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Functions235 in github.com/Sentdex/NNfSiX

⨍ Functions 235◇ Types & classes 56
↓ 8 callersFunctionrandom
C++/p005-ReLU-Activation.cpp:21
↓ 6 callersMethodadd
(double[] input1, double[] input2)
Java/P003DotProduct.java:38
↓ 6 callersFunctionlinspace
(start, end float64, num int)
Go/p005-ReLU-Activation.go:145
↓ 5 callersMethodforward
(self, inputs)
Python/p008-Categorical-Cross-Entropy-Loss-applied.py:17
↓ 5 callersMethodrandn
Implementation of randn function from numpy, uses the built in gaussian distribution from the java random class. @param rows How many rows the array
Java/P004LayersAndObjects.java:73
↓ 4 callersMethodForward
C++/p006-Softmax-Activation.cpp:696
↓ 4 callersMethodforward
C+nnfs c++ part 6.cpp:603
↓ 4 callersMethodforward
(self, inputs)
Python/p006-Softmax-Activation.py:13
↓ 4 callersMethodoutput
C++/p005-ReLU-Activation.cpp:117
↓ 4 callersFunctionspiral_data
@brief Generate a randome range in a uniform distribution. * @Note Credit to shreeviknesh (#106) saved alot of time. * * @param[in] points Num
C/p005-ReLU-Activation.c:194
↓ 3 callersMethodnew
(n_inputs: usize, n_neurons: usize)
Rust/p005-ReLU-Activation.rs:24
↓ 3 callersMethodoutput
C++/p004-Layers-and-Object.cpp:81
↓ 3 callersFunctionvectorSum
C+nnfs c++ part 6.cpp:204
↓ 3 callersFunctionvector_sum
C++/p006-Softmax-Activation.cpp:219
↓ 2 callersMethodForward
Forward function
C#/p005-ReLU-Activation.cs:100
↓ 2 callersMethodForward
(double[][] inputs)
C#/p004-Layers-and-Objects.cs:32
↓ 2 callersMethodForward
(input *mat.Dense)
Go/p004-Layers-and-Object.go:31
↓ 2 callersMethodMatrix
C+nnfs c++ part 6.cpp:550
↓ 2 callersMethodMatrix
C++/p006-Softmax-Activation.cpp:643
↓ 2 callersFunctionNewLayerDense
(numberOfInputs, numberOfNeurons int)
Go/p004-Layers-and-Object.go:20
↓ 2 callersMethodRandom
To make generate the same set of random numbers (for debugging) comment out: std::mt19937 random_engine(random_seed()); uncomment: std::mt19937 random
C++/p006-Softmax-Activation.cpp:614
↓ 2 callersFunctiondeloc_layer
free the memory allocated by a layer
C/p006-Softmax-Activation.c:127
↓ 2 callersFunctiondeloc_layer
free the memory allocated by a layer
C/p004-Batches-Layers.c:113
↓ 2 callersFunctionforward
@brief Does a forward pass in the network from one layer to the next. * * @param[in] previos_layer Pointer the previos layer struct. * @para
C/p006-Softmax-Activation.c:144
↓ 2 callersFunctionforward
@brief Does a forward pass in the network from one layer to the next. * * @param[in] previos_layer Pointer the previos layer struct. * @para
C/p004-Batches-Layers.c:130
↓ 2 callersMethodforward
(inputs)
Javascript/p004-Layers-and-Object.js:19
↓ 2 callersMethodforward
(inputs)
Javascript/p005-ReLU-Activation.js:51
↓ 2 callersMethodforward
(&mut self, inputs: Array2<f64>)
Rust/p006-Softmax-Activation.rs:39
↓ 2 callersMethodforward
(&mut self, inputs: Array2<f64>)
Rust/p004-Layers-and-Object.rs:41
↓ 2 callersMethodforward
(self, inputs)
Python/p005-ReLU-Activation.py:14
↓ 2 callersMethodforward
(self, inputs)
Python/p004-Layers-and-Object.py:18
↓ 2 callersMethodforward
forward pass
C++/p004-Layers-and-Object.cpp:77
↓ 2 callersMethodforward
Feed forward method used to calculate output of layer. @param inputs Input to be used to calculate output.
