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Newnius 2020-04-29 18:50:53 +08:00
parent dca3c383ea
commit 30ea099b2b
3 changed files with 34 additions and 34 deletions
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53
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4
main.py
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@ -164,11 +164,11 @@ def main(config):
if config.do_train: if config.do_train:
train_X, valid_X, train_Y, valid_Y = data_gainer.get_train_and_valid_data() train_X, valid_X, train_Y, valid_Y = data_gainer.get_train_and_valid_data()
model = train(config, train_X, train_Y, valid_X, valid_Y) train(config, train_X, train_Y, valid_X, valid_Y)
if config.do_predict: if config.do_predict:
test_X, test_Y = data_gainer.get_test_data(return_label_data=True) test_X, test_Y = data_gainer.get_test_data(return_label_data=True)
pred_result = predict(config, test_X, model) pred_result = predict(config, test_X)
draw(config, data_gainer, pred_result) draw(config, data_gainer, pred_result)

11
model/model_tensorflow.py
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@ -76,19 +76,18 @@ def train(config, train_X, train_Y, valid_X, valid_Y):
if bad_epoch >= config.patience: if bad_epoch >= config.patience:
print(" The training stops early in epoch {}".format(epoch)) print(" The training stops early in epoch {}".format(epoch))
break break
return model
def predict(config, test_X, model): def predict(config, test_X):
config.dropout_rate = 0.1 config.dropout_rate = 0.1
#with tf.variable_scope("stock_predict", reuse=tf.AUTO_REUSE): with tf.variable_scope("stock_predict", reuse=tf.AUTO_REUSE):
#model = Model(config) model = Model(config)
test_len = len(test_X) test_len = len(test_X)
with tf.Session() as sess: with tf.Session() as sess:
#module_file = tf.train.latest_checkpoint(config.model_save_path) module_file = tf.train.latest_checkpoint(config.model_save_path + config.model_name)
#model.saver.restore(sess, module_file) model.saver.restore(sess, module_file)
result = np.zeros((test_len * config.time_step, config.output_size)) result = np.zeros((test_len * config.time_step, config.output_size))
for step in range(test_len): for step in range(test_len):