UnicodeDecodeError in ch14_part2.ipynb

[niwak01]

I met an error executing following code.
I would like to know how to solve the problem.

In[15]:
###########################
EPOCHS = 1000
BATCH_SIZE = 8
total_steps = EPOCHS * int(np.ceil(len(df_train) / BATCH_SIZE))
print('Training Steps: ', total_steps)

regressor.train(input_fn = lambda: train_input_fn(df_train_norm, batch_size = BATCH_SIZE),
steps = total_steps)

result:
###########################
Training Steps: 40000
WARNING:tensorflow:From C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\training\training_util.py:396: Variable.initialized_value (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version.
Instructions for updating:
Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts.
INFO:tensorflow:Calling model_fn.
WARNING:tensorflow:From C:\ProgramData\Anaconda3\lib\site-packages\keras\optimizers\optimizer_v2\adagrad.py:90: calling Constant.init (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Call initializer instance with the dtype argument instead of passing it to the constructor
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:Calling checkpoint listeners before saving checkpoint 0...
INFO:tensorflow:Saving checkpoints for 0 into models/autompg-dnnregressor/model.ckpt.

UnicodeDecodeError Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_19944/1793407232.py in
6 print('Training Steps: ', total_steps)
7
----> 8 regressor.train(input_fn = lambda: train_input_fn(df_train_norm, batch_size = BATCH_SIZE),
9 steps = total_steps)

C:\ProgramData\Anaconda3\lib\site-packages\tensorflow_estimator\python\estimator\estimator.py in train(self, input_fn, hooks, steps, max_steps, saving_listeners)
358
359 saving_listeners = _check_listeners_type(saving_listeners)
--> 360 loss = self._train_model(input_fn, hooks, saving_listeners)
361 logging.info('Loss for final step: %s.', loss)
362 return self

C:\ProgramData\Anaconda3\lib\site-packages\tensorflow_estimator\python\estimator\estimator.py in _train_model(self, input_fn, hooks, saving_listeners)
1184 return self._train_model_distributed(input_fn, hooks, saving_listeners)
1185 else:
-> 1186 return self._train_model_default(input_fn, hooks, saving_listeners)
1187
1188 def _train_model_default(self, input_fn, hooks, saving_listeners):

C:\ProgramData\Anaconda3\lib\site-packages\tensorflow_estimator\python\estimator\estimator.py in _train_model_default(self, input_fn, hooks, saving_listeners)
1215 self.config)
1216 global_step_tensor = tf.compat.v1.train.get_global_step(g)
-> 1217 return self._train_with_estimator_spec(estimator_spec, worker_hooks,
1218 hooks, global_step_tensor,
1219 saving_listeners)

C:\ProgramData\Anaconda3\lib\site-packages\tensorflow_estimator\python\estimator\estimator.py in _train_with_estimator_spec(self, estimator_spec, worker_hooks, hooks, global_step_tensor, saving_listeners)
1510 output_dir=self._config.model_dir))
1511
-> 1512 with training.MonitoredTrainingSession(
1513 master=self._config.master,
1514 is_chief=self._config.is_chief,

C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\training\monitored_session.py in MonitoredTrainingSession(master, is_chief, checkpoint_dir, scaffold, hooks, chief_only_hooks, save_checkpoint_secs, save_summaries_steps, save_summaries_secs, config, stop_grace_period_secs, log_step_count_steps, max_wait_secs, save_checkpoint_steps, summary_dir, save_graph_def)
607 if hooks:
608 all_hooks.extend(hooks)
--> 609 return MonitoredSession(
610 session_creator=session_creator,
611 hooks=all_hooks,

C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\training\monitored_session.py in init(self, session_creator, hooks, stop_grace_period_secs)
1052 hooks=None,
1053 stop_grace_period_secs=120):
-> 1054 super(MonitoredSession, self).init(
1055 session_creator,
1056 hooks,

C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\training\monitored_session.py in init(self, session_creator, hooks, should_recover, stop_grace_period_secs)
755 stop_grace_period_secs=stop_grace_period_secs)
756 if should_recover:
--> 757 self._sess = _RecoverableSession(self._coordinated_creator)
758 else:
759 self._sess = self._coordinated_creator.create_session()

C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\training\monitored_session.py in init(self, sess_creator)
1261 """
1262 self._sess_creator = sess_creator
-> 1263 _WrappedSession.init(self, self._create_session())
1264
1265 def _create_session(self):

