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Functions858 in github.com/AnicetNgrt/jiro-nn

⨍ Functions 858◇ Types & classes 90

Methodaccept_full_dense

(mut self, model: FullDenseConvLayerModel)

src/model/conv_network_model.rs:38

src/model/full_dense_conv_layer_model.rs:169

src/model/full_direct_conv_layer_model.rs:166

Methodadd_shared

( &mut self, transformation: Rc<RefCell<dyn DataTransformation>>, )

src/preprocessing/mod.rs:88

src/preprocessing/map.rs:21

Methodall_epochs_r2

Enables computing the R2 score of the model at the end of each epoch and reporting it if a real time reporter is attached. /!\ Requires `all_epochs_v

src/trainers/kfolds.rs:84

Methodall_epochs_validation

Enables computing the validation score of the model at the end of each epoch and reporting it if a real time reporter is attached. /!\ Is time consum

src/trainers/kfolds.rs:95

Methodappend_columns

(&self, columns: Vec<Series>)

src/datatable.rs:52

Methodas_dropout_layer

src/vision/conv_layer/full_conv_layer.rs:133

Methodas_dropout_layer

src/vision/conv_layer/avg_pooling_layer.rs:64

Methodas_learnable_layer

src/vision/conv_layer/full_conv_layer.rs:125

Methodas_learnable_layer

src/vision/conv_layer/avg_pooling_layer.rs:56

Methodas_learnable_layer_mut

src/vision/conv_layer/full_conv_layer.rs:129

Methodas_learnable_layer_mut

src/vision/conv_layer/avg_pooling_layer.rs:60

Methodattach_real_time_reporter

Attaches a real time reporter to the trainer. The reporter is a closure that takes as arguments: - the current epoch - the evaluation of the current

src/trainers/split.rs:74

Methodattach_real_time_reporter

Attaches a real time reporter to the trainer. The reporter is a closure that takes as arguments: - the current fold - the current epoch - the evaluat

src/trainers/kfolds.rs:109

(networks: &Vec<Self>)

src/network/params.rs:12

`output_gradient` has shape `(j, n)` where `j` is the number of outputs and `n` is the number of samples. Returns `input_gradient` which has shape `(

src/layer/dense_layer.rs:71

(&mut self, epoch: usize, output_gradient: Matrix)

src/layer/full_layer.rs:75

(&mut self, epoch: usize, error_gradient: Matrix)

src/vision/conv_network.rs:44

(&mut self, epoch: usize, output_gradient: Image)

src/vision/conv_layer/dense_conv_layer.rs:73

(&mut self, epoch: usize, output_gradient: Image)

src/vision/conv_layer/full_conv_layer.rs:92

(&mut self, _epoch: usize, output_gradient: Image)

src/vision/conv_layer/avg_pooling_layer.rs:35

(&mut self, epoch: usize, output_gradient: Image)

src/vision/conv_layer/direct_conv_layer.rs:81

(&mut self, _epoch: usize, output_gradient: Matrix)

src/activation/mod.rs:58

Methodbasic_single_pass

Creates a pipeline that does every possible operations once. This may not fit your exact usecase, but it's a good starting point. The pipeline is: -

src/preprocessing/mod.rs:73

(y_true: &Matrix, y_pred: &Matrix)

src/loss/bce.rs:15

Functionbce_prime

(y_true: &Matrix, y_pred: &Matrix)

src/loss/bce.rs:25

Functionbce_vec

(y_pred: &Vec<Scalar>, y_true: &Vec<Scalar>)

src/loss/bce.rs:6

Methodbiases_init_glorot_uniform

src/model/full_dense_layer_model.rs:133

Methodbiases_init_glorot_uniform

src/model/full_dense_conv_layer_model.rs:141

Methodbiases_init_glorot_uniform

src/model/full_direct_conv_layer_model.rs:138

Methodbiases_init_uniform

src/model/full_dense_layer_model.rs:125

Methodbiases_init_uniform

src/model/full_dense_conv_layer_model.rs:133

Methodbiases_init_uniform

src/model/full_direct_conv_layer_model.rs:130

Methodbiases_init_uniform_signed

src/model/full_dense_layer_model.rs:129

Methodbiases_init_uniform_signed

src/model/full_dense_conv_layer_model.rs:137

Methodbiases_init_uniform_signed

src/model/full_direct_conv_layer_model.rs:134

Methodbiases_init_zeros

src/model/full_dense_layer_model.rs:121

Methodbiases_init_zeros

src/model/full_dense_conv_layer_model.rs:129

Methodbiases_init_zeros

src/model/full_direct_conv_layer_model.rs:126

Methodbiases_optimizer_adam

src/model/full_dense_layer_model.rs:173

Methodbiases_optimizer_adam

src/model/full_dense_conv_layer_model.rs:181

Methodbiases_optimizer_adam

src/model/full_direct_conv_layer_model.rs:178

Methodbiases_optimizer_momentum

src/model/full_dense_layer_model.rs:169

Methodbiases_optimizer_momentum

src/model/full_dense_conv_layer_model.rs:177

Methodbiases_optimizer_momentum

src/model/full_direct_conv_layer_model.rs:174

Methodbiases_optimizer_sgd

src/model/full_dense_layer_model.rs:165

Methodbiases_optimizer_sgd

src/model/full_dense_conv_layer_model.rs:173

Methodbiases_optimizer_sgd

src/model/full_direct_conv_layer_model.rs:170

src/model/network_model.rs:50

(&mut self, working_dir: &str)

