional, intent(in) :: batch_size | ||
| 48 | integer, optional, intent(in) :: num_channels, num_inputs | ||
| 49 | real(real12), optional, intent(in) :: momentum, epsilon | ||
| 50 | real(real12), optional, intent(in) :: gamma_init_mean, gamma_init_std | ||
| 51 | real(real12), optional, intent(in) :: beta_init_mean, beta_init_std | ||
| 52 | character(*), optional, intent(in) :: & | ||
| 53 | kernel_initialiser, bias_initialiser, & | ||
| 54 | moving_mean_initialiser, moving_variance_initialiser | ||
| 55 | type(batchnorm1d_layer_type) :: layer | ||
| 56 | end function layer_setup | ||
| 57 | end interface batchnorm1d_layer_type | ||
| 58 | |||
| 59 | |||
| 60 | private | ||
| 61 | public :: batchnorm1d_layer_type | ||
| 62 | public :: read_batchnorm1d_layer | ||
| 63 | |||
| 64 | |||
| 65 | contains | ||
| 66 | |||
| 67 | !!!############################################################################# | ||
| 68 | !!! layer reduction | ||
| 69 | !!!############################################################################# | ||
| 70 | 1 | subroutine layer_reduction(this, rhs) | |
| 71 | implicit none | ||
| 72 | class(batchnorm1d_layer_type), intent(inout) :: this | ||
| 73 | class(learnable_layer_type), intent(in) :: rhs | ||
| 74 | |||
| 75 | select type(rhs) | ||
| 76 | class is(batchnorm1d_layer_type) | ||
| 77 |
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3 | this%dg = this%dg + rhs%dg |
| 78 |
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4 | this%db = this%db + rhs%db |
| 79 | end select | ||
| 80 | |||
| 81 | 1 | end subroutine layer_reduction | |
| 82 | !!!############################################################################# | ||
| 83 | |||
| 84 | |||
| 85 | !!!############################################################################# | ||
| 86 | !!! layer addition | ||
| 87 | !!!############################################################################# | ||
| 88 | 1 | function layer_add(a, b) result(output) | |
| 89 | implicit none | ||
| 90 | class(batchnorm1d_layer_type), intent(in) :: a, b | ||
| 91 | type(batchnorm1d_layer_type) :: output | ||
| 92 | |||
| 93 |
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|
1 | output = a |
| 94 |
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3 | output%dg = output%dg + b%dg |
| 95 |
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3 | output%db = output%db + b%db |
| 96 | |||
| 97 |
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2 | end function layer_add |
| 98 | !!!############################################################################# | ||
| 99 | |||
| 100 | |||
| 101 | !!!############################################################################# | ||
| 102 | !!! layer merge | ||
| 103 | !!!############################################################################# | ||
| 104 | 1 | subroutine layer_merge(this, input) | |
| 105 | implicit none | ||
| 106 | class(batchnorm1d_layer_type), intent(inout) :: this | ||
| 107 | class(learnable_layer_type), intent(in) :: input | ||
| 108 | |||
| 109 | select type(input) | ||
| 110 | class is(batchnorm1d_layer_type) | ||
| 111 |
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3 | this%dg = this%dg + input%dg |
| 112 |
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4 | this%db = this%db + input%db |
| 113 | end select | ||
| 114 | |||
| 115 | 1 | end subroutine layer_merge | |
| 116 | !!!############################################################################# | ||
| 117 | |||
| 118 | |||
| 119 | !!!##########################################################################!!! | ||
| 120 | !!! * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * !!! | ||
| 121 | !!!##########################################################################!!! | ||
| 122 | |||
| 123 | |||
| 124 | !!!############################################################################# | ||
| 125 | !!! get layer outputs | ||
| 126 | !!!############################################################################# | ||
| 127 | 4 | pure subroutine get_output_batchnorm1d(this, output) | |
| 128 | implicit none | ||
| 129 | class(batchnorm1d_layer_type), intent(in) :: this | ||
| 130 | real(real12), allocatable, dimension(..), intent(out) :: output | ||
| 131 | |||
| 132 | select rank(output) | ||
| 133 | rank(1) | ||
| 134 |
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5 | output = reshape(this%output, [size(this%output)]) |
| 135 | rank(2) | ||
| 136 | output = reshape(this%output, & | ||
| 137 |
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12 | [product(this%output_shape(:)), this%batch_size]) |
| 138 | rank(3) | ||
| 139 | ✗ | output = this%output | |
| 140 | end select | ||
| 141 | |||
| 142 | 4 | end subroutine get_output_batchnorm1d | |
| 143 | !!!############################################################################# | ||
| 144 | |||
| 145 | |||
| 146 | !!!##########################################################################!!! | ||
| 147 | !!! * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * !!! | ||
| 148 | !!!##########################################################################!!! | ||
| 149 | |||
| 150 | |||
| 151 | !!!############################################################################# | ||
| 152 | !!! forward propagation assumed rank handler | ||
| 153 | !!!############################################################################# | ||
| 154 | 2 | pure subroutine forward_rank(this, input) | |
| 155 | implicit none | ||
| 156 | class(batchnorm1d_layer_type), intent(inout) :: this | ||
| 157 | real(real12), dimension(..), intent(in) :: input | ||
| 158 | |||
| 159 | select rank(input) | ||
| 160 | rank(2) | ||
| 161 | 2 | call forward_3d(this, input) | |
| 162 | rank(3) | ||
| 163 | ✗ | call forward_3d(this, input) | |
| 164 | end select | ||
| 165 | 2 | end subroutine forward_rank | |
| 166 | !!!############################################################################# | ||
| 167 | |||
| 168 | |||
| 169 | !!!############################################################################# | ||
| 170 | !!! backward propagation assumed rank handler | ||
| 171 | !!!############################################################################# | ||
| 172 | 1 | pure subroutine backward_rank(this, input, gradient) | |
| 173 | implicit none | ||
| 174 | class(batchnorm1d_layer_type), intent(inout) :: this | ||
| 175 | real(real12), dimension(..), intent(in) :: input | ||
| 176 | real(real12), dimension(..), intent(in) :: gradient | ||
| 177 | |||
| 178 | select rank(input); rank(2) | ||
| 179 | 1 | select rank(gradient); rank(2) | |
| 180 | 1 | call backward_3d(this, input, gradient) | |
| 181 | end select | ||
| 182 | end select | ||
| 183 | select rank(input); rank(3) | ||
| 184 | ✗ | select rank(gradient); rank(3) | |
| 185 | ✗ | call backward_3d(this, input, gradient) | |
| 186 | end select | ||
| 187 | end select | ||
| 188 | 1 | end subroutine backward_rank | |
| 189 | !!!############################################################################# | ||
| 190 | |||
| 191 | |||
| 192 | !!!##########################################################################!!! | ||
| 193 | !!! * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * !!! | ||
| 194 | !!!##########################################################################!!! | ||
| 195 | |||
| 196 | |||
| 197 | !!!############################################################################# | ||
| 198 | !!! set up layer | ||
| 199 | !!!############################################################################# | ||
| 200 | 6 | module function layer_setup( & | |
| 201 |
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6 | input_shape, batch_size, & |
| 202 | num_channels, num_inputs, & | ||
| 203 | momentum, epsilon, & | ||
| 204 | gamma_init_mean, gamma_init_std, & | ||
| 205 | beta_init_mean, beta_init_std, & | ||
| 206 | kernel_initialiser, bias_initialiser, & | ||
| 207 | moving_mean_initialiser, moving_variance_initialiser & | ||
| 208 | ) result(layer) | ||
| 209 | use initialiser, only: get_default_initialiser | ||
| 210 | implicit none | ||
| 211 | integer, dimension(:), optional, intent(in) :: input_shape | ||
| 212 | integer, optional, intent(in) :: batch_size | ||
| 213 | integer, optional, intent(in) :: num_channels, num_inputs | ||
| 214 | real(real12), optional, intent(in) :: momentum, epsilon | ||
| 215 | real(real12), optional, intent(in) :: gamma_init_mean, gamma_init_std | ||
| 216 | real(real12), optional, intent(in) :: beta_init_mean, beta_init_std | ||
| 217 | character(*), optional, intent(in) :: & | ||
| 218 | kernel_initialiser, bias_initialiser, & | ||
| 219 | moving_mean_initialiser, moving_variance_initialiser | ||
| 220 | |||
| 221 | type(batchnorm1d_layer_type) :: layer | ||
| 222 | |||
| 223 | |||
| 224 |
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6 | layer%name = "batchnorm1d" |
| 225 | 6 | layer%input_rank = 1 | |
| 226 | !!-------------------------------------------------------------------------- | ||
| 227 | !! initialise batch size | ||
| 228 | !!-------------------------------------------------------------------------- | ||
| 229 |
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6 | if(present(batch_size)) layer%batch_size = batch_size |
| 230 | |||
| 231 | |||
| 232 | !!-------------------------------------------------------------------------- | ||
| 233 | !! set up number of channels (alt. number of features) | ||
| 234 | !!-------------------------------------------------------------------------- | ||
| 235 | ! layer%num_channels = -1 | ||
| 236 | ! if(present(num_channels).and.present(num_inputs))then | ||
| 237 | ! write(0,*) "ERROR: both num_channels and num_inputs present" | ||
