borisd13

This project revisits text generation as detailed in The Unreasonable Effectiveness of Recurrent Neural Networks and tries to bring more understanding to RNN's and how to optimize them. Github repository: https://github.com/borisd13/char-RNN

Project visibility

Public

Last active

2/4/2019, 3:30:27 AM

Owner

Boris Dayma

Contributors

1 user

Total runs

Total compute

2 days

Export & update data

Use our Public API to export data or update data, such as editing the config of existing runs. Learn more in the docs →

Find the run path

To use the public API, you'll often need the run path which is <entity>/<project>/<run_id>. In the app UI, open a run page, then click on the Overview tab to see the run path.

Update config for an existing run

This example updates one of your configuration settings.

import wandb api = wandb.Api() run = api.run("borisd13/char-RNN/<run_id>") run.config["key"] = updated_value run.update()

Export metrics from a single run to a CSV file

This snippet finds all the metrics saved for a single run and saves them to a CSV file.

import wandb api = wandb.Api() # run is specified by <entity>/<project>/<run_id> run = api.run("borisd13/char-RNN/<run_id>") # save the metrics for the run to a csv file metrics_dataframe = run.history() metrics_dataframe.to_csv("metrics.csv")

Read metrics for a run

Pull down the accuracy and timestamps for logged metric data. In this example, data was logged with wandb.log({"accuracy": acc}) to a run with the run path <entity>/<project>/<run_id>.

import wandb api = wandb.Api() run = api.run("borisd13/char-RNN/<run_id>") if run.state == "finished": for i, row in run.history().iterrows(): print(row["_timestamp"], row["accuracy"])

Get unsampled metric data

When you pull data from history, by default it's sampled to 500 points. Get all the logged data points using run.scan_history(). Here's an example downloading all the loss data points logged in history.

import wandb api = wandb.Api() run = api.run("borisd13/char-RNN/<run_id>") history = run.scan_history() losses = [row["loss"] for row in history]

Download the best model file from a sweep

This snippet downloads the model file with the highest validation accuracy from a sweep with runs that saved model files to model.h5.

import wandb api = wandb.Api() sweep = api.sweep("borisd13/char-RNN/<sweep_id>") runs = sorted(sweep.runs, key=lambda run: run.summary.get("val_acc", 0), reverse=True) val_acc = runs[0].summary.get("val_acc", 0) print(f"Best run {runs[0].name} with {val_acc}% validation accuracy") runs[0].file("model.h5").download(replace=True) print("Best model saved to model-best.h5")