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Build neural networks in 1 line

Project description

Neural Network Builder (nn_builder)

Image contributions welcome

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.

First run from nn_builder.pytorch.NN import NN and then NN takes the below arguments:

Field Description Default
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

Coming soon.


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:

  1. Add tests to the tests folder that cover any code you write
  2. Write comments for every function
  3. Create a colab notebook demonstrating how any extra functionality you created works

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