nn | Fast and simple Neural Network for node.js | Runtime Evironment library
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kandi X-RAY | nn Summary
Fast and simple Neural Network for node.js
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QUESTION
I'd like to run a simple neural network model which uses Keras on a Rasperry microcontroller. I get a problem when I use a layer. The code is defined like this:
...ANSWER
Answered 2021-May-25 at 01:08I had the same problem, man. I want to transplant tflite to the development board of CEVA. There is no problem in compiling. In the process of running, there is also an error in AddBuiltin(full_connect). At present, the only possible situation I guess is that some devices can not support tflite.
QUESTION
I have a pyTorch-code to train a model that should be able to detect placeholder-images among product-images. I didn't write the code by myself as I am very unexperienced with CNNs and Machine Learning.
My boss told me to calculate the f1-score for that model and i found out that the formula for that is ((precision * recall)/(precision + recall))
but I don't know how I get precision and recall. Is someone able to tell me how I can get those two parameters from that following code?
(Sorry for the long piece of code, but I didn't really know what is necessary and what isn't)
ANSWER
Answered 2021-Jun-13 at 15:17You can use sklearn to calculate f1_score
QUESTION
I am new in R. I tried to gather the verbs ("/VB","/VBD","/VBG","/VBN","/VBP","/VBZ") using "openNLP" package (Note that 'udpipe' does not work in my environment). I have a sentence mixed with the tag as below.
"Doing/VBG work/NN as/IN always/RB ./. playing/VBG soccer/NN is/VBZ good/JJ ./. I/PRP do/VBP that/IN"
How can I achieve the verbs without POS tags? The answer I am trying to get in this example is
..."doing", "playing", "is", "do"
ANSWER
Answered 2021-Jun-13 at 20:09QUESTION
ANSWER
Answered 2021-Jun-13 at 13:55First of all, BoundingBoxHelper
is now BoxHelper
. It seems you are using a very old release of three.js
.
Besides, your collada()
function is not synchronous. After executing collada("14",pobj);
and collada("7",pobj);
the assets are not yet loaded which means you are computing the bounding box for an empty group object.
I suggest you wait until the Collada models have been loaded via THREE.LoadingManager and then add the helper to your scene.
Alternatively, ensure to call helper.update()
in your animation loop. This approach is wasteful however if the object is static and does not change. So it's probably better to use the onLoad()
callback of a loading manager.
QUESTION
I have png image. I want to upsample it using bicubic interpolation. I found this function in pytorch:
...ANSWER
Answered 2021-Jun-13 at 12:16You can do this
QUESTION
I want to force the Huggingface transformer (BERT) to make use of CUDA.
nvidia-smi showed that all my CPU cores were maxed out during the code execution, but my GPU was at 0% utilization. Unfortunately, I'm new to the Hugginface library as well as PyTorch and don't know where to place the CUDA attributes device = cuda:0
or .to(cuda:0)
.
The code below is basically a customized part from german sentiment BERT working example
...ANSWER
Answered 2021-Jun-12 at 16:19You can make the entire class inherit torch.nn.Module
like so:
QUESTION
I have a NET like (exemple from here)
...ANSWER
Answered 2021-Jun-07 at 14:26The most naive way to do it would be to instantiate both models, sum the two predictions and compute the loss with it. This will backpropagate through both models:
QUESTION
I'm new on PyTorch and I'm trying to code with it
so I have a function called OH
which tack a number and return a vector like this
ANSWER
Answered 2021-Apr-30 at 23:19the problem is that you are receiving a tensor on the act function on the Network and then save it as a tensor just remove the tensor in the action like this
QUESTION
If I need to freeze the output layer of this model which is doing the classification as I don't need it.
...ANSWER
Answered 2021-Jun-11 at 15:33You are confusing a few things here (I think)
Freezing layersYou freeze the layer if you don't want them to be trained (and don't want them to be part of the graph also).
Usually we freeze part of the network creating features, in your case it would be everything up to self.head
.
After that, we usually only train bottleneck (self.head
in this case) to fine-tune it for the task at hand.
In case of your model it would be:
QUESTION
After instantiating a 2D convolution with conv = nn.Conv2d(8, 8, 3, bias=False)
, whose member bias
should be None
, is it able to give conv
a legal bias again (whether with random initialization or determined values)?
I observed that bias in other default convolution modules is of the type Parameter
, so I suspect there are extra procedures beyond simply conv.bias = torch.tensor(...)
to make the new bias legal for conv
.
ANSWER
Answered 2021-Jun-12 at 12:48Yes, it is possible to set the bias of the conv layer after instantiating. You can use the nn.Parameter class to create bias parameter and assign to conv object's bias attribute.
To show this I have created a simple Conv2d layer and assigned zero to the weights and ones to bias.
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