class TestBaseConvolution(unittest.TestCase):
def setUp(self):
self.model = KerasGraphModel()
# self.graph = Graph()
def test_base_convolution(self):
self.model.graph.add_input(name='input', input_shape=(3,100,100))
output_name = self.model.base_convolution(input_name='input',
nb_filters=4,
layer_nb=2,
conv_nb=1)
self.assertEqual(output_name, 'relu2_1')
def setUp(self):
self.model = KerasGraphModel()
from pathlib import Path
from data_preparation.image_preparation import ImageLoader
from keras_models import KerasGraphModel
im_files = list(Path('data/train_photos').glob('*[0-9].jpg'))
# No validation data for now
image_loader = ImageLoader()
train_im_func = image_loader.graph_train_generator(im_files, batch_size=100)
# test_images = next(train_im_func)
model = KerasGraphModel()
#model.build_residual_network(nb_blocks=[1, 2, 2, 2, 2], initial_nb_filters=4)
model.load_graph('18_layer_4_filters')
# Fit on 30 mini-batches of 200 samples for 3 epoch
h = model.graph.fit_generator(train_im_func, 200*20, 3)
# test_data, test_photo_ids = next(test_data_generator)
# test_labels = model.graph.predict(test_data)
model.save_model(model.graph, model_stem='18_layer_4_filters', overwrite=True)
test_data_generator = image_loader.test_image_generator(im_files, batch_size=2000)
test = model.generate_submission(test_data_generator)
