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Functions858 in github.com/AnicetNgrt/jiro-nn

⨍ Functions 858◇ Types & classes 90

Functiondefault_biases_optimizer

src/vision/conv_layer/defaults.rs:14

Functiondefault_kernels_initializer

src/vision/conv_layer/defaults.rs:10

Functiondefault_kernels_optimizer

src/vision/conv_layer/defaults.rs:18

Functiondefault_weights_initializer

src/layer/defaults.rs:10

Functiondefault_weights_optimizer

src/layer/defaults.rs:18

src/datatable.rs:20

Methoddisable_dropout

src/vision/conv_network.rs:113

Methoddisable_dropout

src/vision/conv_layer/full_conv_layer.rs:119

(&self, other: &Self)

src/linalg/arrayfire_matrix.rs:192

(self, dropped_rate: f32)

src/model/full_dense_conv_layer_model.rs:77

(self, dropped_rate: f32)

src/model/full_direct_conv_layer_model.rs:74

Methodenable_dropout

src/vision/conv_network.rs:107

Methodenable_dropout

src/vision/conv_layer/full_conv_layer.rs:115

src/model/full_dense_layer_model.rs:61

src/model/full_dense_conv_layer_model.rs:73

src/model/conv_network_model.rs:21

src/model/full_direct_conv_layer_model.rs:70

src/model/network_model.rs:43

Methodend_with_message

src/monitor/mod.rs:284

Methodepochs_std_test_loss

src/benchmarking.rs:74

Methodepochs_std_train_loss

src/benchmarking.rs:47

(value: MapValue)

src/preprocessing/map.rs:25

Methodequal_scalar

(value: Scalar)

src/preprocessing/map.rs:29

src/linalg/nalgebra_matrix.rs:247

src/linalg/arrayfire_matrix.rs:288

src/vision/image/nalgebra_image.rs:175

src/vision/image/arrayfire_image.rs:411

src/vision/image/nalgebra_image.rs:119

Methodflatten_to_vector

src/datatable.rs:500

(&self, f: &mut fmt::Formatter)

src/linalg/ndarray_matrix.rs:262

(&self, f: &mut fmt::Formatter<'_>)

src/linalg/arrayfire_matrix.rs:335

(&self, f: &mut fmt::Formatter<'_>)

src/layer/dense_layer.rs:109

(&self, f: &mut fmt::Formatter<'_>)

src/vision/image/arrayfire_image.rs:473

(&self, f: &mut fmt::Formatter<'_>)

src/vision/conv_activation/mod.rs:61

(&self, f: &mut fmt::Formatter<'_>)

src/activation/mod.rs:96

`input` has shape `(i, n)` where `i` is the number of inputs and `n` is the number of samples. Returns output which has shape `(j, n)` where `j` is t

src/layer/dense_layer.rs:52

(&mut self, mut input: Matrix)

src/layer/full_layer.rs:54

(&mut self, input: Matrix)

src/vision/conv_network.rs:26

(&mut self, input: Image)

src/vision/conv_layer/dense_conv_layer.rs:58

(&mut self, mut input: Image)

src/vision/conv_layer/full_conv_layer.rs:70

(&mut self, input: Image)

src/vision/conv_layer/avg_pooling_layer.rs:28

(&mut self, input: Image)

src/vision/conv_layer/direct_conv_layer.rs:56

(&mut self, input: Matrix)

src/activation/mod.rs:51

Methodfrom_array

(size: usize, samples: usize, array: &Array<Scalar>)

src/linalg/arrayfire_matrix.rs:326

Methodfrom_binary_compressed

src/network/params.rs:52

Methodfrom_column_leading_vector2

(m: &Vec<Vec<Scalar>>)

src/linalg/ndarray_matrix.rs:70

Methodfrom_column_leading_vector2

(m: &Vec<Vec<Scalar>>)

src/linalg/nalgebra_matrix.rs:102

Methodfrom_column_leading_vector2

(m: &Vec<Vec<Scalar>>)

src/linalg/arrayfire_matrix.rs:110

Methodfrom_column_matrices

(columns: &[Self])

src/linalg/ndarray_matrix.rs:85

Methodfrom_column_matrices

(columns: &[Self])

src/linalg/nalgebra_matrix.rs:122

Methodfrom_column_matrices

(columns: &[Self])

src/linalg/arrayfire_matrix.rs:149

Methodfrom_column_vector

(v: &Vec<Scalar>)

src/linalg/ndarray_matrix.rs:75

Methodfrom_column_vector

fills a column vector row by row with values of index 0 to v.len()

src/linalg/nalgebra_matrix.rs:109

Methodfrom_column_vector

fills a column vector row by row with values of index 0 to v.len()

src/linalg/arrayfire_matrix.rs:122

Methodfrom_columns

(columns: Vec<Series>)

src/datatable.rs:32

Methodfrom_csv_file

src/datatable.rs:296

Methodfrom_dataframe

(dataframe: DataFrame)

src/datatable.rs:28

Methodfrom_features_tags

The `from_features_tags` method is a constructor function for creating a `Dataset` object from a collection of `FeatureTags`. This method takes in a

src/dataset.rs:198

Methodfrom_file

Create a new dataset from a data file (csv, parquet...) by adding all its columns as default features.

