mahmoudmohsen213 / airs Goto Github PK

I had followed your steps and trained/tested my model, and I was trying to create a .pb file from the multiple model files that are generated (the checkpoint files, .pbtxt graph, etc.)

I have tried using freeze_graph.py, but I am unable to find the "output node name/s" that need to be input into the freeze_graph.py function. Do you know what the output nodes are for this trained model/graph?

@mahmoudmohsen213
flash@flash:~/airs$ python3 train.py
2019-03-21 13:46:40.578816: loading data files
WARNING:tensorflow:From train.py:15: load_csv_without_header (from tensorflow.contrib.learn.python.learn.datasets.base) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.data instead.
WARNING:tensorflow:From train.py:33: RunConfig.init (from tensorflow.contrib.learn.python.learn.estimators.run_config) is deprecated and will be removed in a future version.
Instructions for updating:
When switching to tf.estimator.Estimator, use tf.estimator.RunConfig instead.
WARNING:tensorflow:From /usr/local/lib/python3.5/dist-packages/tensorflow/contrib/learn/python/learn/estimators/dnn.py:378: multi_class_head (from tensorflow.contrib.learn.python.learn.estimators.head) is deprecated and will be removed in a future version.
Instructions for updating:
Please switch to tf.contrib.estimator._head.
WARNING:tensorflow:From /usr/local/lib/python3.5/dist-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py:1180: BaseEstimator.init (from tensorflow.contrib.learn.python.learn.estimators.estimator) is deprecated and will be removed in a future version.
Instructions for updating:
Please replace uses of any Estimator from tf.contrib.learn with an Estimator from tf.estimator.
2019-03-21 13:46:49.793051: training...
WARNING:tensorflow:Casting <dtype: 'int64'> labels to bool.
WARNING:tensorflow:Casting <dtype: 'int64'> labels to bool.
WARNING:tensorflow:Trapezoidal rule is known to produce incorrect PR-AUCs; please switch to "careful_interpolation" instead.
WARNING:tensorflow:Trapezoidal rule is known to produce incorrect PR-AUCs; please switch to "careful_interpolation" instead.
WARNING:tensorflow:From /usr/local/lib/python3.5/dist-packages/tensorflow/contrib/learn/python/learn/estimators/head.py:678: ModelFnOps.new (from tensorflow.contrib.learn.python.learn.estimators.model_fn) is deprecated and will be removed in a future version.
Instructions for updating:
When switching to tf.estimator.Estimator, use tf.estimator.EstimatorSpec. You can use the estimator_spec method to create an equivalent one.
2019-03-21 13:46:51.593519: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2019-03-21 13:46:51.620702: W tensorflow/core/framework/op_kernel.cc:1273] OP_REQUIRES failed at save_restore_v2_ops.cc:184 : Invalid argument: tensor_name = dnn/hiddenlayer_0/weights; shape in shape_and_slice spec [75,100] does not match the shape stored in checkpoint: [1,100]
Traceback (most recent call last):
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py", line 1334, in _do_call
return fn(*args)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py", line 1319, in _run_fn
options, feed_dict, fetch_list, target_list, run_metadata)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py", line 1407, in _call_tf_sessionrun
run_metadata)
tensorflow.python.framework.errors_impl.InvalidArgumentError: tensor_name = dnn/hiddenlayer_0/weights; shape in shape_and_slice spec [75,100] does not match the shape stored in checkpoint: [1,100]
[[{{node save/RestoreV2}} = RestoreV2[dtypes=[DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, ..., DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_INT64], _device="/job:localhost/replica:0/task:0/device:CPU:0"](_arg_save/Const_0_0, save/RestoreV2/tensor_names, save/RestoreV2/shape_and_slices)]]

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/training/saver.py", line 1546, in restore
{self.saver_def.filename_tensor_name: save_path})
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py", line 929, in run
run_metadata_ptr)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py", line 1152, in _run
feed_dict_tensor, options, run_metadata)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py", line 1328, in _do_run
run_metadata)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py", line 1348, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.InvalidArgumentError: tensor_name = dnn/hiddenlayer_0/weights; shape in shape_and_slice spec [75,100] does not match the shape stored in checkpoint: [1,100]
[[node save/RestoreV2 (defined at /usr/local/lib/python3.5/dist-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py:1092) = RestoreV2[dtypes=[DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, ..., DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_INT64], _device="/job:localhost/replica:0/task:0/device:CPU:0"](_arg_save/Const_0_0, save/RestoreV2/tensor_names, save/RestoreV2/shape_and_slices)]]

