<filename>Epoch1-BasicSeq2Seq/config/config.py<gh_stars>10-100
import torch
# Define hyper parameter
use_cuda = True if torch.cuda.is_available() else False

# for dataset
dataset_path = 'dataset/Google-10000-English.txt'

# for training
num_epochs = 10
batch_size = 128
learning_rate = 1e-3

# for model
encoder_embedding_size = 256
encoder_output_size = 256
decoder_hidden_size = encoder_output_size
teacher_forcing_ratio = .5
# max_length = 20

# for logging
checkpoint_name = 'auto_encoder.pt'
<filename>Epoch1-BasicSeq2Seq/config/config.py<gh_stars>10-100
import torch
# Define hyper parameter
use_cuda = True if torch.cuda.is_available() else False

# for dataset
dataset_path = 'dataset/Google-10000-English.txt'

# for training
num_epochs = 10
batch_size = 128
learning_rate = 1e-3

# for model
encoder_embedding_size = 256
encoder_output_size = 256
decoder_hidden_size = encoder_output_size
teacher_forcing_ratio = .5
# max_length = 20

# for logging
checkpoint_name = 'auto_encoder.pt'
# Define hyper parameter # for dataset # for training # for model # max_length = 20 # for logging