Usage of LSTM/GRU and Flatten throws dimensional incompatibility error - tensorflow

Question

I want to make use of a promising NN I found at towardsdatascience for my case study.
The data shapes I have are:
X_train:(1200,18,15)
y_train:(1200,18,1)
Here the NN, which possesses among other layers GRU, Flatten and Dense.
def twds_model(layer1=32, layer2=32, layer3=16, dropout_rate=0.5, optimizer='Adam'
, learning_rate=0.001, activation='relu', loss='mse'):
model = Sequential()
model.add(Bidirectional(GRU(layer1, return_sequences=True),input_shape=(X_train.shape[1],X_train.shape[2])))
model.add(AveragePooling1D(2))
model.add(Conv1D(layer2, 3, activation=activation, padding='same',
name='extractor'))
model.add(Flatten())
model.add(Dense(layer3,activation=activation))
model.add(Dropout(dropout_rate))
model.add(Dense(1))
model.compile(optimizer=optimizer,loss=loss)
return model
twds_model=twds_model()
print(twds_model.summary())
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
bidirectional_4 (Bidirection (None, 18, 64) 9216
_________________________________________________________________
average_pooling1d_1 (Average (None, 9, 64) 0
_________________________________________________________________
extractor (Conv1D) (None, 9, 32) 6176
_________________________________________________________________
flatten_1 (Flatten) (None, 288) 0
_________________________________________________________________
dense_3 (Dense) (None, 16) 4624
_________________________________________________________________
dropout_4 (Dropout) (None, 16) 0
_________________________________________________________________
dense_4 (Dense) (None, 1) 17
=================================================================
Total params: 20,033
Trainable params: 20,033
Non-trainable params: 0
_________________________________________________________________
None
Unfortunately, I step into a kind of contradictory error trap, where input and output shapes do not match. Here the error under the upper circumstances.
InvalidArgumentError: Incompatible shapes: [144,1] vs. [144,18,1]
[[{{node loss_2/dense_4_loss/sub}}]]
[[{{node loss_2/mul}}]]
Train on 10420 samples, validate on 1697 samples
Epoch 1/8
---------------------------------------------------------------------------
InvalidArgumentError Traceback (most recent call last)
<ipython-input-30-3f5256ff03ec> in <module>
----> 1 Test_tdws=twds_model.fit(X_train, y_train, epochs=8, batch_size=144, verbose=2, validation_split=(0.14), shuffle=False) #callbacks=[tensorboard])
~\Anaconda3\envs\Tensorflow\lib\site-packages\tensorflow\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, max_queue_size, workers, use_multiprocessing, **kwargs)
878 initial_epoch=initial_epoch,
879 steps_per_epoch=steps_per_epoch,
--> 880 validation_steps=validation_steps)
881
882 def evaluate(self,
~\Anaconda3\envs\Tensorflow\lib\site-packages\tensorflow\python\keras\engine\training_arrays.py in model_iteration(model, inputs, targets, sample_weights, batch_size, epochs, verbose, callbacks, val_inputs, val_targets, val_sample_weights, shuffle, initial_epoch, steps_per_epoch, validation_steps, mode, validation_in_fit, **kwargs)
327
328 # Get outputs.
