matrix-spam-ml

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commit 94ca84b4cd7e3c4374f499a906a0bce4c0c338d7
parent ff58efb39835caea8c9f63529ad83a0f0603c291
Author: MTRNord <mtrnord1@gmail.com>
Date:   Tue,  6 Dec 2022 19:50:42 +0100

Fix hyperparam training

Diffstat:
A.vscode/settings.json | 4++++
Minput/MatrixData.tsv | 2++
Mmodel_v2.py | 1-
3 files changed, 6 insertions(+), 1 deletion(-)

diff --git a/.vscode/settings.json b/.vscode/settings.json @@ -0,0 +1,3 @@ +{ + "python.formatting.provider": "black" +} +\ No newline at end of file diff --git a/input/MatrixData.tsv b/input/MatrixData.tsv @@ -5620,3 +5620,4 @@ spam If you have a Gmail account, a computer or a smartphone and you have 2 hour spam I'll help the community how to earn $30k within 3 days and hours but you will reimburse me 10% of your dividend when you collect it. Note: only interested people should involve, Whatsapp +1 (561) 788 1421 immediately spam Win up to $1000 in crypto trading when you invest with just the minimum of $50 Signup and start investing your crypto with. 💎NO STRESS 💎NO REFERRAL NEEDED!! 💎NO REGISTRATION FEE!! spam If you love to earn from #BITCOIN try GTFx only interested persons No KYC, no VPN + 30+ $BTC daily withdraws (without KYC) $5,000 DEPOSIT BONUS👇 +ham set_many_ordered_items takes an argument that is an iterator of types that implement `Into<AnyBase>`\nAll activitystreams types implement the Extends and ExtendsExt traits, which provide the `into_any_base` method, so you could write something like this:\n\n```rust\nlet v = items\n .into_iter()\n .map(|item| item.into_any_base())\n .collect::<Result<Vec<_>,_>>()?;\ncollection.set_many_ordered_items(v);\n``` +\ No newline at end of file diff --git a/model_v2.py b/model_v2.py @@ -195,7 +195,6 @@ def train_hyperparamters( The hyperparameter search is complete. The optimal number of units in the first densely-connected layer is {best_hps.get('dense1')} and the optimal learning rate for the optimizer is {best_hps.get('learning_rate')}. The optimal dropout rate is {best_hps.get('dropout')} and the optimal l2 rate is {best_hps.get('l2')}. - The optimal embedding_dim is {best_hps.get('embedding_dim')}. """ )