model.rp_nn module

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class model.rp_nn.NeuralNetwork(cost_func=<function mse>, patience=inf, gpu_enable=False)[source]

Bases: object

Abstract base class for wrapping all neural network functionality from RosenPy. This is a superclass.

accuracy(y, y_pred)[source]

Computes the accuracy of the predictions.

Parameters:

yarray-like: The true labels or target values.
y_predarray-like: The predicted values.

Returns:

float: The accuracy of the predictions as a percentage.

add_layer()[source]

convert_data(data)[source]

Converts the input data to the appropriate format for the current backend (NUMPY or CUPY).

Parameters:

dataarray-like: The input data.

Returns:

array-like: The converted input data.

denormalize_outputs(normalized_output_data, mean=0, std_dev=0)[source]

Denormalizes the output data based on the provided mean and standard deviation.

Parameters:

normalized_output_dataarray-like: The data to be denormalized.
meanfloat, optional: The mean used for normalization. Default is 0.
std_devfloat, optional: The standard deviation used for normalization. Default is 0.

Returns:

array-like: The denormalized data.

fit(x_train, y_train, x_val=None, y_val=None, epochs=100, verbose=10, batch_gen=<function batch_sequential>, batch_size=1, optimizer=<model.rp_optimizer.GradientDescent object>)[source]

Trains the neural network on the provided training data.

Parameters:

x_trainarray-like: The input training data.
y_trainarray-like: The target training data.
x_valarray-like, optional: The input validation data. Default is None.
y_valarray-like, optional: The target validation data. Default is None.
epochsint, optional: The number of training epochs. Default is 100.
verboseint, optional: Controls the verbosity of the training process. Default is 10.
batch_genfunction, optional: The batch generation function to use during training. Default is batch_gen_func.batch_sequential.
batch_sizeint, optional: The batch size to use during training. Default is 1.
optimizerOptimizer, optional: The optimizer to use during training. Default is GradientDescent with specified parameters.

get_history()[source]

Returns the training history of the neural network.

Returns:

dict: A dictionary containing the training history.

normalize_data(input_data, mean=0, std_dev=0)[source]

Normalizes the input data based on the provided mean and standard deviation.

Parameters:

input_dataarray-like: The data to be normalized.
meanfloat, optional: The mean for normalization. Default is 0.
std_devfloat, optional: The standard deviation for normalization. Default is 0.

Returns:

array-like: The normalized data.

predict(x, status=1)[source]

Predicts the output for the given input data.

Parameters:

xarray-like: The input data for prediction.

Returns:

array-like: The predicted output for the input data.

update_learning_rate(epoch)[source]

Updates the learning rates of all layers based on the current epoch.

Parameters:

epochint: The current epoch number.

verify_input(data)[source]

Verifies the input data type for optimal performance of the RosenPY framework.

Parameters:

dataarray-like: The input data.