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
This is a very large question. And a hard task. I think, the easy way is to extract one frame by second if the source is a video stream. Then use OpenCV to make a face detection. Once you got the faces, feed a NN for recognitions. Some links for face recognition in Deep Learning: … 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
The keyword is “multilabel classification“. In the output layer you have multiple neurons, each neuron representing one of your classes. Now you should use a binary classification for each neuron independently. So if you have 3 classes, the output of your network could be [0.1, 0.8, 0.99] which means the following: The first class is … 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
Your most versatile function for matrix multiplication is torch.einsum: it allows you specify the dimensions along which to multiply and the order of the dimensions of the output tensor. In your case it would look like: dot_product = torch.einsum(‘bij,bj->bi’) solved How to perform multiplication along axes in pytorch?