ValueError Traceback (most recent call
last) /tmp/ipykernel_2113989/1063976035.py in
11
12 # we should specify shape of the input tensor
---> 13 k_model = pytorch_to_keras(model, input_var, [(3, 224, 224,)], verbose=True)
14 #k_model = pytorch_to_keras(model, input_var, [(3, None, None,)], verbose=True)
15
~/anaconda3/envs/torch/lib/python3.7/site-packages/pytorch2keras/converter.py
in pytorch_to_keras(model, args, input_shapes, change_ordering,
verbose, name_policy, use_optimizer, do_constant_folding)
82 k_model = onnx_to_keras(onnx_model=onnx_model, input_names=input_names,
83 input_shapes=input_shapes, name_policy=name_policy,
---> 84 verbose=verbose, change_ordering=change_ordering)
85
86 return k_model
~/anaconda3/envs/torch/lib/python3.7/site-packages/onnx2keras/converter.py
in onnx_to_keras(onnx_model, input_names, input_shapes, name_policy,
verbose, change_ordering)
179 lambda_funcs,
180 node_name,
--> 181 keras_names
182 )
183 if isinstance(keras_names, list):
~/anaconda3/envs/torch/lib/python3.7/site-packages/onnx2keras/operation_layers.py
in convert_clip(node, params, layers, lambda_func, node_name,
keras_name)
39 lambda_func[keras_name] = target_layer
40
---> 41 layers[node_name] = layer(input_0)
42
43
~/anaconda3/envs/torch/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/base_layer.py
in call(self, inputs, *args, **kwargs)
819 self.name)
820 graph = backend.get_graph()
--> 821 with graph.as_default(), backend.name_scope(self._name_scope()):
822 # Build layer if applicable (if the build method has been
823 # overridden).
~/anaconda3/envs/torch/lib/python3.7/site-packages/tensorflow_core/python/framework/ops.py
in enter(self) 6442 else: 6443 scope =
get_default_graph().name_scope(self._name)
-> 6444 scope_name = scope.enter() 6445 self._exit_fns.append(scope.exit) 6446 return scope_name
~/anaconda3/envs/torch/lib/python3.7/contextlib.py in enter(self)
110 del self.args, self.kwds, self.func
111 try:
--> 112 return next(self.gen)
113 except StopIteration:
114 raise RuntimeError("generator didn't yield") from None
~/anaconda3/envs/torch/lib/python3.7/site-packages/tensorflow_core/python/framework/ops.py
in name_scope(self, name) 4127 # op name regex, which
constrains the initial character. 4128 if not
_VALID_OP_NAME_REGEX.match(name):
-> 4129 raise ValueError("'%s' is not a valid scope name" % name) 4130 old_stack = self._name_stack 4131 if not
name: # Both for name=None and name="" we re-set to empty scope.
ValueError: 'onnx::Conv_369' is not a valid scope name
I am trying to convert pytorch pretrained model to keras via pytorch2keras (https://github.com/gmalivenko/pytorch2keras).
I had tried to install older version of Onnx (1.8.1) and tried to install Onnx from main branch (https://github.com/onnx/onnx).
However, I still face the error. May I know how to solve it?
Try setting name_policy='renumerate' or name_policy='short' for onnx_2_keras.
Related
I am pretty new to tensorflow Keras and there is a Problem Running Cross Validation that I could not fix. It all worked before I installed featurewiz (conda install -c conda-forge featurewiz).
from sklearn.model_selection import KFold, cross_validate, cross_val_score
from scikeras.wrappers import KerasClassifier
estimator = KerasClassifier(model, epochs=500, batch_size=10) #, verbose = 0
kfold = KFold(n_splits=5, shuffle=True)
results = cross_validate(estimator, X, y, cv=kfold, scoring=['accuracy', 'precision_weighted', 'recall_weighted', 'f1_weighted'], return_train_score=True)
print(results)
Error:
WARNING:absl:Found untraced functions such as _update_step_xla while saving (showing 1 of 1). These functions will not be directly callable after loading.
INFO:tensorflow:Assets written to: ram:///var/folders/c4/ywdtx99d1vl0ptsg1fy494_40000gn/T/tmpsuvxkjb9/assets
INFO:tensorflow:Assets written to: ram:///var/folders/c4/ywdtx99d1vl0ptsg1fy494_40000gn/T/tmpsuvxkjb9/assets
---------------------------------------------------------------------------
Empty Traceback (most recent call last)
File ~/tensorflow-test/env/lib/python3.8/site-packages/joblib/parallel.py:862, in Parallel.dispatch_one_batch(self, iterator)
861 try:
--> 862 tasks = self._ready_batches.get(block=False)
863 except queue.Empty:
864 # slice the iterator n_jobs * batchsize items at a time. If the
865 # slice returns less than that, then the current batchsize puts
(...)
868 # accordingly to distribute evenly the last items between all
869 # workers.
File ~/tensorflow-test/env/lib/python3.8/queue.py:167, in Queue.get(self, block, timeout)
166 if not self._qsize():
--> 167 raise Empty
168 elif timeout is None:
Empty:
During handling of the above exception, another exception occurred:
AttributeError Traceback (most recent call last)
Cell In[5], line 6
4 estimator = KerasClassifier(model, epochs=500, batch_size=10) #, verbose = 0
5 kfold = KFold(n_splits=5, shuffle=True) #seed, damit shuffle gleich bleibt , random_state=1337
----> 6 results = cross_validate(estimator, X, y, cv=kfold, scoring=['accuracy', 'precision_weighted', 'recall_weighted', 'f1_weighted'], return_train_score=True)
8 print(results)
File ~/tensorflow-test/env/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:266, in cross_validate(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch, return_train_score, return_estimator, error_score)
263 # We clone the estimator to make sure that all the folds are
264 # independent, and that it is pickle-able.
265 parallel = Parallel(n_jobs=n_jobs, verbose=verbose, pre_dispatch=pre_dispatch)
--> 266 results = parallel(
267 delayed(_fit_and_score)(
268 clone(estimator),
269 X,
270 y,
271 scorers,
272 train,
273 test,
274 verbose,
275 None,
276 fit_params,
277 return_train_score=return_train_score,
278 return_times=True,
279 return_estimator=return_estimator,
280 error_score=error_score,
281 )
282 for train, test in cv.split(X, y, groups)
283 )
285 _warn_or_raise_about_fit_failures(results, error_score)
287 # For callabe scoring, the return type is only know after calling. If the
288 # return type is a dictionary, the error scores can now be inserted with
289 # the correct key.
File ~/tensorflow-test/env/lib/python3.8/site-packages/joblib/parallel.py:1085, in Parallel.__call__(self, iterable)
1076 try:
1077 # Only set self._iterating to True if at least a batch
1078 # was dispatched. In particular this covers the edge
(...)
1082 # was very quick and its callback already dispatched all the
1083 # remaining jobs.
1084 self._iterating = False
-> 1085 if self.dispatch_one_batch(iterator):
1086 self._iterating = self._original_iterator is not None
1088 while self.dispatch_one_batch(iterator):
File ~/tensorflow-test/env/lib/python3.8/site-packages/joblib/parallel.py:873, in Parallel.dispatch_one_batch(self, iterator)
870 n_jobs = self._cached_effective_n_jobs
871 big_batch_size = batch_size * n_jobs
--> 873 islice = list(itertools.islice(iterator, big_batch_size))
874 if len(islice) == 0:
875 return False
File ~/tensorflow-test/env/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:268, in <genexpr>(.0)
263 # We clone the estimator to make sure that all the folds are
264 # independent, and that it is pickle-able.
265 parallel = Parallel(n_jobs=n_jobs, verbose=verbose, pre_dispatch=pre_dispatch)
266 results = parallel(
267 delayed(_fit_and_score)(
--> 268 clone(estimator),
269 X,
270 y,
271 scorers,
272 train,
273 test,
274 verbose,
275 None,
276 fit_params,
277 return_train_score=return_train_score,
278 return_times=True,
279 return_estimator=return_estimator,
280 error_score=error_score,
281 )
282 for train, test in cv.split(X, y, groups)
283 )
285 _warn_or_raise_about_fit_failures(results, error_score)
287 # For callabe scoring, the return type is only know after calling. If the
288 # return type is a dictionary, the error scores can now be inserted with
289 # the correct key.
File ~/tensorflow-test/env/lib/python3.8/site-packages/sklearn/base.py:89, in clone(estimator, safe)
87 new_object_params = estimator.get_params(deep=False)
88 for name, param in new_object_params.items():
---> 89 new_object_params[name] = clone(param, safe=False)
90 new_object = klass(**new_object_params)
91 params_set = new_object.get_params(deep=False)
File ~/tensorflow-test/env/lib/python3.8/site-packages/sklearn/base.py:70, in clone(estimator, safe)
68 elif not hasattr(estimator, "get_params") or isinstance(estimator, type):
69 if not safe:
---> 70 return copy.deepcopy(estimator)
71 else:
72 if isinstance(estimator, type):
File ~/tensorflow-test/env/lib/python3.8/copy.py:153, in deepcopy(x, memo, _nil)
151 copier = getattr(x, "__deepcopy__", None)
152 if copier is not None:
--> 153 y = copier(memo)
154 else:
155 reductor = dispatch_table.get(cls)
File ~/tensorflow-test/env/lib/python3.8/site-packages/scikeras/_saving_utils.py:117, in deepcopy_model(model, memo)
116 def deepcopy_model(model: keras.Model, memo: Dict[Hashable, Any]) -> keras.Model:
--> 117 _, (model_bytes, optimizer_weights) = pack_keras_model(model)
118 new_model = unpack_keras_model(model_bytes, optimizer_weights)
119 memo[model] = new_model
File ~/tensorflow-test/env/lib/python3.8/site-packages/scikeras/_saving_utils.py:108, in pack_keras_model(model)
106 optimizer_weights = None
107 if model.optimizer is not None:
--> 108 optimizer_weights = model.optimizer.get_weights()
109 model_bytes = np.asarray(memoryview(b.read()))
110 return (
111 unpack_keras_model,
112 (model_bytes, optimizer_weights),
113 )
AttributeError: 'Adam' object has no attribute 'get_weights'
I created a Tensorflow enviroment on my M1 Macbook following https://github.com/mrdbourke/m1-machine-learning-test.
It all worked, I got following results:
TensorFlow has access to the following devices:
[PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU'), PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]
TensorFlow version: 2.11.0
I also installed featurewiz, I am not sure if there are some Problems installing it (I did conda install -c conda-forge featurewiz)
SciKeras doesn't work with TensorFlow 2.11. The TensorFlow team release a breaking change in a minor version bump (they removed the get_weights() method). It will be fixed in SciKeras soon: https://github.com/adriangb/scikeras/pull/287
Edit: that PR was merged so the new version of SciKeras (v0.10.0) should solve this issue.
I am trying to load built-in dataset lvis. It turns out that the tfds and lvis should be imported and installed respectively, however, I did possible all, it still does not work.
import os
import tensorflow as tf
from matplotlib import pyplot as plt
%matplotlib inline
!pip install lvis
!pip install tfds-nightly
import tensorflow_datasets as tfds
train_data, info = tfds.load('lvis', split='train', as_supervised=True, with_info=True)
validation_data = tfds.load('lvis', split='validation', as_supervised=True)
test_data = tfds.load('lvis', split='test', as_supervised=True)
There are some odd outputs after running upon codes in colab.
otFoundError Traceback (most recent call last)
/usr/local/lib/python3.7/dist-packages/tensorflow_datasets/core/utils/py_utils.py in try_reraise(*args, **kwargs)
391 try:
--> 392 yield
393 except Exception as e: # pylint: disable=broad-except
15 frames
/usr/local/lib/python3.7/dist-packages/tensorflow_datasets/core/load.py in builder(name, try_gcs, **builder_kwargs)
167 with py_utils.try_reraise(prefix=f'Failed to construct dataset {name}: '):
--> 168 return cls(**builder_kwargs) # pytype: disable=not-instantiable
169
/usr/local/lib/python3.7/dist-packages/tensorflow_datasets/core/dataset_builder.py in __init__(self, file_format, **kwargs)
917 """
--> 918 super().__init__(**kwargs)
919 self.info.set_file_format(file_format)
/usr/local/lib/python3.7/dist-packages/tensorflow_datasets/core/dataset_builder.py in __init__(self, data_dir, config, version)
184 else: # Use the code version (do not restore data)
--> 185 self.info.initialize_from_bucket()
186
/usr/local/lib/python3.7/dist-packages/tensorflow_datasets/core/utils/py_utils.py in __get__(self, obj, objtype)
145 if cached is None:
--> 146 cached = self.fget(obj) # pytype: disable=attribute-error
147 setattr(obj, attr, cached)
/usr/local/lib/python3.7/dist-packages/tensorflow_datasets/core/dataset_builder.py in info(self)
328 "the restored dataset.")
--> 329 info = self._info()
330 if not isinstance(info, dataset_info.DatasetInfo):
/usr/local/lib/python3.7/dist-packages/tensorflow_datasets/object_detection/lvis/lvis.py in _info(self)
94 names_file=tfds.core.tfds_path(
---> 95 'object_detection/lvis/lvis_classes.txt'))
96 return tfds.core.DatasetInfo(
/usr/local/lib/python3.7/dist-packages/tensorflow_datasets/core/features/class_label_feature.py in __init__(self, num_classes, names, names_file)
67 else:
---> 68 self.names = _load_names_from_file(names_file)
69
/usr/local/lib/python3.7/dist-packages/tensorflow_datasets/core/features/class_label_feature.py in _load_names_from_file(names_filepath)
198 name.strip()
--> 199 for name in tf.compat.as_text(f.read()).split("\n")
200 if name.strip() # Filter empty names
/usr/local/lib/python3.7/dist-packages/tensorflow/python/lib/io/file_io.py in read(self, n)
116 """
--> 117 self._preread_check()
118 if n == -1:
/usr/local/lib/python3.7/dist-packages/tensorflow/python/lib/io/file_io.py in _preread_check(self)
79 self._read_buf = _pywrap_file_io.BufferedInputStream(
---> 80 compat.path_to_str(self.__name), 1024 * 512)
81
NotFoundError: /usr/local/lib/python3.7/dist-packages/tensorflow_datasets/object_detection/lvis/lvis_classes.txt; No such file or directory
The above exception was the direct cause of the following exception:
RuntimeError Traceback (most recent call last)
<ipython-input-4-b8c819fe5c62> in <module>()
----> 1 train_data, info = tfds.load('lvis', split='train', as_supervised=True, with_info=True)
2 validation_data = tfds.load('lvis', split='validation', as_supervised=True)
3 test_data = tfds.load('lvis', split='test', as_supervised=True)
/usr/local/lib/python3.7/dist-packages/tensorflow_datasets/core/load.py in load(name, split, data_dir, batch_size, shuffle_files, download, as_supervised, decoders, read_config, with_info, builder_kwargs, download_and_prepare_kwargs, as_dataset_kwargs, try_gcs)
315 builder_kwargs = {}
316
--> 317 dbuilder = builder(name, data_dir=data_dir, try_gcs=try_gcs, **builder_kwargs)
318 if download:
319 download_and_prepare_kwargs = download_and_prepare_kwargs or {}
/usr/local/lib/python3.7/dist-packages/tensorflow_datasets/core/load.py in builder(name, try_gcs, **builder_kwargs)
166 if cls:
167 with py_utils.try_reraise(prefix=f'Failed to construct dataset {name}: '):
--> 168 return cls(**builder_kwargs) # pytype: disable=not-instantiable
169
170 # If neither the code nor the files are found, raise DatasetNotFoundError
/usr/lib/python3.7/contextlib.py in __exit__(self, type, value, traceback)
128 value = type()
129 try:
--> 130 self.gen.throw(type, value, traceback)
131 except StopIteration as exc:
132 # Suppress StopIteration *unless* it's the same exception that
/usr/local/lib/python3.7/dist-packages/tensorflow_datasets/core/utils/py_utils.py in try_reraise(*args, **kwargs)
392 yield
393 except Exception as e: # pylint: disable=broad-except
--> 394 reraise(e, *args, **kwargs)
395
396
/usr/local/lib/python3.7/dist-packages/tensorflow_datasets/core/utils/py_utils.py in reraise(e, prefix, suffix)
359 else:
360 exception = RuntimeError(f'{type(e).__name__}: {msg}')
--> 361 raise exception from e
362 # Otherwise, modify the exception in-place
363 elif len(e.args) <= 1:
RuntimeError: NotFoundError: Failed to construct dataset lvis: /usr/local/lib/python3.7/dist-packages/tensorflow_datasets/object_detection/lvis/lvis_classes.txt; No such file or directory
This is what I did to get it to work on Colab Notebook:
!pip install -q tfds-nightly tensorflow tensorflow-datasets matplotlib lvis pycocotools apache_beam
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
import tensorflow_datasets as tfds
Since the tfds object detection lvis folder isn't up to date, I deleted that folder then redownloaded it from the tfds github page.
First install github-clone so we can download specific repo subfolders
!pip install github-clone
Then remove the lvis folder and redownload it from github:
!rm -rf ../usr/local/lib/python3.7/dist-packages/tensorflow_datasets/object_detection/lvis
!ghclone https://github.com/tensorflow/datasets/tree/master/tensorflow_datasets/object_detection/lvis
!mv ./lvis ../usr/local/lib/python3.7/dist-packages/tensorflow_datasets/object_detection/
After that I could get it to work, this next chunk of code worked for me:
ds, info = tfds.load('lvis', split='train[:25%]', with_info=True,
data_dir= '../content/tensorflow_datasets/',
decoders=tfds.decode.PartialDecoding({
'image': True,
'features': tfds.features.FeaturesDict({'image/id':True,
'objects':tfds.features.Sequence({
'id': True,
'bbox': True,
'label': tfds.features.ClassLabel(names=['skateboard','shoe'])
})
})
})
)
I already looked at the other similar questions, but they did not help me. I'm attempting to use GridSearchCV. I'm using three pipelines to predict nfl play data. It works pretty well until the grid search part.
Here is my code.
pipe_nfl1_1 = Pipeline([
('ssc', StandardScaler()),
('lr', LogisticRegression(random_state=42))
])
pipe_nfl1_2 = Pipeline([
('mms', MinMaxScaler()),
('rfc', RandomForestClassifier(random_state=42))
])
pipe_nfl1_3 = Pipeline([
('mms', MinMaxScaler()),
('svc', svm.SVC(random_state=42))
])
pipelines1 = [pipe_nfl1_1, pipe_nfl1_2, pipe_nfl1_3]
pipe_dict1 = {0: 'Logistic Regression', 1: 'Random Forest', 2: 'SVC'}
for pipe in pipelines1:
pipe.fit(X_train1, y_train1)
print('Pipeline test accuracy for predicting 1st downs:')
for idx, val in enumerate(pipelines1):
print(' %s: %.4f' % (pipe_dict1[idx], val.score(X_test1, y_test1)))
best_acc1 = 0.0
best_clf1 = 0
best_pipe1 = ''
for idx, val in enumerate(pipelines1):
if val.score(X_test1, y_test1) > best_acc1:
best_acc1 = val.score(X_test1, y_test1)
best_pipe1 = val
best_clf1 = idx
best_acc1 *= 100
print('Classifier with best accuracy for predicting 1st downs is %s with %.2f' % (pipe_dict1[best_clf1], best_acc1) + '%')
param_grid1 = {
'lr__n_estimators': [2, 4, 6]
}
grid_search1 = GridSearchCV(pipe_nfl1_1, param_grid1, cv=2)
# fine-tune the hyperparameters
grid_search1.fit(X_train1, y_train1)
# get the best model
final_model1 = grid_search1.best_estimator_
grid_search.best_score_
But I'm getting an error:
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-33-6b0007d9b8f1> in <module>
2
3 # fine-tune the hyperparameters
----> 4 grid_search1.fit(X_train1, y_train1)
5
6 # get the best model
~\AppData\Local\Programs\Python\Python38\lib\site-packages\sklearn\utils\validation.py in inner_f(*args, **kwargs)
70 FutureWarning)
71 kwargs.update({k: arg for k, arg in zip(sig.parameters, args)})
---> 72 return f(**kwargs)
73 return inner_f
74
~\AppData\Local\Programs\Python\Python38\lib\site-packages\sklearn\model_selection\_search.py in fit(self, X, y, groups, **fit_params)
734 return results
735
--> 736 self._run_search(evaluate_candidates)
737
738 # For multi-metric evaluation, store the best_index_, best_params_ and
~\AppData\Local\Programs\Python\Python38\lib\site-packages\sklearn\model_selection\_search.py in _run_search(self, evaluate_candidates)
1186 def _run_search(self, evaluate_candidates):
1187 """Search all candidates in param_grid"""
-> 1188 evaluate_candidates(ParameterGrid(self.param_grid))
1189
1190
~\AppData\Local\Programs\Python\Python38\lib\site-packages\sklearn\model_selection\_search.py in evaluate_candidates(candidate_params)
706 n_splits, n_candidates, n_candidates * n_splits))
707
--> 708 out = parallel(delayed(_fit_and_score)(clone(base_estimator),
709 X, y,
710 train=train, test=test,
~\AppData\Local\Programs\Python\Python38\lib\site-packages\joblib\parallel.py in __call__(self, iterable)
1027 # remaining jobs.
1028 self._iterating = False
-> 1029 if self.dispatch_one_batch(iterator):
1030 self._iterating = self._original_iterator is not None
1031
~\AppData\Local\Programs\Python\Python38\lib\site-packages\joblib\parallel.py in dispatch_one_batch(self, iterator)
845 return False
846 else:
--> 847 self._dispatch(tasks)
848 return True
849
~\AppData\Local\Programs\Python\Python38\lib\site-packages\joblib\parallel.py in _dispatch(self, batch)
763 with self._lock:
764 job_idx = len(self._jobs)
--> 765 job = self._backend.apply_async(batch, callback=cb)
766 # A job can complete so quickly than its callback is
767 # called before we get here, causing self._jobs to
~\AppData\Local\Programs\Python\Python38\lib\site-packages\joblib\_parallel_backends.py in apply_async(self, func, callback)
206 def apply_async(self, func, callback=None):
207 """Schedule a func to be run"""
--> 208 result = ImmediateResult(func)
209 if callback:
210 callback(result)
~\AppData\Local\Programs\Python\Python38\lib\site-packages\joblib\_parallel_backends.py in __init__(self, batch)
570 # Don't delay the application, to avoid keeping the input
571 # arguments in memory
--> 572 self.results = batch()
573
574 def get(self):
~\AppData\Local\Programs\Python\Python38\lib\site-packages\joblib\parallel.py in __call__(self)
250 # change the default number of processes to -1
251 with parallel_backend(self._backend, n_jobs=self._n_jobs):
--> 252 return [func(*args, **kwargs)
253 for func, args, kwargs in self.items]
254
~\AppData\Local\Programs\Python\Python38\lib\site-packages\joblib\parallel.py in <listcomp>(.0)
250 # change the default number of processes to -1
251 with parallel_backend(self._backend, n_jobs=self._n_jobs):
--> 252 return [func(*args, **kwargs)
253 for func, args, kwargs in self.items]
254
~\AppData\Local\Programs\Python\Python38\lib\site-packages\sklearn\model_selection\_validation.py in _fit_and_score(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, return_estimator, error_score)
518 cloned_parameters[k] = clone(v, safe=False)
519
--> 520 estimator = estimator.set_params(**cloned_parameters)
521
522 start_time = time.time()
~\AppData\Local\Programs\Python\Python38\lib\site-packages\sklearn\pipeline.py in set_params(self, **kwargs)
139 self
140 """
--> 141 self._set_params('steps', **kwargs)
142 return self
143
~\AppData\Local\Programs\Python\Python38\lib\site-packages\sklearn\utils\metaestimators.py in _set_params(self, attr, **params)
51 self._replace_estimator(attr, name, params.pop(name))
52 # 3. Step parameters and other initialisation arguments
---> 53 super().set_params(**params)
54 return self
55
~\AppData\Local\Programs\Python\Python38\lib\site-packages\sklearn\base.py in set_params(self, **params)
259
260 for key, sub_params in nested_params.items():
--> 261 valid_params[key].set_params(**sub_params)
262
263 return self
~\AppData\Local\Programs\Python\Python38\lib\site-packages\sklearn\base.py in set_params(self, **params)
247 key, delim, sub_key = key.partition('__')
248 if key not in valid_params:
--> 249 raise ValueError('Invalid parameter %s for estimator %s. '
250 'Check the list of available parameters '
251 'with `estimator.get_params().keys()`.' %
ValueError: Invalid parameter n_estimators for estimator LogisticRegression(random_state=42). Check the list of available parameters with `estimator.get_params().keys()`.
I've done LogisticRegression.get_params().keys() to get the keys, but it returns get_params() missing 1 required positional argument: 'self'.
You shouldn't have the leading underscores in the parameter names. You want your param_grid1 dict to consist of keys that are actually parameters accepted by the model you're using. That would be n_estimators for RandomForest, and C for LogisticRegression. With that said, n_estimators is a parameter for the model RandomForest, but it's not a parameter for LogisticRegression. C is a parameter for LogisticRegression.
I think what you want to do is a grid search over the parameter space of the model that performs best, right? In that case, your param_grid1 variable should be updated to the model that performs best. The parameters accepted by the models you're testing vary from model to model.
I want to calculate semantic similarity between sentences using BERT. I found this code on github for an already fine-tuned BERT for semantic similarity:
from semantic_text_similarity.models import WebBertSimilarity
from semantic_text_similarity.models import ClinicalBertSimilarity
web_model = WebBertSimilarity(device='cpu', batch_size=10)
It downloads 100% and gives me the following error (this is the final line):
TypeError: init_weights() takes 1 positional argument but 2 were given
I tried to read about this error, but I don't understand where is the 2 positional arguments that where given instead of one.
the model I downloaded is from https://github.com/AndriyMulyar/semantic-text-similarity
I'd appreciate any hint on where to look.
Thanks!
-------------------------EDIT QUESTION------------------------------
This is the whole error:
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-73-97be4030b59e> in <module>()
----> 1 web_model = WebBertSimilarity(device='cpu', batch_size=10) #defaults to GPU prediction
/anaconda3/lib/python3.6/site-packages/semantic_text_similarity/models/bert/web_similarity.py in __init__(self, device, batch_size, model_name)
6 def __init__(self, device='cuda', batch_size=10, model_name="web-bert-similarity"):
7 model_path = get_model_path(model_name)
----> 8 super().__init__(device=device, batch_size=batch_size, bert_model_path=model_path)
/anaconda3/lib/python3.6/site-packages/semantic_text_similarity/models/bert/similarity.py in __init__(self, args, device, bert_model_path, batch_size, learning_rate, weight_decay, additional_features)
80 config.pretrained_config_archive_map['additional_features'] = additional_features
81
---> 82 self.regressor_net = BertSimilarityRegressor.from_pretrained(self.args['bert_model_path'], config=config)
83 self.optimizer = torch.optim.Adam(
84 self.regressor_net.parameters(),
/anaconda3/lib/python3.6/site-packages/pytorch_transformers/modeling_utils.py in from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs)
534 if hasattr(model, 'tie_weights'):
535 model.tie_weights() # make sure word embedding weights are still tied
--> 536
537 # Set model in evaluation mode to desactivate DropOut modules by default
538 model.eval()
/anaconda3/lib/python3.6/site-packages/semantic_text_similarity/models/bert/similarity.py in __init__(self, bert_model_config)
25 )
26
---> 27 self.apply(self.init_weights)
28
29
/anaconda3/lib/python3.6/site-packages/torch/nn/modules/module.py in apply(self, fn)
291 """
292 for module in self.children():
--> 293 module.apply(fn)
294 fn(self)
295 return self
/anaconda3/lib/python3.6/site-packages/torch/nn/modules/module.py in apply(self, fn)
291 """
292 for module in self.children():
--> 293 module.apply(fn)
294 fn(self)
295 return self
/anaconda3/lib/python3.6/site-packages/torch/nn/modules/module.py in apply(self, fn)
291 """
292 for module in self.children():
--> 293 module.apply(fn)
294 fn(self)
295 return self
/anaconda3/lib/python3.6/site-packages/torch/nn/modules/module.py in apply(self, fn)
292 for module in self.children():
293 module.apply(fn)
--> 294 fn(self)
295 return self
296
TypeError: init_weights() takes 1 positional argument but 2 were given
I am having an issue restoring a tf.estimator.BoostedTreesRegressor model using tf.SavedModel. When reloading the model from the saved model directory using tf.contrib.predictor.from_saved_model() I receive the following error:
KeyError: "The name 'boosted_trees/QuantileAccumulator/' refers to an
Operation not in the graph."
This error only occurs when using numeric features (e.g. tf.feature_column.numeric_column). Reloading the model works fine when using only categorical columns
When I'm not saving/restoring, BoostedTreesRegressor evaluates and predicts successfully with all features.
The following estimator save/restore scenarios have worked successfully:
- DNNRegressor with numeric and categorical features
- LinearRegressor with numeric and categorical features
- BoostedTreeRegressor with just categorical features
fc = tf.feature_column
feature_columns = [
fc.numeric_column('f1', dtype=tf.int64),
fc.numeric_column('f2', dtype=tf.int64),
fc.indicator_column(
fc.categorical_column_with_vocabulary_list('f3',f3)),
fc.indicator_column(
fc.categorical_column_with_vocabulary_list('f4',f4))
]
feature_spec = fc.make_parse_example_spec(feature_columns)
params = {
'feature_columns' : feature_columns,
'n_batches_per_layer' : n_batches,
'n_trees': 200,
'max_depth': 6,
'learning_rate': 0.01
}
regressor = tf.estimator.BoostedTreesRegressor(**params)
regressor.train(train_input_fn, max_steps=400)
serving_input_receiver_fn = tf.estimator.export.build_parsing_serving_input_receiver_fn(feature_spec)
regressor.export_saved_model('saved_model', serving_input_receiver_fn)
.
.
.
# latest is path to saved model
predict_fn = predictor.from_saved_model(latest[:-4])
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-101-ee20beae4424> in <module>
----> 1 predict_fn = predictor.from_saved_model(latest[:-4])
/usr/local/anaconda3/envs/zume/lib/python3.7/site-packages/tensorflow/contrib/predictor/predictor_factories.py in from_saved_model(export_dir, signature_def_key, signature_def, input_names, output_names, tags, graph, config)
151 tags=tags,
152 graph=graph,
--> 153 config=config)
/usr/local/anaconda3/envs/zume/lib/python3.7/site-packages/tensorflow/contrib/predictor/saved_model_predictor.py in __init__(self, export_dir, signature_def_key, signature_def, input_names, output_names, tags, graph, config)
151 with self._graph.as_default():
152 self._session = session.Session(config=config)
--> 153 loader.load(self._session, tags.split(','), export_dir)
154
155 if input_names is None:
/usr/local/anaconda3/envs/zume/lib/python3.7/site-packages/tensorflow/python/util/deprecation.py in new_func(*args, **kwargs)
322 'in a future version' if date is None else ('after %s' % date),
323 instructions)
--> 324 return func(*args, **kwargs)
325 return tf_decorator.make_decorator(
326 func, new_func, 'deprecated',
/usr/local/anaconda3/envs/zume/lib/python3.7/site-packages/tensorflow/python/saved_model/loader_impl.py in load(sess, tags, export_dir, import_scope, **saver_kwargs)
267 """
268 loader = SavedModelLoader(export_dir)
--> 269 return loader.load(sess, tags, import_scope, **saver_kwargs)
270
271
/usr/local/anaconda3/envs/zume/lib/python3.7/site-packages/tensorflow/python/saved_model/loader_impl.py in load(self, sess, tags, import_scope, **saver_kwargs)
418 with sess.graph.as_default():
419 saver, _ = self.load_graph(sess.graph, tags, import_scope,
--> 420 **saver_kwargs)
421 self.restore_variables(sess, saver, import_scope)
422 self.run_init_ops(sess, tags, import_scope)
/usr/local/anaconda3/envs/zume/lib/python3.7/site-packages/tensorflow/python/saved_model/loader_impl.py in load_graph(self, graph, tags, import_scope, **saver_kwargs)
348 with graph.as_default():
349 return tf_saver._import_meta_graph_with_return_elements( # pylint: disable=protected-access
--> 350 meta_graph_def, import_scope=import_scope, **saver_kwargs)
351
352 def restore_variables(self, sess, saver, import_scope=None):
/usr/local/anaconda3/envs/zume/lib/python3.7/site-packages/tensorflow/python/training/saver.py in _import_meta_graph_with_return_elements(meta_graph_or_file, clear_devices, import_scope, return_elements, **kwargs)
1455 import_scope=import_scope,
1456 return_elements=return_elements,
-> 1457 **kwargs))
1458
1459 saver = _create_saver_from_imported_meta_graph(
/usr/local/anaconda3/envs/zume/lib/python3.7/site-packages/tensorflow/python/framework/meta_graph.py in import_scoped_meta_graph_with_return_elements(meta_graph_or_file, clear_devices, graph, import_scope, input_map, unbound_inputs_col_name, restore_collections_predicate, return_elements)
850 for value in field.value:
851 col_op = graph.as_graph_element(
--> 852 ops.prepend_name_scope(value, scope_to_prepend_to_names))
853 graph.add_to_collection(key, col_op)
854 elif kind == "int64_list":
/usr/local/anaconda3/envs/zume/lib/python3.7/site-packages/tensorflow/python/framework/ops.py in as_graph_element(self, obj, allow_tensor, allow_operation)
3476
3477 with self._lock:
-> 3478 return self._as_graph_element_locked(obj, allow_tensor, allow_operation)
3479
3480 def _as_graph_element_locked(self, obj, allow_tensor, allow_operation):
/usr/local/anaconda3/envs/zume/lib/python3.7/site-packages/tensorflow/python/framework/ops.py in _as_graph_element_locked(self, obj, allow_tensor, allow_operation)
3536 if name not in self._nodes_by_name:
3537 raise KeyError("The name %s refers to an Operation not in the "
-> 3538 "graph." % repr(name))
3539 return self._nodes_by_name[name]
3540
KeyError: "The name 'boosted_trees/QuantileAccumulator/' refers to an Operation not in the graph."
If you are using Tensorflow Version, 1.x (1.14, 1.15), you can use
tf.compat.v1.saved_model.load or tf.compat.v1.saved_model.loader.load or
tf.saved_model.loader.load to Load a Saved Model.
If you are using Tensorflow Version 2, below is the code where Saving and Restoring is Successful using tf.estimator.BoostedTreesClassifier:
n_batches = 1
est = tf.estimator.BoostedTreesClassifier(feature_columns,
n_batches_per_layer=n_batches)
# The model will stop training once the specified number of trees is built, not
# based on the number of steps.
est.train(train_input_fn, max_steps=100)
# Eval.
result = est.evaluate(eval_input_fn)
clear_output()
print(pd.Series(result))
feature_spec = fc.make_parse_example_spec(feature_columns)
serving_input_receiver_fn = tf.estimator.export.build_parsing_serving_input_receiver_fn(feature_spec)
Exported_Path = est.export_saved_model('saved_model', serving_input_receiver_fn)
imported = tf.saved_model.load(Exported_Path)
For the complete working code using Tensorflow Version 2, please find this Github Gist.