| 238 | ! write(0,*) "These represent the same parameter, so are conflicting" | ||
| 239 | ! stop 1 | ||
| 240 | ! end if | ||
| 241 | |||
| 242 | |||
| 243 | !!-------------------------------------------------------------------------- | ||
| 244 | !! set up momentum and epsilon | ||
| 245 | !!-------------------------------------------------------------------------- | ||
| 246 |
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6 | if(present(momentum))then |
| 247 | 1 | layer%momentum = momentum | |
| 248 | else | ||
| 249 | 5 | layer%momentum = 0._real12 | |
| 250 | end if | ||
| 251 |
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6 | if(present(epsilon))then |
| 252 | 1 | layer%epsilon = epsilon | |
| 253 | else | ||
| 254 | 5 | layer%epsilon = 1.E-5_real12 | |
| 255 | end if | ||
| 256 | |||
| 257 | |||
| 258 | !!-------------------------------------------------------------------------- | ||
| 259 | !! set up initialiser mean and standard deviations | ||
| 260 | !!-------------------------------------------------------------------------- | ||
| 261 |
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6 | if(present(gamma_init_mean)) layer%gamma_init_mean = gamma_init_mean |
| 262 |
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6 | if(present(gamma_init_std)) layer%gamma_init_std = gamma_init_std |
| 263 |
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6 | if(present(beta_init_mean)) layer%beta_init_mean = beta_init_mean |
| 264 |
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6 | if(present(beta_init_std)) layer%beta_init_std = beta_init_std |
| 265 | |||
| 266 | |||
| 267 | !!-------------------------------------------------------------------------- | ||
| 268 | !! define gamma and beta initialisers | ||
| 269 | !!-------------------------------------------------------------------------- | ||
| 270 |
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6 | if(present(kernel_initialiser)) layer%kernel_initialiser = kernel_initialiser |
| 271 |
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6 | if(trim(layer%kernel_initialiser).eq.'') & |
| 272 | 5 | layer%kernel_initialiser = 'ones' | |
| 273 | !get_default_initialiser("batch") | ||
| 274 | write(*,'("BATCHNORM1D kernel (gamma) initialiser: ",A)') & | ||
| 275 |
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6 | trim(layer%kernel_initialiser) |
| 276 |
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6 | if(present(bias_initialiser)) layer%bias_initialiser = bias_initialiser |
| 277 |
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6 | if(trim(layer%bias_initialiser).eq.'') & |
| 278 | 5 | layer%bias_initialiser = 'zeros' | |
| 279 | !get_default_initialiser("batch") | ||
| 280 | write(*,'("BATCHNORM1D bias (beta) initialiser: ",A)') & | ||
| 281 |
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6 | trim(layer%bias_initialiser) |
| 282 | |||
| 283 |
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6 | if(present(moving_mean_initialiser)) & |
| 284 |
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1 | layer%moving_mean_initialiser = moving_mean_initialiser |
| 285 |
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6 | if(trim(layer%moving_mean_initialiser).eq.'') & |
| 286 | 5 | layer%moving_mean_initialiser = 'zeros' | |
| 287 | !get_default_initialiser("batch") | ||
| 288 | write(*,'("BATCHNORM1D moving mean initialiser: ",A)') & | ||
| 289 |
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6 | trim(layer%moving_mean_initialiser) |
| 290 |
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6 | if(present(moving_variance_initialiser)) & |
| 291 |
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1 | layer%moving_variance_initialiser = moving_variance_initialiser |
| 292 |
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6 | if(trim(layer%moving_variance_initialiser).eq.'') & |
| 293 | 5 | layer%moving_variance_initialiser = 'ones' | |
| 294 | !get_default_initialiser("batch") | ||
| 295 | write(*,'("BATCHNORM1D moving variance initialiser: ",A)') & | ||
| 296 |
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6 | trim(layer%moving_variance_initialiser) |
| 297 | |||
| 298 | |||
| 299 | !!-------------------------------------------------------------------------- | ||
| 300 | !! initialise layer shape | ||
| 301 | !!-------------------------------------------------------------------------- | ||
| 302 |
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6 | if(present(input_shape))then |
| 303 |
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4 | call layer%init(input_shape=input_shape) |
| 304 |
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2 | elseif(present(num_channels).and.present(num_inputs))then |
| 305 | ✗ | call layer%init(input_shape=[num_inputs, num_channels]) | |
| 306 |
