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#example training w/ trainable dropout layer | |
for epoch in range(epochs): | |
for (train_batch, test_batch) in zip(train_dataloader, test_dataloader): | |
features, target = train_batch | |
test_features, test_target= test_batch | |
unfreeze_all_but_dropout(model); | |
optimizer.zero_grad(); |
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$myArr = [38, 18, 10, 7, "15"]; | |
echo in_array(10, $myArr); // TRUE | |
echo in_array(19, $myArr); // TRUE | |
// Without strict check | |
echo in_array("18", $myArr); // TRUE | |
// With strict check | |
echo in_array("18", $myArr, true); // FALSE |
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$x = new stdClass(); |
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find ./ -type f -name "*work*" -exec wc -w {} + |
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.top1 { | |
height:390px; | |
background-color:#FFFFFF; | |
margin-top:10px; | |
overflow: hidden; | |
} | |
.top1 img { | |
height:100%; | |
} |
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import numpy as np | |
a = np.array([1,8,3]) |
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for %a in (*.*) do ren "%a" "prefix - %a" |
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<component | |
(open)="open($event)" | |
></component> |
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dataframe.column.nunique() |
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