Awesome

nn_builder

nn_builder lets you build neural networks with less boilerplate code. You specify the type of network you want and it builds it.

Install

pip install nn_builder

Support

Network Type	NN	CNN	RNN
PyTorch	:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:
TensorFlow 2.0	:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:

Examples

On the left is how you can create the PyTorch neural network on the right in only 1 line of code using nn_builder:

Screenshot

Similarly for TensorFlow on the left is how you can create the CNN on the right in only 1 line of code using nn_builder:

Screenshot

Usage

See this colab notebook for lots of examples of how to use the module. 3 types of PyTorch and TensorFlow network are currently supported: NN, CNN and RNN. Each network takes the following arguments:

Field	Description	Default
input_dim	Dimension of the input into the network. See below for more detail. Not needed for Tensorflow.	N/A
layers_info	List to indicate the layers of the network you want. Exact requirements depend on network type, see below for more detail	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	PyTorch & TF 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 column numbers of the data that you want to be put through an embedding layer before being fed through the hidden 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 of the form: [ [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
random_seed	Integer to indicate the random seed you want to use	0
return_final_seq_only	Only needed for RNN. Boolean to indicate whether you only want to return the output for the final timestep (True) or if you want to return the output for all timesteps (False)	True

Each network type has slightly different requirements for input_dim and layers_info as explained below:

1. NN

input_dim: # Features in PyTorch, not needed for TensorFlow
layers_info: List of integers to indicate number of hidden units you want per linear layer.
For example:

from nn_builder.pytorch.NN import NN   
model = NN(input_dim=5, layers_info=[10, 10, 1], output_activation=None, hidden_activations="relu", 
           dropout=0.0, initialiser="xavier", batch_norm=False)

2. CNN

input_dim: (# Channels, Height, Width) in PyTorch, not needed for TensorFlow
layers_info: We expect the field layers_info to be a list of lists indicating the size and type of layers that you want. Each layer in a CNN can be one of these 4 forms:
- ["conv", channels, kernel size, stride, padding]
- ["maxpool", kernel size, stride, padding]
- ["avgpool", kernel size, stride, padding]
- ["linear", units]
For a PyTorch network kernel size, stride, padding and units must be integers. For TensorFlow they must all be integers except for padding which must be one of {“valid”, “same”}
For example:

from nn_builder.pytorch.CNN import CNN   
model = CNN(input_dim=(3, 64, 64), 
            layers_info=[["conv", 32, 3, 1, 0], ["maxpool", 2, 2, 0], 
                         ["conv", 64, 3, 1, 2], ["avgpool", 2, 2, 0], 
                         ["linear", 10]],
            hidden_activations="relu", output_activation="softmax", dropout=0.0,
            initialiser="xavier", batch_norm=True)

3. RNN

input_dim: # Features in PyTorch, not needed for TensorFlow
layers_info: We expect the field layers_info to be a list of lists indicating the size and type of layers that you want. Each layer in a CNN can be one of these 4 forms:
- ["lstm", units]
- ["gru", units]
- ["linear", units]
For example:

from nn_builder.pytorch.CNN import CNN   
model = RNN(input_dim=5, layers_info=[["gru", 50], ["lstm", 10], ["linear", 2]],
            hidden_activations="relu", output_activation="softmax", 
            batch_norm=False, dropout=0.0, initialiser="xavier")

Contributing

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

To help you remember things you learn about machine learning in general checkout Gizmo