import argparse
import numpy as np
def config_poem_train(args=''):
parser = argparse.ArgumentParser()
# Data and vocabulary file
# parser.add_argument('--data_file', type=str,
# default='../data/poem/poems_space.txt',
# help='data file')
parser.add_argument('--data_path', type=str,
default='./datasets/yangsaisai-poetrydatasets/',
help='data path')
parser.add_argument('--encoding', type=str,
default='utf-8',
help='the encoding of the data file.')
# Parameters for saving models.
parser.add_argument('--output_dir', type=str, default='output_model',
help=('directory to store final and'
' intermediate results and models.'))
# Parameters for using saved best models.
parser.add_argument('--init_dir', type=str, default='',
help='continue from the outputs in the given directory')
# Parameters to configure the neural network.
parser.add_argument('--hidden_size', type=int, default=128,#128,
help='size of RNN hidden state vector')
parser.add_argument('--embedding_size', type=int, default=128,#0,
help='size of character embeddings, 0 for one-hot')
parser.add_argument('--num_layers', type=int, default=2,
help='number of layers in the RNN')
parser.add_argument('--num_unrollings', type=int, default=64,#10,
help='number of unrolling steps.')
parser.add_argument('--cell_type', type=str, default='lstm',
help='which model to use (rnn, lstm or gru).')
# Parameters to control the training.
parser.add_argument('--num_epochs', type=int, default=15,
help='number of epochs')
parser.add_argument('--batch_size', type=int, default=16,
help='minibatch size')
parser.add_argument('--train_frac', type=float, default=0.9,
help='fraction of data used for training.')
parser.add_argument('--valid_frac', type=float, default=0.05,
help='fraction of data used for validation.')
# test_frac is computed as (1 - train_frac - valid_frac).
parser.add_argument('--dropout', type=float, default=0.0,
help='dropout rate, default to 0 (no dropout).')
parser.add_argument('--input_dropout', type=float, default=0.0,
help=('dropout rate on input layer, default to 0 (no dropout),'
'and no dropout if using one-hot representation.'))
# Parameters for gradient descent.
parser.add_argument('--max_grad_norm', type=float, default=5.,
help='clip global grad norm')
parser.add_argument('--learning_rate', type=float, default=5e-3,
help='initial learning rate')
# Parameters for logging.
parser.add_argument('--progress_freq', type=int, default=100,
help=('frequency for progress report in training and evalution.'))
parser.add_argument('--verbose', type=int, default=0,
help=('whether to show progress report in training and evalution.'))
# Parameters to feed in the initial model and current best model.
parser.add_argument('--init_model', type=str,
default='', help=('initial model'))
parser.add_argument('--best_model', type=str,
default='', help=('current best model'))
parser.add_argument('--best_valid_ppl', type=float,
default=np.Inf, help=('current valid perplexity'))
# # Parameters for using saved best models.
# parser.add_argument('--model_dir', type=str, default='',
# help='continue from the outputs in the given directory')
# Parameters for debugging.
parser.add_argument('--debug', dest='debug', action='store_true',
help='show debug information')
parser.set_defaults(debug=False)
# Parameters for unittesting the implementation.
parser.add_argument('--test', dest='test', action='store_true',
help=('use the first 1000 character to as data to test the implementation'))
parser.set_defaults(test=False)
# input_args = '--data_path ./data/poem --output_dir output_poem --hidden_size 256 --embedding_size 128 --num_unrollings 128 --debug --encoding utf-8'
args = parser.parse_args(args.split())
return args
def config_sample(args=''):
parser = argparse.ArgumentParser()
# hyper-parameters for using saved best models.
# 学习日志和结果相关的超参数
logging_args = parser.add_argument_group('Logging_Options')
logging_args.add_argument('--model_dir', type=str,
default='demo_model/',
help='continue from the outputs in the given directory')
logging_args.add_argument('--data_dir', type=str,
default='./datasets/yangsaisai-poetrydatasets',
help='data file path')
logging_args.add_argument('--best_model', type=str,
default='', help=('current best model'))
# hyper-parameters for sampling.
# 设置sampling相关的超参数
testing_args = parser.add_argument_group('Sampling Options')
testing_args.add_argument('--max_prob', dest='max_prob', action='store_true',
help='always pick the most probable next character in sampling')
testing_args.set_defaults(max_prob=False)
testing_args.add_argument('--start_text', type=str,
default='The meaning of life is ',
help='the text to start with')
testing_args.add_argument('--length', type=int,
default=100,
help='length of sampled sequence')
testing_args.add_argument('--seed', type=int,
default=-1,
help=('seed for sampling to replicate results, '
'an integer between 0 and 4294967295.'))
args = parser.parse_args(args.split())
return args