--> 329 batch_outs = f(ins_batch)
330 if not isinstance(batch_outs, list):
331 batch_outs = [batch_outs]
~\Anaconda3\envs\Tensorflow\lib\site-packages\tensorflow\python\keras\backend.py in __call__(self, inputs)
3074
3075 fetched = self._callable_fn(*array_vals,
-> 3076 run_metadata=self.run_metadata)
3077 self._call_fetch_callbacks(fetched[-len(self._fetches):])
3078 return nest.pack_sequence_as(self._outputs_structure,
~\Anaconda3\envs\Tensorflow\lib\site-packages\tensorflow\python\client\session.py in __call__(self, *args, **kwargs)
1437 ret = tf_session.TF_SessionRunCallable(
1438 self._session._session, self._handle, args, status,
-> 1439 run_metadata_ptr)
1440 if run_metadata:
1441 proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)
~\Anaconda3\envs\Tensorflow\lib\site-packages\tensorflow\python\framework\errors_impl.py in __exit__(self, type_arg, value_arg, traceback_arg)
526 None, None,
527 compat.as_text(c_api.TF_Message(self.status.status)),
--> 528 c_api.TF_GetCode(self.status.status))
529 # Delete the underlying status object from memory otherwise it stays alive
530 # as there is a reference to status from this from the traceback due to
InvalidArgumentError: Incompatible shapes: [144,1] vs. [144,18,1]
[[{{node loss_2/dense_4_loss/sub}}]]
[[{{node loss_2/mul}}]]
And for completion the expectable error where y_train was reshaped to (1200*18,1):
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-47-2a6d0761b794> in <module>
----> 1 Test_tdws=twds_model.fit(X_train, y_train_flat, epochs=8, batch_size=144, verbose=2, validation_split=(0.14), shuffle=False) #callbacks=[tensorboard])
~\Anaconda3\envs\Tensorflow\lib\site-packages\tensorflow\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, max_queue_size, workers, use_multiprocessing, **kwargs)
774 steps=steps_per_epoch,
775 validation_split=validation_split,
--> 776 shuffle=shuffle)
777
778 # Prepare validation data.
~\Anaconda3\envs\Tensorflow\lib\site-packages\tensorflow\python\keras\engine\training.py in _standardize_user_data(self, x, y, sample_weight, class_weight, batch_size, check_steps, steps_name, steps, validation_split, shuffle)
2434 # Check that all arrays have the same length.
2435 if not self._distribution_strategy:
-> 2436 training_utils.check_array_lengths(x, y, sample_weights)
2437 if self._is_graph_network and not self.run_eagerly:
2438 # Additional checks to avoid users mistakenly using improper loss fns.
~\Anaconda3\envs\Tensorflow\lib\site-packages\tensorflow\python\keras\engine\training_utils.py in check_array_lengths(inputs, targets, weights)
454 'the same number of samples as target arrays. '
455 'Found ' + str(list(set_x)[0]) + ' input samples '
--> 456 'and ' + str(list(set_y)[0]) + ' target samples.')
457 if len(set_w) > 1:
458 raise ValueError('All sample_weight arrays should have '
ValueError: Input arrays should have the same number of samples as target arrays. Found 12117 input samples and 218106 target samples
Utilized versions are:
Package Version
---------------------- --------------------
- nsorflow-gpu
-ensorflow-gpu 1.13.1
-rotobuf 3.11.3
-umpy 1.18.1
