def train_once(self, iteration, presets=None, masks=None):
tf.reset_default_graph()
sess = tf.Session()
dataset = dataset_mnist.ConstructedDatasetMnist(
train_len=self.train_len)
input_tensor, label_tensor = dataset.placeholders
hyperparameters = {
'layers': [(300, tf.nn.relu), (100, tf.nn.relu), (10, None)]
}
model = model_fc.ModelFc(hyperparameters,
input_tensor,
label_tensor,
presets=presets,
masks=masks)
params = {
'test_interval': 100,
'save_summaries': True,
'save_network': True,
}
return trainer.train(sess,
dataset,
model,
functools.partial(
tf.train.GradientDescentOptimizer, .1),
('iterations', 50000),
output_dir=paths.run(self.output_dir, iteration),
**params)
def train_model(sess, level, dataset, model):
params = {
'test_interval': 100,
'save_summaries': True,
'save_network': True,
}
return trainer_wgan.train(sess,
dataset,
model,
constants.OPTIMIZER_FN,
training_len,
output_dir=paths.run(output_dir, level,
experiment_name),
**params)
def run(trial_name, level, experiment_name='same_init', run_id=''): return paths.run(trial(trial_name), level, experiment_name, run_id)
print(text.format(*self.counters[text][trial]))
avg_printer = AveragePrinter(len(sys.argv) > 2 and sys.argv[2] == '-a')
exp_path = paths.experiment(constants.EXPERIMENT_PATH, sys.argv[1])
trial_nums = [
int(re.findall('\d+', trial_dir)[0]) for trial_dir in os.listdir(exp_path)
]
print("Found {} trials".format(max(trial_nums)))
for trial in range(1, max(trial_nums) + 1):
trial_path = paths.trial(exp_path, trial)
if not os.path.isdir(trial_path):
print("Warning: skipping trial {}, does not exist".format(trial))
continue
first_run_path = paths.run(trial_path, 0)
first_run_train_acc = float(
subprocess.check_output(
['tail', '-n', '1',
paths.log(first_run_path, 'train')]).strip().split(',')[-1])
avg_printer.do_print(trial, '\tFirst run train acc: {}',
[first_run_train_acc])
first_run_test_acc = float(
subprocess.check_output(
['tail', '-n', '1',
paths.log(first_run_path, 'test')]).strip().split(',')[-1])
avg_printer.do_print(trial, '\tFirst run test acc: {}',
[first_run_test_acc])
runs = map(int, os.listdir(trial_path))
second_last_run = sorted(runs)[-2] if len(runs) > 1 else runs[0]