classifier | A PHP Bayesian classifier algorithm library

Im attempting to find model performance metrics (F1 score, accuracy, recall) following this guide https://machinelearningmastery.com/how-to-calculate-precision-recall-f1-and-more-for-deep-learning-models/

This exact code was working a few months ago but now returning all sorts of errors, very confusing since i havent changed one character of this code. Maybe a package update has changed things?

I fit the sequential model with model.fit, then used model.evaluate to find test accuracy. Now i am attempting to use model.predict_classes to make class predictions (model is a multi-class classifier). Code shown below:

I try to implement a fully-connected model for classification using the MNIST dataset. A part of the code is the following:

There was introduced a new feature Gradle managed devices (see for example here: https://developer.android.com/studio/preview/features?hl=fr)

The setup seems to be pretty straightforward, just copy a few lines to the module level build.gradle file and everything should work.

Sadly it is not the case for me and I strive for some advice, please. The code is red and the script doesn't succeed. See my build.gradle.kts file:

The underlined ManagedVirtualDevice shows the following error:

My Android studio version is Android Studio Bumblebee | 2021.1.1 Canary 11 Build #AI-211.7628.21.2111.7676841, built on August 26, 2021.

Syncing Gradle shows this:

I have created a class for word2vec vectorisation which is working fine. But when I create a model pickle file and use that pickle file in a Flask App, I am getting an error like:

AttributeError: module '__main__' has no attribute 'GensimWord2VecVectorizer'

I am creating the model on Google Colab.

Code in Jupyter Notebook:

This worked fine for me be building under Java 8. Now under Java 17.01 I get this when I do mvn deploy.

mvn install works fine. I tried 3.6.3 and 3.8.4 and updated (I think) all my plugins to the newest versions.

Any ideas?

I'm trying to use GridSearchCV to find the best hyperparameters for an LSTM model, including the best parameters for vocab size and the word embeddings dimension. First, I prepared my testing and training data.

I am attempting to fine-tune a BERT model on Google Colab from the Tensorflow Hub using this link.

However, I run into the following error:

I have built a pixel classifier for images, and for each pixel in the image, I want to define to which pre-defined color cluster it belongs. It works, but at some 5 minutes per image, I think I am doing something unpythonic that can for sure be optimized.

How can we map the function directly over the list of lists?

I have built a number of sklearn classifier models to perform multi-label classification and I would like to calibrate their predict_proba outputs so that I can obtain confidence scores. I would also like to use metrics such as sklearn.metrics.recall_score to evaluate them.

I have 4 labels to predict and the true labels are multi-hot encoded (e.g. [0, 1, 1, 1]). As a result, CalibratedClassifierCV does not directly accept my data: