Training Function

Overview

The train function is responsible for training a neural network model using gradient descent while incorporating simulation-based optimizations. The function handles data preprocessing, model initialization, training, and simulation-driven improvements.

Function Signature

train(x_train, x_test, y_train, y_test, labels, path, model_name, epochs, generations, input_size, hidden_size, output_size, input_shape, kernel_size, deepth, batch_size=128, simulation_set_size=20, simulation_alg=montecarlo_alg, sim_set_generator=create_simulation_set_SAMLE, simulation_scheduler=SimulationScheduler(SimulationScheduler.PROGRESS_CHECK, simulation_time=60, simulation_epochs=20), lr_scheduler=LearningRateScheduler(LearningRateScheduler.PROGRESIVE, 0.03, 0.8), loss_function=Loss.multiclass_cross_entropy, activation_fun=Activations.Sigmoid, input_paths=1, sample_sub_generator=None, simulation_score=Simulation_score(), optimizer=SGDOptimizer())

Parameters

Parameter	Type	Description
x_train	array-like	Training feature data
x_test	array-like	Testing feature data
y_train	array-like	Training labels
y_test	array-like	Testing labels
labels	list	List of label names
path	str	Directory path for saving model and history
model_name	str	Name of the model to be saved
epochs	int	Number of epochs for each training phase
generations	int	Number of training iterations with simulations
input_size	int	Number of input neurons
hidden_size	int	Number of hidden neurons
output_size	int	Number of output neurons
input_shape	tuple or None	Shape of input data (for convolutional mode)
kernel_size	int	Size of convolution kernel
deepth	int	Depth of convolution layers
batch_size	int	Batch size for training (default: 128)
simulation_set_size	int	Number of samples used in simulation (default: 20)
simulation_alg	object	Algorithm used for simulations (default: montecarlo_alg)
sim_set_generator	function	Function for generating simulation set
simulation_scheduler	object	Scheduler controlling simulation frequency
lr_scheduler	object	Learning rate scheduler
loss_function	function	Loss function used during training
activation_fun	function	Activation function used in the model
input_paths	int	Number of input paths for model
sample_sub_generator	function or None	Function for generating sample subsets (default: None)
simulation_score	object	Scoring function for simulations
optimizer	object	Optimizer used for gradient descent (default: SGDOptimizer)

Returns

• A trained Model instance after applying training and simulation steps.

Example Usage

Basic Training Example

from training_module import train

# Sample data (replace with actual dataset)
x_train = [[0.1, 0.2], [0.3, 0.4]]
x_test = [[0.5, 0.6]]
y_train = [0, 1]
y_test = [1]
labels = ['class_0', 'class_1']

# Define parameters
path = "./model_output/"
model_name = "neural_net"
epochs = 10
generations = 5
input_size = 2
hidden_size = 4
output_size = 2
input_shape = None
kernel_size = 3
deepth = 2

# Train model
model = train(x_train, x_test, y_train, y_test, labels, path, model_name, epochs, generations, input_size, hidden_size, output_size, input_shape, kernel_size, deepth)

Training with Custom Learning Rate Scheduler

from training_module import train, LearningRateScheduler

lr_scheduler = LearningRateScheduler(LearningRateScheduler.EXPONENTIAL, 0.05, 0.9)

model = train(x_train, x_test, y_train, y_test, labels, path, model_name, epochs, generations, input_size, hidden_size, output_size, input_shape, kernel_size, deepth, lr_scheduler=lr_scheduler)

Training with Simulation Algorithm

from training_module import train, montecarlo_alg, create_simulation_set_SAMLE

sim_alg = montecarlo_alg
sim_set_gen = create_simulation_set_SAMLE

model = train(x_train, x_test, y_train, y_test, labels, path, model_name, epochs, generations, input_size, hidden_size, output_size, input_shape, kernel_size, deepth, simulation_alg=sim_alg, sim_set_generator=sim_set_gen)

Notes

• Ensure that the dataset is properly formatted before passing it to the train function.
• The function includes a simulation-based improvement mechanism that optimizes model performance through iterative refinements.
• Various parameters such as simulation_alg, lr_scheduler, and optimizer allow customization of the training process.

References

• Simulation Scheduler: Used to control the timing and execution of simulations.
• Learning Rate Scheduler: Defines the learning rate adaptation strategy.
• Simulation Algorithms: Improve model performance through reinforcement learning-based exploration.