src/dataset.rs:134

Methodfrom_file

src/datatable.rs:181

(nrows: usize, ncols: usize, mut f: F)

src/linalg/ndarray_matrix.rs:57

(nrows: usize, ncols: usize, f: F)

src/linalg/nalgebra_matrix.rs:72

(nrows: usize, ncols: usize, mut f: F)

src/linalg/arrayfire_matrix.rs:81

(nrows: usize, ncols: usize, nchan: usize, samples: usize, f: F)

src/vision/image/nalgebra_image.rs:40

(nrows: usize, ncols: usize, nchan: usize, samples: usize, mut f: F)

src/vision/image/arrayfire_image.rs:102

(nrows: usize, ncols: usize, nchan: usize, samples: usize, f: F)

src/vision/image/ndarray_image.rs:40

Methodfrom_ipc_file

src/datatable.rs:251

Methodfrom_iter

(nrow: usize, ncol: usize, data: impl Iterator<Item = Scalar>)

src/linalg/ndarray_matrix.rs:43

Methodfrom_iter

Fills the matrix with the iterator columns after columns by chunking the data by n_rows. ```txt Your data : [[col1: row0 row1 ... rowNrow][col2]...[co

src/linalg/nalgebra_matrix.rs:66

Methodfrom_iter

Fills the matrix with the iterator columns after columns by chunking the data by n_rows. ```txt Your data : [[col1: row0 row1 ... rowNrow][col2]...[co

src/linalg/arrayfire_matrix.rs:69

Methodfrom_json

src/network/params.rs:37

Methodfrom_json_file

src/benchmarking.rs:88

Methodfrom_json_file

src/model/mod.rs:123

Methodfrom_json_string

src/model/mod.rs:136

Methodfrom_parquet_file

src/datatable.rs:274

Methodfrom_row_leading_vector2

(m: &Vec<Vec<Scalar>>)

src/linalg/ndarray_matrix.rs:65

Methodfrom_row_leading_vector2

```txt Your data : [ [row0: col0 col1 ... colNcol], [row1: col0 col1 ... colNcol], ... [rowNrow: col0 col1 ... colNcol], ] Result : [ [col0: row0 row

src/linalg/nalgebra_matrix.rs:96

Methodfrom_row_leading_vector2

```txt Your data : [ [row0: col0 col1 ... colNcol], [row1: col0 col1 ... colNcol], ... [rowNrow: col0 col1 ... colNcol], ] ```

src/linalg/arrayfire_matrix.rs:105

Methodfrom_row_vector

(v: &Vec<Scalar>)

src/linalg/ndarray_matrix.rs:80

Methodfrom_row_vector

fills a row vector column by column with values of index 0 to v.len()

src/linalg/nalgebra_matrix.rs:114

Methodfrom_row_vector

fills a row vector column by column with values of index 0 to v.len()

src/linalg/arrayfire_matrix.rs:132

Methodfrom_samples

(samples: &Matrix, channels: usize)

src/vision/image/nalgebra_image.rs:47

Methodfrom_samples

(samples: &Matrix, channels: usize)

src/vision/image/arrayfire_image.rs:135

Methodfrom_samples

(samples: &Matrix, channels: usize)

src/vision/image/ndarray_image.rs:47

Methodfrom_tags

The `from_tags` method is a constructor function for creating a `Feature` object from a list of `FeatureTags`. See the documentation for `FeatureTags

src/dataset.rs:256

Methodfrom_vectors

( columns_names: &[S], columns_vectors: &Vec<Vec<Scalar>>, )

src/datatable.rs:451

Methodfull_dense

(self, kernels_count: usize, kernels_size: usize)

src/model/conv_network_model.rs:25

Methodfull_direct

(self, kernels_size: usize)

src/model/conv_network_model.rs:29

Methodget_channel

(&self, channel: usize)

src/vision/image/nalgebra_image.rs:139

Methodget_channel

(&self, channel: usize)

src/vision/image/arrayfire_image.rs:336

Methodget_channel_across_samples

(&self, channel: usize)

src/vision/image/nalgebra_image.rs:143

Methodget_channel_across_samples

(&self, channel: usize)

src/vision/image/arrayfire_image.rs:348

Methodget_column_as_matrix

(&self, idx: usize)

src/linalg/nalgebra_matrix.rs:118

Methodget_column_as_matrix

(&self, idx: usize)

src/linalg/arrayfire_matrix.rs:141

Methodget_data_col_leading

src/linalg/nalgebra_matrix.rs:186

Methodget_data_row_leading

src/linalg/nalgebra_matrix.rs:196

Methodget_data_row_leading

src/linalg/arrayfire_matrix.rs:235

Methodget_final_test_loss_avg

src/benchmarking.rs:121

Methodget_final_test_loss_std

src/benchmarking.rs:125

Methodget_hashed_id

src/preprocessing/feature_cached.rs:26

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