A pure numpy-based implementation of transposed convolutions which are used for upscaling the tensors and dilated convolutions proposed in Multi-Scale Context Aggregation by Dilated Convolutions by Yu et al. The convolutions are performed by matrix multiplications by transforming the image and the filers into matrices. The loops in the transformation routines are numba-compatible and therefore can be compiled using the numba JIT.
- Python 3
- Numpy
- Numba (recommended)
- tqdm
The Network class in network.py represents a single neural network. The various available layers are also present in network.py. These include convolutional layers (with dilation), transposed convolutional layers, pooling layers, fully connected layers, various activations, cross-entropy and squared error losses and various padding/reshaping layers. These layers can be added to the network using the network.add_layer function, with the last layer being a loss function. To train a model, you must run the model.forward function, which gives you the loss, the model.backward function which calculates the derivatives, and the model.adam_trainstep function which updates the parameters using the ADAM optimizer. For inference, model.run can be used.
The tests for the convolution gradient implementation can by run by
python conv_tests.py
python mnist_example.py
The mnist_example.py runs two networks on the MNIST dataset present in the repository. The first is a CNN classifier, which should achieve a test accuracy of about 0.97, and the second is a convolution-transposed convolution autoencoder over MNIST trained using a sum of squared errors. The convolution shape arithmetic should be kept in mind when designing such autoencoders.