def test_tiny_conv_prelu_random(self):
np.random.seed(1988)
# Define a model
from keras.layers.advanced_activations import PReLU
model = Sequential()
model.add(Convolution2D(input_shape = (10, 10, 3),
nb_filter = 3, nb_row = 5, nb_col = 5, border_mode = 'same'))
model.add(PReLU(shared_axes=[1, 2]))
model.set_weights([np.random.rand(*w.shape) for w in model.get_weights()])
# Get the coreml model
self._test_keras_model(model)
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