def sample(self):
climbs, _ = self.preprocess()
discriminator_path = local_file_path(__file__, "discriminator.h5")
generator_path = local_file_path(__file__, "generator.h5")
gan = GAN(climbs,
discriminator_path,
generator_path,
node_scale_factor=8)
gan.load_models()
images = gan.sample_image()
print(images)
def train(self):
climbs, _ = self.preprocess()
discriminator_path = local_file_path(__file__, "discriminator.h5")
generator_path = local_file_path(__file__, "generator.h5")
gan = GAN(climbs,
discriminator_path,
generator_path,
node_scale_factor=8)
gan.train(epochs=5000, batch_size=32, sample_interval=200)
def main(year):
# Load generated climbsets
file_data = pickle.load(
open(local_file_path(__file__, year + ".pickle"), "rb"))
# Split training and test data
original_climbs = file_data["original"].climbs
original_grades = np.asarray(
[i.grade.grade_number for i in original_climbs])
x_train, x_test, y_train, _ = Configuration.split_function(
original_climbs, original_grades)
# Only keep a sample of the test set for original climbs
file_data["original"].climbs = x_test[:500]
# Train the models
trained_models = map(train_model, model_setups((x_train, y_train)))
# Format the data for the website
data = website_json_structure(file_data)
# Add grades to the website data
for config in trained_models:
add_grades(data["original"], config, file_data["original"].climbs)
add_grades(data["lstm"], config, file_data["lstm"].climbs)
# Save to file
repo_base_directory = Path(__file__).resolve().parent.parent.parent
website_climb_directory = repo_base_directory.joinpath(
"website-moon", "moon", "climbs")
with open(website_climb_directory.joinpath(f"{year}.js"), "w") as handle:
handle.write(f"var climbs_{year} = " + json.dumps(data))
def main(year):
num_climbs = 500
# Load climbset
climbset = load_climbset(year)
# Sample generators
lstm = keras_lstm_gen.Model()
sampling_parameters = params.Parameters()
sample = lstm.sample(climbset, num_climbs, sampling_parameters)
# Save to file
file_data = {"original": climbset, "lstm": sample}
print(f"Saving {len(file_data)} climbsets")
pickle.dump(file_data,
open(local_file_path(__file__, f"{year}.pickle"), "wb"))
def load_sample(self):
return pickle.load(
open(local_file_path(__file__, "sample.pickle"), "rb"))