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我正在嘗試使用tensorflow LSTMs進行時間序列預測。我使用的lstm-for-epf.py修改後的版本從repo時間序列的tensorflow lstm模型
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])
這是給錯誤。
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'