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2 | elseif(present(num_channels))then |
| 307 |
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2 | call layer%init(input_shape=[num_channels]) |
| 308 |
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1 | elseif(present(num_inputs))then |
| 309 |
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2 | call layer%init(input_shape=[num_inputs]) |
| 310 | end if | ||
| 311 | |||
| 312 |
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12 | end function layer_setup |
| 313 | !!!############################################################################# | ||
| 314 | |||
| 315 | |||
| 316 | !!!############################################################################# | ||
| 317 | !!! initialise layer | ||
| 318 | !!!############################################################################# | ||
| 319 |
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6 | subroutine init_batchnorm1d(this, input_shape, batch_size, verbose) |
| 320 | use initialiser, only: initialiser_setup | ||
| 321 | implicit none | ||
| 322 | class(batchnorm1d_layer_type), intent(inout) :: this | ||
| 323 | integer, dimension(:), intent(in) :: input_shape | ||
| 324 | integer, optional, intent(in) :: batch_size | ||
| 325 | integer, optional, intent(in) :: verbose | ||
| 326 | |||
| 327 | integer :: verbose_ = 0 | ||
| 328 | 12 | class(initialiser_type), allocatable :: t_initialiser | |
| 329 | |||
| 330 | |||
| 331 | !!-------------------------------------------------------------------------- | ||
| 332 | !! initialise optional arguments | ||
| 333 | !!-------------------------------------------------------------------------- | ||
| 334 |
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6 | if(present(verbose)) verbose_ = verbose |
| 335 |
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6 | if(present(batch_size)) this%batch_size = batch_size |
| 336 | |||
| 337 | |||
| 338 | !!-------------------------------------------------------------------------- | ||
| 339 | !! initialise input shape | ||
| 340 | !!-------------------------------------------------------------------------- | ||
| 341 |
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6 | if(.not.allocated(this%input_shape))then |
| 342 |
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6 | if(size(input_shape).eq.1.or.size(input_shape).eq.2)then |
| 343 |
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6 | this%input_rank = size(input_shape) |
| 344 | end if | ||
| 345 |
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6 | call this%set_shape(input_shape) |
| 346 | end if | ||
| 347 | |||
| 348 | |||
| 349 | !!-------------------------------------------------------------------------- | ||
| 350 | !! set up output shape | ||
| 351 | !!-------------------------------------------------------------------------- | ||
| 352 |
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12 | this%output_shape = this%input_shape |
| 353 |
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6 | this%num_channels = this%input_shape(this%input_rank) |
| 354 |
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6 | if(size(this%input_shape).eq.2)then |
| 355 | ✗ | this%num_inputs = this%input_shape(1) | |
| 356 | end if | ||
| 357 | |||
| 358 | |||
| 359 | !!-------------------------------------------------------------------------- | ||
| 360 | !! allocate mean, variance, gamma, beta, dg, db | ||
| 361 | !!-------------------------------------------------------------------------- | ||
| 362 |
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|
42 | allocate(this%mean(this%num_channels), source=0._real12) |
| 363 |
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42 | allocate(this%variance, source=this%mean) |
| 364 |
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42 | allocate(this%gamma, source=this%mean) |
| 365 |
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42 | allocate(this%beta, source=this%mean) |
| 366 |
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42 | allocate(this%dg, source=this%mean) |
| 367 |
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|
42 | allocate(this%db, source=this%mean) |
| 368 | |||
| 369 | |||
| 370 | !!-------------------------------------------------------------------------- | ||
| 371 | !! initialise gamma | ||
| 372 | !!-------------------------------------------------------------------------- | ||
| 373 |
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|
12 | allocate(t_initialiser, source=initialiser_setup(this%kernel_initialiser)) |
| 374 | 6 | t_initialiser%mean = this%gamma_init_mean | |
| 375 | 6 | t_initialiser%std = this%gamma_init_std | |
| 376 | 24 | call t_initialiser%initialise(this%gamma, & | |
| 377 | fan_in =this%num_channels, & | ||
| 378 |
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6 | fan_out=this%num_channels) |
