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                                            UpCoder

Exception: RNN : Input dimension should be rank 3 : [batch_size, n_steps, n_features]

@sainisanjay @GuangmingZhu Did you solve the problem? I also meet the problem. Can you give me some suggestion. Thank you very much!

Exception: RNN : Input dimension should be rank 3 : [batch_size, n_steps, n_features]

Maybe it can be solved by this way. As the mentioned at the README.md, we should modify the source code of Tensorlay, we need to replace the RNNLayer code with...

Exception: RNN : Input dimension should be rank 3 : [batch_size, n_steps, n_features]

@sainisanjay As far as I known, you need to replace the RNNLayer of TensorLayer Library with the tensorlayer-rnnlayer.py. It can be almost solved.

Exception: RNN : Input dimension should be rank 3 : [batch_size, n_steps, n_features]

I think you can google it, then you can find the answer in the first link. [Google it](https://github.com/tensorflow/models/issues/933)

any recommendation for parameters?

@sedghi @mbickel @skrish13 Hi, how is the effect of default parameters? I found it is strange when I use the default parameters. It seem that the effect of CRF operation...

CRF returning all 0's

same problem here. Do you solve it?

How to save LSHIndex ?

Is it supported now?

can't download vgg16.npy file

thanks

RoI Pooling return <unknown> Tensor

@guozhiyao Maybe we must set the shape of input_image, then the size of the feature map will not be unknown.

[BUG]: WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 73958 closing signal SIGTERM

1. you can set export TORCH_CPP_LOG_LEVEL=DEBUG to print more info 2. you can decrease the number of multi-gpus, such as 8->2, to make sure the memory is sufficient.