is a Lua-based deep learning program distributed under the
. It has been developed from the ground up, with many of its algorithms implemented in Lua for the first time. This highly configurable artificial intelligence tool enables users to experiment with diverse architectures of dense neural networks in a user-friendly manner.
Unlike its primary use for straightforward classification tasks, this software is primarily developed to tackle complex logical problems that are often beyond human solving capabilities. Its potential applications span across various fields such as science, economics, and more.
Users can experiment with hyperparameter tuning using configuration files. The number of hidden layers can be adjusted from 2 to 7, resulting in a total of 4 to 9 layers. The number of neurons in the input layer and each hidden layer can be freely configured and is virtually limitless. The number of output neurons is set to 1.
In general, 2 hidden layers are suitable for highly nonlinear separation tasks, while 3 hidden layers excel at handling tasks that challenge human capabilities. Architectures with 4 or more hidden layers theoretically have the potential to tackle tasks beyond human capabilities but may pose challenges during training.
Neurosculptor offers a wide array of activation functions, including CoLU, GELU, Identity, Mish, ReLU, Sigmoid, SiLU, Smish, Swish, Tanh, and TanhExp. Additionally, the software provides a choice between two gradient descent optimisation algorithms: AdamW and SGD. While both options are available, it's worth noting that AdamW, known for its advanced capabilities, is the recommended choice for most use cases.
Neurosculptor v0.5.0 has been verified to work properly with Lua 5.4.6 and LuaJIT 2.1.1692716794. In terms of performance, it's noteworthy that LuaJIT outperforms Lua by a factor of 14 when benchmarked on a neural network consisting of 250 neurons using the Sigmoid activation function and the SGD algorithm.
activation_input.conf - Set the activation function for the input layer.
activation_hidden.conf - Set the activation function for the hidden layers.
activation_output.conf - Set the activation function for the output layer.
optimisation.conf - Set the gradient descent optimisation algorithm.
learning_rate.conf - Set a real number when using the SGD algorithm.
weight_decay.conf - Set a real number when using the AdamW algorithm. Alternatively, a value of 0 can be set if preferring the Adam algorithm over AdamW.
iterations.conf - Specify an integer representing the number of epochs.
hidden_layers.conf - Set the number of hidden layers as an integer within the range of 2 to 7.
hidden_1_size.conf - Configure the number of neurons in the first hidden layer.
hidden_2_size.conf - Configure the number of neurons in the second hidden layer.
hidden_3_size.conf - Configure the number of neurons in the third hidden layer.
hidden_4_size.conf - Configure the number of neurons in the fourth hidden layer.
hidden_5_size.conf - Configure the number of neurons in the fifth hidden layer.
hidden_6_size.conf - Configure the number of neurons in the sixth hidden layer.
hidden_7_size.conf - Configure the number of neurons in the seventh hidden layer.
train_file_x.csv - In the simplest scenario, when using the Sigmoid activation function, this file should contain a binary matrix consisting of the numbers 0 and 1.
train_file_y.txt - In the simplest scenario, when using the Sigmoid activation function, this file should contain a single column of the numbers 0 and 1, corresponding to the columns in train_file_x.csv.
test_file.csv - Testing data should be formatted like train_file_x.csv but can include any number of lines, with each line representing a separate input for analysis.
Please note that the number of input neurons is automatically calculated based on the data in train_file_x.csv, while the number of output neurons is fixed at 1.
train_file_x.csv contains a matrix of binary representations for numbers ranging from 0 to 10. This matrix comprises eleven rows and four columns. In contrast, within the train_file_y.txt, each row from train_file_x.csv is assigned a 1 or a 0, depending on whether the corresponding number is even or odd.
Furthermore, the test_file.csv encompasses binary representations of numbers spanning from 11 to 15. As illustrated in the image above, a 3-hidden-layer neural network accurately classifies them as either even or odd, despite these numbers not being present in the training data.