def main():
# dataLoader is created outside, because for now, none of the
# hyperparameters we search over below affects the reading of the data.
# Reading data is incredibly slow, so we want to do it only once over
# all models, if the data will be the same over all models.
# If we ever do change things like LSTM size, which affect reading in
# the data, that should be the outermost loop, so that we have to
# read in data as few times as possible
pool = Pool()
default_config = Config()
dataLoader = reddit_data.DataLoader(default_config)
with open('results.txt', 'a+') as f:
hidden_sizes = [10, 20, 30, 50]
learning_rates = [0.0001, 0.0005, 0.001, 0.005, 0.01]
annealing = [1.0, 1.2, 1.5]
l2_regs = [0.001, 0.01, 0.1, 1.0]
for hidden_size in hidden_sizes:
for learning_rate in learning_rates:
for anneal_by in annealing:
for l2_reg in l2_regs:
config = Config()
config.hidden_size = hidden_size
config.learning_rate = learning_rate
config.anneal_by = anneal_by
config.l2_reg = l2_reg
with tf.Graph().as_default():
# baselineModel = BaselineModel(config, dataLoader)
# best_val_pp = train_baseline_model(baselineModel)
result = pool.apply_async(train_baseline_thread, [config, dataLoader])
best_val_acc = result.get()
f.write(str(config) + '\n')
f.write('Best validation accuracy: ' + str(best_val_acc) + '\n')
f.write('=-=' * 5 + '\n')
f.write('=-=' * 5 + '\n')
def get_config(args):
config = Config()
if args.hiddensize:
config.hidden_size = args.hiddensize
if args.lr:
config.learning_rate = args.lr
if args.annealby:
config.anneal_by = args.annealby
if args.l2reg:
config.l2_reg = args.l2reg
if args.lstmsize:
config.lstm_size = args.lstmsize
if args.epochs:
config.max_epochs = args.epochs
if args.testfile:
config.test_file = args.testfile
return config
