From 5373838f244f3ce3624c4a40e758f04d6a57affc Mon Sep 17 00:00:00 2001 From: github-actions <${GITHUB_ACTOR}@users.noreply.github.com> Date: Sun, 11 Oct 2020 14:35:44 +0000 Subject: [PATCH] Documentation for 895ae31cd7742d37ac6b892278f1797dbd775568 --- ...1_1layers_1_1_dense_layer__coll__graph.md5 | 2 +- ...1_1layers_1_1_dense_layer__coll__graph.svg | 2 +- ...ork_1_1layers_1_1_dense_layer-members.html | 6 +- ...1_1neural__network_1_1_neural_network.html | 60 +++++++++---------- ...g_1_1neural__network_1_1_neural_network.js | 2 +- ...5d132aa38b9c9aab6716663a751b82_cgraph.map} | 0 ...5d132aa38b9c9aab6716663a751b82_cgraph.md5} | 0 ...5d132aa38b9c9aab6716663a751b82_cgraph.svg} | 0 ...etwork_1_1_neural_network__coll__graph.md5 | 2 +- ...etwork_1_1_neural_network__coll__graph.svg | 2 +- ...l__network_1_1_neural_network-members.html | 2 +- d8/d95/vector__ops_8hpp_source.html | 6 +- ...al__network_1_1layers_1_1_dense_layer.html | 50 ++++++++-------- ...ural__network_1_1layers_1_1_dense_layer.js | 6 +- ...046825be0b6dbb73fbe834aa49200e_cgraph.map} | 0 ...046825be0b6dbb73fbe834aa49200e_cgraph.md5} | 0 ...046825be0b6dbb73fbe834aa49200e_cgraph.svg} | 0 ...ab6f1b2840f89a858ca36b78739b69_cgraph.map} | 0 ...ab6f1b2840f89a858ca36b78739b69_cgraph.md5} | 0 ...ab6f1b2840f89a858ca36b78739b69_cgraph.svg} | 0 functions_d.html | 2 +- functions_func.html | 2 +- navtreeindex1.js | 2 +- navtreeindex4.js | 6 +- navtreeindex5.js | 2 +- search/all_4.js | 2 +- search/all_e.js | 2 +- search/functions_4.js | 2 +- search/functions_e.js | 2 +- 29 files changed, 81 insertions(+), 81 deletions(-) rename d4/df4/{classmachine__learning_1_1neural__network_1_1_neural_network_a39cb437b5043d750dca3d013caf3687d_cgraph.map => classmachine__learning_1_1neural__network_1_1_neural_network_a215d132aa38b9c9aab6716663a751b82_cgraph.map} (100%) rename d4/df4/{classmachine__learning_1_1neural__network_1_1_neural_network_a39cb437b5043d750dca3d013caf3687d_cgraph.md5 => classmachine__learning_1_1neural__network_1_1_neural_network_a215d132aa38b9c9aab6716663a751b82_cgraph.md5} (100%) rename d4/df4/{classmachine__learning_1_1neural__network_1_1_neural_network_a39cb437b5043d750dca3d013caf3687d_cgraph.svg => classmachine__learning_1_1neural__network_1_1_neural_network_a215d132aa38b9c9aab6716663a751b82_cgraph.svg} (100%) rename dc/d93/{classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer_a51c2b942ecf10625780c6bb9d5c50ff1_cgraph.map => classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer_a11046825be0b6dbb73fbe834aa49200e_cgraph.map} (100%) rename dc/d93/{classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer_a51c2b942ecf10625780c6bb9d5c50ff1_cgraph.md5 => classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer_a11046825be0b6dbb73fbe834aa49200e_cgraph.md5} (100%) rename dc/d93/{classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer_a51c2b942ecf10625780c6bb9d5c50ff1_cgraph.svg => classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer_a11046825be0b6dbb73fbe834aa49200e_cgraph.svg} (100%) rename dc/d93/{classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer_a04b8e21316458436c8851959928c3964_cgraph.map => classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer_a35ab6f1b2840f89a858ca36b78739b69_cgraph.map} (100%) rename dc/d93/{classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer_a04b8e21316458436c8851959928c3964_cgraph.md5 => classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer_a35ab6f1b2840f89a858ca36b78739b69_cgraph.md5} (100%) rename dc/d93/{classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer_a04b8e21316458436c8851959928c3964_cgraph.svg => classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer_a35ab6f1b2840f89a858ca36b78739b69_cgraph.svg} (100%) diff --git a/d3/d65/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer__coll__graph.md5 b/d3/d65/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer__coll__graph.md5 index e484487bb..484c43f31 100644 --- a/d3/d65/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer__coll__graph.md5 +++ b/d3/d65/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer__coll__graph.md5 @@ -1 +1 @@ -516cf9217a406a91914a458c29f398ce \ No newline at end of file +9f030e5e7f8e212477b135560c801cf0 \ No newline at end of file diff --git a/d3/d65/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer__coll__graph.svg b/d3/d65/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer__coll__graph.svg index d97977460..da0421385 100644 --- a/d3/d65/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer__coll__graph.svg +++ b/d3/d65/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer__coll__graph.svg @@ -50,7 +50,7 @@ Node3->Node1 - kernal + kernel diff --git a/d3/d98/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer-members.html b/d3/d98/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer-members.html index 47e9b5368..142f01e20 100644 --- a/d3/d98/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer-members.html +++ b/d3/d98/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer-members.html @@ -100,11 +100,11 @@ $(document).ready(function(){initNavTree('dc/d93/classmachine__learning_1_1neura activation (defined in machine_learning::neural_network::layers::DenseLayer)machine_learning::neural_network::layers::DenseLayer activation_function (defined in machine_learning::neural_network::layers::DenseLayer)machine_learning::neural_network::layers::DenseLayer dactivation_function (defined in machine_learning::neural_network::layers::DenseLayer)machine_learning::neural_network::layers::DenseLayer - DenseLayer(const int &neurons, const std::string &activation, const std::pair< size_t, size_t > &kernal_shape, const bool &random_kernal)machine_learning::neural_network::layers::DenseLayerinline - DenseLayer(const int &neurons, const std::string &activation, const std::vector< std::valarray< double >> &kernal)machine_learning::neural_network::layers::DenseLayerinline + DenseLayer(const int &neurons, const std::string &activation, const std::pair< size_t, size_t > &kernel_shape, const bool &random_kernel)machine_learning::neural_network::layers::DenseLayerinline + DenseLayer(const int &neurons, const std::string &activation, const std::vector< std::valarray< double >> &kernel)machine_learning::neural_network::layers::DenseLayerinline DenseLayer(const DenseLayer &layer)=defaultmachine_learning::neural_network::layers::DenseLayer DenseLayer(DenseLayer &&)=defaultmachine_learning::neural_network::layers::DenseLayer - kernal (defined in machine_learning::neural_network::layers::DenseLayer)machine_learning::neural_network::layers::DenseLayer + kernel (defined in machine_learning::neural_network::layers::DenseLayer)machine_learning::neural_network::layers::DenseLayer neurons (defined in machine_learning::neural_network::layers::DenseLayer)machine_learning::neural_network::layers::DenseLayer operator=(const DenseLayer &layer)=defaultmachine_learning::neural_network::layers::DenseLayer operator=(DenseLayer &&)=defaultmachine_learning::neural_network::layers::DenseLayer diff --git a/d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html b/d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html index f900e56be..459757db0 100644 --- a/d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html +++ b/d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html @@ -145,8 +145,8 @@ Public Member Functions - - + +

