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@ -3,6 +3,8 @@
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4
main.py
4
main.py
@ -164,11 +164,11 @@ def main(config):
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if config.do_train:
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train_X, valid_X, train_Y, valid_Y = data_gainer.get_train_and_valid_data()
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model = train(config, train_X, train_Y, valid_X, valid_Y)
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train(config, train_X, train_Y, valid_X, valid_Y)
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if config.do_predict:
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test_X, test_Y = data_gainer.get_test_data(return_label_data=True)
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pred_result = predict(config, test_X, model)
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pred_result = predict(config, test_X)
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draw(config, data_gainer, pred_result)
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@ -76,19 +76,18 @@ def train(config, train_X, train_Y, valid_X, valid_Y):
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if bad_epoch >= config.patience:
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print(" The training stops early in epoch {}".format(epoch))
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break
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return model
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def predict(config, test_X, model):
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def predict(config, test_X):
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config.dropout_rate = 0.1
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#with tf.variable_scope("stock_predict", reuse=tf.AUTO_REUSE):
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#model = Model(config)
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with tf.variable_scope("stock_predict", reuse=tf.AUTO_REUSE):
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model = Model(config)
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test_len = len(test_X)
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with tf.Session() as sess:
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#module_file = tf.train.latest_checkpoint(config.model_save_path)
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#model.saver.restore(sess, module_file)
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module_file = tf.train.latest_checkpoint(config.model_save_path + config.model_name)
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model.saver.restore(sess, module_file)
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result = np.zeros((test_len * config.time_step, config.output_size))
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for step in range(test_len):
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