NEDAS.models.qg.fortran.emulator.netutils module

class NEDAS.models.qg.fortran.emulator.netutils.Att_Res_UNet(list_predictors, list_targets, patch_dim, batch_size, n_filters, activation, kernel_initializer, batch_norm, pooling_type, dropout)

Bases: object

repeat_elem(tensor, rep)

gating_signal(x, n_filters, batch_norm=False)

attention_block(x, g, inter_shape)

residual_conv_block(x, n_filters, padding: Literal['valid', 'same'] = 'same', kernel_size=(3, 3))

downsample_block(x, n_filters, pool_size=(2, 2), kernel_size=(3, 3), strides=2)

upsample_block(x, conv_features, n_filters, kernel_size=(3, 3), strides=2, padding='same')

make_unet_model()

featname2tuple(feature_name)

class NEDAS.models.qg.fortran.emulator.netutils.Data_generator(*args: Any, **kwargs: Any)

Bases: Sequence

index2rs(index)

on_epoch_end()