Man pages for metANN
Metaheuristic and Gradient-Based Optimization for Neural Network Training and Continuous Problems
| abc_optimize | Artificial Bee Colony Engine |
| activation_derivative | Derivative of Activation Functions |
| activation_leaky_relu | Leaky Rectified Linear Unit Activation Function |
| activation_linear | Linear Activation Function |
| activation_relu | Rectified Linear Unit Activation Function |
| activation_sigmoid | Sigmoid Activation Function |
| activation_softmax | Softmax Activation Function |
| activation_tanh | Hyperbolic Tangent Activation Function |
| as_activation | Convert Character Input to an Activation Object |
| as_loss | Convert Character Input to a Loss Object |
| as_metric | Convert Character Input to a Metric Object |
| as_metrics | Convert Multiple Inputs to Metric Objects |
| as_optimizer | Convert Character Input to an Optimizer Object |
| available_activations | List Available Activation Functions |
| available_gradient_optimizers | List Available Gradient-Based Optimizers |
| available_losses | List Available Loss Functions |
| available_metaheuristics | List Available Metaheuristic Optimizers |
| available_metrics | List Available Performance Metrics |
| available_optimizers | List Available Optimizers |
| classification_loss_value | Classification Loss Value |
| classification_metric_values | Classification Metric Values |
| coef.metann | Extract Weights from a metANN Model |
| coef.met_optimize_result | Extract the Best Parameters from a metANN Optimization Result |
| compute_metric_values | Compute Metric Values |
| count_parameters | Count the Number of Trainable Parameters in an MLP... |
| decode_classification_prediction | Decode Classification Predictions |
| decode_weights | Decode an MLP Weight Vector |
| dense_layer | Create a Dense Layer |
| de_optimize | Differential Evolution Engine |
| detect_task | Detect Task Type |
| encode_classification_response | Encode Classification Response |
| evaluate | Evaluate a metANN Model |
| forward_pass | Forward Pass for an MLP |
| ga_optimize | Genetic Algorithm Engine |
| gradient_optimize | Gradient-Based Optimization Engine |
| gwo_optimize | Grey Wolf Optimizer Engine |
| initialize_weights | Initialize MLP Weights |
| is_activation | Check Whether an Object is a metANN Activation |
| is_architecture | Check Whether an Object is a metANN Architecture |
| is_dense_layer | Check Whether an Object is a Dense Layer |
| is_layer | Check Whether an Object is a metANN Layer |
| is_loss | Check Whether an Object is a metANN Loss |
| is_metric | Check Whether an Object is a metANN Metric |
| is_mlp_architecture | Check Whether an Object is an MLP Architecture |
| is_optimizer | Check Whether an Object is a metANN Optimizer |
| levy_flight | Levy Flight Step |
| loss_binary_crossentropy | Binary Cross-Entropy Loss |
| loss_crossentropy | Categorical Cross-Entropy Loss |
| loss_huber | Huber Loss |
| loss_log_cosh | Log-Cosh Loss |
| loss_mae | Mean Absolute Error Loss |
| loss_mse | Mean Squared Error Loss |
| metann | Train an Artificial Neural Network with metANN |
| met_mlp | Train a Feed-Forward Multilayer Perceptron |
| met_optimize | General-Purpose Optimization |
| metric_accuracy | Accuracy Metric |
| metric_f1 | F1 Score Metric |
| metric_mae | Mean Absolute Error Metric |
| metric_mse | Mean Squared Error Metric |
| metric_precision | Precision Metric |
| metric_r2 | Coefficient of Determination Metric |
| metric_recall | Recall Metric |
| metric_rmse | Root Mean Squared Error Metric |
| mlp_architecture | Create an MLP Architecture |
| mlp_binary_crossentropy_gradient | Compute Binary Cross-Entropy Gradient for an MLP |
| mlp_crossentropy_gradient | Compute Multi-Class Cross-Entropy Gradient for an MLP |
| mlp_forward_cache | Forward Pass with Cache |
| mlp_mse_gradient | Compute MSE Gradient for an MLP |
| new_activation | Create an Activation Function Object |
| new_loss | Create a Loss Function Object |
| new_metric | Create a Metric Function Object |
| new_optimizer | Create an Optimizer Object |
| optimizer_abc | Artificial Bee Colony Optimizer |
| optimizer_adam | Adam Optimizer |
| optimizer_de | Differential Evolution Optimizer |
| optimizer_ga | Genetic Algorithm Optimizer |
| optimizer_gwo | Grey Wolf Optimizer |
| optimizer_hybrid | Hybrid Optimizer |
| optimizer_info | Get Optimizer Information |
| optimizer_pso | Particle Swarm Optimization Optimizer |
| optimizer_sboa | Secretary Bird Optimization Algorithm Optimizer |
| optimizer_sgd | Stochastic Gradient Descent Optimizer |
| optimizer_tlbo | Teaching-Learning-Based Optimization Optimizer |
| optimizer_woa | Whale Optimization Algorithm Optimizer |
| plot.metann | Plot a metANN Model |
| plot.met_optimize_result | Plot Optimization Convergence |
| plot_network | Plot Neural Network Architecture |
| predict.metann | Predict with a metANN Model |
| print.metann | Print a metANN Model |
| print.metann_evaluation | Print metANN Evaluation Results |
| print.met_dense_layer | Print a Dense Layer |
| print.met_mlp_architecture | Print an MLP Architecture |
| print.met_optimizer | Print a metANN Optimizer |
| print.met_optimize_result | Print a metANN Optimization Result |
| print.met_optimizer_info | Print Optimizer Information |
| pso_optimize | Particle Swarm Optimization Engine |
| sboa_optimize | Secretary Bird Optimization Algorithm Engine |
| summary.metann | Summarize a metANN Model |
| summary.met_optimize_result | Summarize a metANN Optimization Result |
| tlbo_optimize | Teaching-Learning-Based Optimization Engine |
| train_mlp_gradient | Train an MLP with a Gradient-Based Optimizer |
| woa_optimize | Whale Optimization Algorithm Engine |
