pytorch-esn

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Sequence to Sequence TIme Series using Deep ESN implementaion

5

How this can be implemented towards sequence to sequence time series using ESN of more than 1 layer (Deep ESN)?

RidhwanAmin

The grad is none, please help me.

1

I don't know why the grad is none.? this is my net code. CNN+ESN class CNN_ESN(nn.Module): def __init__(self, output_size,channel_num, device,drop_prob=0.5): super(CNN_ESN, self).__init__() self.conv = nn.Sequential( nn.Conv2d(channel_num, 64, (5, 5), padding='same'),...

yiyi123123132

If I want to improve the accuracy (say around 95%) for classification on the MNIST dataset, do I need to tune the hyperparameters? Or do I need to change anything...

Shadab442

Using svd to train mackey-glass data, the error can decrease to 3.2e-11. While I change to gradient descent, after 100 epochs, the error becomes about 1e-4. I have no idea...

ruitang-git

I managed to train a ESN model for timeseries prediction and exported same to ONNX file. Later on while inference it ran into issue. 

sn25-dev

mnist: RuntimeError: linalg.solve: A must be batches of square matrices, but they are 501 by 10 matrices

1

Running the example/mnist.py I get: ``` /torchesn/nn/echo_state_network.py", line 237, in fit W = torch.linalg.solve(self.XTy, RuntimeError: linalg.solve: A must be batches of square matrices, but they are 501 by 10 matrices...

jabowery