-
Download stock training data from Kaggle
-
Divide the file into
test.csv
,train.csv
files and put them outside this folder -
Download
GloVe
and set corresponding path in the flag of this program. -
In your terminal, type
python main.py --FLAGNAME FLAGVALUE
All the flags can be found at the beginning of main.py
:
flags.DEFINE_string("model", 'hlstm', "which deep learning model to use [hlstm]") # only hlstm currently
flags.DEFINE_integer("batch_size", 32, "training batch size [32]")
flags.DEFINE_integer("iterations", 400, "number of iterations totally [400]")
flags.DEFINE_float("learning_lr", 0.001, "init learning rate [0.01]")
flags.DEFINE_boolean("reload_word_emb", False, "reload wordembedding from GloVe or use saved one [False]")
flags.DEFINE_string("word_emb_path", "../glove.6B/glove.6B.50d.txt", "GloVe source file location")
flags.DEFINE_integer("word_emb_dim", 50, "word embedding vectors dimension [50]")
flags.DEFINE_integer("emb_dim", 50, "embedding dimension for LSTM layers [50]")
flags.DEFINE_string("train_data_path", "../train.csv", "training data set path [../train.csv]")
flags.DEFINE_string("test_data_path", "../test.csv", "testing data set path [../test.csv]")
flags.DEFINE_string("checkpoint_dir", "../checkpoints", "checkpoint directory [../checkpoints]")
flags.DEFINE_integer("class_cnt", 2, "number of classes in the dataset [2]")
flags.DEFINE_boolean("debug", False, "debug mode [False]")
flags.DEFINE_boolean("show", True, "show learning progress [True]"
- Python3
- TensorFlow
- pip installable packages:
numpy
,pandas
,progress