Java/P004LayersAndObjects.java:53
↓ 2 callersMethodforward
(inputs)
Ruby/p004_Layers_And_Object.rb:22
↓ 2 callersMethodgetOutput
Get the Output array. @return The output array of the layer;
Java/P004LayersAndObjects.java:62
↓ 2 callersFunctionlayer_init
@brief Setup a layer with random weights and bais as well as allocating memory for the storage buffers. * * @param[in] layer Pointer to an em
C/p006-Softmax-Activation.c:94
↓ 2 callersFunctionlayer_init
@brief Setup a layer with random weights and bias as well as allocating memory for the storage buffers. * * @param[in] layer Pointer to an em
C/p004-Batches-Layers.c:80
↓ 2 callersFunctionmatrixGetMaxInAxis
axis = 0 means max value in each row axis = 1 means max value in each column
C+nnfs c++ part 6.cpp:315
↓ 2 callersFunctionmatrix_max_in_axis
axis = 0 means max value in each row axis = 1 means max value in each column
C++/p006-Softmax-Activation.cpp:344
↓ 1 callersMethodForward
Forward forwards the input
Go/p005-ReLU-Activation.go:36
↓ 1 callersMethodForward
Forward forwards the activation result
Go/p005-ReLU-Activation.go:59
↓ 1 callersFunctionNewActivationRelu
NewActivationRelu returns ActivationRelu with nil Output
Go/p005-ReLU-Activation.go:54
↓ 1 callersFunctionNewLayerDense
NewLayerDense creates layer generated by random numbers based on given number of inputs and neurons
Go/p005-ReLU-Activation.go:24
↓ 1 callersFunctionT
Transpose matrix
C++/p004-Layers-and-Object.cpp:21
↓ 1 callersFunctionT
matrix transpose function
C++/p005-ReLU-Activation.cpp:27
↓ 1 callersFunctionactivation_ReLU
ReLU activation function
C/p005-ReLU-Activation.c:156
↓ 1 callersFunctionactivation_ReLU
ReLU activation function
C/p006-Softmax-Activation.c:157
↓ 1 callersFunctionactivation_relu
(input: Array2<f64>)
Rust/p005-ReLU-Activation.rs:38
↓ 1 callersFunctionactivation_relu
(input: Array2<f64>)
Rust/p006-Softmax-Activation.rs:44
↓ 1 callersFunctionactivation_softmax
@brief Gets the sum of the output layer and normalizes each output value. * * @note C can not do inline summation of arrays. This can only be done a
C/p006-Softmax-Activation.c:256
↓ 1 callersFunctionadd
($a, $b)
PHP/p003-Dot-Product.php:32
↓ 1 callersFunctionadd
Kotlin/P003DotProduct.kt:36
↓ 1 callersMethodadd
Updated to add a 2 dimensional array to a one dimensional array. @param input1 Two dimensional array @param input2 One dimensional array. @return New
Java/P004LayersAndObjects.java:111
↓ 1 callersMethodadd
Updated to add a 2 dimensional array to a one dimensional array. @param input1 Two dimensional array @param input2 One dimensional array. @return New
Java/P005ReLUActivationAndSpiralDataset.java:127
↓ 1 callersFunctionaddMatrix
(vector1: number[], vector2: number[])
TypeScript/p003-Dot-Product.ts:19
↓ 1 callersMethodcalculate
(self, output, y)
Python/p008-Categorical-Cross-Entropy-Loss-applied.py:32
↓ 1 callersMethodcreate_data
(int points, int classes)
Java/P005ReLUActivationAndSpiralDataset.java:37
↓ 1 callersFunctiondeloc_layer
free the memory allocated by a layer
C/p005-ReLU-Activation.c:126
↓ 1 callersFunctiondeloc_spiral
@brief Free the allocated memory for the spiral data. * * @param[in] data Structure holding the generated spiral data. */
C/p005-ReLU-Activation.c:238
↓ 1 callersFunctiondeloc_spiral
@brief Free the allocated memory for the spiral data. * * @param[in] data Structure holding the generated spiral data. */
C/p006-Softmax-Activation.c:239
↓ 1 callersFunctiondot
($a, $b)
PHP/p003-Dot-Product.php:7
↓ 1 callersFunctiondotProduct
Kotlin/P003DotProduct.kt:23
↓ 1 callersFunctiondotProduct
(weights: number[][], inputs: number[])
TypeScript/p003-Dot-Product.ts:7
↓ 1 callersFunctiondotProduct
(l1, l2)
Ruby/p003_Dot_Product.rb:4
↓ 1 callersMethoddotProduct
Updated to calculate the dot product of multidimensional arrays. @param input1 input array 1. @param input2 input array 2 @return Newly created dot p
Java/P004LayersAndObjects.java:90
↓ 1 callersMethoddotProduct
(double[][] input1, double[] input2)
Java/P003DotProduct.java:24
↓ 1 callersMethoddotProduct
Updated to calculate the dot product of multidimensional arrays. @param input1 input array 1. @param input2 input array 2 @return Newly created dot p
Java/P005ReLUActivationAndSpiralDataset.java:106
↓ 1 callersFunctiondot_product
C/p005-ReLU-Activation.c:45
↓ 1 callersFunctiondot_product
C/p003-Dot-Product.c:20
↓ 1 callersFunctiondot_product
C/p006-Softmax-Activation.c:46
↓ 1 callersFunctiondot_product
C/p004-Batches-Layers.c:36
↓ 1 callersMethodfill
fill vector with num
C+nnfs c++ part 6.cpp:54
↓ 1 callersMethodfill
fill vector with num
C++/p006-Softmax-Activation.cpp:58
↓ 1 callersFunctionforward
@brief Does a forward pass in the network from one layer to the next. * * @param[in] previos_layer Pointer the previos layer struct. * @para
C/p005-ReLU-Activation.c:143
↓ 1 callersMethodforward
(&mut self, inputs: Array2<f64>)
Rust/p005-ReLU-Activation.rs:33
↓ 1 callersMethodforward
forward pass
C++/p005-ReLU-Activation.cpp:113
↓ 1 callersMethodforward
Feed forward method used to calculate output of layer. @param inputs Input to be used to calculate output.
