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Follow the instruction, I can already 1. convert the yolo2 weight (for detecting 80 classes) to keras weight, load the yolo2 model in keras and do correct prediction. 2. convert...

How to support multiple output y

1

The `sklearn` support for muliple output, i.e. the shape of y can be (B,L) rather than (B,) For example, ``` from sklearn.ensemble import RandomForestRegressor x_train = np.random.randn(100,100) y_train = np.random.randn(100,50)...

How to predict via converted yolo9000 model

2

I now want to work on https://github.com/philipperemy/yolo-9000 Follow the instruction, I can already 1. convert the yolo2 weight (for detecting 80 classes in coco_classes.txt) to keras weight, load the yolo2...

replicated script request

1

Do you plan to share a replicated script like [PA-Graph-Transformer](https://github.com/benatorc/PA-Graph-Transformer) did. For example, a `train.py` script for the QM7 dataset and achieve around your best score **43.9**

Hi, I'd like to use `functorh` to realize following loss: ### Question demonstrate assume the - the dimension of output tensor is $O$ and we will use $y^\gamma$ mark each...

Inference model trained on Multiple GPU

1

For multiple GPU train, the saved model ( in .meta file) is split to two clones. So all the tensor names are changed by default like source_ph --> clone_0/ source_ph...

the way to calculate `time_means` in script get_stats.py is wrong

3

Please see: https://github.com/NVlabs/FourCastNet/blob/master/data_process/get_stats.py ``` **time_means = np.zeros((1,21,721, 1440))** for ii, year in enumerate(years): with h5py.File('/pscratch/sd/s/shas1693/data/era5/train/'+ str(year) + '.h5', 'r') as f: rnd_idx = np.random.randint(0, 1460-500) global_means += np.mean(f['fields'][rnd_idx:rnd_idx+500], keepdims=True, axis...

Enhenced String report and stacked bar visulization.

2

1. make the high level profile possible. Original profile build the hook for the least leaf module in a model For example, only the `nn.Linear`, `nn.Tanh` and that elements won't...

the code location: tensornetwork/matrixproductstates/infinite_mps.py line 287 to 298 ``` U, singvals, V, _ = self.backend.svd( tmp, pivot_axis=1, max_singular_values=D, max_truncation_error=truncation_threshold, relative=True) lam = self.backend.diagflat(singvals) self.tensors[0] = ncon([lam, V, inv_sqrtr, self.tensors[0]], [[-1,...