Im using a Pytorch Unet model to which i am feeding in a image as input and along with that i am feeding the label as the input image mask and traning the dataset on it. The Unet model i have picked up from somewhere else, and i am using the cross-entropy loss as a … Read more
All you need to do is install pillow: Then you should be all set. Found this after hours of searching.
The biggest difference between the models you’re building from a “features” point of view is that Naive Bayes treats them as independent, whereas SVM looks at the interactions between them to a certain degree, as long as you’re using a non-linear kernel (Gaussian, rbf, poly etc.). So if you have interactions, and, given your problem, … Read more
If my goal is to fine-tune the network for the entire dataset It is not clear what you mean by “fine-tune”, or even what exactly is your purpose for performing cross-validation (CV); in general, CV serves one of the following purposes: Model selection (choose the values of hyperparameters) Model assessment Since you don’t define any … Read more
Argh! I keep getting the following error when attempting to compute with my neural network: I can’t figure out what the problem is. Below I’ll provide you with an example data and formatting from my matrices and then I’ll show you the code I’m attempting to run. matrix.train1 is used for training the network> matrix.train1 (Intercept) survived pclass … Read more
Please read carefully the documentation on backward() to better understand it. By default, pytorch expects backward() to be called for the last output of the network – the loss function. The loss function always outputs a scalar and therefore, the gradients of the scalar loss w.r.t all other variables/parameters is well defined (using the chain rule). Thus, by default, backward() is called on a scalar … Read more
The idea of word2vec is to maximise the similarity (dot product) between the vectors for words which appear close together (in the context of each other) in text, and minimise the similarity of words that do not. In equation (3) of the paper you link to, ignore the exponentiation for a moment. You have The numerator is … Read more
Cross-entropy is commonly used to quantify the difference between two probability distributions. In the context of machine learning, it is a measure of error for categorical multi-class classification problems. Usually the “true” distribution (the one that your machine learning algorithm is trying to match) is expressed in terms of a one-hot distribution. For example, suppose … Read more
For this question, it’s a simple answer from the primary author: With fit_generator, you can use a generator for the validation data as well. In general, I would recommend using fit_generator, but using train_on_batch works fine too. These methods only exist for the sake of convenience in different use cases, there is no “correct” method. train_on_batch allows you to expressly update … Read more
In the following TensorFlow function, we must feed the activation of artificial neurons in the final layer. That I understand. But I don’t understand why it is called logits? Isn’t that a mathematical function?