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⨍ Functions 121◇ Types & classes 18

↓ 28 callersMethodmap

src/matrix.rs:145

↓ 26 callersMethodadd_layer

To add a new layer to the network Example: ``` # #[macro_use] extern crate juggernaut; # fn main() { use juggernaut::sample::Sample; use juggernaut:

↓ 13 callersMethodget

Returns the element in the position M,N

src/matrix.rs:111

↓ 11 callersMethodtrain

To train the network. It calls the forward pass and updates the weights using backpropagation

↓ 10 callersMethodcols

Number of the Matrix columns

src/matrix.rs:106

↓ 7 callersMethodcalc

Calculates the SoftMax of input `x`

src/activation/softmax.rs:14

↓ 7 callersMethodrows

Number of the Matrix rows

src/matrix.rs:101

↓ 5 callersMethodevaluate

Use this function to evaluate a trained neural network This function simply passes the given sample to the `forward` function and returns the output

↓ 5 callersMethodforward

This is the forward method of the network which calculates the random weights and multiplies the inputs of given samples to the weights matrix. Thinks

↓ 5 callersMethodrow

Returns a row of Matrix

src/matrix.rs:96

↓ 4 callersMethoddot

↓ 4 callersFunctionfrom_vec

src/matrix.rs:231

↓ 4 callersMethodmap_row

Map for each row of Matrix

src/matrix.rs:150

↓ 4 callersMethodtranspose

Transpose of a Matrix

src/matrix.rs:140

↓ 4 callersMethodweights

weights without bias node

↓ 3 callersMethodcalc

(&self, prediction: &Matrix, target: &Matrix)

src/cost/squared_error.rs:19

↓ 3 callersMethodcalc

Calculates the Sigmoid of input `x`

src/activation/sigmoid.rs:14

↓ 2 callersMethodbiases

↓ 2 callersMethodcalc

(&self, prediction: &Matrix, target: &Matrix)

src/cost/cross_entropy.rs:20

↓ 2 callersMethodderivative

Calculates the Derivative Sigmoid of input `x`

src/activation/sigmoid.rs:21

↓ 2 callersFunctiongenerate_by_row

src/matrix.rs:270

↓ 2 callersMethodon_error

To add a callback function and receive the errors of the network during training process Please note that there is another function that basically cal

↓ 2 callersFunctionsample_input_to_matrix

(sample: &Sample)

↓ 2 callersMethodset_cost_function

To set a cost function for the network

↓ 1 callersMethodderivative

Calculates the Derivative SoftMax of input `x`

src/activation/softmax.rs:31

↓ 1 callersMethodemit_on_epoch

To emit the `on_epoch` callback

↓ 1 callersMethodemit_on_error

To emit the `on_error` callback

↓ 1 callersMethoderror

This function calculates the error rate of network during training and calls the `on_error_fn` if it is available

↓ 1 callersMethodget_inputs_count

src/sample.rs:23

↓ 1 callersMethodget_layers

To get the layers of the network

↓ 1 callersMethodget_outputs_count

src/sample.rs:27

↓ 1 callersMethodinputs

↓ 1 callersMethodneurons

↓ 1 callersMethodon_epoch

To add a callback function to get called after each epoch

↓ 1 callersFunctionsample_output_to_matrix

(sample: &Sample)

src/utils.rs:13

↓ 1 callersMethodset_biases

(&mut self, weights: Matrix)

↓ 1 callersMethodset_shuffle_data

To set shuffle data flag Enabling this option shuffles data before each iteration

