Question

我正在尝试使用tensorflow LSTM进行时间序列预测。我正在使用repo

中 lstm-for-epf.py 的修改版本

import numpy as np
import pandas as pd
from matplotlib import pyplot as plt

from tensorflow.contrib import learn
from sklearn.metrics import mean_squared_error, mean_absolute_error
from lstm_predictor import generate_data, load_csvdata, lstm_model


LOG_DIR = './ops_logs'
TIMESTEPS = 10
RNN_LAYERS = [{'steps': TIMESTEPS}]
DENSE_LAYERS = [10, 10]
TRAINING_STEPS = 100000
BATCH_SIZE = 100
PRINT_STEPS = TRAINING_STEPS / 100

dateparse = lambda dates: pd.datetime.strptime(dates, '%d/%m/%Y %H:%M')
rawdata = pd.read_csv("RealMarketPriceDataPT.csv", 
                   parse_dates={'timeline': ['date', '(UTC)']}, 
                   index_col='timeline', date_parser=dateparse)


X, y = load_csvdata(rawdata, TIMESTEPS, seperate=False)


regressor = learn.TensorFlowEstimator(model_fn=lstm_model(TIMESTEPS, RNN_LAYERS, DENSE_LAYERS), 
                                      n_classes=0,
                                      verbose=1,  
                                      steps=TRAINING_STEPS, 
                                      optimizer='Adagrad',
                                      learning_rate=0.03,
                                      batch_size=BATCH_SIZE )




validation_monitor = learn.monitors.ValidationMonitor(X['val'], y['val'],
                                                      every_n_steps=PRINT_STEPS,
                                                      early_stopping_rounds=1000,
                                                      batch_size=BATCH_SIZE )

regressor.fit(X['train'], y['train'], monitors=[validation_monitor], logdir=LOG_DIR)


predicted = regressor.predict(X['test'])
mse = mean_absolute_error(y['test'], predicted)
print ("Error: %f" % mse)

# plot_predicted, = plt.plot(predicted, label='predicted')
# plot_test, = plt.plot(y['test'], label='test')
# plt.legend(handles=[plot_predicted, plot_test])

它给出了错误。

Traceback (most recent call last):
  File "lstm-for-epf.py", line 43, in <module>
    regressor.fit(X['train'], y['train'], monitors=[validation_monitor], logdir=LOG_DIR)
  File "/home/tensorflow/anaconda3/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/estimators/base.py", line 166, in fit
    monitors=monitors)
  File "/home/tensorflow/anaconda3/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py", line 578, in _train_model
    max_steps=max_steps)
  File "/home/tensorflow/anaconda3/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/graph_actions.py", line 280, in _supervised_train
    None)
  File "/home/tensorflow/anaconda3/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/supervised_session.py", line 270, in run
    run_metadata=run_metadata)
  File "/home/tensorflow/anaconda3/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/recoverable_session.py", line 54, in run
    run_metadata=run_metadata)
  File "/home/tensorflow/anaconda3/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/coordinated_session.py", line 70, in run
    self._coord.join(self._coordinated_threads_to_join)
  File "/home/tensorflow/anaconda3/lib/python3.5/site-packages/tensorflow/python/training/coordinator.py", line 357, in join
    six.reraise(*self._exc_info_to_raise)
  File "/home/tensorflow/anaconda3/lib/python3.5/site-packages/six.py", line 686, in reraise
    raise value
  File "/home/tensorflow/anaconda3/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/coordinated_session.py", line 66, in run
    return self._sess.run(*args, **kwargs)
  File "/home/tensorflow/anaconda3/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/monitored_session.py", line 107, in run
    induce_stop = monitor.step_end(monitors_step, monitor_outputs)
  File "/home/tensorflow/anaconda3/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/monitors.py", line 396, in step_end
    return self.every_n_step_end(step, output)
  File "/home/tensorflow/anaconda3/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/monitors.py", line 687, in every_n_step_end
    steps=self.eval_steps, metrics=self.metrics, name=self.name)
TypeError: evaluate() got an unexpected keyword argument 'batch_size'

Answer 1

您是否尝试将“batch_size = BATCH_SIZE”移动到适合调用中，例如：regressor.fit（... batch_size = BATCH_SIZE ...）

具有类似代码的来源： https://github.com/tgjeon/TensorFlow-Tutorials-for-Time-Series/blob/master/lstm-for-sine-wave.ipynb

tensorflow lstm模型用于时间序列

1 个答案: