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
It seems the task you are trying to solve is regression: predicting the price. However, you are training a classification model, that assigns a class to every input. ROC-AUC score is meant for classification problems where the output is the probability of the input belonging to a class. If you do a multi-class classification, then … Read more
There are a couple of ways. If timing the results with the following code: We get: So the multiplication seems to be the fastest.
Remove scoring=’roc_auc’ and it will work as roc_auc curve does not support categorical data.
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
(It is possible that my interpretation of the question is wrong. If the question is how to get from a discrete PDF into a discrete CDF, then np.cumsum divided by a suitable constant will do if the samples are equispaced. If the array is not equispaced, then np.cumsum of the array multiplied by the distances between the points will … Read more
Single layer To initialize the weights of a single layer, use a function from torch.nn.init. For instance: Alternatively, you can modify the parameters by writing to conv1.weight.data (which is a torch.Tensor). Example: The same applies for biases: nn.Sequential or custom nn.Module Pass an initialization function to torch.nn.Module.apply. It will initialize the weights in the entire … Read more
Your understanding is pretty much spot on, albeit very, very basic. TensorFlow is more of a low-level library. Basically, we can think of TensorFlow as the Lego bricks (similar to NumPy and SciPy) that we can use to implement machine learning algorithms whereas Scikit-Learn comes with off-the-shelf algorithms, e.g., algorithms for classification such as SVMs, … Read more