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LARNN: Linear Attention Recurrent Neural Network

A fixed-size, go-back-k recurrent attention module on an RNN so as to have linear short-term memory by the means of attention. The LARNN model can be easily used inside a loop on the cell state just like any other RNN. The cell state keeps the k last states for its multi-head attention mechanism.

The LARNN is derived from the Long Short-Term Memory (LSTM) cell. The LARNN introduces attention on the state's past values up to a certain range, limited by a time window k to keep the forward processing linear in time in terms sequence length (time steps).

Therefore, multi-head attention with positional encoding is used on the most recent past values of the inner state cell so as to enable a better mid-term memory, such that at each new time steps, the cell looks back at it's own previous cell state values with an attention query.

The LARNN Cell

Note that the positional encoding is concatenated rather than added. Also, the ELU activation is used in the cell. There is also batch normalization at many places (not drawn). The Multi-Head Attention Mechanism uses an ELU activation rather than unactivated Linears, for the keys and values and the query. There is here only one query rather than many queries.

Yes, it LARNNs.

Downloading the dataset

cd data
cd ..

Meta-optimize the LARNN

This will launch a round of meta-optimisation which will save the results under a new ./results/ folder.

python3 --dataset UCIHAR --device cuda

Two training rounds have been executed and renamed under the folders ./results_round_1/ and ./results_round_2/ for now.

Visualize the results

You can visually inspect the effect of every hyperparameter on the accuracy, and their correlated effect, by navigating at:

You could also copy and run one of those files on new results by simply changing the results folder in the jupyter-notebook such that your new folder is taken.

The hyperparameters space searched with meta-optimization

Here are the hyperparameters and their respective value ranges, which have been explored:

    ### Optimization parameters
    # This loguniform scale will multiply the learning rate, so as to make
    # it vary exponentially, in a multiplicative fashion rather than in
    # a linear fashion, to handle his exponentialy varying nature:
    'learning_rate': 0.005 * hp.loguniform('learning_rate_mult', -0.5, 0.5),
    # How many epochs before the learning_rate is multiplied by 0.75
    'decay_each_N_epoch': hp.quniform('decay_each_N_epoch', 3 - 0.499, 10 + 0.499, 1),
    # L2 weight decay:
    'l2_weight_reg': 0.005 * hp.loguniform('l2_weight_reg_mult', -1.3, 1.3),
    # Number of loops on the whole train dataset
    'training_epochs': 25,
    # Number of examples fed per training step
    'batch_size': 256,

    ### LSTM/RNN parameters
    # The dropout on the hidden unit on top of each LARNN cells
    'dropout_drop_proba': hp.uniform('dropout_drop_proba', 0.05, 0.5),
    # Let's multiply the "default" number of hidden units:
    'hidden_size': 64 * hp.loguniform('hidden_size_mult', -0.6, 0.6),
    # The number 'h' of attention heads: from 1 to 20 attention heads.
    'attention_heads': hp.quniform('attention_heads', 6 - 0.499, 36 + 0.499, 1),

    ### LARNN (Linear Attention RNN) parameters
    # How restricted is the attention back in time steps (across sequence)
    'larnn_window_size': hp.uniform('larnn_window_size', 10, 50),
    # How the new attention is placed in the LSTM
    'larnn_mode': hp.choice('larnn_mode', [
        'residual',  # Attention will be added to Wx and Wh as `Wx*x + Wh*h + Wa*a + b`.
        'layer'  # Attention will be post-processed like `Wa*(concat(x, h, a)) + bs`
        # Note:
        #     `a(K, Q, V) = MultiHeadSoftmax(Q*K'/sqrt(dk))*V` like in Attention Is All You Need (AIAYN).
        #     `Q = Wxh*concat(x, h) + bxh`
        #     `V = K = Wk*(a "larnn_window_size" number of most recent cells)`
    # Wheter or not to use Positional Encoding similar to the one used in
    'use_positional_encoding': hp.choice('use_positional_encoding', [False, True]),
    # Wheter or not to use BN(ELU(.)) in the Linear() layers of the keys and values in the multi-head attention.
    'activation_on_keys_and_values': hp.choice('activation_on_keys_and_values', [False, True]),

    # Number of layers, either stacked or residualy stacked:
    'num_layers': hp.choice('num_layers', [2, 3]),
    # Use residual connections for the 2nd (stacked) layer?
    'is_stacked_residual': hp.choice('is_stacked_residual', [False, True])

The best results were found with those hyperparameters, for a test accuracy of 91.924%:

    "activation_on_keys_and_values": true,
    "attention_heads": 27,
    "batch_size": 256,
    "decay_each_N_epoch": 26,
    "dropout_drop_proba": 0.08885391813337816,
    "hidden_size": 81,
    "is_stacked_residual": true,
    "l2_weight_reg": 0.0006495900377590891,
    "larnn_mode": "residual",
    "larnn_window_size": 38,
    "learning_rate": 0.006026504115228934,
    "num_layers": 3,
    "training_epochs": 100,
    "use_positional_encoding": false

Retrain on best hyperparameters found by meta-optimization

You can re-train on the best hyperparameters found with this command:

python3 --dataset UCIHAR --device cuda

Note: before being able to run this command, you will need to have .json files from training results under the path ./results/UCIHAR/. Currently, the best results are found within ./results_round_2/UCIHAR/, the folder could be renamed to make this command work.

Debug the LARNN model

This command is practical if you want to edit the model and potentially print-debug its dimensions:


Some thoughts

The LARNN cell obtains better results than the LSTM cell as explored here.

However, it's yet uncompared to a bidirectional deeply stacked setup such as done here, where better results are obtained.

It seems that the positional encoding is not helpful for the learning.


The current project contains code derived from those other projects:

More information on which pieces of code comes from where in the headers of each Python files. All of those references are licensed under permissive open-source licenses, such as the MIT License and the Apache 2.0 License.


My project is freely available under the terms of the MIT License.

Copyright (c) 2018 Guillaume Chevalier

Note: my drawings are specially available under the CC-BY license rather than under the MIT License.

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