def main():
data_directory = os.path.dirname(os.path.realpath(__file__))
# should load the data
p1b1_runner.run(data_directory, "2")
# data should now be loaded
assert p1b1_runner.X_train is not None
assert p1b1_runner.X_test is not None
p1b1_runner.run(data_directory, "2")
def main():
hyper_parameter_map = {'epochs': 2}
hyper_parameter_map['framework'] = 'keras'
hyper_parameter_map['model_name'] = 'p1b1'
hyper_parameter_map['timeout'] = 3600
hyper_parameter_map['save'] = './p1b1_output'
p1b1_runner.run(hyper_parameter_map, "val_corr")
hyper_parameter_map = {'epochs': 2}
hyper_parameter_map['framework'] = 'keras'
hyper_parameter_map['model_name'] = 'p1b1'
hyper_parameter_map['save'] = './p1b1_output'
p1b1_runner.run(hyper_parameter_map, "val_loss")
def main():
hyper_parameter_map = {'epochs': 1}
hyper_parameter_map['batch_size'] = 40
hyper_parameter_map['dense'] = [1900, 500]
hyper_parameter_map['framework'] = 'keras'
hyper_parameter_map['save'] = './p1bl1_output'
validation_loss = p1b1_runner.run(hyper_parameter_map)
print("Validation Loss: ", validation_loss)
#List of hyperparameters - edit this to add or remove a parameter
epochs, batch_size, d1, d2, ld, lr = parameterString.split(',')
hyper_parameter_map = {'epochs': int(epochs)}
hyper_parameter_map['framework'] = 'keras'
hyper_parameter_map['batch_size'] = int(batch_size)
hyper_parameter_map['dense'] = [int(d1), int(d2)]
hyper_parameter_map['latent_dim'] = int(ld)
hyper_parameter_map['learning_rate'] = float(lr)
hyper_parameter_map['run_id'] = parameterString
# hyper_parameter_map['instance_directory'] = os.environ['TURBINE_OUTPUT']
hyper_parameter_map['save'] = os.environ[
'TURBINE_OUTPUT'] + "/output-" + os.environ['PMI_RANK']
sys.argv = ['p1b1_runner']
val_loss = p1b1_runner.run(hyper_parameter_map)
print(val_loss)
sfn = os.environ['TURBINE_OUTPUT'] + "/output-" + os.environ[
'PMI_RANK'] + "/procname-" + parameterString
with open(sfn, 'w') as sfile:
sfile.write(socket.getfqdn())
proc_id = "-" + str(os.getpid())
sfile.write(proc_id)
# works around this error:
# https://github.com/tensorflow/tensorflow/issues/3388
from keras import backend as K
K.clear_session()