CompositionalLanguageModel/trainV1.py at master · leedtan/CompositionalLanguageModel · GitHub

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"""
Script of training model V1
"""

import modelV1_tf as m1
import argparse
import pickle


if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument("--trainFile", default='./')
    parser.add_argument("--testFile", default='./')
    parser.add_argument("--mapFile", default='./')
    parser.add_argument("--outputPath")
    parser.add_argument("--batchSize", type = int, default = 32)
    parser.add_argument("--nIter", type=int, default=100000)
    parser.add_argument("--seed", type=int, default=100)
    parser.add_argument("--testIter", type=int, default=500)
    parser.add_argument("--flgSave", action='store_true')

    parser.add_argument("--hidden_filters", type=int, default=128)
    parser.add_argument("--hidden_filters_subprogram", type=int, default=128)
    parser.add_argument("--num_layers_encoder", type=int, default=2)
    parser.add_argument("--num_layers_subprogram", type=int, default=2)
    parser.add_argument("--size_emb", type=int, default=64)
    parser.add_argument("--init_mag", type=float, default=1e-3)
    parser.add_argument("--l2_lambda", type=float, default=1e-3)
    parser.add_argument("--lrInit", type=float, default=0.1)

    args = parser.parse_args()

    max_cmd_len = 10
    max_actions_per_subprogram = 9
    max_num_subprograms = 7
    num_cmd = 14
    num_act = 9

    train_paras = {'batchSize': args.batchSize, 'nIter': args.nIter, 'seed': args.seed, 'testIter': args.testIter,
                   'flgSave': args.flgSave, 'savePath': args.outputPath, 'lrInit': args.lrInit}
    model_paras = {'hidden_filters': args.hidden_filters, 'num_layers_encoder': args.num_layers_encoder,
                   'size_emb': args.size_emb, 'num_cmd': num_cmd, 'num_act': num_act, 'init_mag': args.init_mag,
                   'max_cmd_len': max_cmd_len, 'max_num_subprograms': max_num_subprograms,
                   'max_actions_per_subprogram': max_actions_per_subprogram, 'l2_lambda': args.l2_lambda,
                   'hidden_filters_subprogram': args.hidden_filters_subprogram, 'num_layers_subprogram': args.num_layers_subprogram, }

    print("Loading Data")
    command_map, _, action_map, _ = pickle.load(open(args.mapFile, 'rb'))

    trainset = m1.DataSet(args.trainFile, command_map, action_map, max_cmd_len, max_actions_per_subprogram, max_num_subprograms, delimiter = ':::', seed=100)
    testset = m1.DataSet(args.testFile, command_map, action_map, max_cmd_len, max_actions_per_subprogram, max_num_subprograms, delimiter = ':::', seed=100)
    print('Length of training set: ', trainset._dataSize)
    print('Length of test set: ', testset._dataSize)

    print('Model parameters: ', model_paras)
    model = m1.m1(model_paras)

    print('Training parameters: ', train_paras)
    trainModel = m1.trainModel(model, train_paras, trainset, testset)
    modelResult, lsTrainAcc, lsTestAcc = trainModel.run()
    pickle.dump([lsTrainAcc, lsTestAcc], open(args.outputPath + 'acc.p', 'wb'))

    print('Best test accuracy: ', max(lsTestAcc))