src/preprocessing/mod.rs:51

src/vision/image/nalgebra_image.rs:127

Methodcolumn_min_max

(&self, column: &str)

src/datatable.rs:606

Methodcolumns_map

(&self, _f: impl Fn(usize, &Vec<Scalar>) -> Vec<Scalar>)

src/linalg/ndarray_matrix.rs:110

Methodcolumns_map

(&self, f: impl Fn(usize, &Vec<Scalar>) -> Vec<Scalar>)

src/linalg/nalgebra_matrix.rs:135

Methodcolumns_map

(&self, _f: impl Fn(usize, &Vec<Scalar>) -> Vec<Scalar>)

src/linalg/arrayfire_matrix.rs:180

Methodcolumns_names_from_csv_file

src/datatable.rs:221

Methodcolumns_names_from_file

src/datatable.rs:201

Methodcolumns_names_from_ipc_file

src/datatable.rs:231

Methodcolumns_names_from_parquet_file

src/datatable.rs:241

Methodcolumns_sum

src/linalg/nalgebra_matrix.rs:162

Methodcolumns_sum

src/linalg/arrayfire_matrix.rs:197

Methodcomponent_add

(&self, other: &Self)

src/linalg/nalgebra_matrix.rs:170

Methodcomponent_add

(&self, other: &Self)

src/linalg/arrayfire_matrix.rs:211

Methodcomponent_add

(&self, other: &Self)

src/vision/image/nalgebra_image.rs:79

Methodcomponent_add

(&self, other: &Self)

src/vision/image/arrayfire_image.rs:210

Methodcomponent_div

(&self, other: &Self)

src/linalg/nalgebra_matrix.rs:178

Methodcomponent_div

(&self, other: &Self)

src/linalg/arrayfire_matrix.rs:219

Methodcomponent_div

(&self, other: &Self)

src/vision/image/nalgebra_image.rs:91

Methodcomponent_div

(&self, other: &Self)

src/vision/image/arrayfire_image.rs:261

Methodcomponent_mul

(&self, other: &Self)

src/linalg/nalgebra_matrix.rs:166

Methodcomponent_mul

(&self, other: &Self)

src/linalg/arrayfire_matrix.rs:207

Methodcomponent_mul

(&self, other: &Self)

src/vision/image/nalgebra_image.rs:87

Methodcomponent_mul

(&self, other: &Self)

src/vision/image/arrayfire_image.rs:244

Methodcomponent_sub

(&self, other: &Self)

src/linalg/nalgebra_matrix.rs:174

Methodcomponent_sub

(&self, other: &Self)

src/linalg/arrayfire_matrix.rs:215

Methodcomponent_sub

(&self, other: &Self)

src/vision/image/nalgebra_image.rs:83

Methodcomponent_sub

(&self, other: &Self)

src/vision/image/arrayfire_image.rs:227

Methodcompute_avg_model

Enables computing the average model of all folds at the final epoch

src/trainers/kfolds.rs:61

(nrow: usize, ncol: usize, value: Scalar)

src/linalg/ndarray_matrix.rs:20

(nrow: usize, ncol: usize, value: Scalar)

src/linalg/nalgebra_matrix.rs:23

(nrow: usize, ncol: usize, value: Scalar)

src/linalg/arrayfire_matrix.rs:26

(nrow: usize, ncol: usize, nchan: usize, samples: usize, value: Scalar)

src/vision/image/nalgebra_image.rs:14

(nrow: usize, ncol: usize, nchan: usize, samples: usize, value: Scalar)

src/vision/image/arrayfire_image.rs:30

(nrow: usize, ncol: usize, nchan: usize, samples: usize, value: Scalar)

src/vision/image/ndarray_image.rs:14

Methodconvolve_full

(&self, kernels: &Self)

src/vision/image/nalgebra_image.rs:115

Methodconvolve_full

(&self, kernels: &Self)

src/vision/image/arrayfire_image.rs:299

src/network/params.rs:60

Methodcross_correlate

(&self, kernels: &Self)

src/vision/image/nalgebra_image.rs:111

Methodcross_correlate

(&self, kernels: &Self)

src/vision/image/arrayfire_image.rs:294

src/vision/conv_optimizer/momentum.rs:31

src/vision/conv_optimizer/sgd.rs:16

src/vision/conv_optimizer/adam.rs:55

src/optimizer/momentum.rs:32

src/optimizer/sgd.rs:15

src/optimizer/adam.rs:54

Functiondefault_biases_initializer

src/layer/defaults.rs:6

Functiondefault_biases_initializer

src/vision/conv_layer/defaults.rs:6

Functiondefault_biases_optimizer

src/layer/defaults.rs:14

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