↓ 1 callersMethodset_weights

(&mut self, weights: Matrix)

src/matrix.rs:258

To get the first element of the Matrix (Vec<Vec<_>>)

src/matrix.rs:155

Calculates the LeakyRectifiedLinearUnit of input `x`

src/activation/leakyrectifiedlinearunit.rs:16

Calculates the tanh of input `x`

src/activation/hyperbolictangent.rs:15

Calculates the Identity of input `x`

src/activation/identity.rs:14

Calculates the SoftPlus of input `x`

src/activation/softplus.rs:15

Calculates the RectifiedLinearUnit of input `x`

src/activation/rectifiedlinearunit.rs:14

Functioncross_entropy_calc_half_test

src/cost/cross_entropy.rs:83

Functioncross_entropy_calc_test

src/cost/cross_entropy.rs:72

Methodderivative

Calculates the Derivative LeakyRectifiedLinearUnit of input `x`

src/activation/leakyrectifiedlinearunit.rs:27

Methodderivative

Calculates the Derivative tanh of input `x`

src/activation/hyperbolictangent.rs:20

Methodderivative

Calculates the Derivative Identity of input `x`

src/activation/identity.rs:19

Methodderivative

Calculates the Derivative SoftPlus of input `x`

src/activation/softplus.rs:20

Methodderivative

Calculates the Derivative RectifiedLinearUnit of input `x`

src/activation/rectifiedlinearunit.rs:21

Multiplication with Matrix

src/matrix.rs:118

Functionempty_matrix_test

src/matrix.rs:165

Functionerror_function_test

Functionforward_test

Functionforward_test_2layers

Generates Matrix from a vector

src/matrix.rs:91

Returns a vector with `m` rows and `n` columns Example: ``` # #[macro_use] extern crate juggernaut; # fn main() { use juggernaut::matrix::Matrix; us

src/matrix.rs:40

Methodgenerate_by_row

Returns a new Matrix with `m` rows and `n` columns Works exactly same as `generate` but accepts a Fn that returns each row of the matrix instead of e

src/matrix.rs:60

Functiongenerate_by_row_variable_rows

src/matrix.rs:282

Functionget_layers_test

Functionidentity_derivative_test

src/activation/identity.rs:36

Functionidentity_test

src/activation/identity.rs:30

Functioninputs_count

src/sample.rs:41

Functionleakyrectifiedlinearunit_derivative_test

src/activation/leakyrectifiedlinearunit.rs:47

Functionleakyrectifiedlinearunit_test

src/activation/leakyrectifiedlinearunit.rs:40

examples/helloworld.rs:10

Functionmap_row_variable_rows

src/matrix.rs:295

src/cost/squared_error.rs:15

src/cost/cross_entropy.rs:16

Functionnetwork_with_two_activations

(neurons: usize, inputs: usize, activation: T)

(inputs: Vec<f64>, outputs: Vec<f64>)

src/sample.rs:9

src/cost/squared_error.rs:9

src/cost/cross_entropy.rs:10

src/activation/leakyrectifiedlinearunit.rs:9

src/activation/hyperbolictangent.rs:8

src/activation/sigmoid.rs:7

src/activation/identity.rs:7

src/activation/softplus.rs:8

src/activation/rectifiedlinearunit.rs:7

src/activation/softmax.rs:7

Functionnew_neural_layer

Functionnew_predict_inputs_count

src/sample.rs:53

Functionnew_predict_output_count

src/sample.rs:59

Functionon_epoch_test

Functionoutputs_count

src/sample.rs:47

(inputs: Vec<f64>)

src/sample.rs:16

Returns a vector with `m` rows and `n` columns with random elements

src/matrix.rs:80

Functionrandom_matrix_get

src/matrix.rs:179

Functionrandom_matrix_test

src/matrix.rs:171

Functionrandom_mul_test1

src/matrix.rs:189

Functionrandom_mul_test2

src/matrix.rs:198

Functionrandom_mul_test3

src/matrix.rs:207

Functionrandom_mul_test4

src/matrix.rs:220

Functionrectifiedlinearunit_derivative_test

src/activation/rectifiedlinearunit.rs:41

Functionrectifiedlinearunit_test

src/activation/rectifiedlinearunit.rs:34

Functionshuffle_data

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