def test_insert_conv(self):
cnn1 = cga.CNN.random(input_shape=self.input_shape,
num_classes=10,
search_space=self.search_space_cnn_force_pool)
cnn1.history = self.history1
# force a new conv layer
conv_search_space = cga.ConvolutionalLayer.SearchSpace()
conv_search_space.kernel_search_space = cga.Kernel.SearchSpace(
depths=[64],
resolutions=[(3, 3)],
strides=[(1, 1)],
padding_types=('V', ))
cnn_search_space = cga.CNN.SearchSpace()
cnn_search_space.mutate = cga.StochasticFunction({
lambda cnn: cnn.insert_layer(conv_search_space, cga.ConvolutionalLayer):
1
})
cnn_search_space.dynamic_child_search_spaces[
cga.ConvolutionalLayer] = conv_search_space
cnn2 = cnn1.mutate(cnn_search_space)
self.assertNotEqual(cnn1, cnn2)
self.assertNotEqual(cnn1.topology, cnn2.topology)
self.assertEqual(len(cnn2._layers), 2)
conv_exists = isinstance(cnn2._layers[0],
cga.ConvolutionalLayer) or isinstance(
cnn2._layers[1], cga.ConvolutionalLayer)
self.assertTrue(conv_exists)
self.assertEqual(cnn2.history.parent1, cnn1)
self.assertEqual(cnn2.history.parent1.history, self.history1)
def test_mutate_conv(self):
cnn1 = cga.CNN.random(input_shape=self.input_shape,
num_classes=10,
search_space=self.search_space_cnn_force_conv)
cnn1.history = self.history1
# force the conv to mutate
search_space = cga.CNN.SearchSpace()
search_space.dynamic_child_search_spaces[
cga.
ConvolutionalLayer].kernel_search_space = cga.Kernel.SearchSpace(
depths=[64],
resolutions=[(3, 3)],
strides=[(1, 1)],
padding_types=('V', ))
search_space.mutate = cga.StochasticFunction(
{lambda cnn: cnn.change_layer(search_space): 1})
cnn2 = cnn1.mutate(search_space)
self.assertNotEqual(cnn1, cnn2)
self.assertNotEqual(cnn1.topology, cnn2.topology)
self.assertEqual(len(cnn2._layers), len(cnn2._layers))
self.assertNotEqual(cnn1._layers[0]._kernel.depth,
cnn2._layers[0]._kernel.depth)
self.assertEqual(cnn2._layers[0]._kernel.depth, 64)
self.assertEqual(cnn1.num_classes, cnn2.num_classes)
self.assertEqual(cnn2.history.parent1, cnn1)
self.assertEqual(cnn2.history.parent1.history, self.history1)
def test_mutate_add_layer(self):
layer = cga.SkipLayer([1, 32, 32], [])
# force the add conv mutation
search_space = cga.SkipLayer.SearchSpace()
search_space.mutate = cga.StochasticFunction(
{lambda layer: layer.add_conv(self.conv_search_space): 1})
new_layer = layer.mutate(search_space)
self.assertNotEqual(new_layer, layer)
self.assertNotEqual(len(layer._convs), len(new_layer._convs))
self.assertArrayEqual(new_layer.output_shape, [888, 32, 32])
def test_mutate_remove_layer(self):
layer = cga.SkipLayer.from_string(
'S{C[064(3x3 1x1 S)];C[032(3x3 1x1 S)];C[128(3x3 1x1 S)]}',
input_shape=[1, 32, 32])
# force the remove conv mutation
search_space = cga.SkipLayer.SearchSpace()
search_space.mutate = cga.StochasticFunction(
{lambda layer: layer.remove_conv(): 1})
new_layer = layer.mutate(search_space)
self.assertNotEqual(new_layer, layer)
self.assertEqual(len(new_layer._convs), 2)
def test_mutate_change_layer(self):
layer = cga.SkipLayer.from_string('S{C[064(3x3 1x1 S)]}',
input_shape=[1, 32, 32])
# force the change random conv mutation
search_space = cga.SkipLayer.SearchSpace()
search_space.mutate = cga.StochasticFunction({
lambda layer: layer.mutate_random_conv(self.conv_search_space):
1
})
new_layer = layer.mutate(search_space)
self.assertNotEqual(new_layer, layer)
self.assertEqual(len(layer._convs), len(new_layer._convs))
self.assertNotEqual(layer._convs[0], new_layer._convs[0])
self.assertArrayEqual(new_layer.output_shape, [888, 32, 32])
def test_mutate_pool(self):
cnn1 = cga.CNN.random(input_shape=self.input_shape,
num_classes=10,
search_space=self.search_space_cnn_force_pool)
cnn1.history = self.history1
# force the pool to mutate
search_space = cga.CNN.SearchSpace()
search_space.mutate = cga.StochasticFunction(
{lambda cnn: cnn.change_layer(search_space): 1})
cnn2 = cnn1.mutate(search_space)
self.assertNotEqual(cnn1, cnn2)
self.assertNotEqual(cnn1.topology, cnn2.topology)
self.assertEqual(len(cnn2._layers), len(cnn2._layers))
self.assertNotEqual(cnn1._layers[0]._pooling_type,
cnn2._layers[0]._pooling_type)
self.assertEqual(cnn1.num_classes, cnn2.num_classes)
self.assertEqual(cnn2.history.parent1, cnn1)
self.assertEqual(cnn2.history.parent1.history, self.history1)