Keras is a high level api which works on top of Tensorflow. It is very easy to learn and implement. Tensorflow allows us to change every minute detail in the network. Thus making it useful in scenarios where flexibility is essential. But building a network and debugging it in Tensorflow is more time consuming than … Read more
Your question is a bit broad, but i’ll try to answer nonetheless. To imitate a subjects behavior you could use and LSTM network which has an understanding of the state it’s in (in your case the state may include information about how fast and in which direction the dot is going and where the pointer … Read more
Well, I got result that was need for me. I found him in another question from Stack Overflow: How to show graph in Visual Studio Code itself? Use #%% for creating cells in that you will run independent fragments of code. It seems like Python Shell(this is Jupyter Notebook that I love). Result that I … Read more
The BN layer tries to zero-mean its output by subtracting an expectation over inputs. So we can expect some of its output values to be negative. So the LeakyReLU following the BN layer will still receive negative values. solved Is use of Leaky ReLu after Batch Normalization (BN) is useful
This problems is a binary segmentation problem. In a sense you learn a mapping from satellite images and predict for each pixel, iff this pixel is part of the road. A simple algorithm to do this would be to check if the pixel color is part of some range. A CNN naturally will learn you … Read more
An inability to overfit may be caused by a lack of network capacity or a bug in your code itself. Are you training the model from scratch? Did you write the model yourself? Are you working on a task at a similar scale to Imagenet classification? Plateauing of a loss is to be expecting during … Read more
TLDR; Reinforcement learning In general, training agents uses reinforcement learning. It is different than what you explained, because it seems as if you want to define a fitness heuristic to tell the agent whether it is doing OK or not, which might be biased. Reinforcement learning also has biases, but they are researchedand studied. A … Read more
As with many domains of computer vision recently, deep learning is not needed to perform the task but does outperform non-DL based methods. There is lots of prior work that uses other techniques, for example SVMs, to perform facial expression recognition. However if we look at more recent work like the FERA 2017 competition, for … Read more
I found an answer in a completely unrelated thread in the forums. Couldn’t find a Googleable answer, so posting here for future users’ sake. Since CUDA calls are executed asynchronously, you should run your code with CUDA_LAUNCH_BLOCKING=1 python script.py This makes sure the right line of code will throw the error message. solved Pytorch crashes … Read more
Yes..you can get a good accuracy in the first epoch as well. It depends more on the complexity of the data and the model you build. sometimes if the learning rate is too high, than also it could so happen you get a higher training accuracy. Also, coming to the adjusting weights and biases part, … Read more
I just copy the reply from there for those facing similar issues. You do not need a pre-trained model. Actually, when you are developing your own, custom architecture, you cannot even get a pre-trained model. 1200 images is not much, but without any other information, one cannot tell if it is enough or not. Maybe … Read more
Why not learn the numerical representations for each base? This is a common problem in Neural Machine Translation, where we seek to encode “words” with a meaning as (naively) numbers. The core idea is that different words should not be represented with simple numbers, but with a learned dense vector. The process of finding this … Read more
One particular dataset is benchmark for image classification in the computer vision and machine learning literature solved One particular dataset is benchmark for image classification in the computer vision and machine learning literature
It sounds as if you’ve trained to a discrete classification, but you want continuous output. Switch your algorithm to do regression, rather than classification. Another possibility is to harness your last-layer output to interpolate. Use the weights given to the top choice and its strongest adjacent choice. For instance, if your classification gives 1 .01 … Read more