| 379 |
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6 | deallocate(t_initialiser) |
| 380 | |||
| 381 | !! initialise beta | ||
| 382 | !!-------------------------------------------------------------------------- | ||
| 383 |
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12 | allocate(t_initialiser, source=initialiser_setup(this%bias_initialiser)) |
| 384 | 6 | t_initialiser%mean = this%beta_init_mean | |
| 385 | 6 | t_initialiser%std = this%beta_init_std | |
| 386 | 24 | call t_initialiser%initialise(this%beta, & | |
| 387 | fan_in =this%num_channels, & | ||
| 388 |
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6 | fan_out=this%num_channels) |
| 389 |
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6 | deallocate(t_initialiser) |
| 390 | |||
| 391 | |||
| 392 | !!-------------------------------------------------------------------------- | ||
| 393 | !! initialise moving mean | ||
| 394 | !!-------------------------------------------------------------------------- | ||
| 395 | ✗ | allocate(t_initialiser, & | |
| 396 |
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12 | source=initialiser_setup(this%moving_mean_initialiser)) |
| 397 | 24 | call t_initialiser%initialise(this%mean, & | |
| 398 | fan_in =this%num_channels, & | ||
| 399 |
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6 | fan_out=this%num_channels) |
| 400 |
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6 | deallocate(t_initialiser) |
| 401 | |||
| 402 | !! initialise moving variance | ||
| 403 | !!-------------------------------------------------------------------------- | ||
| 404 | ✗ | allocate(t_initialiser, & | |
| 405 |
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12 | source=initialiser_setup(this%moving_variance_initialiser)) |
| 406 | 24 | call t_initialiser%initialise(this%variance, & | |
| 407 | fan_in =this%num_channels, & | ||
| 408 |
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6 | fan_out=this%num_channels) |
| 409 |
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6 | deallocate(t_initialiser) |
| 410 | |||
| 411 | |||
| 412 | !!-------------------------------------------------------------------------- | ||
| 413 | !! initialise batch size-dependent arrays | ||
| 414 | !!-------------------------------------------------------------------------- | ||
| 415 |
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6 | if(this%batch_size.gt.0) call this%set_batch_size(this%batch_size) |
| 416 | |||
| 417 |
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30 | end subroutine init_batchnorm1d |
| 418 | !!!############################################################################# | ||
| 419 | |||
| 420 | |||
| 421 | !!!############################################################################# | ||
| 422 | !!! set batch size | ||
| 423 | !!!############################################################################# | ||
| 424 | 6 | subroutine set_batch_size_batchnorm1d(this, batch_size, verbose) | |
| 425 | implicit none | ||
| 426 | class(batchnorm1d_layer_type), intent(inout) :: this | ||
| 427 | integer, intent(in) :: batch_size | ||
| 428 | integer, optional, intent(in) :: verbose | ||
| 429 | |||
| 430 | integer :: verbose_ = 0 | ||
| 431 | |||
| 432 | |||
| 433 | !!-------------------------------------------------------------------------- | ||
| 434 | !! initialise optional arguments | ||
| 435 | !!-------------------------------------------------------------------------- | ||
| 436 |
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6 | if(present(verbose)) verbose_ = verbose |
| 437 | 6 | this%batch_size = batch_size | |
| 438 | |||
| 439 | |||
| 440 | !!-------------------------------------------------------------------------- | ||
| 441 | !! set norm | ||
| 442 | !!-------------------------------------------------------------------------- | ||
| 443 | 6 | this%norm = real(this%batch_size, real12) | |
| 444 | |||
| 445 | |||
| 446 | !!-------------------------------------------------------------------------- | ||
| 447 | !! allocate arrays | ||
| 448 | !!-------------------------------------------------------------------------- | ||
| 449 |
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6 | if(allocated(this%input_shape))then |
| 450 |
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6 | if(allocated(this%output)) deallocate(this%output) |
| 451 | ✗ | allocate(this%output( & | |
| 452 | this%num_inputs, & | ||
| 453 | this%num_channels, & | ||
| 454 | this%batch_size), & | ||
| 455 |
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|
356 | source=0._real12) |
| 456 |
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6 | if(allocated(this%di)) deallocate(this%di) |
| 457 |
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|
356 | allocate(this%di, source=this%output) |
| 458 | end if | ||
| 459 | |||
| 460 | 6 | end subroutine set_batch_size_batchnorm1d | |
| 461 | !!!############################################################################# | ||
| 462 | |||
| 463 | |||
| 464 | !!!##########################################################################!!! | ||
| 465 | !!! * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * !!! | ||
| 466 | !!!##########################################################################!!! | ||
| 467 | |||
| 468 | |||
| 469 | !!!############################################################################# | ||
| 470 | !!! print layer to file | ||
| 471 | !!!############################################################################# | ||
| 472 | ✗ | subroutine print_batchnorm1d(this, file) | |
| 473 | implicit none | ||
| 474 | class(batchnorm1d_layer_type), intent(in) :: this | ||
| 475 | character(*), intent(in) :: file | ||
| 476 | |||
| 477 | integer :: unit | ||
| 478 | integer :: m | ||
| 479 | |||
| 480 | !! open file with new unit | ||
| 481 | !!-------------------------------------------------------------------------- | ||
| 482 | ✗ | open(newunit=unit, file=trim(file), access='append') | |
| 483 | |||
| 484 | !! write convolution initial parameters | ||
| 485 | !!-------------------------------------------------------------------------- | ||
| 486 | ✗ | write(unit,'("BATCHNORM1D")') | |
| 487 | ✗ | write(unit,'(3X,"INPUT_SHAPE = ",2(1X,I0))') this%input_shape | |
| 488 | ✗ | write(unit,'(3X,"MOMENTUM = ",F0.9)') this%momentum | |
| 489 | ✗ | write(unit,'(3X,"EPSILON = ",F0.9)') this%epsilon | |
| 490 | ✗ | write(unit,'(3X,"NUM_CHANNELS = ",I0)') this%num_channels | |
| 491 | ✗ | write(unit,'("GAMMA")') | |
| 492 | ✗ | do m=1,this%num_channels | |
| 493 | ✗ | write(unit,'(5(E16.8E2))') this%gamma(m) | |
| 494 | end do | ||
| 495 | ✗ | write(unit,'("END GAMMA")') | |
| 496 | ✗ | write(unit,'("BETA")') | |
| 497 | ✗ | do m=1,this%num_channels | |
| 498 | ✗ | write(unit,'(5(E16.8E2))') this%beta(m) | |
| 499 | end do | ||
| 500 | ✗ | write(unit,'("END BATCHNORM1D")') | |
| 501 | |||
| 502 | !! close unit | ||
| 503 | !!-------------------------------------------------------------------------- | ||
| 504 | ✗ | close(unit) | |
| 505 | |||
| 506 | ✗ | end subroutine print_batchnorm1d | |
| 507 | !!!############################################################################# | ||
| 508 | |||
| 509 | |||
| 510 | !!!############################################################################# | ||
| 511 | !!! read layer from file | ||
| 512 | !!!############################################################################# | ||
| 513 | ✗ | function read_batchnorm1d_layer(unit, verbose) result(layer) | |
| 514 | use infile_tools, only: assign_val, assign_vec | ||
| 515 | use misc, only: to_lower, icount | ||
| 516 | implicit none | ||
| 517 | integer, intent(in) :: unit | ||
| 518 | integer, optional, intent(in) :: verbose | ||
| 519 | |||
| 520 | class(batchnorm1d_layer_type), allocatable :: layer | ||
| 521 | |||
| 522 | integer :: stat, verbose_ = 0 | ||
| 523 | integer :: itmp1, c, i, j, k | ||
| 524 | integer :: num_channels | ||
| 525 | real(real12) :: momentum = 0._real12, epsilon = 1.E-5_real12 | ||
| 526 | logical :: found_gamma=.false., found_beta=.false. | ||
| 527 | character(14) :: kernel_initialiser='', bias_initialiser='' | ||
| 528 | character(256) :: buffer, tag | ||
| 529 | |||
| 530 | integer, dimension(3) :: input_shape | ||
| 531 | ✗ | real(real12), allocatable, dimension(:) :: data_list | |
| 532 | |||
| 533 | |||
| 534 | !!-------------------------------------------------------------------------- | ||
| 535 | !! initialise optional arguments | ||
| 536 | !!-------------------------------------------------------------------------- | ||
| 537 | ✗ | if(present(verbose)) verbose_ = verbose | |
| 538 | |||
| 539 | |||
| 540 | !!-------------------------------------------------------------------------- | ||
| 541 | !! loop over tags in layer card | ||
| 542 | !!-------------------------------------------------------------------------- | ||
| 543 | ✗ | tag_loop: do | |
| 544 | |||
| 545 | !! check for end of file | ||
| 546 | ✗ | read(unit,'(A)',iostat=stat) buffer | |
| 547 | ✗ | if(stat.ne.0)then | |
| 548 | ✗ | write(0,*) "ERROR: file encountered error (EoF?) before END BATCHNORM1D" | |
| 549 | ✗ | stop "Exiting..." | |
| 550 | end if | ||
| 551 | ✗ | if(trim(adjustl(buffer)).eq."") cycle tag_loop | |
| 552 | |||
| 553 | !! check for end of convolution card | ||
| 554 | ✗ | if(trim(adjustl(buffer)).eq."END BATCHNORM1D")then | |
| 555 | ✗ | backspace(unit) | |
| 556 | ✗ | exit tag_loop | |
| 557 | end if | ||
| 558 | |||
| 559 | ✗ | tag=trim(adjustl(buffer)) | |
| 560 | ✗ | if(scan(buffer,"=").ne.0) tag=trim(tag(:scan(tag,"=")-1)) | |
| 561 | |||
| 562 | !! read parameters from save file | ||
| 563 | ✗ | select case(trim(tag)) | |