absl-py 0.9.0
antlr4-python3-runtime 4.8
asn1crypto 1.3.0
astor 0.7.1
astropy 3.2.1
astunparse 1.6.3
attrs 19.3.0
audioread 2.1.8
autopep8 1.5.3
backcall 0.1.0
beautifulsoup4 4.9.0
bezier 0.8.0
bkcharts 0.2
bleach 3.1.4
blis 0.2.4
bokeh 1.1.0
boto3 1.9.253
botocore 1.12.253
Bottleneck 1.3.2
cachetools 4.1.0
certifi 2020.4.5.1
cffi 1.14.0
chardet 3.0.4
click 6.7
cloudpickle 0.5.3
cmdstanpy 0.4.0
color 0.1
colorama 0.4.3
colorcet 0.9.1
convertdate 2.2.1
copulas 0.2.5
cryptography 2.8
ctgan 0.2.1
cycler 0.10.0
cymem 2.0.2
Cython 0.29.17
dash 0.26.0
dash-core-components 0.27.2
dash-html-components 0.11.0
dash-renderer 0.13.2
dask 0.18.1
dataclasses 0.6
datashader 0.7.0
datashape 0.5.2
datawig 0.1.10
deap 1.3.0
decorator 4.4.2
defusedxml 0.6.0
deltapy 0.1.1
dill 0.2.9
distributed 1.22.1
docutils 0.14
entrypoints 0.3
ephem 3.7.7.1
et-xmlfile 1.0.1
exrex 0.10.5
Faker 4.0.3
fastai 1.0.60
fastprogress 0.2.2
fbprophet 0.6
fire 0.3.1
Flask 1.0.2
Flask-Compress 1.4.0
future 0.17.1
gast 0.3.3
geojson 2.4.1
geomet 0.2.0.post2
google-auth 1.14.0
google-auth-oauthlib 0.4.1
google-pasta 0.2.0
gplearn 0.4.1
graphviz 0.13.2
grpcio 1.29.0
h5py 2.10.0
HeapDict 1.0.0
holidays 0.10.2
holoviews 1.12.1
html2text 2018.1.9
hyperas 0.4.1
hyperopt 0.1.2
idna 2.6
imageio 2.5.0
imbalanced-learn 0.3.3
imblearn 0.0
importlib-metadata 1.5.0
impyute 0.0.8
ipykernel 5.1.4
ipython 7.13.0
ipython-genutils 0.2.0
ipywidgets 7.5.1
itsdangerous 0.24
jdcal 1.4
jedi 0.16.0
Jinja2 2.11.1
jmespath 0.9.5
joblib 0.13.2
jsonschema 3.2.0
jupyter 1.0.0
jupyter-client 6.1.2
jupyter-console 6.0.0
jupyter-core 4.6.3
Keras 2.2.5
Keras-Applications 1.0.8
Keras-Preprocessing 1.1.2
keras-rectified-adam 0.17.0
kiwisolver 1.2.0
korean-lunar-calendar 0.2.1
librosa 0.7.2
llvmlite 0.32.1
lml 0.0.1
locket 0.2.0
LunarCalendar 0.0.9
Markdown 2.6.11
MarkupSafe 1.1.1
matplotlib 3.2.1
missingpy 0.2.0
mistune 0.8.4
mkl-fft 1.0.15
mkl-random 1.1.0
mkl-service 2.3.0
mock 4.0.2
msgpack 0.5.6
multipledispatch 0.6.0
murmurhash 1.0.2
mxnet 1.4.1
nb-conda 2.2.1
nb-conda-kernels 2.2.3
nbconvert 5.6.1
nbformat 5.0.4
nbstripout 0.3.7
networkx 2.1
notebook 6.0.3
numba 0.49.1
numexpr 2.7.1
numpy 1.19.0
oauthlib 3.1.0
olefile 0.46
opencv-python 4.2.0.34
openpyxl 2.5.5
opt-einsum 3.2.1
packaging 20.3
pandas 1.0.3
pandasvault 0.0.3
pandocfilters 1.4.2
param 1.9.0
parso 0.6.2
partd 0.3.8
patsy 0.5.1
pbr 5.1.3
pickleshare 0.7.5
Pillow 7.0.0
pip 20.0.2
plac 0.9.6
plotly 4.7.1
plotly-express 0.4.1
preshed 2.0.1
prometheus-client 0.7.1
prompt-toolkit 3.0.4
protobuf 3.11.3
psutil 5.4.7
py 1.8.0
pyasn1 0.4.8
pyasn1-modules 0.2.8
pycodestyle 2.6.0
pycparser 2.20
pyct 0.4.5
pyensae 1.3.839
pyexcel 0.5.8
pyexcel-io 0.5.7
Pygments 2.6.1
pykalman 0.9.5
PyMeeus 0.3.7
pymongo 3.8.0
pyOpenSSL 19.1.0
pyparsing 2.4.7
pypi 2.1
pyquickhelper 1.9.3418
pyrsistent 0.16.0
PySocks 1.7.1
pystan 2.19.1.1
python-dateutil 2.8.1
pytz 2019.3
pyviz-comms 0.7.2
PyWavelets 0.5.2
pywin32 227
pywinpty 0.5.7
PyYAML 5.3.1
pyzmq 18.1.1
qtconsole 4.4.4
rdt 0.2.1
RegscorePy 1.1
requests 2.23.0
requests-oauthlib 1.3.0
resampy 0.2.2
retrying 1.3.3
rsa 4.0
s3transfer 0.2.1
