torch | TensorLy-Torch : Deep Tensor Learning with TensorLy | Machine Learning library

The installation on the m1 chip for the following packages: Numpy 1.21.1, pandas 1.3.0, torch 1.9.0 and a few other ones works fine for me. They also seem to work properly while testing them. However when I try to install scipy or scikit-learn via pip this error appears:

ERROR: Failed building wheel for numpy

Failed to build numpy

ERROR: Could not build wheels for numpy which use PEP 517 and cannot be installed directly

Why should Numpy be build again when I have the latest version from pip already installed?

Every previous installation was done using python3.9 -m pip install ... on Mac OS 11.3.1 with the apple m1 chip.

Maybe somebody knows how to deal with this error or if its just a matter of time.

I want to use a dataloader in my script.

normaly the default function call would be like this.

Does it make sense to use Conda + Poetry for a Machine Learning project? Allow me to share my (novice) understanding and please correct or enlighten me:

As far as I understand, Conda and Poetry have different purposes but are largely redundant:

• Conda is primarily a environment manager (in fact not necessarily Python), but it can also manage packages and dependencies.
• Poetry is primarily a Python package manager (say, an upgrade of pip), but it can also create and manage Python environments (say, an upgrade of Pyenv).

My idea is to use both and compartmentalize their roles: let Conda be the environment manager and Poetry the package manager. My reasoning is that (it sounds like) Conda is best for managing environments and can be used for compiling and installing non-python packages, especially CUDA drivers (for GPU capability), while Poetry is more powerful than Conda as a Python package manager.

I've managed to make this work fairly easily by using Poetry within a Conda environment. The trick is to not use Poetry to manage the Python environment: I'm not using commands like poetry shell or poetry run, only poetry init, poetry install etc (after activating the Conda environment).

For full disclosure, my environment.yml file (for Conda) looks like this:

Goal: I am trying to import a graph FROM networkx into PyTorch geometric and set labels and node features.

(This is in Python)

Question(s):

1. How do I do this [the conversion from networkx to PyTorch geometric]? (presumably by using the from_networkx function)
2. How do I transfer over node features and labels? (more important question)

I have seen some other/previous posts with this question but they weren't answered (correct me if I am wrong).

Attempt: (I have just used an unrealistic example below, as I cannot post anything real on here)

Let us imagine we are trying to do a graph learning task (e.g. node classification) on a group of cars (not very realistic as I said). That is, we have a group of cars, an adjacency matrix, and some features (e.g. price at the end of the year). We want to predict the node label (i.e. brand of the car).

I will be using the following adjacency matrix: (apologies, cannot use latex to format this)

A = [(0, 1, 0, 1, 1), (1, 0, 1, 1, 0), (0, 1, 0, 0, 1), (1, 1, 0, 0, 0), (1, 0, 1, 0, 0)]

Here is the code (for Google Colab environment):

I'm working through the lessons on building a neural network and I'm confused as to why 512 is used for the linear_relu_stack in the example code:

This question is the same with How can I check a confusion_matrix after fine-tuning with custom datasets?, on Data Science Stack Exchange.

I would like to check a confusion_matrix, including precision, recall, and f1-score like below after fine-tuning with custom datasets.

Fine tuning process and the task are Sequence Classification with IMDb Reviews on the Fine-tuning with custom datasets tutorial on Hugging face.

After finishing the fine-tune with Trainer, how can I check a confusion_matrix in this case?

An image of confusion_matrix, including precision, recall, and f1-score original site: just for example output image

I am trying to understand an example snippet that makes use of the PyTorch transposed convolution function, with documentation here, where in the docs the author writes:

"The padding argument effectively adds dilation * (kernel_size - 1) - padding amount of zero padding to both sizes of the input."

Consider the snippet below where a [1, 1, 4, 4] sample image of all ones is input to a ConvTranspose2D operation with arguments stride=2 and padding=1 with a weight matrix of shape (1, 1, 4, 4) that has entries from a range between 1 and 16 (in this case dilation=1 and added_padding = 1*(4-1)-1 = 2)

When training a MaskRCNN on my multi-class instance segmentation custom data set, given an input formatted as:

Why does knn always predict the same number? How can I solve this? The dataset is here.

Code: