Customizing_adanet tutorial error

[zhouhao-learning]

In customizing_adanet notebook, When I ran the Customizing_adanet tutorial error notebook, I got the following error at the end:

TypeError                                 Traceback (most recent call last)
<ipython-input-10-9f72d27cf659> in <module>
     26     eval_spec=tf.estimator.EvalSpec(
     27         input_fn=input_fn("test", training=False, batch_size=BATCH_SIZE),
---> 28         steps=None))
     29 print("Accuracy:", results["accuracy"])
     30 print("Loss:", results["average_loss"])

~/anaconda3/envs/Adanet/lib/python3.5/site-packages/tensorflow/python/estimator/training.py in train_and_evaluate(estimator, train_spec, eval_spec)
    445         '(with task id 0).  Given task id {}'.format(config.task_id))
    446 
--> 447   return executor.run()
    448 
    449 

~/anaconda3/envs/Adanet/lib/python3.5/site-packages/tensorflow/python/estimator/training.py in run(self)
    529         config.task_type != run_config_lib.TaskType.EVALUATOR):
    530       logging.info('Running training and evaluation locally (non-distributed).')
--> 531       return self.run_local()
    532 
    533     # Distributed case.

~/anaconda3/envs/Adanet/lib/python3.5/site-packages/tensorflow/python/estimator/training.py in run_local(self)
    667           input_fn=self._train_spec.input_fn,
    668           max_steps=self._train_spec.max_steps,
--> 669           hooks=train_hooks)
    670 
    671       if not self._continuous_eval_listener.before_eval():

~/anaconda3/envs/Adanet/lib/python3.5/site-packages/adanet/core/estimator.py in train(self, input_fn, hooks, steps, max_steps, saving_listeners)
    440             hooks=hooks,
    441             max_steps=max_steps,
--> 442             saving_listeners=saving_listeners)
    443 
    444         # If training ended because the maximum number of training steps

~/anaconda3/envs/Adanet/lib/python3.5/site-packages/tensorflow/python/estimator/estimator.py 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

~/anaconda3/envs/Adanet/lib/python3.5/site-packages/tensorflow/python/estimator/estimator.py 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):

~/anaconda3/envs/Adanet/lib/python3.5/site-packages/tensorflow/python/estimator/estimator.py in _train_model_default(self, input_fn, hooks, saving_listeners)
   1130       worker_hooks.extend(input_hooks)
   1131       estimator_spec = self._call_model_fn(
-> 1132           features, labels, model_fn_lib.ModeKeys.TRAIN, self.config)
   1133       return self._train_with_estimator_spec(estimator_spec, worker_hooks,
   1134                                              hooks, global_step_tensor,

~/anaconda3/envs/Adanet/lib/python3.5/site-packages/tensorflow/python/estimator/estimator.py in _call_model_fn(self, features, labels, mode, config)
   1105 
   1106     logging.info('Calling model_fn.')
-> 1107     model_fn_results = self._model_fn(features=features, **kwargs)
   1108     logging.info('Done calling model_fn.')
   1109 

~/anaconda3/envs/Adanet/lib/python3.5/site-packages/adanet/core/estimator.py in _model_fn(self, features, labels, mode, params)
   1044           mode=mode,
   1045           previous_ensemble_summary=previous_ensemble_summary,
-> 1046           previous_ensemble_spec=previous_ensemble_spec)
   1047 
   1048     # Variable which allows us to read the current iteration from a checkpoint.

~/anaconda3/envs/Adanet/lib/python3.5/site-packages/adanet/core/iteration.py in build_iteration(self, iteration_number, subnetwork_builders, features, mode, labels, previous_ensemble_summary, previous_ensemble_spec)
    213             mode=mode,
    214             iteration_step=iteration_step_tensor,
--> 215             labels=labels)
    216         candidate = self._candidate_builder.build_candidate(
    217             ensemble_spec=ensemble_spec,

~/anaconda3/envs/Adanet/lib/python3.5/site-packages/adanet/core/ensemble.py in append_new_subnetwork(self, ensemble_spec, subnetwork_builder, iteration_step, summary, features, mode, labels)
    321               iteration_step=iteration_step,
    322               summary=summary,
--> 323               previous_ensemble=ensemble)
    324           trainable_vars_after = tf.trainable_variables()
    325           var_list = list(

<ipython-input-8-13ccd77121f3> in build_subnetwork(self, features, logits_dimension, training, iteration_step, summary, previous_ensemble)
     25                        previous_ensemble=None):
     26     """See `adanet.subnetwork.Builder`."""
---> 27     images = features.values()[0]
     28     kernel_initializer = tf.keras.initializers.he_normal(seed=self._seed)
     29     x = tf.layers.conv2d(

TypeError: 'dict_values' object does not support indexing

This is your latest code, how should I solve this problem?

[dtrippas]

Wrap list() around features.values() to fix this error. Solution taken from stackoverflow

[cweill]

These tutorials were not tested with Python 3: #18. @dtrippas's response is correct.

[smyrbdr]

Just replaced the corresponding line (images=features.vaules()[0]) in build_subnetwork function with the following line;
images = tf.to_float(features[FEATURES_KEY])
and this solved the issue for me on Python 3.