scikit-image 0.15.0
scikit-learn 0.23.2
scipy 1.4.1
sdv 0.3.2
seaborn 0.9.0
seasonal 0.3.1
Send2Trash 1.5.0
sentinelsat 0.12.2
setuptools 46.3.0
setuptools-git 1.2
six 1.14.0
sklearn 0.0
sortedcontainers 2.0.4
SoundFile 0.10.3.post1
soupsieve 2.0
spacy 2.1.8
srsly 0.1.0
statsmodels 0.9.0
stopit 1.1.2
sugartensor 1.0.0.2
ta 0.5.25
tb-nightly 1.14.0a20190603
tblib 1.3.2
tensorboard 1.13.1
tensorboard-plugin-wit 1.6.0.post3
tensorflow-estimator 1.13.0
tensorflow-gpu 1.13.1
termcolor 1.1.0
terminado 0.8.3
testpath 0.4.4
text-unidecode 1.3
texttable 1.4.0
tf-estimator-nightly 1.14.0.dev2019060501
Theano 1.0.4
thinc 7.0.8
threadpoolctl 2.1.0
toml 0.10.1
toolz 0.10.0
torch 1.4.0
torchvision 0.5.0
tornado 6.0.4
TPOT 0.10.2
tqdm 4.45.0
traitlets 4.3.3
transforms3d 0.3.1
tsaug 0.2.1
typeguard 2.7.1
typing 3.6.6
update-checker 0.16
urllib3 1.22
utm 0.4.2
wasabi 0.2.2
wcwidth 0.1.9
webencodings 0.5.1
Werkzeug 1.0.1
wheel 0.34.2
widgetsnbextension 3.5.1
win-inet-pton 1.1.0
wincertstore 0.2
wrapt 1.11.2
xarray 0.10.8
xlrd 1.1.0
yahoo-historical 0.3.2
zict 0.1.3
zipp 2.2.0
A lot of thanks in advance for every hint that points towards a running code ;-)!
EDITEDITEDIT
After updating tensorflow and keras to the latest version, I received the error below. The error persisted, although tensorlfow, CUDA 10.1 and cudnn 8.0.2 were completely deleted and reinstalled. The error was produced both with my original and with Fallen Aparts example code.
UnknownError: Fail to find the dnn implementation.
[[{{node CudnnRNN}}]]
[[sequential/bidirectional/forward_gru/PartitionedCall]] [Op:__inference_train_function_5731]
Function call stack:
train_function -> train_function -> train_function
None
Epoch 1/4
---------------------------------------------------------------------------
UnknownError Traceback (most recent call last)
<ipython-input-1-64eb8afffe02> in <module>
27 print(twds_model.summary())
28
---> 29 twds_model.fit(X_train, y_train, epochs=4)
~\Anaconda3\envs\Tensorflow\lib\site-packages\tensorflow\python\keras\engine\training.py in _method_wrapper(self, *args, **kwargs)
106 def _method_wrapper(self, *args, **kwargs):
107 if not self._in_multi_worker_mode(): # pylint: disable=protected-access
--> 108 return method(self, *args, **kwargs)
109
110 # Running inside `run_distribute_coordinator` already.
~\Anaconda3\envs\Tensorflow\lib\site-packages\tensorflow\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_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)
1096 batch_size=batch_size):
1097 callbacks.on_train_batch_begin(step)
-> 1098 tmp_logs = train_function(iterator)
1099 if data_handler.should_sync:
1100 context.async_wait()
~\Anaconda3\envs\Tensorflow\lib\site-packages\tensorflow\python\eager\def_function.py in __call__(self, *args, **kwds)
778 else:
779 compiler = "nonXla"
--> 780 result = self._call(*args, **kwds)
781
782 new_tracing_count = self._get_tracing_count()
~\Anaconda3\envs\Tensorflow\lib\site-packages\tensorflow\python\eager\def_function.py in _call(self, *args, **kwds)
838 # Lifting succeeded, so variables are initialized and we can run the
839 # stateless function.
