There's a couple of other questions similar to this, but I couldn't find a solution which seems to fit. I am using LightGBM with Scikit-Optimize BayesSearchCV.
full_pipeline = skl.Pipeline(steps=[('preprocessor', pre_processor),
('estimator', lgbm.sklearn.LGBMClassifier())])
scorer=make_scorer(fl.lgb_focal_f1_score)
lgb_tuner = sko.BayesSearchCV(full_pipeline, hyper_space, cv=5, refit=True, n_iter=num_calls,scoring=scorer)
lgb_tuner.fit(balanced_xtrain, balanced_ytrain)
Training runs for a while, before it errors with the following:
Traceback (most recent call last):
File "/var/training.py", line 134, in <module>
lgb_tuner.fit(balanced_xtrain, balanced_ytrain)
File "/usr/local/lib/python3.6/site-packages/skopt/searchcv.py", line 694, in fit
groups=groups, n_points=n_points_adjusted
File "/usr/local/lib/python3.6/site-packages/skopt/searchcv.py", line 579, in _step
self._fit(X, y, groups, params_dict)
File "/usr/local/lib/python3.6/site-packages/skopt/searchcv.py", line 423, in _fit
for parameters in parameter_iterable
File "/usr/local/lib/python3.6/site-packages/joblib/parallel.py", line 1041, in __call__
if self.dispatch_one_batch(iterator):
File "/usr/local/lib/python3.6/site-packages/joblib/parallel.py", line 859, in dispatch_one_batch
self._dispatch(tasks)
File "/usr/local/lib/python3.6/site-packages/joblib/parallel.py", line 777, in _dispatch
job = self._backend.apply_async(batch, callback=cb)
File "/usr/local/lib/python3.6/site-packages/joblib/_parallel_backends.py", line 208, in apply_async
result = ImmediateResult(func)
File "/usr/local/lib/python3.6/site-packages/joblib/_parallel_backends.py", line 572, in __init__
self.results = batch()
File "/usr/local/lib/python3.6/site-packages/joblib/parallel.py", line 263, in __call__
for func, args, kwargs in self.items]
File "/usr/local/lib/python3.6/site-packages/joblib/parallel.py", line 263, in <listcomp>
for func, args, kwargs in self.items]
File "/usr/local/lib/python3.6/site-packages/sklearn/model_selection/_validation.py", line 531, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "/usr/local/lib/python3.6/site-packages/sklearn/pipeline.py", line 335, in fit
self._final_estimator.fit(Xt, y, **fit_params_last_step)
File "/usr/local/lib/python3.6/site-packages/lightgbm/sklearn.py", line 857, in fit
callbacks=callbacks, init_model=init_model)
File "/usr/local/lib/python3.6/site-packages/lightgbm/sklearn.py", line 617, in fit
callbacks=callbacks, init_model=init_model)
File "/usr/local/lib/python3.6/site-packages/lightgbm/engine.py", line 252, in train
booster.update(fobj=fobj)
File "/usr/local/lib/python3.6/site-packages/lightgbm/basic.py", line 2467, in update
return self.__boost(grad, hess)
File "/usr/local/lib/python3.6/site-packages/lightgbm/basic.py", line 2503, in __boost
ctypes.byref(is_finished)))
File "/usr/local/lib/python3.6/site-packages/lightgbm/basic.py", line 55, in _safe_call
raise LightGBMError(decode_string(_LIB.LGBM_GetLastError()))
lightgbm.basic.LightGBMError: Check failed: (best_split_info.left_count) > (0) at /__w/1/s/python-package/compile/src/treelearner/serial_tree_learner.cpp, line 651 .
Some answers to similar questions have suggested it might be as a consequence of using the GPU, but I do not have a GPU available. I don't know what else is causing it or how to try and fix it. Can anyone suggest anything?
I think this was due to my hyperparameter limits being wrong, causing one hyperparameter to be set to zero which shouldn't have been, though I'm not sure which one.
Related
Traceback:
File "C:\Users\spand\AppData\Local\Programs\Python\Python310\lib\site-packages\streamlit\runtime\scriptrunner\script_runner.py", line 565, in _run_script
exec(code, module.dict)
File "C:\Users\spand\Desktop\laptop price prediction\app.py", line 68, in
st.title(int(np.exp(pipe.predict(query))))
File "C:\Users\spand\AppData\Local\Programs\Python\Python310\lib\site-packages\sklearn\pipeline.py", line 457, in predict
Xt = transform.transform(Xt)
File "C:\Users\spand\AppData\Local\Programs\Python\Python310\lib\site-packages\sklearn\compose_column_transformer.py", line 763, in transform
Xs = self._fit_transform(
File "C:\Users\spand\AppData\Local\Programs\Python\Python310\lib\site-packages\sklearn\compose_column_transformer.py", line 621, in _fit_transform
return Parallel(n_jobs=self.n_jobs)(
File "C:\Users\spand\AppData\Local\Programs\Python\Python310\lib\site-packages\joblib\parallel.py", line 1085, in call
if self.dispatch_one_batch(iterator):
File "C:\Users\spand\AppData\Local\Programs\Python\Python310\lib\site-packages\joblib\parallel.py", line 901, in dispatch_one_batch
self._dispatch(tasks)
File "C:\Users\spand\AppData\Local\Programs\Python\Python310\lib\site-packages\joblib\parallel.py", line 819, in _dispatch
job = self._backend.apply_async(batch, callback=cb)
File "C:\Users\spand\AppData\Local\Programs\Python\Python310\lib\site-packages\joblib_parallel_backends.py", line 208, in apply_async
result = ImmediateResult(func)
File "C:\Users\spand\AppData\Local\Programs\Python\Python310\lib\site-packages\joblib_parallel_backends.py", line 597, in init
self.results = batch()
File "C:\Users\spand\AppData\Local\Programs\Python\Python310\lib\site-packages\joblib\parallel.py", line 288, in call
return [func(*args, **kwargs)
File "C:\Users\spand\AppData\Local\Programs\Python\Python310\lib\site-packages\joblib\parallel.py", line 288, in
return [func(*args, **kwargs)
File "C:\Users\spand\AppData\Local\Programs\Python\Python310\lib\site-packages\sklearn\utils\fixes.py", line 117, in call
return self.function(*args, **kwargs)
File "C:\Users\spand\AppData\Local\Programs\Python\Python310\lib\site-packages\sklearn\pipeline.py", line 853, in _transform_one
res = transformer.transform(X)
File "C:\Users\spand\AppData\Local\Programs\Python\Python310\lib\site-packages\sklearn\preprocessing_encoders.py", line 888, in transform
self._map_infrequent_categories(X_int, X_mask)
File "C:\Users\spand\AppData\Local\Programs\Python\Python310\lib\site-packages\sklearn\preprocessing_encoders.py", line 726, in _map_infrequent_categories
if not self._infrequent_enabled:
How to overcome this problem no i can't find the reason behind it
Please check the version of streamlit (update the sklearn version)
ColumnTransformer(transformers=[('col_tnf',OneHotEncoder(sparse=False,drop='first'),0,1,3,8,11])],remainder='passthrough')
I am trying to initialize resnet50 as backbone for a model in TF 1.15, and the model is run on google TPU V2. My code is this:
backbone_model=tf.keras.applications.ResNet50(include_top=False, weights='imagenet',pooling=None)
I get following errors.
<pre><code>
E1014 09:57:09.458413 140497105635136 tpu.py:425] Operation of type Placeholder (input_1) is not supported on the TPU. Execution will fail if this op is used in the graph.
app.run(main)
File "/home/usman/anaconda3/envs/py37/lib/python3.7/site-packages/absl/app.py", line 300, in run
_run_main(main, args)
File "/home/usman/anaconda3/envs/py37/lib/python3.7/site-packages/absl/app.py", line 251, in _run_main
sys.exit(main(argv))
File "/home/usman/nas/tpu.py", line 232, in main
max_steps=FLAGS.train_steps)
File "/home/usman/anaconda3/envs/py37/lib/python3.7/site-packages/tensorflow_estimator/python/estimator/tpu/tpu_estimator.py", line 3035, in train
rendezvous.raise_errors()
File "/home/usman/anaconda3/envs/py37/lib/python3.7/site-packages/tensorflow_estimator/python/estimator/tpu/error_handling.py", line 136, in raise_errors
six.reraise(typ, value, traceback)
File "/home/usman/anaconda3/envs/py37/lib/python3.7/site-packages/six.py", line 703, in reraise
raise value
File "/home/usman/anaconda3/envs/py37/lib/python3.7/site-packages/tensorflow_estimator/python/estimator/tpu/tpu_estimator.py", line 3030, in train
saving_listeners=saving_listeners)
File "/home/usman/anaconda3/envs/py37/lib/python3.7/site-packages/tensorflow_estimator/python/estimator/estimator.py", line 370, in train
loss = self._train_model(input_fn, hooks, saving_listeners)
File "/home/usman/anaconda3/envs/py37/lib/python3.7/site-packages/tensorflow_estimator/python/estimator/estimator.py", line 1161, in _train_model
return self._train_model_default(input_fn, hooks, saving_listeners)
File "/home/usman/anaconda3/envs/py37/lib/python3.7/site-packages/tensorflow_estimator/python/estimator/estimator.py", line 1191, in _train_model_default
features, labels, ModeKeys.TRAIN, self.config)
File "/home/usman/anaconda3/envs/py37/lib/python3.7/site-packages/tensorflow_estimator/python/estimator/tpu/tpu_estimator.py", line 2857, in _call_model_fn
config)
File "/home/usman/anaconda3/envs/py37/lib/python3.7/site-packages/tensorflow_estimator/python/estimator/estimator.py", line 1149, in _call_model_fn
model_fn_results = self._model_fn(features=features, **kwargs)
File "/home/usman/anaconda3/envs/py37/lib/python3.7/site-packages/tensorflow_estimator/python/estimator/tpu/tpu_estimator.py", line 3159, in _model_fn
_train_on_tpu_system(ctx, model_fn_wrapper, dequeue_fn))
File "/home/usman/anaconda3/envs/py37/lib/python3.7/site-packages/tensorflow_estimator/python/estimator/tpu/tpu_estimator.py", line 3604, in _train_on_tpu_system
device_assignment=ctx.device_assignment)
File "/home/usman/anaconda3/envs/py37/lib/python3.7/site-packages/tensorflow_core/python/tpu/tpu.py", line 1277, in split_compile_and_shard
name=name)
File "/home/usman/anaconda3/envs/py37/lib/python3.7/site-packages/tensorflow_core/python/tpu/tpu.py", line 992, in split_compile_and_replicate
outputs = computation(*computation_inputs)
File "/home/usman/anaconda3/envs/py37/lib/python3.7/site-packages/tensorflow_estimator/python/estimator/tpu/tpu_estimator.py", line 3589, in multi_tpu_train_steps_on_single_shard
inputs=[0, _INITIAL_LOSS])
File "/home/usman/anaconda3/envs/py37/lib/python3.7/site-packages/tensorflow_core/python/tpu/training_loop.py", line 178, in while_loop
condition_wrapper, body_wrapper, inputs, name="", parallel_iterations=1)
File "/home/usman/anaconda3/envs/py37/lib/python3.7/site-packages/tensorflow_core/python/ops/control_flow_ops.py", line 2753, in while_loop
return_same_structure)
File "/home/usman/anaconda3/envs/py37/lib/python3.7/site-packages/tensorflow_core/python/ops/control_flow_ops.py", line 2245, in BuildLoop
pred, body, original_loop_vars, loop_vars, shape_invariants)
File "/home/usman/anaconda3/envs/py37/lib/python3.7/site-packages/tensorflow_core/python/ops/control_flow_ops.py", line 2170, in _BuildLoop
body_result = body(*packed_vars_for_body)
File "/home/usman/anaconda3/envs/py37/lib/python3.7/site-packages/tensorflow_core/python/tpu/training_loop.py", line 121, in body_wrapper
outputs = body(*(inputs + dequeue_ops))
File "/home/usman/anaconda3/envs/py37/lib/python3.7/site-packages/tensorflow_estimator/python/estimator/tpu/tpu_estimator.py", line 3588, in <lambda>
lambda i, loss: [i + 1, single_tpu_train_step(i)],
File "/home/usman/anaconda3/envs/py37/lib/python3.7/site-packages/tensorflow_estimator/python/estimator/tpu/tpu_estimator.py", line 1715, in train_step
self._call_model_fn(features, labels))
File "/home/usman/anaconda3/envs/py37/lib/python3.7/site-packages/tensorflow_estimator/python/estimator/tpu/tpu_estimator.py", line 1994, in _call_model_fn
estimator_spec = self._model_fn(features=features, **kwargs)
File "/home/usman/nas/tpu.py", line 120, in model
pooling=None)
File "/home/usman/anaconda3/envs/py37/lib/python3.7/site-packages/tensorflow_core/python/keras/applications/__init__.py", line 49, in wrapper
return base_fun(*args, **kwargs)
File "/home/usman/anaconda3/envs/py37/lib/python3.7/site-packages/tensorflow_core/python/keras/applications/resnet.py", line 33, in ResNet50
return resnet.ResNet50(*args, **kwargs)
File "/home/usman/anaconda3/envs/py37/lib/python3.7/site-packages/keras_applications/resnet_common.py", line 435, in ResNet50
**kwargs)
File "/home/usman/anaconda3/envs/py37/lib/python3.7/site-packages/keras_applications/resnet_common.py", line 411, in ResNet
model.load_weights(weights_path)
File "/home/usman/anaconda3/envs/py37/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training.py", line 182, in load_weights
return super(Model, self).load_weights(filepath, by_name)
File "/home/usman/anaconda3/envs/py37/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/network.py", line 1373, in load_weights
saving.load_weights_from_hdf5_group(f, self.layers)
File "/home/usman/anaconda3/envs/py37/lib/python3.7/site-packages/tensorflow_core/python/keras/saving/hdf5_format.py", line 693, in load_weights_from_hdf5_group
K.batch_set_value(weight_value_tuples)
File "/home/usman/anaconda3/envs/py37/lib/python3.7/site-packages/tensorflow_core/python/keras/backend.py", line 3259, in batch_set_value
get_session().run(assign_ops, feed_dict=feed_dict)
File "/home/usman/anaconda3/envs/py37/lib/python3.7/site-packages/tensorflow_core/python/keras/backend.py", line 486, in get_session
_initialize_variables(session)
File "/home/usman/anaconda3/envs/py37/lib/python3.7/site-packages/tensorflow_core/python/keras/backend.py", line 903, in _initialize_variables
[variables_module.is_variable_initialized(v) for v in candidate_vars])
File "/home/usman/anaconda3/envs/py37/lib/python3.7/site-packages/tensorflow_core/python/client/session.py", line 956, in run
run_metadata_ptr)
File "/home/usman/anaconda3/envs/py37/lib/python3.7/site-packages/tensorflow_core/python/client/session.py", line 1165, in _run
self._graph, fetches, feed_dict_tensor, feed_handles=feed_handles)
File "/home/usman/anaconda3/envs/py37/lib/python3.7/site-packages/tensorflow_core/python/client/session.py", line 488, in __init__
self._assert_fetchable(graph, fetch.op)
File "/home/usman/anaconda3/envs/py37/lib/python3.7/site-packages/tensorflow_core/python/client/session.py", line 505, in _assert_fetchable
% op.name)
tensorflow.python.framework.errors_impl.InaccessibleTensorError: Operation 'VarIsInitializedOp' has been marked as not fetchable. Typically this happens when it is defined in another function or code block. Use return values,explicit Python locals or TensorFlow collections to access it.
</code></pre>
From what I have searched , following is the closest guess to the error I am getting , it is not possible to fetch the result of an op created inside the while loop's body, because the body might execute 0 or more times, based on the loop condition. To get a value out of the loop, you need to return it from the body function (as one of the loop variables), and its final value after all the iterations will be returned from tf.while_loop(). But this happens inside tf code , not externally , because i am only running a single line code for initialization of a model.
I'm trying to get the predictions inside the function on_epoch_end of keras' Callback.
At the moment, to get the predictions, I execute self.model.predict with batch_size of 2, but at the 3rd epochs I get this error:
RuntimeError: Dst tensor is not initialized in Tensorflow
Reading on the web, I notice that this error appears when the GPU goes out of memory. In my case, reading the stack trace, this error is triggered by self.model.predict inside on_epoch_end, it says:
File "mlp_keras.py", line 20, in on_epoch_end predictions =
self.model.predict(self.dataset)
This is the full stack trace:
Traceback (most recent call last):
File "mlp_keras.py", line 150, in <module>
callbacks=[KendallTauHistory(training_dataset, training_dataset_labels, groups_id_count)])
File "/usr/home/studenti/sp171412/word_ordering/mlp/env/lib/python2.7/site-packages/tensorflow_core/python/keras/engine/training.py", line 819, in fit
use_multiprocessing=use_multiprocessing)
File "/usr/home/studenti/sp171412/word_ordering/mlp/env/lib/python2.7/site-packages/tensorflow_core/python/keras/engine/training_v2.py", line 397, in fit
prefix='val_')
File "/usr/lib64/python2.7/contextlib.py", line 24, in __exit__
self.gen.next()
File "/usr/home/studenti/sp171412/word_ordering/mlp/env/lib/python2.7/site-packages/tensorflow_core/python/keras/engine/training_v2.py", line 771, in on_epoch
self.callbacks.on_epoch_end(epoch, epoch_logs)
File "/usr/home/studenti/sp171412/word_ordering/mlp/env/lib/python2.7/site-packages/tensorflow_core/python/keras/callbacks.py", line 302, in on_epoch_end
callback.on_epoch_end(epoch, logs)
File "mlp_keras.py", line 20, in on_epoch_end
predictions = self.model.predict(self.dataset)
File "/usr/home/studenti/sp171412/word_ordering/mlp/env/lib/python2.7/site-packages/tensorflow_core/python/keras/engine/training.py", line 1013, in predict
use_multiprocessing=use_multiprocessing)
File "/usr/home/studenti/sp171412/word_ordering/mlp/env/lib/python2.7/site-packages/tensorflow_core/python/keras/engine/training_v2.py", line 498, in predict
workers=workers, use_multiprocessing=use_multiprocessing, **kwargs)
File "/usr/home/studenti/sp171412/word_ordering/mlp/env/lib/python2.7/site-packages/tensorflow_core/python/keras/engine/training_v2.py", line 426, in _model_iteration
use_multiprocessing=use_multiprocessing)
File "/usr/home/studenti/sp171412/word_ordering/mlp/env/lib/python2.7/site-packages/tensorflow_core/python/keras/engine/training_v2.py", line 706, in _process_inputs
use_multiprocessing=use_multiprocessing)
File "/usr/home/studenti/sp171412/word_ordering/mlp/env/lib/python2.7/site-packages/tensorflow_core/python/keras/engine/data_adapter.py", line 357, in __init__
dataset = self.slice_inputs(indices_dataset, inputs)
File "/usr/home/studenti/sp171412/word_ordering/mlp/env/lib/python2.7/site-packages/tensorflow_core/python/keras/engine/data_adapter.py", line 383, in slice_inputs
dataset_ops.DatasetV2.from_tensors(inputs).repeat()
File "/usr/home/studenti/sp171412/word_ordering/mlp/env/lib/python2.7/site-packages/tensorflow_core/python/data/ops/dataset_ops.py", line 566, in from_tensors
return TensorDataset(tensors)
File "/usr/home/studenti/sp171412/word_ordering/mlp/env/lib/python2.7/site-packages/tensorflow_core/python/data/ops/dataset_ops.py", line 2765, in __init__
element = structure.normalize_element(element)
File "/usr/home/studenti/sp171412/word_ordering/mlp/env/lib/python2.7/site-packages/tensorflow_core/python/data/util/structure.py", line 113, in normalize_element
ops.convert_to_tensor(t, name="component_%d" % i))
File "/usr/home/studenti/sp171412/word_ordering/mlp/env/lib/python2.7/site-packages/tensorflow_core/python/framework/ops.py", line 1314, in convert_to_tensor
ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
File "/usr/home/studenti/sp171412/word_ordering/mlp/env/lib/python2.7/site-packages/tensorflow_core/python/framework/tensor_conversion_registry.py", line 52, in _default_conversion_function
return constant_op.constant(value, dtype, name=name)
File "/usr/home/studenti/sp171412/word_ordering/mlp/env/lib/python2.7/site-packages/tensorflow_core/python/framework/constant_op.py", line 258, in constant
allow_broadcast=True)
File "/usr/home/studenti/sp171412/word_ordering/mlp/env/lib/python2.7/site-packages/tensorflow_core/python/framework/constant_op.py", line 266, in _constant_impl
t = convert_to_eager_tensor(value, ctx, dtype)
File "/usr/home/studenti/sp171412/word_ordering/mlp/env/lib/python2.7/site-packages/tensorflow_core/python/framework/constant_op.py", line 96, in convert_to_eager_tensor
return ops.EagerTensor(value, ctx.device_name, dtype)
RuntimeError: Dst tensor is not initialized.
My question is: is there a way to get the predictions without performing predict inside on_epoch_end? Thanks in advance.
Alright, after seeing your last comment, what you could do:
epochs = 100
for epoch in range(epochs):
model.fit(x_train, y_train)
y_predict = model.predict(x_test)
EDIT2
Ok so far i have tried with python3.5 -tf 1.10 and python 2.7 tf 1.10
I m still getting this error
Traceback (most recent call last):
File "object_detection/model_main.py", line 101, in <module>
tf.app.run()
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/platform/app.py", line 125, in run
_sys.exit(main(argv))
File "object_detection/model_main.py", line 97, in main
tf.estimator.train_and_evaluate(estimator, train_spec, eval_specs[0])
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/estimator/training.py", line 455, in train_and_evaluate
return executor.run()
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/estimator/training.py", line 594, in run
return self.run_local()
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/estimator/training.py", line 695, in run_local
saving_listeners=saving_listeners)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/estimator/estimator.py", line 354, in train
loss = self._train_model(input_fn, hooks, saving_listeners)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/estimator/estimator.py", line 1179, in _train_model
return self._train_model_default(input_fn, hooks, saving_listeners)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/estimator/estimator.py", line 1209, in _train_model_default
features, labels, model_fn_lib.ModeKeys.TRAIN, self.config)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/estimator/estimator.py", line 1167, in _call_model_fn
model_fn_results = self._model_fn(features=features, **kwargs)
File "/home/nvidia/tensorflow/models/research/object_detection/model_lib.py", line 287, in model_fn
prediction_dict, features[fields.InputDataFields.true_image_shape])
File "/home/nvidia/tensorflow/models/research/object_detection/meta_architectures/ssd_meta_arch.py", line 686, in loss
keypoints, weights)
File "/home/nvidia/tensorflow/models/research/object_detection/meta_architectures/ssd_meta_arch.py", line 859, in _assign_targets
groundtruth_weights_list)
File "/home/nvidia/tensorflow/models/research/object_detection/core/target_assigner.py", line 481, in batch_assign_targets
anchors, gt_boxes, gt_class_targets, unmatched_class_label, gt_weights)
File "/home/nvidia/tensorflow/models/research/object_detection/core/target_assigner.py", line 180, in assign
match = self._matcher.match(match_quality_matrix, **params)
File "/home/nvidia/tensorflow/models/research/object_detection/core/matcher.py", line 239, in match
return Match(self._match(similarity_matrix, **params),
File "/home/nvidia/tensorflow/models/research/object_detection/matchers/argmax_matcher.py", line 190, in _match
_match_when_rows_are_non_empty, _match_when_rows_are_empty)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/util/deprecation.py", line 488, in new_func
return func(*args, **kwargs)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/control_flow_ops.py", line 2074, in cond
orig_res_t, res_t = context_t.BuildCondBranch(true_fn)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/control_flow_ops.py", line 1920, in BuildCondBranch
original_result = fn()
File "/home/nvidia/tensorflow/models/research/object_detection/matchers/argmax_matcher.py", line 153, in _match_when_rows_are_non_empty
-1)
File "/home/nvidia/tensorflow/models/research/object_detection/matchers/argmax_matcher.py", line 203, in _set_values_using_indicator
indicator = tf.cast(1-indicator, x.dtype)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/math_ops.py", line 878, in r_binary_op_wrapper
x = ops.convert_to_tensor(x, dtype=y.dtype.base_dtype, name="x")
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 1028, in convert_to_tensor
as_ref=False)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 1124, in internal_convert_to_tensor
ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/constant_op.py", line 228, in _constant_tensor_conversion_function
return constant(v, dtype=dtype, name=name)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/constant_op.py", line 207, in constant
value, dtype=dtype, shape=shape, verify_shape=verify_shape))
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/tensor_util.py", line 442, in make_tensor_proto
_AssertCompatible(values, dtype)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/tensor_util.py", line 353, in _AssertCompatible
(dtype.name, repr(mismatch), type(mismatch).__name__))
TypeError: Expected bool, got 1 of type 'int' instead.
Has anybody tried to train on TX2 or is it for my case only and i did something wrong?
ORIGINAL
Trying to train on mobilenet ssd on Jetson TX2 (I know it is not for taining but i have no better option)
followed these guides
https://towardsdatascience.com/how-to-train-your-own-object-detector-with-tensorflows-object-detector-api-bec72ecfe1d9
https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/running_locally.md
Training runs on my laptop (CPU) fine but i get the following error on my TX2
Traceback (most recent call last):
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/op_def_library.py", line 510, in _apply_op_helper
preferred_dtype=default_dtype)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py", line 1040, in internal_convert_to_tensor
ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py", line 883, in _TensorTensorConversionFunction
(dtype.name, t.dtype.name, str(t)))
ValueError: Tensor conversion requested dtype int32 for Tensor with dtype float32: 'Tensor("Loss/Loss/huber_loss/Sub_1:0", shape=(24, 1917, 4), dtype=float32)'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "object_detection/model_main.py", line 101, in <module>
tf.app.run()
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/platform/app.py", line 126, in run
_sys.exit(main(argv))
File "object_detection/model_main.py", line 97, in main
tf.estimator.train_and_evaluate(estimator, train_spec, eval_specs[0])
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/estimator/training.py", line 425, in train_and_evaluate
executor.run()
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/estimator/training.py", line 504, in run
self.run_local()
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/estimator/training.py", line 636, in run_local
hooks=train_hooks)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/estimator/estimator.py", line 355, in train
loss = self._train_model(input_fn, hooks, saving_listeners)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/estimator/estimator.py", line 824, in _train_model
features, labels, model_fn_lib.ModeKeys.TRAIN, self.config)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/estimator/estimator.py", line 805, in _call_model_fn
model_fn_results = self._model_fn(features=features, **kwargs)
File "/home/nvidia/tensorflow/models/research/object_detection/model_lib.py", line 287, in model_fn
prediction_dict, features[fields.InputDataFields.true_image_shape])
File "/home/nvidia/tensorflow/models/research/object_detection/meta_architectures/ssd_meta_arch.py", line 708, in loss
weights=batch_reg_weights)
File "/home/nvidia/tensorflow/models/research/object_detection/core/losses.py", line 74, in __call__
return self._compute_loss(prediction_tensor, target_tensor, **params)
File "/home/nvidia/tensorflow/models/research/object_detection/core/losses.py", line 157, in _compute_loss
reduction=tf.losses.Reduction.NONE
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/losses/losses_impl.py", line 444, in huber_loss
math_ops.multiply(delta, linear))
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/math_ops.py", line 326, in multiply
return gen_math_ops.mul(x, y, name)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/gen_math_ops.py", line 4689, in mul
"Mul", x=x, y=y, name=name)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/op_def_library.py", line 546, in _apply_op_helper
inferred_from[input_arg.type_attr]))
TypeError: Input 'y' of 'Mul' Op has type float32 that does not match type int32 of argument 'x'.
NOTE:
Used precompiled wheels to install tensorflow
There was an error with protobuf compiler that has been solved by
removing this line
reserved 6; (line number 104)
in ssd.proto on object_detection/protos folder
I found this solution here but i couldnt find the link
Here is the script to start training
PIPELINE_CONFIG_PATH=/home/nvidia/testtraining/models/model/ssd_mobilenet_v1_pets.config
MODEL_DIR=/home/nvidia/testtraining/models/model/
NUM_TRAIN_STEPS=50000
NUM_EVAL_STEPS=2000
python3 object_detection/model_main.py \
--pipeline_config_path=${PIPELINE_CONFIG_PATH} \
--model_dir=${MODEL_DIR} \
--num_train_steps=${NUM_TRAIN_STEPS} \
--num_eval_steps=${NUM_EVAL_STEPS} \
--alsologtostderr
Laptop TF version 1.10.0
Jetson TX2 tf version 1.6.0-rc1
I m new to Ubuntu and Tensorflow so go easy on me :)
Thanks
EDIT:
It seems like line 546, in _apply_op_helper is some sort of error handling line.
I tried to fix this error with following edit. Added these. In /usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/math_ops.py Added these to line 236 just after define statement
import tensorflow as tf
y = tf.cast(y, x.dtype)
This created some other error message which is solved by editing /home/nvidia/tensorflow/models/research/object_detection/matchers/argmax_matcher.py line 203-204 to these
indicator = tf.cast(1-indicator, x.dtype)
return tf.add(tf.multiply(x, indicator), val * indicator)
But i m still getting error
Traceback (most recent call last):
File "object_detection/model_main.py", line 101, in <module>
tf.app.run()
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/platform/app.py", line 126, in run
_sys.exit(main(argv))
File "object_detection/model_main.py", line 97, in main
tf.estimator.train_and_evaluate(estimator, train_spec, eval_specs[0])
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/estimator/training.py", line 425, in train_and_evaluate
executor.run()
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/estimator/training.py", line 504, in run
self.run_local()
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/estimator/training.py", line 636, in run_local
hooks=train_hooks)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/estimator/estimator.py", line 355, in train
loss = self._train_model(input_fn, hooks, saving_listeners)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/estimator/estimator.py", line 824, in _train_model
features, labels, model_fn_lib.ModeKeys.TRAIN, self.config)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/estimator/estimator.py", line 805, in _call_model_fn
model_fn_results = self._model_fn(features=features, **kwargs)
File "/home/nvidia/tensorflow/models/research/object_detection/model_lib.py", line 287, in model_fn
prediction_dict, features[fields.InputDataFields.true_image_shape])
File "/home/nvidia/tensorflow/models/research/object_detection/meta_architectures/ssd_meta_arch.py", line 686, in loss
keypoints, weights)
File "/home/nvidia/tensorflow/models/research/object_detection/meta_architectures/ssd_meta_arch.py", line 859, in _assign_targets
groundtruth_weights_list)
File "/home/nvidia/tensorflow/models/research/object_detection/core/target_assigner.py", line 481, in batch_assign_targets
anchors, gt_boxes, gt_class_targets, unmatched_class_label, gt_weights)
File "/home/nvidia/tensorflow/models/research/object_detection/core/target_assigner.py", line 180, in assign
match = self._matcher.match(match_quality_matrix, **params)
File "/home/nvidia/tensorflow/models/research/object_detection/core/matcher.py", line 239, in match
return Match(self._match(similarity_matrix, **params),
File "/home/nvidia/tensorflow/models/research/object_detection/matchers/argmax_matcher.py", line 190, in _match
_match_when_rows_are_non_empty, _match_when_rows_are_empty)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/util/deprecation.py", line 432, in new_func
return func(*args, **kwargs)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/control_flow_ops.py", line 2047, in cond
orig_res_t, res_t = context_t.BuildCondBranch(true_fn)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/control_flow_ops.py", line 1897, in BuildCondBranch
original_result = fn()
File "/home/nvidia/tensorflow/models/research/object_detection/matchers/argmax_matcher.py", line 153, in _match_when_rows_are_non_empty
-1)
File "/home/nvidia/tensorflow/models/research/object_detection/matchers/argmax_matcher.py", line 203, in _set_values_using_indicator
indicator = tf.cast(1-indicator, x.dtype)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/math_ops.py", line 983, in r_binary_op_wrapper
x = ops.convert_to_tensor(x, dtype=y.dtype.base_dtype, name="x")
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py", line 950, in convert_to_tensor
as_ref=False)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py", line 1040, in internal_convert_to_tensor
ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/constant_op.py", line 235, in _constant_tensor_conversion_function
return constant(v, dtype=dtype, name=name)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/constant_op.py", line 214, in constant
value, dtype=dtype, shape=shape, verify_shape=verify_shape))
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/tensor_util.py", line 433, in make_tensor_proto
_AssertCompatible(values, dtype)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/tensor_util.py", line 344, in _AssertCompatible
(dtype.name, repr(mismatch), type(mismatch).__name__))
TypeError: Expected bool, got 1 of type 'int' instead.
And this one is out of my leage
I think there is a huge compatability issues and i will just install tf 1.1 instead
I m open to new ideas though
The key to your problem is here(bottom of traceback):
TypeError: Input 'y' of 'Mul' Op has type float32 that does not match type int32 of argument 'x'.
your y has type float32, but argument - x(place where you pass this y to), needs to be int32.
Try using tf.cast(y, tf.int32) or something like that.
Sometimes there are some changes in tf/you use some older model versions. So this may happen from time to time.
So just open
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/op_def_library.py"
find line 546, and do that cast. (using sudo vim, i guess)
I am running the example code from below link to create a model and validating with iris data set.
http://machinelearningmastery.com/machine-learning-in-python-step-by-step/
In the above link, they used online dataset and it is working fine.
I have downloaded iris dataset in a csv format and ran the same program in linux box, but it throws the following error.
Traceback (most recent call last):
File "/home/nn/Desktop/iris.py", line 44, in <module>
cv_results = model_selection.cross_val_score(model, X_train, Y_train, cv=kfold, scoring=scoring)
File "/home/nn/.local/lib/python3.5/site-packages/sklearn/model_selection/_validation.py", line 140, in cross_val_score
for train, test in cv_iter)
File "/home/nn/.local/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 758, in __call__
while self.dispatch_one_batch(iterator):
File "/home/nn/.local/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 608, in dispatch_one_batch
self._dispatch(tasks)
File "/home/nn/.local/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 571, in _dispatch
job = self._backend.apply_async(batch, callback=cb)
File "/home/nn/.local/lib/python3.5/site-packages/sklearn/externals/joblib/_parallel_backends.py", line 109, in apply_async
result = ImmediateResult(func)
File "/home/nn/.local/lib/python3.5/site-packages/sklearn/externals/joblib/_parallel_backends.py", line 326, in __init__
self.results = batch()
File "/home/nn/.local/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 131, in __call__
return [func(*args, **kwargs) for func, args, kwargs in self.items]
File "/home/nn/.local/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 131, in <listcomp>
return [func(*args, **kwargs) for func, args, kwargs in self.items]
File "/home/nn/.local/lib/python3.5/site-packages/sklearn/model_selection/_validation.py", line 238, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "/home/nn/.local/lib/python3.5/site-packages/sklearn/linear_model/logistic.py", line 1173, in fit
order="C")
File "/home/nn/.local/lib/python3.5/site-packages/sklearn/utils/validation.py", line 521, in check_X_y
ensure_min_features, warn_on_dtype, estimator)
File "/home/nn/.local/lib/python3.5/site-packages/sklearn/utils/validation.py", line 382, in check_array
array = np.array(array, dtype=dtype, order=order, copy=copy)
ValueError: could not convert string to float: 'Petal.Length'
Can you check if when you are reading the data using read_csv are you ignoring the header?
You can use in read_csv:
header='infer'