def test_sequential_generation(self):
input_shape = [3, 100, 100]
layer_list = model_generation.generate_random_model(
10, (100, 100), AVAILABLE_MODULES, MAX_N_KERNELS,
MAX_CONV_KERNEL_SIZE, MAX_CONV_STRIDE, MAX_CONV_DILATION,
MAX_CONV_OUT_CHANNELS, MAX_POOLING_KERNEL_SIZE, MAX_POOLING_STRIDE)
sequential = generate_sequential(layer_list,
input_shape=input_shape,
output_size=10)
self.assertEqual(len(sequential), len(layer_list) + 2)
def create_individual(self, data):
individual = generate_random_model(
self.model_n_layers, self.X_train.shape[1:],
self.available_modules, self.max_n_kernels,
self.max_conv_kernel_size, self.max_conv_stride,
self.max_conv_dilation, self.max_conv_out_channels,
self.max_pooling_kernel_size, self.max_pooling_stride)
return generate_sequential(individual,
input_shape=self.X_train.shape[1:],
output_size=len(np.unique(self.y_train)))
def test_gen_population(self):
population = []
for i in range(100):
population.append(
model_generation.generate_random_model(
10, (100, 100), AVAILABLE_MODULES, MAX_N_KERNELS,
MAX_CONV_KERNEL_SIZE, MAX_CONV_STRIDE, MAX_CONV_DILATION,
MAX_CONV_OUT_CHANNELS, MAX_POOLING_KERNEL_SIZE,
MAX_POOLING_STRIDE))
self.assertEqual(len(population[-1]), 10)
self.assertEqual(len(population), 100)
def test_sequential_to_layer_list(self):
input_shape = [3, 100, 100]
layer_list = model_generation.generate_random_model(
30, (100, 100), AVAILABLE_MODULES, MAX_N_KERNELS,
MAX_CONV_KERNEL_SIZE, MAX_CONV_STRIDE, MAX_CONV_DILATION,
MAX_CONV_OUT_CHANNELS, MAX_POOLING_KERNEL_SIZE, MAX_POOLING_STRIDE)
sequential = generate_sequential(layer_list,
input_shape=input_shape,
output_size=10)
new_layer_list = sequential_to_layer_list(sequential)
for new_layer, layer in zip(new_layer_list, layer_list):
self.assertEqual(type(new_layer), type(layer))
def test_score_model(self):
input_shape = [1, 20]
layer_list = model_generation.generate_random_model(
10, input_shape, AVAILABLE_MODULES, MAX_N_KERNELS,
MAX_CONV_KERNEL_SIZE, MAX_CONV_STRIDE, MAX_CONV_DILATION,
MAX_CONV_OUT_CHANNELS, MAX_POOLING_KERNEL_SIZE, MAX_POOLING_STRIDE)
X, y = make_classification(100)
X = X[:, None, :, None]
sequential = generate_sequential(layer_list,
input_shape=input_shape,
output_size=10)
print(
get_model_score(sequential, X[:50], y[:50], X[50:], y[50:], 32, 1))
def test_simple_model_gen(self):
model = model_generation.generate_random_model(
10, (100, 100), AVAILABLE_MODULES, MAX_N_KERNELS,
MAX_CONV_KERNEL_SIZE, MAX_CONV_STRIDE, MAX_CONV_DILATION,
MAX_CONV_OUT_CHANNELS, MAX_POOLING_KERNEL_SIZE, MAX_POOLING_STRIDE)
self.assertEqual(len(model), 10)