--> 840 return self._stateless_fn(*args, **kwds)
841 else:
842 canon_args, canon_kwds = \
~\Anaconda3\envs\Tensorflow\lib\site-packages\tensorflow\python\eager\function.py in __call__(self, *args, **kwargs)
2827 with self._lock:
2828 graph_function, args, kwargs = self._maybe_define_function(args, kwargs)
-> 2829 return graph_function._filtered_call(args, kwargs) # pylint: disable=protected-access
2830
2831 #property
~\Anaconda3\envs\Tensorflow\lib\site-packages\tensorflow\python\eager\function.py in _filtered_call(self, args, kwargs, cancellation_manager)
1846 resource_variable_ops.BaseResourceVariable))],
1847 captured_inputs=self.captured_inputs,
-> 1848 cancellation_manager=cancellation_manager)
1849
1850 def _call_flat(self, args, captured_inputs, cancellation_manager=None):
~\Anaconda3\envs\Tensorflow\lib\site-packages\tensorflow\python\eager\function.py in _call_flat(self, args, captured_inputs, cancellation_manager)
1922 # No tape is watching; skip to running the function.
1923 return self._build_call_outputs(self._inference_function.call(
-> 1924 ctx, args, cancellation_manager=cancellation_manager))
1925 forward_backward = self._select_forward_and_backward_functions(
1926 args,
~\Anaconda3\envs\Tensorflow\lib\site-packages\tensorflow\python\eager\function.py in call(self, ctx, args, cancellation_manager)
548 inputs=args,
549 attrs=attrs,
--> 550 ctx=ctx)
551 else:
552 outputs = execute.execute_with_cancellation(
~\Anaconda3\envs\Tensorflow\lib\site-packages\tensorflow\python\eager\execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
58 ctx.ensure_initialized()
59 tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
---> 60 inputs, attrs, num_outputs)
61 except core._NotOkStatusException as e:
62 if name is not None:
UnknownError: Fail to find the dnn implementation.
[[{{node CudnnRNN}}]]
[[sequential/bidirectional/forward_gru/PartitionedCall]] [Op:__inference_train_function_5731]
Function call stack:
train_function -> train_function -> train_function
The respective version list:
Package Version
------------------------ ---------------
- nsorflow-gpu
-ensorflow-gpu 2.3.0
-rotobuf 3.11.3
absl-py 0.9.0
antlr4-python3-runtime 4.8
asn1crypto 1.3.0
astor 0.7.1
astropy 3.2.1
astunparse 1.6.3
attrs 19.3.0
audioread 2.1.8
autopep8 1.5.3
backcall 0.1.0
beautifulsoup4 4.9.0
bezier 0.8.0
bkcharts 0.2
bleach 3.1.4
blis 0.2.4
bokeh 1.1.0
boto3 1.9.253
botocore 1.12.253
Bottleneck 1.3.2
cachetools 4.1.0
certifi 2020.4.5.1
cffi 1.14.0
chardet 3.0.4
click 6.7
cloudpickle 0.5.3
cmdstanpy 0.4.0
color 0.1
colorama 0.4.3
colorcet 0.9.1
convertdate 2.2.1
copulas 0.2.5
cryptography 2.8
ctgan 0.2.1
cycler 0.10.0
cymem 2.0.2
Cython 0.29.17
dash 0.26.0
dash-core-components 0.27.2
dash-html-components 0.11.0
dash-renderer 0.13.2
dask 0.18.1
dataclasses 0.6
datashader 0.7.0
datashape 0.5.2
datawig 0.1.10
