R: Metaheuristic and Gradient-Based Optimization for Neural Network Training and Continuous Problems

# Metaheuristic and Gradient-Based Optimization for Neural Network Training and Continuous Problems

## Documentation for package ‘metANN’ version 0.1.0

- DESCRIPTION file.

## Help Pages

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
coef.metann | Extract Weights from a metANN Model
coef.met_optimize_result | Extract the Best Parameters from a metANN Optimization Result
count_parameters | Count the Number of Trainable Parameters in an MLP Architecture
decode_weights | Decode an MLP Weight Vector
dense_layer | Create a Dense Layer
evaluate | Evaluate a metANN Model
forward_pass | Forward Pass for an MLP
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
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
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
met_mlp | Train a Feed-Forward Multilayer Perceptron
met_optimize | General-Purpose Optimization
mlp_architecture | Create an MLP Architecture
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_optimizer_info | Print Optimizer Information
print.met_optimize_result | Print a metANN Optimization Result
summary.metann | Summarize a metANN Model
summary.met_optimize_result | Summarize a metANN Optimization Result