| 564 | case("INPUT_SHAPE") | ||
| 565 | ✗ | call assign_vec(buffer, input_shape, itmp1) | |
| 566 | case("MOMENTUM") | ||
| 567 | ✗ | call assign_val(buffer, momentum, itmp1) | |
| 568 | case("EPSILON") | ||
| 569 | ✗ | call assign_val(buffer, epsilon, itmp1) | |
| 570 | case("KERNEL_INITIALISER") | ||
| 571 | ✗ | call assign_val(buffer, kernel_initialiser, itmp1) | |
| 572 | case("BIAS_INITIALISER") | ||
| 573 | ✗ | call assign_val(buffer, bias_initialiser, itmp1) | |
| 574 | case("GAMMA") | ||
| 575 | ✗ | found_gamma = .true. | |
| 576 | ✗ | kernel_initialiser = 'zeros' | |
| 577 | ✗ | bias_initialiser = 'zeros' | |
| 578 | ✗ | exit tag_loop | |
| 579 | case("beta") | ||
| 580 | ✗ | found_beta = .true. | |
| 581 | ✗ | kernel_initialiser = 'zeros' | |
| 582 | ✗ | bias_initialiser = 'zeros' | |
| 583 | ✗ | exit tag_loop | |
| 584 | case default | ||
| 585 | !! don't look for "e" due to scientific notation of numbers | ||
| 586 | !! ... i.e. exponent (E+00) | ||
| 587 | ✗ | if(scan(to_lower(trim(adjustl(buffer))),& | |
| 588 | 'abcdfghijklmnopqrstuvwxyz').eq.0)then | ||
| 589 | ✗ | cycle tag_loop | |
| 590 | ✗ | elseif(tag(:3).eq.'END')then | |
| 591 | ✗ | cycle tag_loop | |
| 592 | end if | ||
| 593 | ✗ | stop "Unrecognised line in input file: "//trim(adjustl(buffer)) | |
| 594 | end select | ||
| 595 | end do tag_loop | ||
| 596 | |||
| 597 | |||
| 598 | !!-------------------------------------------------------------------------- | ||
| 599 | !! set transfer activation function | ||
| 600 | !!-------------------------------------------------------------------------- | ||
| 601 | ✗ | num_channels = input_shape(size(input_shape)) | |
| 602 | layer = batchnorm1d_layer_type( input_shape=input_shape, & | ||
| 603 | momentum = momentum, epsilon = epsilon & | ||
| 604 | ✗ | ) | |
| 605 | |||
| 606 | |||
| 607 | !!-------------------------------------------------------------------------- | ||
| 608 | !! check if WEIGHTS card was found | ||
| 609 | !!-------------------------------------------------------------------------- | ||
| 610 | ✗ | allocate(data_list(num_channels), source=0._real12) | |
| 611 | ✗ | do i=1,2 | |
| 612 | ✗ | if(found_gamma.or.found_beta)then | |
| 613 | ✗ | c = 1 | |
| 614 | ✗ | k = 1 | |
| 615 | ✗ | data_list = 0._real12 | |
| 616 | ✗ | data_concat_loop: do while(c.le.num_channels) | |
| 617 | ✗ | read(unit,'(A)',iostat=stat) buffer | |
| 618 | ✗ | if(stat.ne.0) exit data_concat_loop | |
| 619 | ✗ | k = icount(buffer) | |
| 620 | ✗ | read(buffer,*,iostat=stat) (data_list(j),j=c,c+k-1) | |
| 621 | ✗ | c = c + k | |
| 622 | end do data_concat_loop | ||
| 623 | ✗ | if(found_gamma)then | |
| 624 | ✗ | layer%gamma = data_list | |
| 625 | ✗ | found_gamma = .false. | |
| 626 | ✗ | elseif(found_beta)then | |
| 627 | ✗ | layer%beta = data_list | |
| 628 | ✗ | found_beta = .false. | |
| 629 | end if | ||
| 630 | ✗ | read(unit,'(A)',iostat=stat) buffer | |
| 631 | ✗ | if(index(trim(adjustl(buffer)),"GAMMA").eq.1) found_gamma = .true. | |
| 632 | ✗ | if(index(trim(adjustl(buffer)),"BETA").eq.1) found_beta = .true. | |
| 633 | end if | ||
| 634 | end do | ||
| 635 | ✗ | deallocate(data_list) | |
| 636 | |||
| 637 | |||
| 638 | !! check for end of layer card | ||
| 639 | !!----------------------------------------------------------------------- | ||
| 640 | ✗ | read(unit,'(A)') buffer | |
| 641 | ✗ | if(trim(adjustl(buffer)).ne."END BATCHNORM1D")then | |
| 642 | ✗ | write(*,*) trim(adjustl(buffer)) | |
| 643 | ✗ | stop "ERROR: END BATCHNORM1D not where expected" | |
| 644 | end if | ||
| 645 | |||
| 646 | ✗ | end function read_batchnorm1d_layer | |
| 647 | !!!############################################################################# | ||
| 648 | |||
| 649 | |||
| 650 | !!!##########################################################################!!! | ||
| 651 | !!! * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * !!! | ||
| 652 | !!!##########################################################################!!! | ||
| 653 | |||
| 654 | |||
| 655 | !!!############################################################################# | ||
| 656 | !!! forward propagation | ||
| 657 | !!!############################################################################# | ||
| 658 | 2 | pure subroutine forward_3d(this, input) | |
| 659 | implicit none | ||
| 660 | class(batchnorm1d_layer_type), intent(inout) :: this | ||
| 661 | real(real12), dimension( & | ||
| 662 | this%num_inputs, & | ||
| 663 | this%num_channels, & | ||
| 664 | this%batch_size), & | ||
| 665 | intent(in) :: input | ||
| 666 | |||
| 667 | integer :: m | ||
| 668 | 4 | real(real12), dimension(this%num_channels) :: t_mean, t_variance | |
| 669 | |||
| 670 | |||
| 671 | 2 | select case(this%inference) | |
| 672 | case(.true.) | ||
| 673 | ✗ | do concurrent(m=1:this%num_channels) | |
| 674 | !! normalize each feature | ||
