Skip to content

Latest commit

 

History

History
98 lines (77 loc) · 3.6 KB

README.md

File metadata and controls

98 lines (77 loc) · 3.6 KB
Raw

TensorFlow.js Example: Jena Weather

This demo showcases

  • visualization of temporal sequential data with the tfjs-vis library
  • prediction of future values based on sequential input data using various model types including
    • linear regressors
    • multilayer perceptrons (MLPs)
    • recurrent neural networks (RNNs, to be added)
  • underfitting, overfitting, and various techniques for reducing overfitting, including
    • L2 regularization
    • dropout
    • recurrent dropout (to be added)

The data used in this demo is the Jena weather archive dataset.

This example also showcases the usage of the following important APIs in TensorFlow.js

  • tf.data.generator(): How to create tf.data.Dataset objects from generator functions.
  • tf.Model.fitDataset(): How to use a tf.data.Dataset object to train a tf.Model and use another tf.data.Dataset object to perform validation of the model at the end of every training epoch.
  • tfvis.show.fitCallbacks(): How to use the convenient method to plot training-set and validation-set losses at the end of batches and epochs of model training.

Training RNNs in Node.js

This example shows how to predict temperature using a few different types of models, including linear regressors, multilayer perceptrons, and recurrent neural networks (RNNs). While training of the first two types of models happens in the browser, the training of RNNs is conducted in Node.js, due to their heavier computational load and longer training time.

For example, to train a gated recurrent unit (GRU) model, use shell commands:

yarn
yarn train-rnn

By default, the training happens on the CPU using the Eigen ops from tfjs-node. If you have a CUDA-enabled GPU and the necessary drivers and libraries (CUDA and CuDNN) installed, you can train the model using the CUDA/CuDNN ops from tfjs-node-gpu. For that, just add the --gpu flag:

yarn
yarn train-rnn --gpu

You can also calculate the prediction error (mean absolute error) based on a commonsense baseline method that is not machine learning: just predict the temperature as the latest temperature data point in the input features. This can be done with the dummy --modelType flag value baseline, i.e.,

yarn
yarn train-rnn --modelType baseline

Monitoring Node.js Training in TensorBoard

The Node.js-based training script allows you to log the loss values from the model to TensorBoard. Relative to printing loss values to the console, which the training script performs by default, logging to tensorboard has the following advantanges:

  1. Persistence of the loss values, so you can have a copy of the training history available even if the system crashes in the middle of the training for some reason, while logs in consoles a more ephemeral.
  2. Visualizing the loss values as curves makes the trends easier to see.
  3. You will be able to monitor the training from a remote machine by accessing the TensorBoard HTTP server.

To do this in this example, add the flag --logDir to the yarn train command, followed by the directory to which you want the logs to be written, e.g.,

yarn train-rnn --gpu --logDir /tmp/jena-weather-logs-1

Then install tensorboard and start it by pointing it to the log directory:

# Skip this step if you have already installed tensorboard.
pip install tensorboard

tensorboard --logdir /tmp/jena-weather-logs-1

tensorboard will print an HTTP URL in the terminal. Open your browser and navigate to the URL to view the loss curves in the Scalar dashboard of TensorBoard.