Merge branch 'master' of github.com:microsoft/pytorch-luxor-lab

README.md

@@ -1,33 +1,4 @@
-Give a short description for your sample here. What does it do and why is it important?
+# "Solving problems with Deep Learning: an in-depth example using PyTorch and its ecosystem" tutorial/lab
-
-## Contents
-
-Outline the file contents of the repository. It helps users navigate the codebase, build configuration and any related assets.
-
-| File/folder       | Description                                |
-|-------------------|--------------------------------------------|
-| `src`             | Sample source code.                        |
-| `.gitignore`      | Define what to ignore at commit time.      |
-| `CHANGELOG.md`    | List of changes to the sample.             |
-| `CONTRIBUTING.md` | Guidelines for contributing to the sample. |
-| `README.md`       | This README file.                          |
-| `LICENSE`         | The license for the sample.                |
-
-## Prerequisites
-
-Outline the required components and tools that a user might need to have on their machine in order to run the sample. This can be anything from frameworks, SDKs, OS versions or IDE releases.
-
-## Setup
-
-Explain how to prepare the sample once the user clones or downloads the repository. The section should outline every step necessary to install dependencies and set up any settings (for example, API keys and output folders).
-
-## Runnning the sample
-
-Outline step-by-step instructions to execute the sample and see its output. Include steps for executing the sample from the IDE, starting specific services in the Azure portal or anything related to the overall launch of the code.
-
-## Key concepts
-
-Provide users with more context on the tools and services used in the sample. Explain some of the code that is being used and how services interact with each other.
 
 ## Contributing
 

luxor/utils.py

@@ -27,7 +27,7 @@ def train(net, data_loader, parameters, device):
                 torch.nn.utils.clip_grad_norm_(net.parameters(), parameters["grad_norm"])
             optimizer.step()
 
-    return(epoch_loss)
+    return epoch_loss
 
 
 def evaluate(net, data_loader, device):