The CMU-MultimodalSDK on which this repo depend has drastically changed its API since this code is written. Hence the code in this repo cannot be run off-the-shelf anymore. However, the code for the model itself can still be of reference.
This is a PyTorch implementation of:
Zadeh, Amir, et al. "Tensor fusion network for multimodal sentiment analysis." EMNLP 2017 Oral.
It requires PyTorch and the CMU Multimodal Data SDK (https://github.com/A2Zadeh/CMU-MultimodalDataSDK) to function properly. The training data (CMU-MOSI dataset) will be automatically downloaded if you run the script for the first time.
The model is defined in model.py, and the training script is train.py.
Here's a list of commandline arguments for train.py:
--dataset: default is 'MOSI', currently don't really support other datasets. Just ignore this option
--epochs: max number of epochs, default is 50
--batch_size: batch size, default is 32
--patience: specifies the early stopping condition, similar to that in Keras, default 20
--cuda: whether or not to use GPU, default False
--model_path: a string that specifies the location for storing trained models, default='models'
--max_len: max sequence length when preprocessing data, default=20
In a nutshell, you can train the model using the following command:
python train.py --epochs 100 --patience 10
The script starts with a randomly selected set of hyper-parameters. If you want to tune it, you can change them yourself in the script.
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