Build neural networks in 1 line
Neural Network Builder (nn_builder)
nn_builder creates neural networks in 1 line. It makes it easier for you to create neural networks and quickly experiment with different architectures.
pip install nn_builder
On the left is the large amount of code you would need to write to create a PyTorch neural network equivalent to the only one line of code on the right using nn_builder:
1. PyTorch NN Module
The PyTorch NN module lets you build feedforward (rather than CNNs or RNNs) PyTorch neural networks in one line.
from nn_builder.pytorch.NN import NN and then NN takes the below arguments:
|input_dim||Integer to indicate the dimension of the input into the network||N/A|
|linear_hidden_units||List of integers to indicate the width and number of linear hidden layers you want in your network||N/A|
|output_dim||Integer to indicate the dimension of the output of the network if you want 1 output head. Provide a list of integers if you want multiple output heads||N/A|
|output_activation||String to indicate the activation function you want the output to go through. Provide a list of strings if you want multiple output heads||No activation|
|hidden_activations||String or list of string to indicate the activations you want used on the output of hidden layers (not including the output layer), default is ReLU and for example "tanh" would have tanh applied on all hidden layer activations||ReLU after every hidden layer|
|dropout||Float to indicate what dropout probability you want applied after each hidden layer||0|
|initialiser||String to indicate which initialiser you want used to initialise all the parameters. All PyTorch initialisers are supported.||PyTorch Default|
|batch_norm||Boolean to indicate whether you want batch norm applied to the output of every hidden layer||False|
|columns_of_data_to_be_embedded||List to indicate the columns numbers of the data that you want to be put through an embedding layer before being fed through the other layers of the network||No embeddings|
|embedding_dimensions||If you have categorical variables you want embedded before flowing through the network then you specify the embedding dimensions here with a list like so: [ [embedding_input_dim_1, embedding_output_dim_1], [embedding_input_dim_2, embedding_output_dim_2] ...]||No embeddings|
|y_range||Tuple of float or integers of the form (y_lower, y_upper) indicating the range you want to restrict the output values to in regression tasks||No range|
|print_model_summary||Boolean to indicate whether you want a model summary printed after model is created||False|
See this colab notebook for demonstrations of how to use this module.
2. Other Modules
Anyone is very welcome to contribute via a pull request. Please see the Issues page for ideas on the best areas to contribute to and try to:
- Add tests to the tests folder that cover any code you write
- Write comments for every function
- Create a colab notebook demonstrating how any extra functionality you created works
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
|Filename, size & hash SHA256 hash help||File type||Python version||Upload date|
|nn_builder-0.0.5-py3-none-any.whl (19.7 kB) Copy SHA256 hash SHA256||Wheel||py3|
|nn_builder-0.0.5.tar.gz (14.6 kB) Copy SHA256 hash SHA256||Source||None|