PyTorch-ESN

PyTorch-ESN is a PyTorch module, written in Python, implementing Echo State Networks with leaky-integrated units. ESN's implementation with more than one layer is based on DeepESN. The readout is trainable by ridge regression or by PyTorch's optimizers.

Prerequisites

• PyTorch

Basic Usage

Offline training (ridge regression)

SVD

Mini-batch mode is not allowed with this method.

from torchesn.nn import ESN
from torchesn.utils import prepare_target

# prepare target matrix for offline training
flat_target = prepare_target(target, seq_lengths, washout)

model = ESN(input_size, hidden_size, output_size)

# train
model(input, washout, hidden, flat_target)

# inference
output, hidden = model(input, washout, hidden)

Cholesky or inverse

from torchesn.nn import ESN
from torchesn.utils import prepare_target

# prepare target matrix for offline training
flat_target = prepare_target(target, seq_lengths, washout)

model = ESN(input_size, hidden_size, output_size, readout_training='cholesky')

# accumulate matrices for ridge regression
for batch in batch_iter:
    model(batch, washout[batch], hidden, flat_target)

# train
model.fit()

# inference
output, hidden = model(input, washout, hidden)

Classification tasks

For classification, just use one of the previous methods and pass 'mean' or 'last' to output_steps argument.

model = ESN(input_size, hidden_size, output_size, output_steps='mean')

For more information see docstrings or section 4.7 of "A Practical Guide to Applying Echo State Networks" by Mantas Lukoševičius.

Online training (PyTorch optimizer)

Same as PyTorch.