WITH sizes AS (SELECT nspname || '.' || relname AS "relation",
pg_total_relation_size(C.oid) AS "total_size"
FROM pg_class C
LEFT JOIN pg_namespace N ON (N.oid = C.relnamespace)
WHERE nspname NOT IN ('pg_catalog', 'information_schema')
AND C.relkind <> 'i'
AND nspname !~ '^pg_toast'
) SELECT pg_size_pretty(SUM(total_size)) FROM sizes;
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class AttentionLSTM(LSTM): | |
"""LSTM with attention mechanism | |
This is an LSTM incorporating an attention mechanism into its hidden states. | |
Currently, the context vector calculated from the attended vector is fed | |
into the model's internal states, closely following the model by Xu et al. | |
(2016, Sec. 3.1.2), using a soft attention model following | |
Bahdanau et al. (2014). | |
The layer expects two inputs instead of the usual one: |
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# start a new virtualenv | |
git clone git@github.com:FragLegs/learning2ski.git . | |
pip install -r requirements.txt | |
python eval.py -e 150 --monitor --seed 42 l2s | |
# If you leave off the --monitor flag, the results are a little better |