deap 1.3.0
decorator 4.4.2
defusedxml 0.6.0
deltapy 0.1.1
dill 0.2.9
distributed 1.22.1
docutils 0.14
entrypoints 0.3
ephem 3.7.7.1
et-xmlfile 1.0.1
exrex 0.10.5
Faker 4.0.3
fastai 1.0.60
fastprogress 0.2.2
fbprophet 0.6
fire 0.3.1
Flask 1.0.2
Flask-Compress 1.4.0
future 0.17.1
gast 0.3.3
geojson 2.4.1
geomet 0.2.0.post2
google-auth 1.14.0
google-auth-oauthlib 0.4.1
google-pasta 0.2.0
gplearn 0.4.1
graphviz 0.13.2
grpcio 1.29.0
h5py 2.10.0
HeapDict 1.0.0
holidays 0.10.2
holoviews 1.12.1
html2text 2018.1.9
hyperas 0.4.1
hyperopt 0.1.2
idna 2.6
imageio 2.5.0
imbalanced-learn 0.3.3
imblearn 0.0
importlib-metadata 1.5.0
impyute 0.0.8
ipykernel 5.1.4
ipython 7.13.0
ipython-genutils 0.2.0
ipywidgets 7.5.1
itsdangerous 0.24
jdcal 1.4
jedi 0.16.0
Jinja2 2.11.1
jmespath 0.9.5
joblib 0.13.2
jsonschema 3.2.0
jupyter 1.0.0
jupyter-client 6.1.2
jupyter-console 6.0.0
jupyter-core 4.6.3
Keras 2.4.3
Keras-Applications 1.0.8
Keras-Preprocessing 1.1.2
keras-rectified-adam 0.17.0
kiwisolver 1.2.0
korean-lunar-calendar 0.2.1
librosa 0.7.2
llvmlite 0.32.1
lml 0.0.1
locket 0.2.0
LunarCalendar 0.0.9
Markdown 2.6.11
MarkupSafe 1.1.1
matplotlib 3.2.1
missingpy 0.2.0
mistune 0.8.4
mkl-fft 1.0.15
mkl-random 1.1.0
mkl-service 2.3.0
mock 4.0.2
msgpack 0.5.6
multipledispatch 0.6.0
murmurhash 1.0.2
mxnet 1.4.1
nb-conda 2.2.1
nb-conda-kernels 2.2.3
nbconvert 5.6.1
nbformat 5.0.4
nbstripout 0.3.7
networkx 2.1
notebook 6.0.3
numba 0.49.1
numexpr 2.7.1
numpy 1.18.5
oauthlib 3.1.0
olefile 0.46
opencv-python 4.2.0.34
openpyxl 2.5.5
opt-einsum 3.2.1
packaging 20.3
pandas 1.0.3
pandasvault 0.0.3
pandocfilters 1.4.2
param 1.9.0
parso 0.6.2
partd 0.3.8
patsy 0.5.1
pbr 5.1.3
pickleshare 0.7.5
Pillow 7.0.0
pip 20.2.2
plac 0.9.6
plotly 4.7.1
plotly-express 0.4.1
preshed 2.0.1
prometheus-client 0.7.1
prompt-toolkit 3.0.4
protobuf 3.11.3
psutil 5.4.7
py 1.8.0
pyasn1 0.4.8
pyasn1-modules 0.2.8
pycodestyle 2.6.0
pycparser 2.20
pyct 0.4.5
pyensae 1.3.839
pyexcel 0.5.8
pyexcel-io 0.5.7
Pygments 2.6.1
pykalman 0.9.5
PyMeeus 0.3.7
pymongo 3.8.0
pyOpenSSL 19.1.0
pyparsing 2.4.7
pypi 2.1
pyquickhelper 1.9.3418
pyrsistent 0.16.0
PySocks 1.7.1
pystan 2.19.1.1
python-dateutil 2.8.1
pytz 2019.3
pyviz-comms 0.7.2
PyWavelets 0.5.2
pywin32 227
pywinpty 0.5.7
PyYAML 5.3.1
pyzmq 18.1.1
qtconsole 4.4.4
rdt 0.2.1
RegscorePy 1.1
requests 2.23.0
requests-oauthlib 1.3.0
resampy 0.2.2
retrying 1.3.3
rsa 4.0
s3transfer 0.2.1
scikit-image 0.15.0
scikit-learn 0.23.2
scipy 1.4.1
sdv 0.3.2
seaborn 0.9.0
seasonal 0.3.1
Send2Trash 1.5.0
sentinelsat 0.12.2
setuptools 46.3.0
setuptools-git 1.2
six 1.14.0
sklearn 0.0
sortedcontainers 2.0.4
SoundFile 0.10.3.post1
soupsieve 2.0
spacy 2.1.8
srsly 0.1.0
statsmodels 0.9.0
stopit 1.1.2
sugartensor 1.0.0.2
ta 0.5.25
tb-nightly 1.14.0a20190603
tblib 1.3.2
tensorboard 2.3.0
tensorboard-plugin-wit 1.7.0
tensorflow-gpu 2.3.0
tensorflow-gpu-estimator 2.3.0
termcolor 1.1.0
terminado 0.8.3
testpath 0.4.4
text-unidecode 1.3
texttable 1.4.0
Theano 1.0.4
thinc 7.0.8
threadpoolctl 2.1.0
toml 0.10.1
toolz 0.10.0
torch 1.4.0
torchvision 0.5.0
tornado 6.0.4
TPOT 0.10.2
tqdm 4.45.0
traitlets 4.3.3
transforms3d 0.3.1
tsaug 0.2.1
typeguard 2.7.1
typing 3.6.6
update-checker 0.16
urllib3 1.22
utm 0.4.2
wasabi 0.2.2
wcwidth 0.1.9
webencodings 0.5.1
Werkzeug 1.0.1
wheel 0.34.2
widgetsnbextension 3.5.1
win-inet-pton 1.1.0
wincertstore 0.2
wrapt 1.11.2
xarray 0.10.8
xlrd 1.1.0
yahoo-historical 0.3.2
zict 0.1.3
zipp 2.2.0

Answer 1

I cannot reproduce your error, check if the following code works for you:
from tensorflow.keras import Sequential
from tensorflow.keras.layers import Conv1D, GRU, Bidirectional, AveragePooling1D, Dense, Flatten, Dropout
import numpy as np
def twds_model(layer1=32, layer2=32, layer3=16, dropout_rate=0.5, optimizer='Adam',
learning_rate=0.001, activation='relu', loss='mse'):
model = Sequential()
model.add(Bidirectional(GRU(layer1, return_sequences=True), input_shape=(X_train.shape[1], X_train.shape[2])))
model.add(AveragePooling1D(2))
model.add(Conv1D(layer2, 3, activation=activation, padding='same',
name='extractor'))
model.add(Flatten())
model.add(Dense(layer3, activation=activation))
model.add(Dropout(dropout_rate))
model.add(Dense(1))
model.compile(optimizer=optimizer, loss=loss)
return model
if __name__ == '__main__':
X_train = np.random.rand(1200, 18, 15)
y_train = np.random.rand(1200, 18, 1)
twds_model = twds_model()
print(twds_model.summary())
twds_model.fit(X_train, y_train, epochs=20)

Answer 2

Okay, here is what worked for me:
Tensorflow 2.3.0
Keras 2.4.2
CUDA 10.1
cuDNN 7.6.5
alongside with this code snippet retrieved from this github issue
import tensorflow as tf
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = '0' # Set to -1 if CPU should be used CPU = -1 , GPU = 0
gpus = tf.config.experimental.list_physical_devices('GPU')
cpus = tf.config.experimental.list_physical_devices('CPU')
if gpus:
try:
# Currently, memory growth needs to be the same across GPUs
for gpu in gpus:
tf.config.experimental.set_memory_growth(gpu, True)
logical_gpus = tf.config.experimental.list_logical_devices('GPU')
print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPUs")
except RuntimeError as e:
# Memory growth must be set before GPUs have been initialized
print(e)
elif cpus:
try:
# Currently, memory growth needs to be the same across GPUs
logical_cpus= tf.config.experimental.list_logical_devices('CPU')
print(len(cpus), "Physical CPU,", len(logical_cpus), "Logical CPU")
except RuntimeError as e:
# Memory growth must be set before GPUs have been initialized
print(e)
A lot of thanks goes to #Fallen Apart who stayed with me. In case of being curious, you might also want to have a brief glimpse into my follow up question here ;-).