TensorFlow 1.3.0

python train_lstm.py

Traceback (most recent call last): File "train_lstm.py", line 173, in estimator = ts_estimators.TimeSeriesRegressor( AttributeError: 'module' object has no attribute 'TimeSeriesRegressor'

It seems TimeSeriesRegressor is in the master branch in tensorflow/tensorflow/contrib/timeseries/python/timeseries/estimators.py

but the latest version 1.3.0 has it named _TimeSeriesRegressor

After I rename TimeSeriesRegressor to _TimeSeriesRegressor in train_lstm.py, I got this error

File "train_lstm.py", line 174, in model=_LSTMModel(num_features=1, num_units=128), TypeError: Can't instantiate abstract class _LSTMModel with abstract methods generate

Which version of TF do you use @hzy46 ?

great code thanks may you clarify : will it work for multivariate time series prediction both regression and classification 1 where all values are continues values weight height age target 1 56 160 34 1.2 2 77 170 54 3.5 3 87 167 43 0.7 4 55 198 72 0.5 5 88 176 32 2.3

2 or even will it work for multivariate time series where values are mixture of continues and categorical values for example 2 dimensions have continues values and 3 dimensions are categorical values

color        weight     gender  height  age  target 

1 black 56 m 160 34 yes 2 white 77 f 170 54 no 3 yellow 87 m 167 43 yes 4 white 55 m 198 72 no 5 white 88 f 176 32 yes

Traceback (most recent call last): File "D:/YINGYONG/pycharm/pycharmProfessional/project/venv/Lib/site-packages/tensorflow/contrib/timeseries/python/timeseries/state_management.py", line 23, in from tensorflow.contrib.timeseries.python.timeseries import feature_keys File "D:\YINGYONG\pycharm\pycharmProfessional\project\venv\lib\site-packages\tensorflow\contrib_init_.py", line 98, in from tensorflow.contrib import timeseries File "D:\YINGYONG\pycharm\pycharmProfessional\project\venv\lib\site-packages\tensorflow\contrib\timeseries_init_.py", line 40, in from tensorflow.contrib.timeseries.python.timeseries import * File "D:\YINGYONG\pycharm\pycharmProfessional\project\venv\lib\site-packages\tensorflow\contrib\timeseries\python_init_.py", line 22, in from tensorflow.contrib.timeseries.python.timeseries import * File "D:\YINGYONG\pycharm\pycharmProfessional\project\venv\lib\site-packages\tensorflow\contrib\timeseries\python\timeseries_init_.py", line 24, in from tensorflow.contrib.timeseries.python.timeseries.ar_model import * File "D:\YINGYONG\pycharm\pycharmProfessional\project\venv\lib\site-packages\tensorflow\contrib\timeseries\python\timeseries\ar_model.py", line 24, in from tensorflow.contrib.timeseries.python.timeseries import model File "D:\YINGYONG\pycharm\pycharmProfessional\project\venv\lib\site-packages\tensorflow\contrib\timeseries\python\timeseries\model.py", line 27, in from tensorflow.contrib.timeseries.python.timeseries import estimators as ts_estimators File "D:\YINGYONG\pycharm\pycharmProfessional\project\venv\lib\site-packages\tensorflow\contrib\timeseries\python\timeseries\estimators.py", line 27, in from tensorflow.contrib.timeseries.python.timeseries import state_management File "D:\YINGYONG\pycharm\pycharmProfessional\project\venv\lib\site-packages\tensorflow\contrib\timeseries\python\timeseries\state_management.py", line 25, in from tensorflow.contrib.timeseries.python.timeseries.model import ModelOutputs ImportError: cannot import name 'ModelOutputs'

Process finished with exit code 1

INFO:tensorflow:Using default config. WARNING:tensorflow:Using temporary folder as model directory: /tmp/tmpzXWlhI INFO:tensorflow:Using config: {'_save_checkpoints_secs': 600, '_session_config': None, '_keep_checkpoint_max': 5, '_task_type': 'worker', '_global_id_in_cluster': 0, '_is_chief': True, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7fc8d7933650>, '_evaluation_master': '', '_save_checkpoints_steps': None, '_keep_checkpoint_every_n_hours': 10000, '_service': None, '_num_ps_replicas': 0, '_tf_random_seed': None, '_master': '', '_device_fn': None, '_num_worker_replicas': 1, '_task_id': 0, '_log_step_count_steps': 100, '_model_dir': '/tmp/tmpzXWlhI', '_train_distribute': None, '_save_summary_steps': 100} INFO:tensorflow:Calling model_fn. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Create CheckpointSaverHook. INFO:tensorflow:Graph was finalized. INFO:tensorflow:Running local_init_op. INFO:tensorflow:Done running local_init_op. INFO:tensorflow:Saving checkpoints for 0 into /tmp/tmpzXWlhI/model.ckpt. ERROR:tensorflow:Model diverged with loss = NaN.

NanLossDuringTrainingErrorTraceback (most recent call last) in () 32 optimizer=tf.train.AdamOptimizer(0.001)) 33 ---> 34 estimator.train(input_fn=train_input_fn, steps=200) 35 evaluation_input_fn = tf.contrib.timeseries.WholeDatasetInputFn(reader) 36 evaluation = estimator.evaluate(input_fn=evaluation_input_fn, steps=1)

/usr/local/anaconda2/lib/python2.7/site-packages/tensorflow/python/estimator/estimator.pyc in train(self, input_fn, hooks, steps, max_steps, saving_listeners) 364 365 saving_listeners = _check_listeners_type(saving_listeners) --> 366 loss = self._train_model(input_fn, hooks, saving_listeners) 367 logging.info('Loss for final step: %s.', loss) 368 return self

/usr/local/anaconda2/lib/python2.7/site-packages/tensorflow/python/estimator/estimator.pyc in _train_model(self, input_fn, hooks, saving_listeners) 1117 return self._train_model_distributed(input_fn, hooks, saving_listeners) 1118 else: -> 1119 return self._train_model_default(input_fn, hooks, saving_listeners) 1120 1121 def _train_model_default(self, input_fn, hooks, saving_listeners):

/usr/local/anaconda2/lib/python2.7/site-packages/tensorflow/python/estimator/estimator.pyc in _train_model_default(self, input_fn, hooks, saving_listeners) 1133 return self._train_with_estimator_spec(estimator_spec, worker_hooks, 1134 hooks, global_step_tensor, -> 1135 saving_listeners) 1136 1137 def _train_model_distributed(self, input_fn, hooks, saving_listeners):

/usr/local/anaconda2/lib/python2.7/site-packages/tensorflow/python/estimator/estimator.pyc in _train_with_estimator_spec(self, estimator_spec, worker_hooks, hooks, global_step_tensor, saving_listeners) 1334 loss = None 1335 while not mon_sess.should_stop(): -> 1336 _, loss = mon_sess.run([estimator_spec.train_op, estimator_spec.loss]) 1337 return loss 1338

/usr/local/anaconda2/lib/python2.7/site-packages/tensorflow/python/training/monitored_session.pyc in run(self, fetches, feed_dict, options, run_metadata) 575 feed_dict=feed_dict, 576 options=options, --> 577 run_metadata=run_metadata) 578 579 def run_step_fn(self, step_fn):

/usr/local/anaconda2/lib/python2.7/site-packages/tensorflow/python/training/monitored_session.pyc in run(self, fetches, feed_dict, options, run_metadata) 1051 feed_dict=feed_dict, 1052 options=options, -> 1053 run_metadata=run_metadata) 1054 except _PREEMPTION_ERRORS as e: 1055 logging.info('An error was raised. This may be due to a preemption in '

/usr/local/anaconda2/lib/python2.7/site-packages/tensorflow/python/training/monitored_session.pyc in run(self, *args, **kwargs) 1142 raise six.reraise(*original_exc_info) 1143 else: -> 1144 raise six.reraise(*original_exc_info) 1145 1146

/usr/local/anaconda2/lib/python2.7/site-packages/tensorflow/python/training/monitored_session.pyc in run(self, *args, **kwargs) 1127 def run(self, *args, **kwargs): 1128 try: -> 1129 return self._sess.run(*args, **kwargs) 1130 except _PREEMPTION_ERRORS: 1131 raise

/usr/local/anaconda2/lib/python2.7/site-packages/tensorflow/python/training/monitored_session.pyc in run(self, fetches, feed_dict, options, run_metadata) 1207 results=outputs[hook] if hook in outputs else None, 1208 options=options, -> 1209 run_metadata=run_metadata)) 1210 self._should_stop = self._should_stop or run_context.stop_requested 1211

/usr/local/anaconda2/lib/python2.7/site-packages/tensorflow/python/training/basic_session_run_hooks.pyc in after_run(self, run_context, run_values) 633 if self._fail_on_nan_loss: 634 logging.error(failure_message) --> 635 raise NanLossDuringTrainingError 636 else: 637 logging.warning(failure_message)

NanLossDuringTrainingError: NaN loss during training.

