Mercurial > repos > bgruening > sklearn_to_categorical
changeset 3:ec69cbe34b73 draft
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 8850f42c2c3763e614f7454c9c006f3d2ff572c0
| author | bgruening |
|---|---|
| date | Fri, 27 May 2022 12:43:28 +0000 |
| parents | 612ca26c197d |
| children | 14180f9c831e |
| files | keras_macros.xml to_categorical.xml |
| diffstat | 2 files changed, 12 insertions(+), 5 deletions(-) [+] |
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--- a/keras_macros.xml Fri Aug 27 09:54:55 2021 +0000 +++ b/keras_macros.xml Fri May 27 12:43:28 2022 +0000 @@ -36,7 +36,7 @@ <option value="tanh" selected="@TANH@">tanh</option> <option value="sigmoid">sigmoid</option> <option value="hard_sigmoid">hard_sigmoid</option> - <option value="exponential">tanh</option> + <option value="exponential">exponential</option> </param> </xml> @@ -701,7 +701,14 @@ <!--Simple key words text parameters, conbined to reduce UI latency--> <xml name="simple_kwargs" token_help="Leave blank for default."> - <param argument="kwargs" type="text" value="" label="Type in key words arguments if different from the default" help="@HELP@"/> + <param argument="kwargs" type="text" value="" label="Type in key words arguments if different from the default" help="@HELP@"> + <sanitizer> + <valid initial="default"> + <add value="'" /> + <add value=""" /> + </valid> + </sanitizer> + </param> </xml> <!-- Keras CallBacks --> @@ -897,7 +904,7 @@ <option value="sparse_categorical_accuracy">sparse_categorical_accuracy</option> <option value="mse">mse / MSE / mean_squared_error</option> <option value="mae">mae / MAE / mean_absolute_error</option> - <option value="mae">mape / MAPE / mean_absolute_percentage_error</option> + <option value="mape">mape / MAPE / mean_absolute_percentage_error</option> <option value="cosine_proximity">cosine_proximity</option> <option value="cosine">cosine</option> <option value="none">none</option> @@ -982,4 +989,4 @@ </citation> </xml> -</macros> \ No newline at end of file +</macros>
--- a/to_categorical.xml Fri Aug 27 09:54:55 2021 +0000 +++ b/to_categorical.xml Fri May 27 12:43:28 2022 +0000 @@ -69,7 +69,7 @@ Converts a class vector (integers) to binary class matrix. tf.keras.utils.to_categorical( - y, num_classes=None, dtype='float32' +y, num_classes=None, dtype='float32' ) E.g. for use with categorical_crossentropy.