Java/P005ReLUActivationAndSpiralDataset.java:78
↓ 1 callersMethodforward
Feed forward method used to calculate ReLU output of layer. @param inputs Input to be used to calculate output.
Java/P005ReLUActivationAndSpiralDataset.java:146
↓ 1 callersFunctionlayer_init
@brief Setup a layer with random weights and bais as well as allocating memory for the storage buffers. * * @param[in] layer Pointer to an em
C/p005-ReLU-Activation.c:93
↓ 1 callersFunctionlayer_output
C/p005-ReLU-Activation.c:69
↓ 1 callersFunctionlayer_output
C/p003-Dot-Product.c:40
↓ 1 callersFunctionlayer_output
C/p006-Softmax-Activation.c:70
↓ 1 callersFunctionlayer_output
C/p004-Batches-Layers.c:56
↓ 1 callersFunctionmatrixAdd
assumes that all the vectors in the matrix will be the same size
C+nnfs c++ part 6.cpp:264
↓ 1 callersFunctionmatrixCapMin
value less than lower, than lower otherwise value (cap values at lower) used for rectified linear activation function in hidden layer
C+nnfs c++ part 6.cpp:282
↓ 1 callersFunctionmatrixDot
Assumes that all the vectors in a matrix will have the same size. The only way for a vectors to have different sizes is if they are set manually, sinc
C+nnfs c++ part 6.cpp:232
↓ 1 callersFunctionmatrixExp
exponentiate all the elements
C+nnfs c++ part 6.cpp:363
↓ 1 callersFunctionmatrixMinusMaxInAxis
0 = rows, 1 = columns
C+nnfs c++ part 6.cpp:396
↓ 1 callersFunctionmatrixNormalizeInAxis
axis 0 means rows are summed to normalize axis 1 means that columns are summed to normalize*/
C+nnfs c++ part 6.cpp:465
↓ 1 callersFunctionmatrixSumInAxis
axis 0 means rows are summed axis 1 means that columns
C+nnfs c++ part 6.cpp:432
↓ 1 callersFunctionmatrix_add
assumes that all the vectors in the matrix will be the same size
C++/p006-Softmax-Activation.cpp:293
↓ 1 callersFunctionmatrix_cap_min
value less than lower, than lower otherwise value (cap values at lower) used for rectified linear activation function in hidden layer
C++/p006-Softmax-Activation.cpp:311
↓ 1 callersFunctionmatrix_dot
Assumes that all the vectors in a matrix will have the same size. The only way for a vectors to have different sizes is if they are set manually, sinc
C++/p006-Softmax-Activation.cpp:261
↓ 1 callersFunctionmatrix_exp
exponentiate all the elements
C++/p006-Softmax-Activation.cpp:392
↓ 1 callersFunctionmatrix_minus_in_axis
0 = rows, 1 = columns
C++/p006-Softmax-Activation.cpp:425
↓ 1 callersFunctionmatrix_normalize_in_axis
axis 0 means rows are summed to normalize axis 1 means that columns are summed to normalize*/
C++/p006-Softmax-Activation.cpp:494
↓ 1 callersFunctionmatrix_sum_in_axis
axis 0 means rows are summed axis 1 means that columns
C++/p006-Softmax-Activation.cpp:461
↓ 1 callersFunctionplotSpiral
(X *mat.Dense, y *mat.Dense)
Go/p005-ReLU-Activation.go:88
↓ 1 callersFunctionprint_array
($a)
PHP/p002-Basic-Neuron-Layer.php:7
↓ 1 callersFunctionprint_array
($a)
PHP/p003-Dot-Product.php:44
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