imdb

Train a LSTM network

This directory includes code and data to train a LSTM network model (inspired by the example in tensorflow.tfjs-examples on sentiment analysit from the Keras.

Training your own sentiment analysis model in tfjs-node

To train the model using tfjs-node, do

yarn
yarn train <MODEL_TYPE>

where MODEL_TYPE is a required argument that specifies what type of model is to be trained. The available options are:

• multihot: A model that takes a multi-hot encoding of the words in the sequence. In terms of data representation and model complexity, this is the simplest model in this example.
• flatten: A model that flattens the embedding vectors of all words in the sequence.
• cnn: A 1D convolutional model, with a dropout layer included.
• simpleRNN: A model that uses a SimpleRNN layer (tf.layers.simpleRNN)
• lstm: A model that uses a LSTM laayer (tf.layers.lstm)
• bidirectionalLSTM: A model that uses a bidirectional LSTM layer (tf.layers.bidirectional and tf.layers.lstm)

By default, the training happens on the CPU using the Eigen kernels from tfjs-node. You can make the training happen on GPU by adding the --gpu flag to the command, e.g.,

yarn train --gpu <MODEL_TYPE>

The training process will download the training data and metadata form the web if they haven't been downloaded before. After the model training completes, the model will be saved to the public/resources folder, alongside a metadata.json file.

Other arguments of the yarn train command include:

• --maxLen allows you to specify the sequence length.

• --numWords allows you to specify the vocabulary size.

• --embeddingSize allows you to adjust the dimensionality of the embedding vectors.

• --epochs, --batchSize, and --validationSplit are training-related settings.

• --modelSavePath allows you to specify where to store the model and metadata after training completes.

• --embeddingFilesPrefix Prefix for the path to which to save the embedding vectors and labels files (optinal). See the section below for details.

• --logDir This optional string lets you log the loss and accuracy values to a tensorboard log directory during training. For example if you start your training with command:

  yarn train lstm --logDir /tmp/my_lstm_logs

  You can use the following commands to start a tensorboard server in a separate terminal:

  pip install tensorboard   # Unless tensorboard is already installed
  tensorboard --logdir /tmp/my_lstm_logs

  Then you can open a browser tab and navigate to the http:// URL indicated by tensorboard (by default: http://localhost:6006) to view the loss and accuracy curves.

  You may encounter error info from node-pre-gyp that "This Node instance does not support builds for N-API version 4" because the library deinitely build on N-API 4. This is a known issue of installing tfjs-node on windows with certain version of node. Node v10.16.3 should work or another command you can try is:

  npm rebuild @tensorflow/tfjs-node --build-from-source