I am a beginner training an image dataset on diabetic retinopathy, using the keras_flow_from_dataframe class. But my model has been underfitting. So I tried preprocessing, by writing a custom preprocessing function to be passed in my image data generator class, using OpenCV's adaptive thresholding implementation. The function works very well when I use it outside of Keras, but when I add it to my image data generator class and fit my model, it returns a type error saying bad argument type for built-in operation before my first epoch starts.
Here's the preprocessing code:
def preprocess(im):
im = cv2.imread(im, 1)
im= cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
im=cv2.resize(im, (300,300))
im.resize(300, 300, 1)
block_size = 73
constant = 2
# ADAPTIVE GAUSSIAN THRESHOLDING
thr2 = cv2.adaptiveThreshold(im, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, block_size, constant)
return thr2
It runs well outside of Keras when I test it with the images in my dataframe, but when I add it to my image data generator class, it throws an error.
train_datagen = ImageDataGenerator(
rotation_range=30,
width_shift_range=0.4,
height_shift_range=0.4,
shear_range=0.3,
zoom_range=0.3,
horizontal_flip = True,
fill_mode='nearest',
preprocessing_function = preprocess)
valid_datagen = ImageDataGenerator(preprocessing_function = preprocess)
Then I load in my dataset from dataframe:
from keras.preprocessing.image import ImageDataGenerator
traingen = train_datagen.flow_from_dataframe(x_train, x_col='path', y_col='level',class_mode='other',
target_size=(300,300), color_mode='grayscale', batch_size=16)
validgen = valid_datagen.flow_from_dataframe(valid, x_col='path', y_col='level',class_mode='other',
target_size=(300,300), color_mode='grayscale', batch_size=16)
Then I fit the model using model.fit_generator, which then throws me the type error: bad argument type for built-in operation.
TypeError Traceback (most recent call last)
<ipython-input-126-30ceb84a2574> in <module>()
2
3 history = model.fit_generator(traingen, validation_data = validgen, epochs=100, steps_per_epoch=10,
----> 4 validation_steps=10, verbose=1, callbacks=[lr_reduction])
5
6
~/var/python/lib/python3.6/site-packages/keras/legacy/interfaces.py in wrapper(*args, **kwargs)
89 warnings.warn('Update your `' + object_name + '` call to the ' +
90 'Keras 2 API: ' + signature, stacklevel=2)
---> 91 return func(*args, **kwargs)
92 wrapper._original_function = func
93 return wrapper
~/var/python/lib/python3.6/site-packages/keras/engine/training.py in fit_generator(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)
1416 use_multiprocessing=use_multiprocessing,
1417 shuffle=shuffle,
-> 1418 initial_epoch=initial_epoch)
1419
1420 #interfaces.legacy_generator_methods_support
~/var/python/lib/python3.6/site-packages/keras/engine/training_generator.py in fit_generator(model, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)
179 batch_index = 0
180 while steps_done < steps_per_epoch:
--> 181 generator_output = next(output_generator)
182
183 if not hasattr(generator_output, '__len__'):
~/var/python/lib/python3.6/site-packages/keras/utils/data_utils.py in get(self)
599 except Exception as e:
600 self.stop()
--> 601 six.reraise(*sys.exc_info())
602
603
~/var/python/lib/python3.6/site-packages/six.py in reraise(tp, value, tb)
691 if value.__traceback__ is not tb:
692 raise value.with_traceback(tb)
--> 693 raise value
694 finally:
695 value = None
~/var/python/lib/python3.6/site-packages/keras/utils/data_utils.py in get(self)
593 try:
594 while self.is_running():
--> 595 inputs = self.queue.get(block=True).get()
596 self.queue.task_done()
597 if inputs is not None:
~/var/python/lib/python3.6/multiprocessing/pool.py in get(self, timeout)
642 return self._value
643 else:
--> 644 raise self._value
645
646 def _set(self, i, obj):
~/var/python/lib/python3.6/multiprocessing/pool.py in worker(inqueue, outqueue, initializer, initargs, maxtasks, wrap_exception)
117 job, i, func, args, kwds = task
118 try:
--> 119 result = (True, func(*args, **kwds))
120 except Exception as e:
121 if wrap_exception and func is not _helper_reraises_exception:
~/var/python/lib/python3.6/site-packages/keras/utils/data_utils.py in get_index(uid, i)
399 The value at index `i`.
400 """
--> 401 return _SHARED_SEQUENCES[uid][i]
402
403
~/var/python/lib/python3.6/site-packages/keras_preprocessing/image/iterator.py in __getitem__(self, idx)
63 index_array = self.index_array[self.batch_size * idx:
64 self.batch_size * (idx + 1)]
---> 65 return self._get_batches_of_transformed_samples(index_array)
66
67 def __len__(self):
~/var/python/lib/python3.6/site-packages/keras_preprocessing/image/iterator.py in _get_batches_of_transformed_samples(self, index_array)
233 params = self.image_data_generator.get_random_transform(x.shape)
234 x = self.image_data_generator.apply_transform(x, params)
--> 235 x = self.image_data_generator.standardize(x)
236 batch_x[i] = x
237 # optionally save augmented images to disk for debugging purposes
~/var/python/lib/python3.6/site-packages/keras_preprocessing/image/image_data_generator.py in standardize(self, x)
695 """
696 if self.preprocessing_function:
--> 697 x = self.preprocessing_function(x)
698 if self.rescale:
699 x *= self.rescale
<ipython-input-112-7bddefa5e731> in preprocess(im)
1 def preprocess(im):
----> 2 im = cv2.imread(im, 1)
3 im= cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
4 im=cv2.resize(im, (300,300))
5 im.resize(300, 300, 1)
TypeError: bad argument type for built-in operation
TypeError Traceback (most recent call last)
<ipython-input-126-30ceb84a2574> in <module>()
2
3 history = model.fit_generator(traingen, validation_data = validgen, epochs=100, steps_per_epoch=10,
----> 4 validation_steps=10, verbose=1, callbacks=[lr_reduction])
5
6
~/var/python/lib/python3.6/site-packages/keras/legacy/interfaces.py in wrapper(*args, **kwargs)
89 warnings.warn('Update your `' + object_name + '` call to the ' +
90 'Keras 2 API: ' + signature, stacklevel=2)
---> 91 return func(*args, **kwargs)
92 wrapper._original_function = func
93 return wrapper
~/var/python/lib/python3.6/site-packages/keras/engine/training.py in fit_generator(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)
1416 use_multiprocessing=use_multiprocessing,
1417 shuffle=shuffle,
-> 1418 initial_epoch=initial_epoch)
1419
1420 #interfaces.legacy_generator_methods_support
~/var/python/lib/python3.6/site-packages/keras/engine/training_generator.py in fit_generator(model, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)
179 batch_index = 0
180 while steps_done < steps_per_epoch:
--> 181 generator_output = next(output_generator)
182
183 if not hasattr(generator_output, '__len__'):
~/var/python/lib/python3.6/site-packages/keras/utils/data_utils.py in get(self)
599 except Exception as e:
600 self.stop()
--> 601 six.reraise(*sys.exc_info())
602
603
~/var/python/lib/python3.6/site-packages/six.py in reraise(tp, value, tb)
691 if value.__traceback__ is not tb:
692 raise value.with_traceback(tb)
--> 693 raise value
694 finally:
695 value = None
~/var/python/lib/python3.6/site-packages/keras/utils/data_utils.py in get(self)
593 try:
594 while self.is_running():
--> 595 inputs = self.queue.get(block=True).get()
596 self.queue.task_done()
597 if inputs is not None:
~/var/python/lib/python3.6/multiprocessing/pool.py in get(self, timeout)
642 return self._value
643 else:
--> 644 raise self._value
645
646 def _set(self, i, obj):
~/var/python/lib/python3.6/multiprocessing/pool.py in worker(inqueue, outqueue, initializer, initargs, maxtasks, wrap_exception)
117 job, i, func, args, kwds = task
118 try:
--> 119 result = (True, func(*args, **kwds))
120 except Exception as e:
121 if wrap_exception and func is not _helper_reraises_exception:
~/var/python/lib/python3.6/site-packages/keras/utils/data_utils.py in get_index(uid, i)
399 The value at index `i`.
400 """
--> 401 return _SHARED_SEQUENCES[uid][i]
402
403
~/var/python/lib/python3.6/site-packages/keras_preprocessing/image/iterator.py in __getitem__(self, idx)
63 index_array = self.index_array[self.batch_size * idx:
64 self.batch_size * (idx + 1)]
---> 65 return self._get_batches_of_transformed_samples(index_array)
66
67 def __len__(self):
~/var/python/lib/python3.6/site-packages/keras_preprocessing/image/iterator.py in _get_batches_of_transformed_samples(self, index_array)
233 params = self.image_data_generator.get_random_transform(x.shape)
234 x = self.image_data_generator.apply_transform(x, params)
--> 235 x = self.image_data_generator.standardize(x)
236 batch_x[i] = x
237 # optionally save augmented images to disk for debugging purposes
~/var/python/lib/python3.6/site-packages/keras_preprocessing/image/image_data_generator.py in standardize(self, x)
695 """
696 if self.preprocessing_function:
--> 697 x = self.preprocessing_function(x)
698 if self.rescale:
699 x *= self.rescale
<ipython-input-112-7bddefa5e731> in preprocess(im)
1 def preprocess(im):
----> 2 im = cv2.imread(im, 1)
3 im= cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
4 im=cv2.resize(im, (300,300))
5 im.resize(300, 300, 1)
TypeError: bad argument type for built-in operation
TypeError Traceback (most recent call last)
<ipython-input-126-30ceb84a2574> in <module>()
2
3 history = model.fit_generator(traingen, validation_data = validgen, epochs=100, steps_per_epoch=10,
----> 4 validation_steps=10, verbose=1, callbacks=[lr_reduction])
5
6
~/var/python/lib/python3.6/site-packages/keras/legacy/interfaces.py in wrapper(*args, **kwargs)
89 warnings.warn('Update your `' + object_name + '` call to the ' +
90 'Keras 2 API: ' + signature, stacklevel=2)
---> 91 return func(*args, **kwargs)
92 wrapper._original_function = func
93 return wrapper
~/var/python/lib/python3.6/site-packages/keras/engine/training.py in fit_generator(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)
1416 use_multiprocessing=use_multiprocessing,
1417 shuffle=shuffle,
-> 1418 initial_epoch=initial_epoch)
1419
1420 #interfaces.legacy_generator_methods_support
~/var/python/lib/python3.6/site-packages/keras/engine/training_generator.py in fit_generator(model, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)
179 batch_index = 0
180 while steps_done < steps_per_epoch:
--> 181 generator_output = next(output_generator)
182
183 if not hasattr(generator_output, '__len__'):
~/var/python/lib/python3.6/site-packages/keras/utils/data_utils.py in get(self)
599 except Exception as e:
600 self.stop()
--> 601 six.reraise(*sys.exc_info())
602
603
~/var/python/lib/python3.6/site-packages/six.py in reraise(tp, value, tb)
691 if value.__traceback__ is not tb:
692 raise value.with_traceback(tb)
--> 693 raise value
694 finally:
695 value = None
~/var/python/lib/python3.6/site-packages/keras/utils/data_utils.py in get(self)
593 try:
594 while self.is_running():
--> 595 inputs = self.queue.get(block=True).get()
596 self.queue.task_done()
597 if inputs is not None:
~/var/python/lib/python3.6/multiprocessing/pool.py in get(self, timeout)
642 return self._value
643 else:
--> 644 raise self._value
645
646 def _set(self, i, obj):
~/var/python/lib/python3.6/multiprocessing/pool.py in worker(inqueue, outqueue, initializer, initargs, maxtasks, wrap_exception)
117 job, i, func, args, kwds = task
118 try:
--> 119 result = (True, func(*args, **kwds))
120 except Exception as e:
121 if wrap_exception and func is not _helper_reraises_exception:
~/var/python/lib/python3.6/site-packages/keras/utils/data_utils.py in get_index(uid, i)
399 The value at index `i`.
400 """
--> 401 return _SHARED_SEQUENCES[uid][i]
402
403
~/var/python/lib/python3.6/site-packages/keras_preprocessing/image/iterator.py in __getitem__(self, idx)
63 index_array = self.index_array[self.batch_size * idx:
64 self.batch_size * (idx + 1)]
---> 65 return self._get_batches_of_transformed_samples(index_array)
66
67 def __len__(self):
~/var/python/lib/python3.6/site-packages/keras_preprocessing/image/iterator.py in _get_batches_of_transformed_samples(self, index_array)
233 params = self.image_data_generator.get_random_transform(x.shape)
234 x = self.image_data_generator.apply_transform(x, params)
--> 235 x = self.image_data_generator.standardize(x)
236 batch_x[i] = x
237 # optionally save augmented images to disk for debugging purposes
~/var/python/lib/python3.6/site-packages/keras_preprocessing/image/image_data_generator.py in standardize(self, x)
695 """
696 if self.preprocessing_function:
--> 697 x = self.preprocessing_function(x)
698 if self.rescale:
699 x *= self.rescale
<ipython-input-112-7bddefa5e731> in preprocess(im)
1 def preprocess(im):
----> 2 im = cv2.imread(im, 1)
3 im= cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
4 im=cv2.resize(im, (300,300))
5 im.resize(300, 300, 1)
TypeError: bad argument type for built-in operation
I have also thought about preprocessing the images and saving them to a folder then I'll load them from that folder to a dataframe, but it's computationally expensive and time consuming.
I ran into a problem like yours and my teacher helped me by pointing it to the docs of the tf preprocess_function, it said the preprocess_function argument is an image, you can read more on this.
That is why it gives you the error at cv2.imread(image). You should remove that line because im is an image that the generator gives you. There no need to load it because it's already loaded
My one work well, hope yours will be fine too.
I think the issue was with Keras parsing the openCV output, Because it ran well when i used another library to perform the processing called ImgAUg. Here's the link. https://pypi.org/project/imgaug/.
So i just scripted a preprocessing function with the library, and then i passed it into the keras imageDataGenerator class. It ran fine and didn't throw any error at me.
Related
My Code:
from sklearn.model_selection import GridSearchCV
from gensim.sklearn_api import W2VTransformer
from sklearn.metrics import accuracy_score, make_scorer
s_obj = W2VTransformer()
params_grid = {
'size': [100,200,300],
'window':[10,15,20],
'min_count': [1,2,3,4,5,6],
'workers': [10,20],
'sg':[0,1],
'negative': [2,3,4,6,5],
'sample':[1e-5]
}
s_model = GridSearchCV(s_obj, params_grid, cv=3,
scoring=make_scorer(accuracy_score))
s_model.fit(sentences)
print(s_model.best_params_)
Error is : " TypeError: _score() missing 1 required positional argument: 'y_true' "
PS: I reached the point that the error is showing something about
y_true i.e needed a labelled data then I am not having labelled data,
working on unsupervised learning, so if I am correct then do we have
any other library to tune the unsupervised model?
full traceback
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-230-37cafb83162e> in <module>
14
15 s_model = GridSearchCV(s_obj,params_grid,cv=3,scoring=make_scorer(accuracy_score))
---> 16 s_model.fit(train)
17
18 print(s_model.best_params_)
~/anaconda3/lib/python3.8/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
~/anaconda3/lib/python3.8/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
~/anaconda3/lib/python3.8/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
~/anaconda3/lib/python3.8/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,
~/anaconda3/lib/python3.8/site-packages/joblib/parallel.py in __call__(self, iterable)
1039 # remaining jobs.
1040 self._iterating = False
-> 1041 if self.dispatch_one_batch(iterator):
1042 self._iterating = self._original_iterator is not None
1043
~/anaconda3/lib/python3.8/site-packages/joblib/parallel.py in dispatch_one_batch(self, iterator)
857 return False
858 else:
--> 859 self._dispatch(tasks)
860 return True
861
~/anaconda3/lib/python3.8/site-packages/joblib/parallel.py in _dispatch(self, batch)
775 with self._lock:
776 job_idx = len(self._jobs)
--> 777 job = self._backend.apply_async(batch, callback=cb)
778 # A job can complete so quickly than its callback is
779 # called before we get here, causing self._jobs to
~/anaconda3/lib/python3.8/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)
~/anaconda3/lib/python3.8/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):
~/anaconda3/lib/python3.8/site-packages/joblib/parallel.py in __call__(self)
260 # change the default number of processes to -1
261 with parallel_backend(self._backend, n_jobs=self._n_jobs):
--> 262 return [func(*args, **kwargs)
263 for func, args, kwargs in self.items]
264
~/anaconda3/lib/python3.8/site-packages/joblib/parallel.py in <listcomp>(.0)
260 # change the default number of processes to -1
261 with parallel_backend(self._backend, n_jobs=self._n_jobs):
--> 262 return [func(*args, **kwargs)
263 for func, args, kwargs in self.items]
264
~/anaconda3/lib/python3.8/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)
558 else:
559 fit_time = time.time() - start_time
--> 560 test_scores = _score(estimator, X_test, y_test, scorer)
561 score_time = time.time() - start_time - fit_time
562 if return_train_score:
~/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py in _score(estimator, X_test, y_test, scorer)
603 scorer = _MultimetricScorer(**scorer)
604 if y_test is None:
--> 605 scores = scorer(estimator, X_test)
606 else:
607 scores = scorer(estimator, X_test, y_test)
~/anaconda3/lib/python3.8/site-packages/sklearn/metrics/_scorer.py in __call__(self, estimator, *args, **kwargs)
85 for name, scorer in self._scorers.items():
86 if isinstance(scorer, _BaseScorer):
---> 87 score = scorer._score(cached_call, estimator,
88 *args, **kwargs)
89 else:
TypeError: _score() missing 1 required positional argument: 'y_true'
Can anyone help me to solve this issue?
I am trying to evaluate my deep learning RNN model but I keep getting this one error. I have absolutely no idea what the error means and I am unable to solve it. Any help is appreciated. Thanks.
This is the code I am using to evaluate my model:
model = keras.models.load_model('D:/Semester 3.2 OFFICIAL/Deep Learning/Assignment 2/test_model_14 files/Model14Checkpoint-188-0.60.h5')
model.load_weights('D:/Semester 3.2 OFFICIAL/Deep Learning/Assignment 2/test_model_14 files/Model14Checkpoint-188-0.60.h5')
evaluate = model.evaluate(X_test_seq_padded, y_test, batch_size=128)
loss = evaluate[0]
acc = evaluate[1] * 100
print("Loss: {:0.3f} - Accuracy: {:0.3f}%".format(loss,acc))
And this is the error that it returns:
128/8510 [..............................] - ETA: 1:05
---------------------------------------------------------------------------
InvalidArgumentError Traceback (most recent call last)
<ipython-input-8-8b3cba2991da> in <module>
13 model = keras.models.load_model('D:/Semester 3.2 OFFICIAL/Deep Learning/Assignment 2/test_model_14 files/Model14Checkpoint-188-0.60.h5')
14 model.load_weights('D:/Semester 3.2 OFFICIAL/Deep Learning/Assignment 2/test_model_14 files/Model14Checkpoint-188-0.60.h5')
---> 15 evaluate = model.evaluate(X_test_seq_padded, y_test, batch_size=128)
16
17 loss = evaluate[0]
~\anaconda3\envs\three point seven\lib\site-packages\tensorflow_core\python\keras\engine\training.py in evaluate(self, x, y, batch_size, verbose, sample_weight, steps, callbacks, max_queue_size, workers, use_multiprocessing)
928 max_queue_size=max_queue_size,
929 workers=workers,
--> 930 use_multiprocessing=use_multiprocessing)
931
932 def predict(self,
~\anaconda3\envs\three point seven\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py in evaluate(self, model, x, y, batch_size, verbose, sample_weight, steps, callbacks, max_queue_size, workers, use_multiprocessing, **kwargs)
488 sample_weight=sample_weight, steps=steps, callbacks=callbacks,
489 max_queue_size=max_queue_size, workers=workers,
--> 490 use_multiprocessing=use_multiprocessing, **kwargs)
491
492 def predict(self, model, x, batch_size=None, verbose=0, steps=None,
~\anaconda3\envs\three point seven\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py in _model_iteration(self, model, mode, x, y, batch_size, verbose, sample_weight, steps, callbacks, max_queue_size, workers, use_multiprocessing, **kwargs)
473 mode=mode,
474 training_context=training_context,
--> 475 total_epochs=1)
476 cbks.make_logs(model, epoch_logs, result, mode)
477
~\anaconda3\envs\three point seven\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py in run_one_epoch(model, iterator, execution_function, dataset_size, batch_size, strategy, steps_per_epoch, num_samples, mode, training_context, total_epochs)
126 step=step, mode=mode, size=current_batch_size) as batch_logs:
127 try:
--> 128 batch_outs = execution_function(iterator)
129 except (StopIteration, errors.OutOfRangeError):
130 # TODO(kaftan): File bug about tf function and errors.OutOfRangeError?
~\anaconda3\envs\three point seven\lib\site-packages\tensorflow_core\python\keras\engine\training_v2_utils.py in execution_function(input_fn)
96 # `numpy` translates Tensors to values in Eager mode.
97 return nest.map_structure(_non_none_constant_value,
---> 98 distributed_function(input_fn))
99
100 return execution_function
~\anaconda3\envs\three point seven\lib\site-packages\tensorflow_core\python\eager\def_function.py in __call__(self, *args, **kwds)
566 xla_context.Exit()
567 else:
--> 568 result = self._call(*args, **kwds)
569
570 if tracing_count == self._get_tracing_count():
~\anaconda3\envs\three point seven\lib\site-packages\tensorflow_core\python\eager\def_function.py in _call(self, *args, **kwds)
636 *args, **kwds)
637 # If we did not create any variables the trace we have is good enough.
--> 638 return self._concrete_stateful_fn._filtered_call(canon_args, canon_kwds) # pylint: disable=protected-access
639
640 def fn_with_cond(*inner_args, **inner_kwds):
~\anaconda3\envs\three point seven\lib\site-packages\tensorflow_core\python\eager\function.py in _filtered_call(self, args, kwargs)
1609 if isinstance(t, (ops.Tensor,
1610 resource_variable_ops.BaseResourceVariable))),
-> 1611 self.captured_inputs)
1612
1613 def _call_flat(self, args, captured_inputs, cancellation_manager=None):
~\anaconda3\envs\three point seven\lib\site-packages\tensorflow_core\python\eager\function.py in _call_flat(self, args, captured_inputs, cancellation_manager)
1690 # No tape is watching; skip to running the function.
1691 return self._build_call_outputs(self._inference_function.call(
-> 1692 ctx, args, cancellation_manager=cancellation_manager))
1693 forward_backward = self._select_forward_and_backward_functions(
1694 args,
~\anaconda3\envs\three point seven\lib\site-packages\tensorflow_core\python\eager\function.py in call(self, ctx, args, cancellation_manager)
543 inputs=args,
544 attrs=("executor_type", executor_type, "config_proto", config),
--> 545 ctx=ctx)
546 else:
547 outputs = execute.execute_with_cancellation(
~\anaconda3\envs\three point seven\lib\site-packages\tensorflow_core\python\eager\execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
65 else:
66 message = e.message
---> 67 six.raise_from(core._status_to_exception(e.code, message), None)
68 except TypeError as e:
69 keras_symbolic_tensors = [
~\anaconda3\envs\three point seven\lib\site-packages\six.py in raise_from(value, from_value)
InvalidArgumentError: 2 root error(s) found.
(0) Invalid argument: indices[28,27] = 10792 is not in [0, 10000)
[[node sequential_3/embedding_3/embedding_lookup (defined at <ipython-input-8-8b3cba2991da>:15) ]]
[[sequential_3/embedding_3/embedding_lookup/_17]]
(1) Invalid argument: indices[28,27] = 10792 is not in [0, 10000)
[[node sequential_3/embedding_3/embedding_lookup (defined at <ipython-input-8-8b3cba2991da>:15) ]]
0 successful operations.
0 derived errors ignored. [Op:__inference_distributed_function_8770]
Errors may have originated from an input operation.
Input Source operations connected to node sequential_3/embedding_3/embedding_lookup:
sequential_3/embedding_3/embedding_lookup/7696 (defined at C:\Users\acer\anaconda3\envs\three point seven\lib\contextlib.py:112)
Input Source operations connected to node sequential_3/embedding_3/embedding_lookup:
sequential_3/embedding_3/embedding_lookup/7696 (defined at C:\Users\acer\anaconda3\envs\three point seven\lib\contextlib.py:112)
Function call stack:
distributed_function -> distributed_function
I am trying to build a Unidirectional RNN for sequence modeling and neural embedding. I have built a custom class to experiment with various architectures and parameters. The data manager class in the code is another class that basically reads text data, processes it and converts it into numeric vectors. tf_train_set is tensorSliceDataset containing numeric vectors and labels of 60% of the dataset. rest 40% are in tf_valid_set.
I have the following code for my RNN:
class UniRNN:
def __init__(self, cell_type= 'gru', embed_size= 128, state_sizes= [128, 64], data_manager= None):
self.cell_type = cell_type
self.state_sizes = state_sizes
self.embed_size = embed_size
self.data_manager = data_manager
self.vocab_size = self.data_manager.vocab_size +1
#return the correspoding memory cell
#staticmethod
def get_layer(cell_type= 'gru', state_size= 128, return_sequences= False, activation = 'tanh'):
if cell_type=='gru':
return tf.keras.layers.GRU(state_size, return_sequences=return_sequences, activation=activation)
elif cell_type== 'lstm':
return tf.keras.layers.LSTM(state_size, return_sequences=return_sequences, activation=activation)
else:
return tf.keras.layers.SimpleRNN(state_size, return_sequences=return_sequences, activation=activation)
def build(self):
x = tf.keras.layers.Input(shape=[None])
h = tf.keras.layers.Embedding(self.vocab_size, self.embed_size, mask_zero=True, trainable=True)(x)
num_layers = len(self.state_sizes)
for i in range(num_layers):
h = self.get_layer(self.cell_type, self.state_sizes[i], return_sequences=True)(h)
h = tf.keras.layers.Dense(dm.num_classes, activation='softmax')(h)
self.model = tf.keras.Model(inputs=x, outputs=h)
def compile_model(self, *args, **kwargs):
self.model.compile(*args, **kwargs)
def fit(self, *args, **kwargs):
return self.model.fit(*args, **kwargs)
def evaluate(self, *args, **kwargs):
self.model.evaluate(*args, **kwargs)
To fit the model, my code is:
uni_rnn = UniRNN(cell_type='basic_rnn', embed_size=128, state_sizes=[128, 128], data_manager=dm) #Insert your code here
uni_rnn.build()
# uni_rnn.model.summary()
opt = tf.keras.optimizers.RMSprop(learning_rate=0.001)
uni_rnn.compile_model(optimizer=opt, loss='sparse_categorical_crossentropy', metrics=['accuracy'])
uni_rnn.fit(dm.tf_train_set.batch(64), epochs=20, validation_data = dm.tf_valid_set.batch(64))
when I run this code, I'm getting the following error:
---------------------------------------------------------------------------
InvalidArgumentError Traceback (most recent call last)
<ipython-input-184-abef9ae0cbcd> in <module>
3 opt = tf.keras.optimizers.RMSprop(learning_rate=0.001)
4 uni_rnn.compile_model(optimizer=opt, loss='sparse_categorical_crossentropy', metrics=['accuracy'])
----> 5 uni_rnn.fit(dm.tf_train_set.batch(64), epochs=20, validation_data = dm.tf_valid_set.batch(64))
<ipython-input-170-53f4c12769ab> in fit(self, *args, **kwargs)
31
32 def fit(self, *args, **kwargs):
---> 33 return self.model.fit(*args, **kwargs)
34
35 def evaluate(self, *args, **kwargs):
~\anaconda3\envs\myenv_tf21_p37\lib\site-packages\tensorflow_core\python\keras\engine\training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)
817 max_queue_size=max_queue_size,
818 workers=workers,
--> 819 use_multiprocessing=use_multiprocessing)
820
821 def evaluate(self,
~\anaconda3\envs\myenv_tf21_p37\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py in fit(self, model, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)
340 mode=ModeKeys.TRAIN,
341 training_context=training_context,
--> 342 total_epochs=epochs)
343 cbks.make_logs(model, epoch_logs, training_result, ModeKeys.TRAIN)
344
~\anaconda3\envs\myenv_tf21_p37\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py in run_one_epoch(model, iterator, execution_function, dataset_size, batch_size, strategy, steps_per_epoch, num_samples, mode, training_context, total_epochs)
126 step=step, mode=mode, size=current_batch_size) as batch_logs:
127 try:
--> 128 batch_outs = execution_function(iterator)
129 except (StopIteration, errors.OutOfRangeError):
130 # TODO(kaftan): File bug about tf function and errors.OutOfRangeError?
~\anaconda3\envs\myenv_tf21_p37\lib\site-packages\tensorflow_core\python\keras\engine\training_v2_utils.py in execution_function(input_fn)
96 # `numpy` translates Tensors to values in Eager mode.
97 return nest.map_structure(_non_none_constant_value,
---> 98 distributed_function(input_fn))
99
100 return execution_function
~\anaconda3\envs\myenv_tf21_p37\lib\site-packages\tensorflow_core\python\eager\def_function.py in __call__(self, *args, **kwds)
566 xla_context.Exit()
567 else:
--> 568 result = self._call(*args, **kwds)
569
570 if tracing_count == self._get_tracing_count():
~\anaconda3\envs\myenv_tf21_p37\lib\site-packages\tensorflow_core\python\eager\def_function.py in _call(self, *args, **kwds)
630 # Lifting succeeded, so variables are initialized and we can run the
631 # stateless function.
--> 632 return self._stateless_fn(*args, **kwds)
633 else:
634 canon_args, canon_kwds = \
~\anaconda3\envs\myenv_tf21_p37\lib\site-packages\tensorflow_core\python\eager\function.py in __call__(self, *args, **kwargs)
2361 with self._lock:
2362 graph_function, args, kwargs = self._maybe_define_function(args, kwargs)
-> 2363 return graph_function._filtered_call(args, kwargs) # pylint: disable=protected-access
2364
2365 #property
~\anaconda3\envs\myenv_tf21_p37\lib\site-packages\tensorflow_core\python\eager\function.py in _filtered_call(self, args, kwargs)
1609 if isinstance(t, (ops.Tensor,
1610 resource_variable_ops.BaseResourceVariable))),
-> 1611 self.captured_inputs)
1612
1613 def _call_flat(self, args, captured_inputs, cancellation_manager=None):
~\anaconda3\envs\myenv_tf21_p37\lib\site-packages\tensorflow_core\python\eager\function.py in _call_flat(self, args, captured_inputs, cancellation_manager)
1690 # No tape is watching; skip to running the function.
1691 return self._build_call_outputs(self._inference_function.call(
-> 1692 ctx, args, cancellation_manager=cancellation_manager))
1693 forward_backward = self._select_forward_and_backward_functions(
1694 args,
~\anaconda3\envs\myenv_tf21_p37\lib\site-packages\tensorflow_core\python\eager\function.py in call(self, ctx, args, cancellation_manager)
543 inputs=args,
544 attrs=("executor_type", executor_type, "config_proto", config),
--> 545 ctx=ctx)
546 else:
547 outputs = execute.execute_with_cancellation(
~\anaconda3\envs\myenv_tf21_p37\lib\site-packages\tensorflow_core\python\eager\execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
65 else:
66 message = e.message
---> 67 six.raise_from(core._status_to_exception(e.code, message), None)
68 except TypeError as e:
69 keras_symbolic_tensors = [
~\anaconda3\envs\myenv_tf21_p37\lib\site-packages\six.py in raise_from(value, from_value)
InvalidArgumentError: assertion failed: [Condition x == y did not hold element-wise:] [x (loss/dense_13_loss/SparseSoftmaxCrossEntropyWithLogits/Shape_1:0) = ] [64 1] [y (loss/dense_13_loss/SparseSoftmaxCrossEntropyWithLogits/strided_slice:0) = ] [64 100]
[[node loss/dense_13_loss/SparseSoftmaxCrossEntropyWithLogits/assert_equal_1/Assert/Assert (defined at <ipython-input-170-53f4c12769ab>:33) ]] [Op:__inference_distributed_function_111597]
Function call stack:
distributed_function
Can someone please explain what the problem is? and maybe how I can fix it?
the last RNN in the loop must have return_sequence = False. to do this you can simply do:
def build(self):
x = tf.keras.layers.Input(shape=[None])
h = tf.keras.layers.Embedding(self.vocab_size, self.embed_size,
mask_zero=True, trainable=True)(x)
num_layers = len(self.state_sizes)
for i in range(num_layers-1):
h = self.get_layer(self.cell_type, self.state_sizes[i], return_sequences=True)(h)
h = self.get_layer(self.cell_type, self.state_sizes[i], return_sequences=False)(h)
h = tf.keras.layers.Dense(dm.num_classes, activation='softmax')(h)
self.model = tf.keras.Model(inputs=x, outputs=h)
I'm trying to use shared Embedding layer for different input length, But I got errors as follows.
Code:
input1 = [Input(name = "input1", shape = (10,))]
input2 = [Input(name = "input2", shape = (10,))]
input3 = [Input(name = 'input3', shape = (1,))]
input4 = [Input(name = 'input4', shape = (1,))]
input5 = [Input(name = 'input5', shape = (1,))]
inputs= input1 + input2 + input3 + input4 + input5
embed = Embedding(name = 'embed', input_dim = 1000, output_dim = 20)
out1 = Flatten(name = 'output1')(embed(inputs[0]))
out2 = Flatten(name = 'output2')(embed(inputs[1]))
out3 = Flatten(name = 'output3')(embed(inputs[2]))
out4 = Flatten(name = 'output4')(embed(inputs[3]))
out5 = Flatten(name = 'output5')(embed(inputs[4]))
concat = Concatenate(name = 'concat')([out1, out2, out3, out4, out5])
result = Dense(name = 'result' + 'dense', units=1, activation='sigmoid')(concat)
model = Model(inputs = inputs, outputs = result)
#model.summary()
optimizer = Adam(learning_rate=0.01)
model.compile(loss='binary_crossentropy', optimizer=optimizer)
input1 = np.random.randint(0, 100, (100,10))
input2 = np.random.randint(0, 100, (100,10))
input3 = np.random.randint(0, 100, (100,1))
input4 = np.random.randint(0, 100, (100,1))
input5 = np.random.randint(0, 100, (100,1))
dummy_y = (input4 > 0).reshape(-1, 1)
model_input = [input1, input2, input3, input4, input5]
model.fit(x = model_input, y = dummy_y)
Error:
TypeError Traceback (most recent call last)
~\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow_core\python\eager\backprop.py in _num_elements(grad)
615 if isinstance(grad, ops.IndexedSlices):
--> 616 return functools.reduce(operator.mul, grad.values._shape_tuple(), 1) # pylint: disable=protected-access
617 raise ValueError("`grad` not a Tensor or IndexedSlices.")
TypeError: unsupported operand type(s) for *: 'int' and 'NoneType'
The above exception was the direct cause of the following exception:
SystemError Traceback (most recent call last)
~\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow_core\python\eager\backprop.py in _aggregate_grads(gradients)
597
--> 598 if len(gradients) == 1:
599 return gradients[0]
SystemError: returned a result with an error set
The above exception was the direct cause of the following exception:
SystemError Traceback (most recent call last)
in
27 dummy_y = (input4 > 0).reshape(-1, 1)
28 model_input = [input1, input2, input3, input4, input5]
---> 29 model.fit(x = model_input, y = dummy_y)
~\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow_core\python\keras\engine\training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)
726 max_queue_size=max_queue_size,
727 workers=workers,
--> 728 use_multiprocessing=use_multiprocessing)
729
730 def evaluate(self,
~\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py in fit(self, model, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, **kwargs)
322 mode=ModeKeys.TRAIN,
323 training_context=training_context,
--> 324 total_epochs=epochs)
325 cbks.make_logs(model, epoch_logs, training_result, ModeKeys.TRAIN)
326
~\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py in run_one_epoch(model, iterator, execution_function, dataset_size, batch_size, strategy, steps_per_epoch, num_samples, mode, training_context, total_epochs)
121 step=step, mode=mode, size=current_batch_size) as batch_logs:
122 try:
--> 123 batch_outs = execution_function(iterator)
124 except (StopIteration, errors.OutOfRangeError):
125 # TODO(kaftan): File bug about tf function and errors.OutOfRangeError?
~\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow_core\python\keras\engine\training_v2_utils.py in execution_function(input_fn)
84 # `numpy` translates Tensors to values in Eager mode.
85 return nest.map_structure(_non_none_constant_value,
---> 86 distributed_function(input_fn))
87
88 return execution_function
~\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow_core\python\eager\def_function.py in __call__(self, *args, **kwds)
455
456 tracing_count = self._get_tracing_count()
--> 457 result = self._call(*args, **kwds)
458 if tracing_count == self._get_tracing_count():
459 self._call_counter.called_without_tracing()
~\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow_core\python\eager\def_function.py in _call(self, *args, **kwds)
501 # This is the first call of __call__, so we have to initialize.
502 initializer_map = object_identity.ObjectIdentityDictionary()
--> 503 self._initialize(args, kwds, add_initializers_to=initializer_map)
504 finally:
505 # At this point we know that the initialization is complete (or less
~\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow_core\python\eager\def_function.py in _initialize(self, args, kwds, add_initializers_to)
406 self._concrete_stateful_fn = (
407 self._stateful_fn._get_concrete_function_internal_garbage_collected( # pylint: disable=protected-access
--> 408 *args, **kwds))
409
410 def invalid_creator_scope(*unused_args, **unused_kwds):
~\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow_core\python\eager\function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)
1846 if self.input_signature:
1847 args, kwargs = None, None
-> 1848 graph_function, _, _ = self._maybe_define_function(args, kwargs)
1849 return graph_function
1850
~\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow_core\python\eager\function.py in _maybe_define_function(self, args, kwargs)
2148 graph_function = self._function_cache.primary.get(cache_key, None)
2149 if graph_function is None:
-> 2150 graph_function = self._create_graph_function(args, kwargs)
2151 self._function_cache.primary[cache_key] = graph_function
2152 return graph_function, args, kwargs
~\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow_core\python\eager\function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
2039 arg_names=arg_names,
2040 override_flat_arg_shapes=override_flat_arg_shapes,
-> 2041 capture_by_value=self._capture_by_value),
2042 self._function_attributes,
2043 # Tell the ConcreteFunction to clean up its graph once it goes out of
~\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow_core\python\framework\func_graph.py in func_graph_from_py_func(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)
913 converted_func)
914
--> 915 func_outputs = python_func(*func_args, **func_kwargs)
916
917 # invariant: `func_outputs` contains only Tensors, CompositeTensors,
~\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow_core\python\eager\def_function.py in wrapped_fn(*args, **kwds)
356 # __wrapped__ allows AutoGraph to swap in a converted function. We give
357 # the function a weak reference to itself to avoid a reference cycle.
--> 358 return weak_wrapped_fn().__wrapped__(*args, **kwds)
359 weak_wrapped_fn = weakref.ref(wrapped_fn)
360
~\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow_core\python\keras\engine\training_v2_utils.py in distributed_function(input_iterator)
71 strategy = distribution_strategy_context.get_strategy()
72 outputs = strategy.experimental_run_v2(
---> 73 per_replica_function, args=(model, x, y, sample_weights))
74 # Out of PerReplica outputs reduce or pick values to return.
75 all_outputs = dist_utils.unwrap_output_dict(
~\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow_core\python\distribute\distribute_lib.py in experimental_run_v2(self, fn, args, kwargs)
758 fn = autograph.tf_convert(fn, ag_ctx.control_status_ctx(),
759 convert_by_default=False)
--> 760 return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
761
762 def reduce(self, reduce_op, value, axis):
~\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow_core\python\distribute\distribute_lib.py in call_for_each_replica(self, fn, args, kwargs)
1785 kwargs = {}
1786 with self._container_strategy().scope():
-> 1787 return self._call_for_each_replica(fn, args, kwargs)
1788
1789 def _call_for_each_replica(self, fn, args, kwargs):
~\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow_core\python\distribute\distribute_lib.py in _call_for_each_replica(self, fn, args, kwargs)
2130 self._container_strategy(),
2131 replica_id_in_sync_group=constant_op.constant(0, dtypes.int32)):
-> 2132 return fn(*args, **kwargs)
2133
2134 def _reduce_to(self, reduce_op, value, destinations):
~\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow_core\python\autograph\impl\api.py in wrapper(*args, **kwargs)
290 def wrapper(*args, **kwargs):
291 with ag_ctx.ControlStatusCtx(status=ag_ctx.Status.DISABLED):
--> 292 return func(*args, **kwargs)
293
294 if inspect.isfunction(func) or inspect.ismethod(func):
~\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow_core\python\keras\engine\training_v2_utils.py in train_on_batch(model, x, y, sample_weight, class_weight, reset_metrics)
262 y,
263 sample_weights=sample_weights,
--> 264 output_loss_metrics=model._output_loss_metrics)
265
266 if reset_metrics:
~\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow_core\python\keras\engine\training_eager.py in train_on_batch(model, inputs, targets, sample_weights, output_loss_metrics)
309 sample_weights=sample_weights,
310 training=True,
--> 311 output_loss_metrics=output_loss_metrics))
312 if not isinstance(outs, list):
313 outs = [outs]
~\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow_core\python\keras\engine\training_eager.py in _process_single_batch(model, inputs, targets, output_loss_metrics, sample_weights, training)
266 model._backwards(tape, scaled_total_loss)
267 else:
--> 268 grads = tape.gradient(scaled_total_loss, trainable_weights)
269 if isinstance(model.optimizer,
270 loss_scale_optimizer.LossScaleOptimizer):
~\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow_core\python\eager\backprop.py in gradient(self, target, sources, output_gradients, unconnected_gradients)
1012 output_gradients=output_gradients,
1013 sources_raw=flat_sources_raw,
-> 1014 unconnected_gradients=unconnected_gradients)
1015
1016 if not self._persistent:
~\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow_core\python\eager\imperative_grad.py in imperative_grad(tape, target, sources, output_gradients, sources_raw, unconnected_gradients)
74 output_gradients,
75 sources_raw,
---> 76 compat.as_str(unconnected_gradients.value))
SystemError: PyEval_EvalFrameEx returned a result with an error set
The error occured when I tried to fit the model, however I try with first four inputs, I can run without the error, any suggestion that would be appreciated.
I jsut adding some traceback errors, Thank you in advance.
I am running the code below in Jupyter notebook. The first chunk of code works okay but when the running the second chunk of code i get an error message "ValueError: None values not supported". I am using tensorflow and keras. Thanks a lot
features=skin_df.drop(columns=['cell_type_idx'],axis=1)
target=skin_df['cell_type_idx']
# Train Test Split
x_train_o, x_test_o, y_train_o, y_test_o = train_test_split(features, target, test_size=0.20,random_state=1234)
x_train = np.asarray(x_train_o['image'].tolist())
x_test = np.asarray(x_test_o['image'].tolist())
x_train_mean = np.mean(x_train)
x_train_std = np.std(x_train)
x_test_mean = np.mean(x_test)
x_test_std = np.std(x_test)
x_train = (x_train - x_train_mean)/x_train_std
x_test = (x_test - x_test_mean)/x_test_std
# Perform one-hot encoding on the labels
y_train = to_categorical(y_train_o, num_classes = 7)
y_test = to_categorical(y_test_o, num_classes = 7)
# Set a learning rate annealer
learning_rate_reduction = ReduceLROnPlateau(monitor='val_acc',
patience=3,
verbose=1,
factor=0.5,
min_lr=0.00001)
#1. Function to plot model's validation loss and validation accuracy
def plot_model_history(model_history):
fig, axs = plt.subplots(1,2,figsize=(15,5))
# summarize history for accuracy
axs[0].plot(range(1,len(model_history.history['acc'])+1),model_history.history['acc'])
axs[0].plot(range(1,len(model_history.history['val_acc'])+1),model_history.history['val_acc'])
axs[0].set_title('Model Accuracy')
axs[0].set_ylabel('Accuracy')
axs[0].set_xlabel('Epoch')
axs[0].set_xticks(np.arange(1,len(model_history.history['acc'])+1),len(model_history.history['acc'])/10)
axs[0].legend(['train', 'val'], loc='best')
# summarize history for loss
axs[1].plot(range(1,len(model_history.history['loss'])+1),model_history.history['loss'])
axs[1].plot(range(1,len(model_history.history['val_loss'])+1),model_history.history['val_loss'])
axs[1].set_title('Model Loss')
axs[1].set_ylabel('Loss')
axs[1].set_xlabel('Epoch')
axs[1].set_xticks(np.arange(1,len(model_history.history['loss'])+1),len(model_history.history['loss'])/10)
axs[1].legend(['train', 'val'], loc='best')
plt.show()
epochs = 50
batch_size = 10
history = model.fit_generator(datagen.flow(x_train,y_train, batch_size=batch_size),
epochs = epochs, validation_data = (x_validate,y_validate),
verbose = 1, steps_per_epoch=x_train.shape[0] // batch_size
, callbacks=[learning_rate_reduction])
error message with trace back
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-74-b36dfc5ecc8e> in <module>
5 epochs = epochs, validation_data = (x_validate,y_validate),
6 verbose = 1, steps_per_epoch=x_train.shape[0] // batch_size
----> 7 , callbacks=[learning_rate_reduction])
~\.conda\envs\Project_2\lib\site-packages\keras\legacy\interfaces.py in wrapper(*args, **kwargs)
89 warnings.warn('Update your `' + object_name + '` call to the ' +
90 'Keras 2 API: ' + signature, stacklevel=2)
---> 91 return func(*args, **kwargs)
92 wrapper._original_function = func
93 return wrapper
~\.conda\envs\Project_2\lib\site-packages\keras\engine\training.py in fit_generator(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, validation_freq, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)
1730 use_multiprocessing=use_multiprocessing,
1731 shuffle=shuffle,
-> 1732 initial_epoch=initial_epoch)
1733
1734 #interfaces.legacy_generator_methods_support
~\.conda\envs\Project_2\lib\site-packages\keras\engine\training_generator.py in fit_generator(model, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, validation_freq, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)
40
41 do_validation = bool(validation_data)
---> 42 model._make_train_function()
43 if do_validation:
44 model._make_test_function()
~\.conda\envs\Project_2\lib\site-packages\keras\engine\training.py in _make_train_function(self)
314 training_updates = self.optimizer.get_updates(
315 params=self._collected_trainable_weights,
--> 316 loss=self.total_loss)
317 updates = self.updates + training_updates
318
~\.conda\envs\Project_2\lib\site-packages\keras\legacy\interfaces.py in wrapper(*args, **kwargs)
89 warnings.warn('Update your `' + object_name + '` call to the ' +
90 'Keras 2 API: ' + signature, stacklevel=2)
---> 91 return func(*args, **kwargs)
92 wrapper._original_function = func
93 return wrapper
~\.conda\envs\Project_2\lib\site-packages\keras\backend\tensorflow_backend.py in symbolic_fn_wrapper(*args, **kwargs)
73 if _SYMBOLIC_SCOPE.value:
74 with get_graph().as_default():
---> 75 return func(*args, **kwargs)
76 else:
77 return func(*args, **kwargs)
~\.conda\envs\Project_2\lib\site-packages\keras\optimizers.py in get_updates(self, loss, params)
541 self.updates.append(K.update(vhat, vhat_t))
542 else:
--> 543 p_t = p - lr_t * m_t / (K.sqrt(v_t) + self.epsilon)
544
545 self.updates.append(K.update(m, m_t))
~\.conda\envs\Project_2\lib\site-packages\tensorflow_core\python\ops\math_ops.py in binary_op_wrapper(x, y)
904 try:
905 y = ops.convert_to_tensor_v2(
--> 906 y, dtype_hint=x.dtype.base_dtype, name="y")
907 except TypeError:
908 # If the RHS is not a tensor, it might be a tensor aware object
~\.conda\envs\Project_2\lib\site-packages\tensorflow_core\python\framework\ops.py in convert_to_tensor_v2(value, dtype, dtype_hint, name)
1254 name=name,
1255 preferred_dtype=dtype_hint,
-> 1256 as_ref=False)
1257
1258
~\.conda\envs\Project_2\lib\site-packages\tensorflow_core\python\framework\ops.py in convert_to_tensor(value, dtype, name, as_ref, preferred_dtype, dtype_hint, ctx, accepted_result_types)
1312
1313 if ret is None:
-> 1314 ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
1315
1316 if ret is NotImplemented:
~\.conda\envs\Project_2\lib\site-packages\tensorflow_core\python\framework\constant_op.py in _constant_tensor_conversion_function(v, dtype, name, as_ref)
315 as_ref=False):
316 _ = as_ref
--> 317 return constant(v, dtype=dtype, name=name)
318
319
~\.conda\envs\Project_2\lib\site-packages\tensorflow_core\python\framework\constant_op.py in constant(value, dtype, shape, name)
256 """
257 return _constant_impl(value, dtype, shape, name, verify_shape=False,
--> 258 allow_broadcast=True)
259
260
~\.conda\envs\Project_2\lib\site-packages\tensorflow_core\python\framework\constant_op.py in _constant_impl(value, dtype, shape, name, verify_shape, allow_broadcast)
294 tensor_util.make_tensor_proto(
295 value, dtype=dtype, shape=shape, verify_shape=verify_shape,
--> 296 allow_broadcast=allow_broadcast))
297 dtype_value = attr_value_pb2.AttrValue(type=tensor_value.tensor.dtype)
298 const_tensor = g._create_op_internal( # pylint: disable=protected-access
~\.conda\envs\Project_2\lib\site-packages\tensorflow_core\python\framework\tensor_util.py in make_tensor_proto(values, dtype, shape, verify_shape, allow_broadcast)
437 else:
438 if values is None:
--> 439 raise ValueError("None values not supported.")
440 # if dtype is provided, forces numpy array to be the type
441 # provided if possible.
ValueError: None values not supported.
Please see the above error code. thanks
I have fixed the issue by creating a new environment and installing Keras and not tensorflow that seems to resolve my issue.
i think in model.compile you have assigned optimizer with predefined variable instead of that use this one """model.compile(optimizer = 'Adam' I wish the best