平台：ubuntu 16.04 tensorflow版本： tensorflow-gpu-1.4 报错如下

WARNING:tensorflow:Skipping summary for observed, must be a float, np.float32, np.int64, np.int32 or int. WARNING:tensorflow:Skipping summary for times, must be a float, np.float32, np.int64, np.int32 or int. WARNING:tensorflow:Skipping summary for mean, must be a float, np.float32, np.int64, np.int32 or int. WARNING:tensorflow:Skipping summary for start_tuple, must be a float, np.float32, np.int64, np.int32 or int. WARNING:tensorflow:Input graph does not contain a QueueRunner. That means predict yields forever. This is probably a mistake. 2018-06-09 16:57:18.216300: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1120] Creating TensorFlow device (/device:GPU:0) -> (device: 0, name: GeForce GTX 1060, pci bus id: 0000:01:00.0, compute capability: 6.1) INFO:tensorflow:Restoring parameters from /tmp/tmpwvnv1vj8/model.ckpt-2000 2018-06-09 16:57:18.318936: W tensorflow/core/framework/op_kernel.cc:1192] Out of range: Reached limit of 1 [[Node: limit_epochs_2/CountUpTo = CountUpToT=DT_INT64, _class=["loc:@limit_epochs_2/epochs"], limit=1, _device="/job:localhost/replica:0/task:0/device:CPU:0"]] 2018-06-09 16:57:18.319003: W tensorflow/core/framework/op_kernel.cc:1192] Out of range: Reached limit of 1 [[Node: limit_epochs_2/CountUpTo = CountUpToT=DT_INT64, _class=["loc:@limit_epochs_2/epochs"], limit=1, _device="/job:localhost/replica:0/task:0/device:CPU:0"]] 2018-06-09 16:57:18.319028: W tensorflow/core/framework/op_kernel.cc:1192] Out of range: Reached limit of 1 [[Node: limit_epochs_2/CountUpTo = CountUpToT=DT_INT64, _class=["loc:@limit_epochs_2/epochs"], limit=1, _device="/job:localhost/replica:0/task:0/device:CPU:0"]] 2018-06-09 16:57:18.319078: W tensorflow/core/framework/op_kernel.cc:1192] Out of range: Reached limit of 1 [[Node: limit_epochs_2/CountUpTo = CountUpToT=DT_INT64, _class=["loc:@limit_epochs_2/epochs"], limit=1, _device="/job:localhost/replica:0/task:0/device:CPU:0"]] 2018-06-09 16:57:18.319125: W tensorflow/core/framework/op_kernel.cc:1192] Out of range: Reached limit of 1 [[Node: limit_epochs_2/CountUpTo = CountUpToT=DT_INT64, _class=["loc:@limit_epochs_2/epochs"], limit=1, _device="/job:localhost/replica:0/task:0/device:CPU:0"]] 2018-06-09 16:57:18.319148: W tensorflow/core/framework/op_kernel.cc:1192] Out of range: Reached limit of 1 [[Node: limit_epochs_2/CountUpTo = CountUpToT=DT_INT64, _class=["loc:@limit_epochs_2/epochs"], limit=1, _device="/job:localhost/replica:0/task:0/device:CPU:0"]] 2018-06-09 16:57:18.319168: W tensorflow/core/framework/op_kernel.cc:1192] Out of range: Reached limit of 1 [[Node: limit_epochs_2/CountUpTo = CountUpToT=DT_INT64, _class=["loc:@limit_epochs_2/epochs"], limit=1, _device="/job:localhost/replica:0/task:0/device:CPU:0"]] 2018-06-09 16:57:18.319191: W tensorflow/core/framework/op_kernel.cc:1192] Out of range: Reached limit of 1 [[Node: limit_epochs_2/CountUpTo = CountUpToT=DT_INT64, _class=["loc:@limit_epochs_2/epochs"], limit=1, _device="/job:localhost/replica:0/task:0/device:CPU:0"]] 2018-06-09 16:57:18.319238: W tensorflow/core/framework/op_kernel.cc:1192] Out of range: Reached limit of 1 [[Node: limit_epochs_2/CountUpTo = CountUpToT=DT_INT64, _class=["loc:@limit_epochs_2/epochs"], limit=1, _device="/job:localhost/replica:0/task:0/device:CPU:0"]] 2018-06-09 16:57:18.319260: W tensorflow/core/framework/op_kernel.cc:1192] Out of range: Reached limit of 1 [[Node: limit_epochs_2/CountUpTo = CountUpToT=DT_INT64, _class=["loc:@limit_epochs_2/epochs"], limit=1, _device="/job:localhost/replica:0/task:0/device:CPU:0"]] 2018-06-09 16:57:18.319306: W tensorflow/core/framework/op_kernel.cc:1192] Out of range: Reached limit of 1 [[Node: limit_epochs_2/CountUpTo = CountUpToT=DT_INT64, _class=["loc:@limit_epochs_2/epochs"], limit=1, _device="/job:localhost/replica:0/task:0/device:CPU:0"]] 2018-06-09 16:57:18.319343: W tensorflow/core/framework/op_kernel.cc:1192] Out of range: Reached limit of 1 [[Node: limit_epochs_2/CountUpTo = CountUpToT=DT_INT64, _class=["loc:@limit_epochs_2/epochs"], limit=1, _device="/job:localhost/replica:0/task:0/device:CPU:0"]] 2018-06-09 16:57:18.319377: W tensorflow/core/framework/op_kernel.cc:1192] Out of range: Reached limit of 1 [[Node: limit_epochs_2/CountUpTo = CountUpToT=DT_INT64, _class=["loc:@limit_epochs_2/epochs"], limit=1, _device="/job:localhost/replica:0/task:0/device:CPU:0"]] 2018-06-09 16:57:18.319399: W tensorflow/core/framework/op_kernel.cc:1192] Out of range: Reached limit of 1 [[Node: limit_epochs_2/CountUpTo = CountUpToT=DT_INT64, _class=["loc:@limit_epochs_2/epochs"], limit=1, _device="/job:localhost/replica:0/task:0/device:CPU:0"]] 2018-06-09 16:57:18.319419: W tensorflow/core/framework/op_kernel.cc:1192] Out of range: Reached limit of 1 [[Node: limit_epochs_2/CountUpTo = CountUpToT=DT_INT64, _class=["loc:@limit_epochs_2/epochs"], limit=1, _device="/job:localhost/replica:0/task:0/device:CPU:0"]] 2018-06-09 16:57:18.319441: W tensorflow/core/framework/op_kernel.cc:1192] Out of range: Reached limit of 1 [[Node: limit_epochs_2/CountUpTo = CountUpToT=DT_INT64, _class=["loc:@limit_epochs_2/epochs"], limit=1, _device="/job:localhost/replica:0/task:0/device:CPU:0"]] 2018-06-09 16:57:18.319461: W tensorflow/core/framework/op_kernel.cc:1192] Out of range: Reached limit of 1 [[Node: limit_epochs_2/CountUpTo = CountUpToT=DT_INT64, _class=["loc:@limit_epochs_2/epochs"], limit=1, _device="/job:localhost/replica:0/task:0/device:CPU:0"]] 2018-06-09 16:57:18.319483: W tensorflow/core/framework/op_kernel.cc:1192] Out of range: Reached limit of 1 [[Node: limit_epochs_2/CountUpTo = CountUpToT=DT_INT64, _class=["loc:@limit_epochs_2/epochs"], limit=1, _device="/job:localhost/replica:0/task:0/device:CPU:0"]] 2018-06-09 16:57:18.319502: W tensorflow/core/framework/op_kernel.cc:1192] Out of range: Reached limit of 1 [[Node: limit_epochs_2/CountUpTo = CountUpToT=DT_INT64, _class=["loc:@limit_epochs_2/epochs"], limit=1, _device="/job:localhost/replica:0/task:0/device:CPU:0"]] 2018-06-09 16:57:18.319518: W tensorflow/core/framework/op_kernel.cc:1192] Out of range: Reached limit of 1 [[Node: limit_epochs_2/CountUpTo = CountUpToT=DT_INT64, _class=["loc:@limit_epochs_2/epochs"], limit=1, _device="/job:localhost/replica:0/task:0/device:CPU:0"]] Traceback (most recent call last): File "/home/jarlan/PycharmProjects/time_serier/train_lstm.py", line 199, in plt.savefig('predict_result.jpg') File "/home/jarlan/.local/lib/python3.5/site-packages/matplotlib/pyplot.py", line 710, in savefig res = fig.savefig(*args, **kwargs) File "/home/jarlan/.local/lib/python3.5/site-packages/matplotlib/figure.py", line 2035, in savefig self.canvas.print_figure(fname, **kwargs) File "/home/jarlan/.local/lib/python3.5/site-packages/matplotlib/backend_bases.py", line 2263, in print_figure **kwargs) File "/home/jarlan/.local/lib/python3.5/site-packages/matplotlib/backends/backend_agg.py", line 584, in print_jpg return background.save(filename_or_obj, format='jpeg', **options) File "/usr/lib/python3/dist-packages/PIL/Image.py", line 1675, in save save_handler(self, fp, filename) File "/usr/lib/python3/dist-packages/PIL/JpegImagePlugin.py", line 708, in _save ImageFile._save(im, fp, [("jpeg", (0, 0)+im.size, 0, rawmode)], bufsize) File "/usr/lib/python3/dist-packages/PIL/ImageFile.py", line 480, in _save e = Image._getencoder(im.mode, e, a, im.encoderconfig) File "/usr/lib/python3/dist-packages/PIL/Image.py", line 431, in _getencoder return encoder(mode, *args + extra) TypeError: integer argument expected, got float

Does the prediction fit for time series with an obvious linear trend plus some periodic term? Such as y=x+sin(x/50)+noise; It seems the Least Square (LS) can do much better than the TFTS in this case. I am not sure whether I use the TFTS correctly.