Flair | This is powerful android framework | Dependency Injection library

I am calling the python script with the flair package with a www-data user (no sudo rights). The models are in path for which that user has access rights, which I have set flair.cache_root = Path("tools/flair")

However, when I run the script with that user I get a Permission Error:

I have a multiindex dataframe like the following:

I have the following df:

I'm creating a tf.dataset object containing 2 images as inputs and a mask as target. All of them are 3D. After applying a custom map, the shape of the object changes from to , , ), types: (tf.float32, tf.float32, tf.int32)>, and when I fit the data, my model throws an error because it only detects one input instead of 2. Here is what I'm doing:

I'm new to Django and programming in general. I'm going through the book Django For Beginners and have added my own flair in the forms section but have run into a problem. Below I have detailed the error and provided my code.
ERROR NoReverseMatch at /drug/12/ Reverse for 'drug_edit' with keyword arguments '{'pk': ''}' not found. 1 pattern(s) tried: ['drug/(?P[0-9]+)/edit/$'] Request Method: GET Request URL: http://127.0.0.1:8000/drug/12/ Django Version: 3.1.3 Exception Type: NoReverseMatch Exception Value:
Reverse for 'drug_edit' with keyword arguments '{'pk': ''}' not found. 1 pattern(s) tried: ['drug/(?P[0-9]+)/edit/$'] Exception Location: C:\Users\scott\django_sites\history_cloud1\history1_env\lib\site-packages\django\urls\resolvers.py, line 685, in _reverse_with_prefix Python Executable: C:\Users\scott\django_sites\history_cloud1\history1_env\Scripts\python.exe Python Version: 3.8.3

models.py

I created a text classifier that uses Tf-Idf using sklearn, and I want to use BERT and Elmo embedding instead of Tf-Idf.

How would one do that ?

I'm getting Bert embedding using the code below:

I am creating a sentiment analysis code for stock market analysis. This is the heart of the code:

I am quite new to the field of semantic segmentation and have recently tried to run the code provided on this paper: Transfer Learning for Brain Tumor Segmentation that was made available on GitHub. It is a semantic segmentation task that uses the BraTS2020 dataset, comprising of 4 modalities, T1, T1ce, T2 and FLAIR. The author utilised a transfer learning approach using Resnet34 weights.

Due to hardware constraints, I had to half the batch size from 24 to 12. However, after training the model, I noticed a significant drop in performance, with the Dice Score (higher is better) of the 3 classes being only around 5-19-11 as opposed to the reported result of 78-87-82 in the paper. The training and validation accuracies however, seem to be performing normally, just that the model does not perform well on test data, I selected the model that was produced before overfitting (validation loss starts increasing but training loss still decreasing) but yielded equally bad results.

So far I have tried:

1. Decreasing the learning rate from 1e-3 to 1e-4, yielded similar results
2. Increased the number of batches fed to the model per training epoch to 200 batches per epoch, to match the number of iterations ran in the paper since I effectively halved the batch size - (100 batches per epoch, batch size of 24)

I noticed that image augmentations were applied to the training and validation dataset to increase the robustness of the model training. Do these augmentations need to be performed on the test set in order to make predictions? There are no resizing transforms, transforms that are present are Gaussian Blur and Noise, change in brightness intensity, rotations, elastic deformation, and mirroring, all implemented using the example here.

I'd greatly appreciate help on these questions:

1. By doubling the number of batches per epoch, it effectively matches the number of iterations performed as in the original paper since the batch size is halved. Is this the correct approach?

2. Does the test set data need to be augmented similarly to the training data in order to perform predictions? (Note: no resizing transformations were performed)

I am trying to implement the 'Bottom-Up Computation' algorithm in data mining (https://www.aaai.org/Papers/FLAIRS/2003/Flairs03-050.pdf).

I need to use the 'pandas' library to create a dataframe and provide it to a recursive function, which should also return a dataframe as output. I am only able to return the final column as output, because I am unable to figure out how to dynamically build a data frame.

Here is the python program: