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Merged
landerlini merged 2 commits into from
Sep 26, 2025
Merged

Towards 0.2.9 #25

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67a7fc1
Added elu and celu
Sep 26, 2025
cd18c36
Fixed failing tests
Sep 26, 2025
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12 changes: 7 additions & 5 deletions README.md
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Expand Up @@ -211,11 +211,13 @@ A few notes:
| `BatchNormalization` | Available | Available | |

#### Keras Activation functions
| Model | Implementation | Test | Notes |
| ---------------------------- | --------------- | --------- | ----------------------------- |
| `tanh` | Available | Available | |
| `sigmoid` | Available | Available | |
| `relu` | Available | Available | |
| Model | Implementation | Test | Notes |
|-----------| --------------- | --------- |--------------------------------|
| `tanh` | Available | Available | |
| `sigmoid` | Available | Available | |
| `relu` | Available | Available | |
| `elu` | Available | | |
| `celu` | Available | | Only for alpha=1 (celu == elu) |


## Running tests
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4 changes: 3 additions & 1 deletion scikinC/layers/BaseLayerConverter.py
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Expand Up @@ -17,7 +17,9 @@ def activate (self, x):
elif activation == 'tanh':
return "%(x)s = tanh(%(x)s);" % {'x':x}
elif activation == 'relu':
return "%(x)s = %(x)s > 0. ? %(x)s : 0.;" % {'x':x}
return "%(x)s = %(x)s > 0. ? %(x)s : 0.;" % {'x':x}
elif activation == 'celu' or activation == 'elu':
return "%(x)s = %(x)s > 0. ? %(x)s : exp(%(x)s) - 1;" % {'x': x}
elif activation == 'linear':
return ""
else:
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2 changes: 1 addition & 1 deletion setup.py
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Expand Up @@ -16,7 +16,7 @@

setup(
name='scikinC', # Required
version="0.2.8", # Required
version="0.2.9", # Required
description='A converter for scikit learn and keras to hardcoded C function',
long_description=long_description,
long_description_content_type='text/markdown', # Optional (see note above)
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2 changes: 2 additions & 0 deletions test/test_keras.py
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Expand Up @@ -42,6 +42,8 @@ def classifier_dense():
tf.keras.layers.Dense(16, activation='tanh'),
tf.keras.layers.Dense(16, activation='sigmoid'),
tf.keras.layers.Dense(16, activation='relu'),
tf.keras.layers.Dense(16, activation='elu' if hasattr(tf.keras.activations, 'elu') else 'linear'),
tf.keras.layers.Dense(16, activation='celu' if hasattr(tf.keras.activations, 'celu') else 'linear'),
tf.keras.layers.Dense(16),
tf.keras.layers.Dense(1, activation='sigmoid')
])
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