# fastai | The fastai deep learning library | Machine Learning library

## kandi X-RAY | fastai Summary

## kandi X-RAY | fastai Summary

fastai is a deep learning library which provides practitioners with high-level components that can quickly and easily provide state-of-the-art results in standard deep learning domains, and provides researchers with low-level components that can be mixed and matched to build new approaches. It aims to do both things without substantial compromises in ease of use, flexibility, or performance. This is possible thanks to a carefully layered architecture, which expresses common underlying patterns of many deep learning and data processing techniques in terms of decoupled abstractions. These abstractions can be expressed concisely and clearly by leveraging the dynamism of the underlying Python language and the flexibility of the PyTorch library. fastai includes:. fastai is organized around two main design goals: to be approachable and rapidly productive, while also being deeply hackable and configurable. It is built on top of a hierarchy of lower-level APIs which provide composable building blocks. This way, a user wanting to rewrite part of the high-level API or add particular behavior to suit their needs does not have to learn how to use the lowest level.

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## fastai Key Features

## fastai Examples and Code Snippets

## Community Discussions

Trending Discussions on fastai

QUESTION

I am trying to load the `Efficientnet-b6`

weights using `PyTorch`

and `Fastai`

:

ANSWER

Answered 2022-Feb-07 at 21:09Given the restrained context, I suspect that the problem resides in `model`

, probably containing an `OrderedDict`

of the EfficientNet model state dict, while the EARUnet expects the EfficientNet `nn.Module`

.

You should instead, try something like:

QUESTION

I'm trying to develop a GAN using FastAi. When converting the Tensor to an Image I get this error.

...ANSWER

Answered 2021-Dec-11 at 17:40I suggest for you to use this code to convert the output of your model from a tensor to a PIL image:

QUESTION

I was trying to create some features from date column using 'add_datepart' function from 'fastai.structured' module in 'fastai' library. I got this error:

...ANSWER

Answered 2021-Oct-28 at 11:10Finally I found the solution to this problem. 'structured' module in 'fastai' has been replaced with 'core' module inside 'tabular' folder in 'fastai' library. So instead of importing 'add_datepart' from 'structured' module import it from 'core'. In short we need to make following changes in our code:

Replace this code-

QUESTION

data source: https://catalog.data.gov/dataset/nyc-transit-subway-entrance-and-exit-data

I tried looking for a similar problem but I can't find an answer and the error does not help much. I'm kinda frustrated at this point. Thanks for the help. I'm calculating the closest distance from a point.

...ANSWER

Answered 2021-Oct-11 at 14:21**geopandas 0.10.1**

- have noted that your data is on kaggle, so start by sourcing it
- there really is only one issue
`shapely.geometry.MultiPoint()`

constructor does not work with a filtered**series**. Pass it a**numpy array**instead and it works. - full code below, have randomly selected a point to serve as
`gpdPoint`

QUESTION

I am drawing a Confusion Matrix in `fastai`

with following code:

ANSWER

Answered 2021-Aug-20 at 02:24If you check the code of the function `ClassificationInterpretation.plot_confusion_matrix`

(in file *fastai / interpret.py*), this is what you see:

QUESTION

I am looking to implement DistilBERT via fastai and huggingface for a mutliclass sequence classification problem. I found a useful tutorial that gave a good example on how to do this with binary classification. The code is below:

...ANSWER

Answered 2021-Aug-25 at 19:09You need to define `num_labels=9`

when loading the model:

QUESTION

I am trying to use the fastai library to build a machine learning model for object detection. I'm not an expert in machine learning, so I searched online and found this tutorial at object detection tutorial
but when I used Google colab to run it, I got error saying:
TypeError: no implementation found for 'torch.nn.functional.smooth_l1_loss' on types that implement **torch_function**: [, ]
And I've also tried a 2018 version of the same tutorial before but ran into the same problem. Does anyone know what's going on?

ANSWER

Answered 2021-Aug-25 at 05:36The repository you referred to in your question is outdated a/c to their README here.

You can find instructions to go to the latest versions of notebooks there.

QUESTION

I am trying to train a densenet model using the fast.ai library. I checked the documentation and I managed to make it work for resnet50. However, for densenet, it seems to be unable to find the module.

I tried to use `arch=models.dn121`

as stated by this forum. But I get the same error.

Can anyone please help?

Here is the code:

...ANSWER

Answered 2021-Aug-10 at 07:17According to this post on the fast.ai forum, this is the solution to use densenet with fast.ai:

QUESTION

I had just installed Anaconda from anaconda.com. The installation proceeded smoothly. After that, I was trying to create a new environment from this environment.yml file. (nbdev.yml)

...ANSWER

Answered 2021-Aug-04 at 05:11After a lot of research, I stumbled on to Mamba doesn't find a solution when mixing conda forge defaults and not specifying Python explicitly 1102. So I just edited nbdev.yml from

QUESTION

I am working through "*Deep Learning for Coders with fastai & Pytorch*". Chapter 4 introduces the autograd function from the PyTorch library on a trivial example.

ANSWER

Answered 2021-Jul-26 at 21:46**TLDR; the derivative of a sum of functions is the sum of their derivatives**

Let `x`

be your input vector made of `x_i`

(where `i`

in `[0,n]`

), `y = x**2`

and `L = sum(y_i)`

. You are looking to compute `dL/dx`

, a vector of the same size as `x`

whose components are the `dL/dx_j`

(where `j`

in `[0,n]`

).

For `j`

in `[0,n]`

, `dL/dx_j`

is simply `dy_j/dx_j`

(derivative of the sum is the sum of derivates and only one of them is different to zero), which is `d(x_j**2)/dx_j`

, *i.e.* `2*x_j`

. Therefore, `dL/dx = [2*x_j where j in [0,n]]`

.

This is the result you get in `x.grad`

when either computing the gradient of `x`

as:

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