Private Member Functions

 NeuralNetwork (const std::vector< std::pair< int, std::string >> &config, const std::vector< std::vector< std::valarray< double >>> &kernals)
 
 NeuralNetwork (const std::vector< std::pair< int, std::string >> &config, const std::vector< std::vector< std::valarray< double >>> &kernels)
 
std::vector< std::vector< std::valarray< double > > > __detailed_single_prediction (const std::vector< std::valarray< double >> &X)
 
@@ -159,8 +159,8 @@ Private Attributes

Detailed Description

NeuralNetwork class is implements MLP. This class is used by actual user to create and train networks.

Constructor & Destructor Documentation

- -

◆ NeuralNetwork() [1/5]

+ +

◆ NeuralNetwork() [1/5]

@@ -178,7 +178,7 @@ Private Attributes
- + @@ -195,7 +195,7 @@ Private Attributes

Private Constructor for class NeuralNetwork. This constructor is used internally to load model.

Parameters
const std::vector< std::vector< std::valarray< double >>> & kernals kernels 
- +
configvector containing pair (neurons, activation)
kernalsvector containing all pretrained kernals
kernelsvector containing all pretrained kernels
@@ -219,14 +219,14 @@ Private Attributes
275  // Reconstructing all pretrained layers
276  for (size_t i = 0; i < config.size(); i++) {
277  layers.emplace_back(neural_network::layers::DenseLayer(
-
278  config[i].first, config[i].second, kernals[i]));
+
278  config[i].first, config[i].second, kernels[i]));
279  }
280  std::cout << "INFO: Network constructed successfully" << std::endl;
281  }
Here is the call graph for this function:
-
+
@@ -305,7 +305,7 @@ Here is the call graph for this function:
329  std::exit(EXIT_FAILURE);
330  }
331  // Separately creating first layer so it can have unit matrix
-
332  // as kernal.
+
332  // as kernel.
333  layers.push_back(neural_network::layers::DenseLayer(
334  config[0].first, config[0].second,
335  {config[0].first, config[0].first}, false));
@@ -447,7 +447,7 @@ Here is the call graph for this function:
291  std::vector<std::valarray<double>> current_pass = X;
292  details.emplace_back(X);
293  for (const auto &l : layers) {
-
294  current_pass = multiply(current_pass, l.kernal);
+
294  current_pass = multiply(current_pass, l.kernel);
295  current_pass = apply_function(current_pass, l.activation_function);
296  details.emplace_back(current_pass);
297  }
@@ -749,13 +749,13 @@ Here is the call graph for this function:
512  predicted;
513  auto activations = this->__detailed_single_prediction(X[i]);
514  // Gradients vector to store gradients for all layers
-
515  // They will be averaged and applied to kernal
+
515  // They will be averaged and applied to kernel
516  std::vector<std::vector<std::valarray<double>>> gradients;
517  gradients.resize(this->layers.size());
518  // First intialize gradients to zero
519  for (size_t i = 0; i < gradients.size(); i++) {
520  zeroes_initialization(
-
521  gradients[i], get_shape(this->layers[i].kernal));
+
521  gradients[i], get_shape(this->layers[i].kernel));
522  }
523  predicted = activations.back(); // Predicted vector
524  cur_error = predicted - Y[i]; // Absoulute error
@@ -776,16 +776,16 @@ Here is the call graph for this function:
539  this->layers[j].dactivation_function));
540  // Calculating gradient for current layer
541  grad = multiply(transpose(activations[j]), cur_error);
-
542  // Change error according to current kernal values
+
542  // Change error according to current kernel values
543  cur_error = multiply(cur_error,
-
544  transpose(this->layers[j].kernal));
+
544  transpose(this->layers[j].kernel));
545  // Adding gradient values to collection of gradients
546  gradients[j] = gradients[j] + grad / double(batch_size);
547  }
548  // Applying gradients
549  for (size_t j = this->layers.size() - 1; j >= 1; j--) {
-
550  // Updating kernal (aka weights)
-
551  this->layers[j].kernal = this->layers[j].kernal -
+
550  // Updating kernel (aka weights)
+
551  this->layers[j].kernel = this->layers[j].kernel -
552  gradients[j] * learning_rate;
553  }
554  }
@@ -1085,7 +1085,7 @@ Here is the call graph for this function:
740  }
741  std::vector<std::pair<int, std::string>> config; // To store config
742  std::vector<std::vector<std::valarray<double>>>
-
743  kernals; // To store pretrained kernals
+
743  kernels; // To store pretrained kernels
744  // Loading model from saved file format
745  size_t total_layers = 0;
746  in_file >> total_layers;
@@ -1093,23 +1093,23 @@ Here is the call graph for this function:
748  int neurons = 0;
749  std::string activation;
750  size_t shape_a = 0, shape_b = 0;
-
751  std::vector<std::valarray<double>> kernal;
+
751  std::vector<std::valarray<double>> kernel;
752  in_file >> neurons >> activation >> shape_a >> shape_b;
753  for (size_t r = 0; r < shape_a; r++) {
754  std::valarray<double> row(shape_b);
755  for (size_t c = 0; c < shape_b; c++) {
756  in_file >> row[c];
757  }
-
758  kernal.push_back(row);
+
758  kernel.push_back(row);
759  }
760  config.emplace_back(make_pair(neurons, activation));
761  ;
-
762  kernals.emplace_back(kernal);
+
762  kernels.emplace_back(kernel);
763  }
764  std::cout << "INFO: Model loaded successfully" << std::endl;
765  in_file.close(); // Closing file
766  return NeuralNetwork(
-
767  config, kernals); // Return instance of NeuralNetwork class
+
767  config, kernels); // Return instance of NeuralNetwork class
768  }
Here is the call graph for this function:
@@ -1204,7 +1204,7 @@ Here is the call graph for this function:

Format in which model is saved:

-

total_layers neurons(1st neural_network::layers::DenseLayer) activation_name(1st neural_network::layers::DenseLayer) kernal_shape(1st neural_network::layers::DenseLayer) kernal_values neurons(Nth neural_network::layers::DenseLayer) activation_name(Nth neural_network::layers::DenseLayer) kernal_shape(Nth neural_network::layers::DenseLayer) kernal_value

+

total_layers neurons(1st neural_network::layers::DenseLayer) activation_name(1st neural_network::layers::DenseLayer) kernel_shape(1st neural_network::layers::DenseLayer) kernel_values neurons(Nth neural_network::layers::DenseLayer) activation_name(Nth neural_network::layers::DenseLayer) kernel_shape(Nth neural_network::layers::DenseLayer) kernel_value

For Example, pretrained model with 3 layers: 

  3
  4 none
@@ -1248,14 +1248,14 @@ Here is the call graph for this function:
 
  670  

 
  671             total_layers

 
  672             neurons(1st neural_network::layers::DenseLayer) activation_name(1st

-
  673            neural_network::layers::DenseLayer) kernal_shape(1st

-
  674            neural_network::layers::DenseLayer) kernal_values

+
  673            neural_network::layers::DenseLayer) kernel_shape(1st

+
  674            neural_network::layers::DenseLayer) kernel_values

 
  675             .

 
  676             .

 
  677             .

 
  678             neurons(Nth neural_network::layers::DenseLayer) activation_name(Nth

-
  679            neural_network::layers::DenseLayer) kernal_shape(Nth

-
  680            neural_network::layers::DenseLayer) kernal_value

+
  679            neural_network::layers::DenseLayer) kernel_shape(Nth

+
  680            neural_network::layers::DenseLayer) kernel_value

 
  681  

 
  682             For Example, pretrained model with 3 layers:

 
  683             <pre>

@@ -1287,9 +1287,9 @@ Here is the call graph for this function:
 
  709         out_file << std::endl;

 
  710         for (const auto &layer : this->layers) {

 
  711             out_file << layer.neurons << ' ' << layer.activation << std::endl;

-
  712             const auto shape = get_shape(layer.kernal);

+
  712             const auto shape = get_shape(layer.kernel);

 
  713             out_file << shape.first << ' ' << shape.second << std::endl;

-
  714             for (const auto &row : layer.kernal) {

+
  714             for (const auto &row : layer.kernel) {

 
  715                 for (const auto &val : row) {

 
  716                     out_file << val << ' ';

 
  717                 }

@@ -1393,8 +1393,8 @@ Here is the call graph for this function:
 
  785                       << layers[i - 1].neurons;  // number of neurons

 
  786             std::cout << ", Activation : "

 
  787                       << layers[i - 1].activation;  // activation

-
  788             std::cout << ", Kernal Shape : "

-
  789                       << get_shape(layers[i - 1].kernal);  // kernal shape

+
  788             std::cout << ", kernel Shape : "

+
  789                       << get_shape(layers[i - 1].kernel);  // kernel shape

 
  790             std::cout << std::endl;

 
  791         }

 
  792         std::cout

diff --git a/d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.js b/d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.js
index 75a42ae23..1b957658b 100644
--- a/d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.js
+++ b/d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.js
@@ -1,6 +1,6 @@
 var classmachine__learning_1_1neural__network_1_1_neural_network =
 [
-    [ "NeuralNetwork", "d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#a39cb437b5043d750dca3d013caf3687d", null ],
+    [ "NeuralNetwork", "d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#a215d132aa38b9c9aab6716663a751b82", null ],
     [ "NeuralNetwork", "d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#ae7cf126a3a8f9d20c81b21584d061a08", null ],
     [ "NeuralNetwork", "d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#a8f984bfd3e32b9b71c33a4f62335c710", null ],
     [ "NeuralNetwork", "d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network.html#a176b955c90ae57d7dbc3c63f27c84c75", null ],
diff --git a/d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network_a39cb437b5043d750dca3d013caf3687d_cgraph.map b/d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network_a215d132aa38b9c9aab6716663a751b82_cgraph.map
similarity index 100%
rename from d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network_a39cb437b5043d750dca3d013caf3687d_cgraph.map
rename to d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network_a215d132aa38b9c9aab6716663a751b82_cgraph.map
diff --git a/d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network_a39cb437b5043d750dca3d013caf3687d_cgraph.md5 b/d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network_a215d132aa38b9c9aab6716663a751b82_cgraph.md5
similarity index 100%
rename from d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network_a39cb437b5043d750dca3d013caf3687d_cgraph.md5
rename to d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network_a215d132aa38b9c9aab6716663a751b82_cgraph.md5
diff --git a/d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network_a39cb437b5043d750dca3d013caf3687d_cgraph.svg b/d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network_a215d132aa38b9c9aab6716663a751b82_cgraph.svg
similarity index 100%
rename from d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network_a39cb437b5043d750dca3d013caf3687d_cgraph.svg
rename to d4/df4/classmachine__learning_1_1neural__network_1_1_neural_network_a215d132aa38b9c9aab6716663a751b82_cgraph.svg
diff --git a/d7/d59/classmachine__learning_1_1neural__network_1_1_neural_network__coll__graph.md5 b/d7/d59/classmachine__learning_1_1neural__network_1_1_neural_network__coll__graph.md5
index 358d45712..15bc74e2c 100644
--- a/d7/d59/classmachine__learning_1_1neural__network_1_1_neural_network__coll__graph.md5
+++ b/d7/d59/classmachine__learning_1_1neural__network_1_1_neural_network__coll__graph.md5
@@ -1 +1 @@
-040d8fc937b68336edec7878d1b4b3a4
\ No newline at end of file
+8a6d92d630474891f83916590477bb71
\ No newline at end of file
diff --git a/d7/d59/classmachine__learning_1_1neural__network_1_1_neural_network__coll__graph.svg b/d7/d59/classmachine__learning_1_1neural__network_1_1_neural_network__coll__graph.svg
index 95b119553..4ec0b7ec8 100644
--- a/d7/d59/classmachine__learning_1_1neural__network_1_1_neural_network__coll__graph.svg
+++ b/d7/d59/classmachine__learning_1_1neural__network_1_1_neural_network__coll__graph.svg
@@ -85,7 +85,7 @@
 Node5->Node3
 
 
- kernal
+ kernel
 
 
 
diff --git a/d8/d27/classmachine__learning_1_1neural__network_1_1_neural_network-members.html b/d8/d27/classmachine__learning_1_1neural__network_1_1_neural_network-members.html
index 0bd9d091b..058e5c63e 100644
--- a/d8/d27/classmachine__learning_1_1neural__network_1_1_neural_network-members.html
+++ b/d8/d27/classmachine__learning_1_1neural__network_1_1_neural_network-members.html
@@ -106,7 +106,7 @@ $(document).ready(function(){initNavTree('d4/df4/classmachine__learning_1_1neura
   get_XY_from_csv(const std::string &file_name, const bool &last_label, const bool &normalize, const int &slip_lines=1)machine_learning::neural_network::NeuralNetworkinline
   layers (defined in machine_learning::neural_network::NeuralNetwork)machine_learning::neural_network::NeuralNetworkprivate
   load_model(const std::string &file_name)machine_learning::neural_network::NeuralNetworkinline
-  NeuralNetwork(const std::vector< std::pair< int, std::string >> &config, const std::vector< std::vector< std::valarray< double >>> &kernals)machine_learning::neural_network::NeuralNetworkinlineprivate
+  NeuralNetwork(const std::vector< std::pair< int, std::string >> &config, const std::vector< std::vector< std::valarray< double >>> &kernels)machine_learning::neural_network::NeuralNetworkinlineprivate
   NeuralNetwork()=defaultmachine_learning::neural_network::NeuralNetwork
   NeuralNetwork(const std::vector< std::pair< int, std::string >> &config)machine_learning::neural_network::NeuralNetworkinlineexplicit
   NeuralNetwork(const NeuralNetwork &model)=defaultmachine_learning::neural_network::NeuralNetwork
diff --git a/d8/d95/vector__ops_8hpp_source.html b/d8/d95/vector__ops_8hpp_source.html
index 2c6b2f14f..aed9fb950 100644
--- a/d8/d95/vector__ops_8hpp_source.html
+++ b/d8/d95/vector__ops_8hpp_source.html
@@ -659,7 +659,6 @@ $(document).ready(function(){initNavTree('d8/d95/vector__ops_8hpp_source.html','
 
linear_probing::putProber
bool putProber(const Entry &entry, int key)
Definition: linear_probing_hash_table.cpp:98

 
machine_learning::pop_front
std::valarray< T > pop_front(const std::valarray< T > &A)
Definition: vector_ops.hpp:102

 
double_hashing
An implementation of hash table using double hashing algorithm.

-
machine_learning::neural_network::layers::DenseLayer::DenseLayer
DenseLayer(const int &neurons, const std::string &activation, const std::pair< size_t, size_t > &kernal_shape, const bool &random_kernal)
Definition: neural_network.cpp:141

 
linear_probing::Entry
Definition: linear_probing_hash_table.cpp:35

 
 
machine_learning::neural_network::NeuralNetwork::~NeuralNetwork
~NeuralNetwork()=default

@@ -787,7 +786,6 @@ $(document).ready(function(){initNavTree('d8/d95/vector__ops_8hpp_source.html','
 
graph::dijkstra
int dijkstra(std::vector< std::vector< std::pair< int, int >>> *adj, int s, int t)
Function runs the dijkstra algorithm for some source vertex and target vertex in the graph and return...
Definition: dijkstra.cpp:66

 
CycleCheck::isCyclicDFSHelper
static bool isCyclicDFSHelper(AdjList const &adjList, std::vector< nodeStates > *state, unsigned int node)
Definition: cycle_check_directed_graph.cpp:170

 
graph::add_undirected_edge
void add_undirected_edge(adjacency_list *graph, int u, int v)
Adds an undirected edge from vertex u to vertex v. Essentially adds too directed edges to the adjacen...
Definition: breadth_first_search.cpp:92

-
machine_learning::neural_network::layers::DenseLayer::DenseLayer
DenseLayer(const int &neurons, const std::string &activation, const std::vector< std::valarray< double >> &kernal)
Definition: neural_network.cpp:183

 
graph::RootedTree::RootedTree
RootedTree(const std::vector< std::pair< int, int > > &undirected_edges, int root_)
Constructs the tree by calculating parent for every vertex. Assumes a valid description of a tree is ...
Definition: lowest_common_ancestor.cpp:93

 
MinHeapNode
Definition: huffman.cpp:7

 
main
int main()
Definition: line_segment_intersection.cpp:92

@@ -847,6 +845,7 @@ $(document).ready(function(){initNavTree('d8/d95/vector__ops_8hpp_source.html','
 
test2
void test2()
Definition: kohonen_som_topology.cpp:451

 
SegmentIntersection::on_segment
bool on_segment(Point first_point, Point second_point, Point third_point)
Definition: line_segment_intersection.cpp:75

 
graph::is_graph_bipartite::Graph::is_bipartite
bool is_bipartite()
function to add edges to our graph
Definition: is_graph_bipartite.cpp:106

+
machine_learning::neural_network::layers::DenseLayer::DenseLayer
DenseLayer(const int &neurons, const std::string &activation, const std::pair< size_t, size_t > &kernel_shape, const bool &random_kernel)
Definition: neural_network.cpp:141

 
double_hashing::rehash
void rehash()
Definition: double_hash_hash_table.cpp:161

 
endl
#define endl
Definition: matrix_exponentiation.cpp:36

 
stack::push
void push(Type item)
Definition: stack.h:83

@@ -889,7 +888,6 @@ $(document).ready(function(){initNavTree('d8/d95/vector__ops_8hpp_source.html','
 
hamilton_cycle
bool hamilton_cycle(const std::vector< std::vector< bool >> &routes)
Definition: hamiltons_cycle.cpp:30

 
data_structures::trie::deleteString
bool deleteString(const std::string &str, int index)
Definition: trie_tree.cpp:134

 
tests
void tests()
Definition: connected_components.cpp:93

-
machine_learning::neural_network::NeuralNetwork::NeuralNetwork
NeuralNetwork(const std::vector< std::pair< int, std::string >> &config, const std::vector< std::vector< std::valarray< double >>> &kernals)
Definition: neural_network.cpp:256

 
data
int data[MAX]
test data
Definition: hash_search.cpp:24

 
machine_learning::neural_network::layers::DenseLayer::DenseLayer
DenseLayer(DenseLayer &&)=default

 
linear_probing::add
void add(int key)
Definition: linear_probing_hash_table.cpp:161

@@ -914,6 +912,7 @@ $(document).ready(function(){initNavTree('d8/d95/vector__ops_8hpp_source.html','
 
std::left
T left(T... args)

 
hash_chain::next
std::shared_ptr< struct Node > next
pointer to the next node
Definition: chaining.cpp:23

 
graph::LowestCommonAncestor
Definition: lowest_common_ancestor.cpp:145

+
machine_learning::neural_network::layers::DenseLayer::DenseLayer
DenseLayer(const int &neurons, const std::string &activation, const std::vector< std::valarray< double >> &kernel)
Definition: neural_network.cpp:183

 
machine_learning::adaline::operator<<
friend std::ostream & operator<<(std::ostream &out, const adaline &ada)
Definition: adaline_learning.cpp:76

 
std::exp
T exp(T... args)

 
std::string::begin
T begin(T... args)

@@ -1023,6 +1022,7 @@ $(document).ready(function(){initNavTree('d8/d95/vector__ops_8hpp_source.html','
 
graph::is_graph_bipartite::Graph::n
int n
size of the graph
Definition: is_graph_bipartite.cpp:53

 
main
int main(int argc, char **argv)
Definition: adaline_learning.cpp:357

 
list
Definition: list_array.cpp:8

+
machine_learning::neural_network::NeuralNetwork::NeuralNetwork
NeuralNetwork(const std::vector< std::pair< int, std::string >> &config, const std::vector< std::vector< std::valarray< double >>> &kernels)
Definition: neural_network.cpp:256

 
std::ostream::precision
T precision(T... args)

 
 
test_circle
void test_circle(std::vector< std::valarray< double >> *data)
Definition: kohonen_som_trace.cpp:196

diff --git a/dc/d93/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer.html b/dc/d93/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer.html
index 8e6a2c430..88fc0cf93 100644
--- a/dc/d93/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer.html
+++ b/dc/d93/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer.html
@@ -107,10 +107,10 @@ Collaboration diagram for machine_learning::neural_network::layers::DenseLayer:<
 
-
-
-
-
+
+
+
+
@@ -136,15 +136,15 @@ int 
-
-
+
+

 

 Public Member Functions
 DenseLayer (const int &neurons, const std::string &activation, const std::pair< size_t, size_t > &kernal_shape, const bool &random_kernal)
 
 DenseLayer (const int &neurons, const std::string &activation, const std::vector< std::valarray< double >> &kernal)
 
 DenseLayer (const int &neurons, const std::string &activation, const std::pair< size_t, size_t > &kernel_shape, const bool &random_kernel)
 
 DenseLayer (const int &neurons, const std::string &activation, const std::vector< std::valarray< double >> &kernel)
 

 
 DenseLayer (const DenseLayer &layer)=default

 
 

 
 ~DenseLayer ()=default
neurons

 

 std::string activation

 
 

-std::vector< std::valarray< double > > kernal
 

+std::vector< std::valarray< double > > kernel
 

 

 
Detailed Description

-
neural_network::layers::DenseLayer class is used to store all necessary information about the layers (i.e. neurons, activation and kernal). This class is used by NeuralNetwork class to store layers. 

+
neural_network::layers::DenseLayer class is used to store all necessary information about the layers (i.e. neurons, activation and kernel). This class is used by NeuralNetwork class to store layers. 

 
Constructor & Destructor Documentation

-
-
◆ DenseLayer() [1/4]

+
+
◆ DenseLayer() [1/4]

 
 

 

@@ -168,13 +168,13 @@ int neurons
           
           
           const std::pair< size_t, size_t > & 
-          kernal_shape, 
+          kernel_shape, 
         
         
           
           
           const bool & 
-          random_kernal 
+          random_kernel 
         
         
           
@@ -192,8 +192,8 @@ int neurons
   
-    
-    
+    
+    

     
neuronsnumber of neurons 

     
activationactivation function for layer 
kernal_shapeshape of kernal 
random_kernalflag for whether to intialize kernal randomly 
kernel_shapeshape of kernel 
random_kernelflag for whether to intialize kernel randomly 

   

   
 
@@ -224,24 +224,24 @@ int neurons
 
  167         }

 
  168         this->activation = activation;  // Setting activation name

 
  169         this->neurons = neurons;        // Setting number of neurons

-
  170         // Initialize kernal according to flag

-
  171         if (random_kernal) {

-
  172             uniform_random_initialization(kernal, kernal_shape, -1.0, 1.0);

+
  170         // Initialize kernel according to flag

+
  171         if (random_kernel) {

+
  172             uniform_random_initialization(kernel, kernel_shape, -1.0, 1.0);

 
  173         } else {

-
  174             unit_matrix_initialization(kernal, kernal_shape);

+
  174             unit_matrix_initialization(kernel, kernel_shape);

 
  175         }

 
  176     }

 

 Here is the call graph for this function:

 

-

+

 

 

 
 

 

-
-
◆ DenseLayer() [2/4]

+
+
◆ DenseLayer() [2/4]

 
 

 

@@ -265,7 +265,7 @@ Here is the call graph for this function:

           
           
           const std::vector< std::valarray< double >> & 
-          kernal 
+          kernel 
         
         
           
@@ -283,7 +283,7 @@ Here is the call graph for this function:

   
-    
+    

     
neuronsnumber of neurons 

     
activationactivation function for layer 
kernalvalues of kernal (useful in loading model) 
kernelvalues of kernel (useful in loading model) 

   

   
 
@@ -314,12 +314,12 @@ Here is the call graph for this function:

 
  208         }

 
  209         this->activation = activation;  // Setting activation name

 
  210         this->neurons = neurons;        // Setting number of neurons

-
  211         this->kernal = kernal;          // Setting supplied kernal values

+
  211         this->kernel = kernel;          // Setting supplied kernel values

 
  212     }

 

 Here is the call graph for this function:

 

-

+

 

 

 
diff --git a/dc/d93/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer.js b/dc/d93/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer.js
index 4b91972b0..78046d566 100644
--- a/dc/d93/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer.js
+++ b/dc/d93/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer.js
@@ -1,7 +1,7 @@
 var classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer =
 [
-    [ "DenseLayer", "dc/d93/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer.html#a51c2b942ecf10625780c6bb9d5c50ff1", null ],
-    [ "DenseLayer", "dc/d93/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer.html#a04b8e21316458436c8851959928c3964", null ],
+    [ "DenseLayer", "dc/d93/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer.html#a11046825be0b6dbb73fbe834aa49200e", null ],
+    [ "DenseLayer", "dc/d93/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer.html#a35ab6f1b2840f89a858ca36b78739b69", null ],
     [ "DenseLayer", "dc/d93/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer.html#a2871146feaaa453558239df67b21e0d2", null ],
     [ "~DenseLayer", "dc/d93/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer.html#ac9cda9453c4a0caf5bae7f9213b019a0", null ],
     [ "DenseLayer", "dc/d93/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer.html#a6c859e3737aa88b29854df0347b29f4e", null ],
@@ -10,6 +10,6 @@ var classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer =
     [ "activation", "dc/d93/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer.html#a891264e2eb1357b2b3282e5532250869", null ],
     [ "activation_function", "dc/d93/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer.html#a8e4c57922478ccc2b7c6277c05608714", null ],
     [ "dactivation_function", "dc/d93/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer.html#acc6cfdcc9d6e5170340abae63234a442", null ],
-    [ "kernal", "dc/d93/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer.html#a2e3fb82813c0fb305d6330867dd42ac8", null ],
+    [ "kernel", "dc/d93/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer.html#a494d39f6c367071d1fd31b3c1caf1a7d", null ],
     [ "neurons", "dc/d93/classmachine__learning_1_1neural__network_1_1layers_1_1_dense_layer.html#ace9c37dd1322d3745de9713c90df8003", null ]
 ];
\ No newline at end of file
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diff --git a/functions_d.html b/functions_d.html
index bdacec1b9..539aa84ae 100644
--- a/functions_d.html
+++ b/functions_d.html
@@ -112,7 +112,7 @@ $(document).ready(function(){initNavTree('functions_d.html',''); initResizable()
 : data_structures::trie
 
 
  • DenseLayer()
-: machine_learning::neural_network::layers::DenseLayer
+: machine_learning::neural_network::layers::DenseLayer
 
    • 
 
  • deQueue()
 : queue< Kind >
diff --git a/functions_func.html b/functions_func.html
index d705a0d56..026c90e34 100644
--- a/functions_func.html
+++ b/functions_func.html
@@ -438,7 +438,7 @@ $(document).ready(function(){initNavTree('functions_func.html',''); initResizabl
 
 
    - n -
      
 
    • NeuralNetwork()
-: machine_learning::neural_network::NeuralNetwork
+: machine_learning::neural_network::NeuralNetwork
 
      • 
 
    • new_val()
 : statistics::stats_computer1< T >
diff --git a/navtreeindex1.js b/navtreeindex1.js
index 49da525b2..0fbf78b79 100644
--- a/navtreeindex1.js
+++ b/navtreeindex1.js
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index 5a5cf765b..e6aa6d86d 100644
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index 5f97ae678..a2ff2602f 100644
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