Caused by op 'save/RestoreV2', defined at:
File "train.py", line 70, in
classifier.fit(input_fn=getTrainData, steps=totalTrainingSteps)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/util/deprecation.py", line 488, in new_func
return func(*args, **kwargs)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py", line 525, in fit
loss = self._train_model(input_fn=input_fn, hooks=hooks)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py", line 1092, in _train_model
config=self._session_config) as mon_sess:
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/training/monitored_session.py", line 504, in MonitoredTrainingSession
stop_grace_period_secs=stop_grace_period_secs)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/training/monitored_session.py", line 921, in init
stop_grace_period_secs=stop_grace_period_secs)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/training/monitored_session.py", line 643, in init
self._sess = _RecoverableSession(self._coordinated_creator)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/training/monitored_session.py", line 1107, in init
_WrappedSession.init(self, self._create_session())
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/training/monitored_session.py", line 1112, in _create_session
return self._sess_creator.create_session()
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/training/monitored_session.py", line 800, in create_session
self.tf_sess = self._session_creator.create_session()
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/training/monitored_session.py", line 557, in create_session
self._scaffold.finalize()
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/training/monitored_session.py", line 215, in finalize
self._saver.build()
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/training/saver.py", line 1114, in build
self._build(self._filename, build_save=True, build_restore=True)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/training/saver.py", line 1151, in _build
build_save=build_save, build_restore=build_restore)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/training/saver.py", line 789, in _build_internal
restore_sequentially, reshape)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/training/saver.py", line 459, in _AddShardedRestoreOps
name="restore_shard"))
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/training/saver.py", line 406, in _AddRestoreOps
restore_sequentially)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/training/saver.py", line 862, in bulk_restore
return io_ops.restore_v2(filename_tensor, names, slices, dtypes)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/gen_io_ops.py", line 1466, in restore_v2
shape_and_slices=shape_and_slices, dtypes=dtypes, name=name)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/op_def_library.py", line 787, in _apply_op_helper
op_def=op_def)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/util/deprecation.py", line 488, in new_func
return func(*args, **kwargs)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py", line 3274, in create_op
op_def=op_def)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py", line 1770, in init
self._traceback = tf_stack.extract_stack()

InvalidArgumentError (see above for traceback): tensor_name = dnn/hiddenlayer_0/weights; shape in shape_and_slice spec [75,100] does not match the shape stored in checkpoint: [1,100]
[[node save/RestoreV2 (defined at /usr/local/lib/python3.5/dist-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py:1092) = RestoreV2[dtypes=[DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, ..., DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_INT64], _device="/job:localhost/replica:0/task:0/device:CPU:0"](_arg_save/Const_0_0, save/RestoreV2/tensor_names, save/RestoreV2/shape_and_slices)]]

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File "train.py", line 70, in
classifier.fit(input_fn=getTrainData, steps=totalTrainingSteps)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/util/deprecation.py", line 488, in new_func
return func(*args, **kwargs)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py", line 525, in fit
loss = self._train_model(input_fn=input_fn, hooks=hooks)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py", line 1092, in _train_model
config=self._session_config) as mon_sess:
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/training/monitored_session.py", line 504, in MonitoredTrainingSession
stop_grace_period_secs=stop_grace_period_secs)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/training/monitored_session.py", line 921, in init
stop_grace_period_secs=stop_grace_period_secs)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/training/monitored_session.py", line 643, in init
self._sess = _RecoverableSession(self._coordinated_creator)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/training/monitored_session.py", line 1107, in init
_WrappedSession.init(self, self._create_session())
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/training/monitored_session.py", line 1112, in _create_session
return self._sess_creator.create_session()
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/training/monitored_session.py", line 800, in create_session
self.tf_sess = self._session_creator.create_session()
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/training/monitored_session.py", line 566, in create_session
init_fn=self._scaffold.init_fn)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/training/session_manager.py", line 288, in prepare_session
config=config)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/training/session_manager.py", line 218, in _restore_checkpoint
saver.restore(sess, ckpt.model_checkpoint_path)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/training/saver.py", line 1582, in restore
err, "a mismatch between the current graph and the graph")
tensorflow.python.framework.errors_impl.InvalidArgumentError: Restoring from checkpoint failed. This is most likely due to a mismatch between the current graph and the graph from the checkpoint. Please ensure that you have not altered the graph expected based on the checkpoint. Original error:

tensor_name = dnn/hiddenlayer_0/weights; shape in shape_and_slice spec [75,100] does not match the shape stored in checkpoint: [1,100]
[[node save/RestoreV2 (defined at /usr/local/lib/python3.5/dist-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py:1092) = RestoreV2[dtypes=[DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, ..., DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_INT64], _device="/job:localhost/replica:0/task:0/device:CPU:0"](_arg_save/Const_0_0, save/RestoreV2/tensor_names, save/RestoreV2/shape_and_slices)]]

Caused by op 'save/RestoreV2', defined at:
File "train.py", line 70, in
classifier.fit(input_fn=getTrainData, steps=totalTrainingSteps)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/util/deprecation.py", line 488, in new_func
return func(*args, **kwargs)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py", line 525, in fit
loss = self._train_model(input_fn=input_fn, hooks=hooks)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py", line 1092, in _train_model
config=self._session_config) as mon_sess:
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/training/monitored_session.py", line 504, in MonitoredTrainingSession
stop_grace_period_secs=stop_grace_period_secs)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/training/monitored_session.py", line 921, in init
stop_grace_period_secs=stop_grace_period_secs)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/training/monitored_session.py", line 643, in init
self._sess = _RecoverableSession(self._coordinated_creator)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/training/monitored_session.py", line 1107, in init
_WrappedSession.init(self, self._create_session())
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/training/monitored_session.py", line 1112, in _create_session
return self._sess_creator.create_session()
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/training/monitored_session.py", line 800, in create_session
self.tf_sess = self._session_creator.create_session()
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/training/monitored_session.py", line 557, in create_session
self._scaffold.finalize()
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/training/monitored_session.py", line 215, in finalize
self._saver.build()
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/training/saver.py", line 1114, in build
self._build(self._filename, build_save=True, build_restore=True)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/training/saver.py", line 1151, in _build
build_save=build_save, build_restore=build_restore)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/training/saver.py", line 789, in _build_internal
restore_sequentially, reshape)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/training/saver.py", line 459, in _AddShardedRestoreOps
name="restore_shard"))
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/training/saver.py", line 406, in _AddRestoreOps
restore_sequentially)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/training/saver.py", line 862, in bulk_restore
return io_ops.restore_v2(filename_tensor, names, slices, dtypes)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/gen_io_ops.py", line 1466, in restore_v2
shape_and_slices=shape_and_slices, dtypes=dtypes, name=name)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/op_def_library.py", line 787, in _apply_op_helper
op_def=op_def)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/util/deprecation.py", line 488, in new_func
return func(*args, **kwargs)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py", line 3274, in create_op
op_def=op_def)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py", line 1770, in init
self._traceback = tf_stack.extract_stack()

InvalidArgumentError (see above for traceback): Restoring from checkpoint failed. This is most likely due to a mismatch between the current graph and the graph from the checkpoint. Please ensure that you have not altered the graph expected based on the checkpoint. Original error:

tensor_name = dnn/hiddenlayer_0/weights; shape in shape_and_slice spec [75,100] does not match the shape stored in checkpoint: [1,100]
[[node save/RestoreV2 (defined at /usr/local/lib/python3.5/dist-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py:1092) = RestoreV2[dtypes=[DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, ..., DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_INT64], _device="/job:localhost/replica:0/task:0/device:CPU:0"](_arg_save/Const_0_0, save/RestoreV2/tensor_names, save/RestoreV2/shape_and_slices)]]

I run convertFeatureFiles.py according to your method but the data is not written to the csv file