| 675 | ✗ | this%output(:,m,:) = this%gamma(m) * (input(:,m,:) - this%mean(m)) / & | |
| 676 | sqrt( & | ||
| 677 | ✗ | this%batch_size / (this%batch_size - 1) * this%variance(m) + & | |
| 678 | this%epsilon) + & | ||
| 679 | ✗ | this%beta(m) | |
| 680 | end do | ||
| 681 | case default | ||
| 682 |
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18 | t_mean = 0._real12 |
| 683 |
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18 | t_variance = 0._real12 |
| 684 |
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2 | do concurrent(m=1:this%num_channels) |
| 685 | !! calculate current mean and variance | ||
| 686 |
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|
176 | t_mean(m) = sum(input(:,m,:)) / this%norm |
| 687 |
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|
176 | t_variance(m) = sum((input(:,m,:) - t_mean(m))**2._real12) / this%norm |
| 688 | |||
| 689 | !! CONVERT TO USING inverse square root of variance (i.e. inverse std) | ||
| 690 | !! would also need to include epsilon in the sqrt denominator | ||
| 691 | |||
| 692 | !! update running averages | ||
| 693 |
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|
16 | if(this%momentum.gt.1.E-8_real12)then |
| 694 | ✗ | this%mean(m) = this%momentum * this%mean(m) + & | |
| 695 | ✗ | (1._real12 - this%momentum) * t_mean(m) | |
| 696 | ✗ | this%variance(m) = this%momentum * this%variance(m) + & | |
| 697 | ✗ | (1._real12 - this%momentum) * t_variance(m) | |
| 698 | else | ||
| 699 |
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|
16 | this%mean(m) = t_mean(m) |
| 700 |
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|
16 | this%variance(m) = t_variance(m) |
| 701 | end if | ||
| 702 | |||
| 703 | !! normalize each feature | ||
| 704 |
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16 | this%output(:,m,:) = this%gamma(m) * (input(:,m,:) - this%mean(m)) / & |
| 705 |
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|
194 | sqrt(this%variance(m) + this%epsilon) + this%beta(m) |
| 706 | |||
| 707 | end do | ||
| 708 | end select | ||
| 709 | |||
| 710 | 2 | end subroutine forward_3d | |
| 711 | !!!############################################################################# | ||
| 712 | |||
| 713 | |||
| 714 | !!!############################################################################# | ||
| 715 | !!! backward propagation | ||
| 716 | !!!############################################################################# | ||
| 717 | 1 | pure subroutine backward_3d(this, input, gradient) | |
| 718 | implicit none | ||
| 719 | class(batchnorm1d_layer_type), intent(inout) :: this | ||
| 720 | real(real12), dimension( & | ||
| 721 | this%num_inputs, & | ||
| 722 | this%num_channels, & | ||
| 723 | this%batch_size), & | ||
| 724 | intent(in) :: input | ||
| 725 | real(real12), dimension( & | ||
| 726 | this%num_inputs, & | ||
| 727 | this%num_channels, & | ||
| 728 | this%batch_size), & | ||
| 729 | intent(in) :: gradient | ||
| 730 | |||
| 731 | integer :: m | ||
| 732 | real(real12), dimension( & | ||
| 733 | this%num_inputs, & | ||
| 734 | this%num_channels, & | ||
| 735 | 2 | this%batch_size) :: x_hat, dx_hat | |
| 736 | |||
| 737 | |||
| 738 | 1 | do concurrent(m=1:this%num_channels) | |
| 739 | !! recalculate x_hat (i.e. normalised input) | ||
| 740 |
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16 | x_hat(:,m,:) = (input(:,m,:) - this%mean(m)) / & |
| 741 |
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|
104 | sqrt(this%variance(m) + this%epsilon) |
| 742 | |||
| 743 | !! calculate gradient of normalised input | ||
| 744 |
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|
88 | dx_hat(:,m,:) = gradient(:,m,:) * this%gamma(m) |
| 745 | |||
| 746 | !! calculate gradient of inputs | ||
| 747 | 96 | this%di(:,m,:) = & | |
| 748 | 16 | 1._real12 / (this%norm * sqrt(this%variance(m) + this%epsilon)) * & | |
| 749 |
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8 | ( this%norm * dx_hat(:,m,:) - & |
| 750 |
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16 | sum(dx_hat(:,m,:)) - x_hat(:,m,:) * & |
| 751 |
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|
272 | sum(dx_hat(:,m,:) * x_hat(:,m,:))) |
| 752 | |||
| 753 | !! calculate gradient of gamma and beta | ||
| 754 |
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88 | this%dg(m) = sum(gradient(:,m,:) * x_hat(:,m,:)) |
| 755 |
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|
89 | this%db(m) = sum(gradient(:,m,:)) |
| 756 | end do | ||
| 757 | |||
| 758 | 1 | end subroutine backward_3d | |
| 759 | !!!############################################################################# | ||
| 760 | |||
| 761 |
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|
41 | end module batchnorm1d_layer |
| 762 | !!!############################################################################# | ||
| 763 |