A homemade machine learning platform modeled after TensorFlow
Project description
Artifice
Artifice is a homemade machine learning platform modeled after TensorFlow, codeveloped and cowritten by William Bidle and Ilana Zane.
Installation
To get started with Artifice, copy the following command and paste it into your command line:
pip install Artifice
To test the installation, run the following code into your Python editor of choice:
from Artifice import Artifice
layer_sequence = [1,'ReLU', 2, 'sigmoid', 3]
loss_function = 'MSLE'
nn = Artifice.NN(layer_sequence, loss_function)
print('activation func library:\n', nn.activation_funcs_library, '\n')
print('loss func library:\n', nn.loss_funcs_library, '\n')
print('current weights:\n', nn.weights, '\n')
print('current activation functions:\n', nn.activation_funcs, '\n')
print('current loss function:\n', nn.loss_func_label, ':', nn.loss_func, '\n')
print('traing error:\n', nn.training_err, '\n')
If there are no errors, then you have successfully installed Artifice! The full list of functions, their usage, as well as some examples can be found within the Artifice.py file.
List of available activation functions
For a given value, $x$, different activation functions are definined by the following.
 "sigmoid" :
$$\frac{1}{1 + e^{x}}$$
 'tanh' :
$$tanh(x)$$
 'ReLU' :
$$f(x) = \begin{cases} x & \text{if } x \geq 0,\ 0 & \text{if } x < 0. \end{cases}$$
List of avaliable loss functions
For a given network output vector, $\vec{y}^{out}$, and true value vector, $\vec{y}^{true}$, with $N$ components each, different loss functions are definined by the following.
 Mean Squared Error ("MSE") :
$$\sum_{i}^N(y_i^{out}  y_i^{true})^2$$
 Mean Absolute Error ("MAE") :
$$\sum_{i}^Ny_i^{out}  y_i^{true}$$

"MAPE" : $$100 * \sum_{i}^N\frac{y_i^{out}  y_i^{true}}{y_i^{out} + y_i^{true}}$$

Mean Squared Logarithmic Error ("MSLE") :
$$\sum_{i}^N(log(y_i^{out} + 1)  log(y_i^{true} + 1))^2$$
 Binary CrossEntropy ("BCE") :
$$\sum_{i}^N(y_i^{true}*log(y_i^{out}) + (1  y_i^{true})*log(1  y_i^{out}))$$
 "Poisson" :
$$\sum_{i}^N(y_i^{out}  y_i^{true} * log(y_i^{out}))$$
Examples
Detailed examples on how to use Artifice can be found in the Artifice_Tutorial.ipynb Jupyter Notebook.
Project details
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