Move activations, metrics, optimizers and regularizers (#94)

keras_contrib/activations.py

@@ -1,3 +0,0 @@
-from __future__ import absolute_import
-from . import backend as K
-from keras.activations import *

keras_contrib/metrics.py

@@ -1,2 +0,0 @@
-from . import backend as K
-from keras.metrics import *

keras_contrib/optimizers.py

@@ -1,3 +0,0 @@
-from __future__ import absolute_import
-from . import backend as K
-from keras.optimizers import *

keras_contrib/regularizers.py

@@ -1,3 +0,0 @@
-from __future__ import absolute_import
-from . import backend as K
-from keras.regularizers import *

keras_contrib/tests/activations.py

@@ -1,10 +1,6 @@
-import pytest
 import numpy as np
-from numpy.testing import assert_allclose
 
 from keras import backend as K
-from keras_contrib import backend as KC
-from keras_contrib import activations
 
 
 def get_standard_values():
@@ -15,5 +11,5 @@ def get_standard_values():
     return np.array([[0, 0.1, 0.5, 0.9, 1.0]], dtype=K.floatx())
 
 
-if __name__ == '__main__':
-    pytest.main([__file__])
+def validate_activation(activation):
+    activation(get_standard_values())

keras_contrib/tests/metrics.py

@@ -0,0 +1,13 @@
+import numpy as np
+
+from keras import backend as K
+
+all_metrics = []
+all_sparse_metrics = []
+
+
+def validate_metric(metric):
+    y_a = K.variable(np.random.random((6, 7)))
+    y_b = K.variable(np.random.random((6, 7)))
+    output = metric(y_a, y_b)
+    assert K.eval(output).shape == ()

keras_contrib/tests/optimizers.py

@@ -1,13 +1,12 @@
 from __future__ import print_function
-from keras.utils.test_utils import get_test_data
-from keras.models import Sequential
-from keras.layers.core import Dense, Activation
-from keras.utils.np_utils import to_categorical
-from keras_contrib import optimizers
-import pytest
-import numpy as np
-np.random.seed(1337)
 
+import numpy as np
+from keras.layers.core import Dense, Activation
+from keras.models import Sequential
+from keras.utils.np_utils import to_categorical
+from keras.utils.test_utils import get_test_data
+
+np.random.seed(1337)
 
 (X_train, y_train), (X_test, y_test) = get_test_data(num_train=1000,
                                                      num_test=200,
@@ -27,7 +26,7 @@ def get_model(input_dim, nb_hidden, output_dim):
     return model
 
 
-def _test_optimizer(optimizer, target=0.89):
+def validate_optimizer(optimizer, target=0.89):
     model = get_model(X_train.shape[1], 10, y_train.shape[1])
     model.compile(loss='categorical_crossentropy',
                   optimizer=optimizer,
@@ -37,7 +36,3 @@ def _test_optimizer(optimizer, target=0.89):
     config = optimizer.get_config()
     assert type(config) == dict
     assert history.history['val_acc'][-1] >= target
-
-
-if __name__ == '__main__':
-    pytest.main([__file__])

keras_contrib/tests/regularizers.py

@@ -1,16 +1,11 @@
-from keras.models import Sequential
-from keras.layers import Dense
-from keras.layers import Activation
-from keras.layers import Flatten
-from keras.layers import ActivityRegularization
-from keras.layers import Embedding
-from keras.datasets import mnist
-from keras.utils import np_utils
-from keras_contrib import regularizers
-import pytest
 import numpy as np
-np.random.seed(1337)
+from keras.datasets import mnist
+from keras.layers import Activation
+from keras.layers import Dense
+from keras.models import Sequential
+from keras.utils import np_utils
 
+np.random.seed(1337)
 
 nb_classes = 10
 batch_size = 128
@@ -40,7 +35,7 @@ def get_data():
     return (X_train, Y_train), (X_test, Y_test), test_ids
 
 
-def create_model(weight_reg=None, activity_reg=None):
+def validate_regularizer(weight_reg=None, activity_reg=None):
     model = Sequential()
     model.add(Dense(50, input_shape=(784,)))
     model.add(Activation('relu'))
@@ -48,7 +43,3 @@ def create_model(weight_reg=None, activity_reg=None):
                     activity_regularizer=activity_reg))
     model.add(Activation('softmax'))
     return model
-
-
-if __name__ == '__main__':
-    pytest.main([__file__])

tests/keras_contrib/metrics_test.py

@@ -1,22 +0,0 @@
-import pytest
-import numpy as np
-
-from keras import backend as K
-from keras_contrib import backend as KC
-from keras_contrib import metrics
-
-
-all_metrics = []
-all_sparse_metrics = []
-
-
-def test_metrics():
-    y_a = K.variable(np.random.random((6, 7)))
-    y_b = K.variable(np.random.random((6, 7)))
-    for metric in all_metrics:
-        output = metric(y_a, y_b)
-        assert K.eval(output).shape == ()
-
-
-if __name__ == '__main__':
-    pytest.main([__file__])