C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\training\monitored_session.py in _create_session(self)
1266 while True:
1267 try:
-> 1268 return self._sess_creator.create_session()
1269 except _PREEMPTION_ERRORS as e:
1270 logging.info(

C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\training\monitored_session.py in create_session(self)
915 # Inform the hooks that a new session has been created.
916 for hook in self._hooks:
--> 917 hook.after_create_session(self.tf_sess, self.coord)
918 return _CoordinatedSession(
919 _HookedSession(self.tf_sess, self._hooks), self.coord,

C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\training\basic_session_run_hooks.py in after_create_session(self, session, coord)
600 self._summary_writer.add_meta_graph(meta_graph_def)
601 # The checkpoint saved here is the state at step "global_step".
--> 602 self._save(session, global_step)
603 self._timer.update_last_triggered_step(global_step)
604

C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\training\basic_session_run_hooks.py in _save(self, session, step)
632
633 logging.info("Saving checkpoints for %d into %s.", step, self._save_path)
--> 634 self._get_saver().save(session, self._save_path, global_step=step,
635 write_meta_graph=self._save_graph_def)
636 self._summary_writer.add_session_log(

C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\training\saver.py in save(self, sess, save_path, global_step, latest_filename, meta_graph_suffix, write_meta_graph, write_state, strip_default_attrs, save_debug_info)
1282 model_checkpoint_path = self.saver_def.save_tensor_name
1283 else:
-> 1284 model_checkpoint_path = sess.run(
1285 self.saver_def.save_tensor_name,
1286 {self.saver_def.filename_tensor_name: checkpoint_file})

C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\client\session.py in run(self, fetches, feed_dict, options, run_metadata)
966
967 try:
--> 968 result = self._run(None, fetches, feed_dict, options_ptr,
969 run_metadata_ptr)
970 if run_metadata:

C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\client\session.py in _run(self, handle, fetches, feed_dict, options, run_metadata)
1189 # or if the call is a partial run that specifies feeds.
1190 if final_fetches or final_targets or (handle and feed_dict_tensor):
-> 1191 results = self._do_run(handle, final_targets, final_fetches,
1192 feed_dict_tensor, options, run_metadata)
1193 else:

C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\client\session.py in _do_run(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)
1369
1370 if handle is None:
-> 1371 return self._do_call(_run_fn, feeds, fetches, targets, options,
1372 run_metadata)
1373 else:

C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\client\session.py in _do_call(self, fn, *args)
1376 def _do_call(self, fn, *args):
1377 try:
-> 1378 return fn(*args)
1379 except errors.OpError as e:
1380 message = compat.as_text(e.message)

C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\client\session.py in _run_fn(feed_dict, fetch_list, target_list, options, run_metadata)
1359 # Ensure any changes to the graph are reflected in the runtime.
1360 self._extend_graph()
-> 1361 return self._call_tf_sessionrun(options, feed_dict, fetch_list,
1362 target_list, run_metadata)
1363

C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\client\session.py in _call_tf_sessionrun(self, options, feed_dict, fetch_list, target_list, run_metadata)
1452 def _call_tf_sessionrun(self, options, feed_dict, fetch_list, target_list,
1453 run_metadata):
-> 1454 return tf_session.TF_SessionRun_wrapper(self._session, options, feed_dict,
1455 fetch_list, target_list,
1456 run_metadata)

UnicodeDecodeError: 'utf-8' codec can't decode byte 0x8e in position 155: invalid start byte

[rasbt]

Hard to say, but my best guess is that the dataframe was maybe not loaded correctly. Could you check and show a screenshot of how the data frame looks like? E.g., maybe screenshot the output from df.tail() or df.head()

[niwak01]

Thank you for your response.
I cannot find any problem in dataframe and csv file.

auto-mpg.csv

[rasbt]

Hm, so strange! I mean, it's nice that the dataframe looks ok, but this makes it harder to debug.

Instead of

regressor = tf.estimator.DNNRegressor(
    feature_columns=all_feature_columns,
    hidden_units=[32, 10],
    model_dir='models/autompg-dnnregressor/')

can you change it to

regressor = tf.estimator.DNNRegressor(
    feature_columns=all_feature_columns,
    hidden_units=[32, 10],
    model_dir="regressor")

[niwak01]

I tried the code as you told, and it caused no errors.
I found following code is also OK.

model_dir = 'models\\autompg-dnnregressor\\')

It was my easy mistake.
I should have replaced '/' with '\' because I am using windows machine...
I am sorry.

Now I can proceed to read the book.
Thank you very much.

[rasbt]

Glad to hear it worked!