def test_dmlrunner_same_train_job_with_split_1( \
runner, mnist_filepath):
split_job = make_split_job(mnist_filepath)
split_job.hyperparams['split'] = 1
job_results = runner.run_job(split_job)
session_filepath = job_results.results['session_filepath']
datapoint_count = job_results.results['datapoint_count']
initialize_job = make_initialize_job()
initial_weights = runner.run_job(initialize_job).results['weights']
train_job = make_train_job(
initial_weights,
session_filepath,
datapoint_count
)
result = runner.run_job(train_job)
assert result.status == 'successful'
results = result.results
new_weights = results['weights']
omega = results['omega']
train_stats = results['train_stats']
assert result.job.job_type is JobTypes.JOB_TRAIN.name
assert type(new_weights) == list
assert type(new_weights[0]) == np.ndarray
assert type(omega) == int or type(omega) == float
assert type(train_stats) == dict
def train_dmlresult_obj(runner, split_dmlresult_obj, init_dmlresult_obj,
small_filepath):
initial_weights = init_dmlresult_obj.results['weights']
session_filepath = split_dmlresult_obj.results['session_filepath']
datapoint_count = split_dmlresult_obj.results['datapoint_count']
train_job = make_train_job(initial_weights, session_filepath,
datapoint_count)
result = runner.run_job(train_job)
return result
def train_dmlresult_obj(config_manager, split_dmlresult_obj,
init_dmlresult_obj):
runner = DMLRunner(config_manager)
initial_weights = init_dmlresult_obj.results['weights']
session_filepath = split_dmlresult_obj.results['session_filepath']
datapoint_count = split_dmlresult_obj.results['datapoint_count']
train_job = make_train_job(make_model_json(), initial_weights,
make_hyperparams(split=1), session_filepath,
datapoint_count)
result = runner.run_job(train_job)
return result
def test_dmlrunner_same_train_job_with_split_1( \
config_manager, mnist_filepath):
model_json = make_model_json()
hyperparams = make_hyperparams(split=1)
runner = DMLRunner(config_manager)
initialize_job = make_initialize_job(model_json)
initial_weights = runner.run_job(initialize_job).results['weights']
split_job = make_split_job(model_json, mnist_filepath)
job_results = runner.run_job(split_job)
session_filepath = job_results.results['session_filepath']
datapoint_count = job_results.results['datapoint_count']
train_job = make_train_job(model_json, initial_weights, hyperparams,
session_filepath, datapoint_count)
result = runner.run_job(train_job)
assert result.status == 'successful'
results = result.results
new_weights = results['weights']
omega = results['omega']
train_stats = results['train_stats']
assert result.job.job_type is JobTypes.JOB_TRAIN.name
assert type(new_weights) == list
assert type(new_weights[0]) == np.ndarray
assert type(omega) == int or type(omega) == float
assert type(train_stats) == dict