I am following the R instructions for using the pre-trained Inception model. (http://mxnet.io/tutorials/r/classifyRealImageWithPretrainedModel.html)
However, whatever picture I send in, it seems to always send back "torch" or "school bus". Also, most scores are exactly zero. Anyone seen this before?
Convolution, there is no explanation of what
pad(although I found that term in Lasagne docs). Any suggestions on what to read? It seems there are many things that get added from separate papers and some list sources (like ADAM) and others don't.
My question has no relation with this topic. I want to know the reason why does people use ::testing::initGoogleTest instead of testing::initGoogleTest when people use gtest to do unit test. => from this, I am confused by the difference of ::testing::initGoogleTest and testing::initGoogleTest? Sorry to take up u guys time here in such stupid question.
Anyone can help me for this confusion?Thanks.
Traceback (most recent call last): File "neuralnet.py", line 1, in <module> import mxnet as mx ImportError: No module named mxnet
Hi all, Im new to mxnet.
Im trying to understand the autoencoder example. There is no documentation.
While I have got it running on a
p2.xlarge using the AWS deep learning AMI, there are many things I dont understand, eg