When I'm trying to retrain the model with tensorflow it shows an error:
**error module 'tensorflow_hub' has no attribute 'KerasLayer'**
The code is:
print("Building model with", MODULE_HANDLE)
model = tf.keras.Sequential([
hub.KerasLayer(MODULE_HANDLE, output_shape=[FV_SIZE],
trainable=do_fine_tuning),
tf.keras.layers.Dropout(rate=0.2),
tf.keras.layers.Dense(train_generator.num_classes,
activation='softmax',
kernel_regularizer=tf.keras.regularizers.l2(0.0001))
])
model.build((None,)+IMAGE_SIZE+(3,))
model.summary()
The error is like:
1 print("Building model with", MODULE_HANDLE)
2 model = tf.keras.Sequential([
----> 3 hub.KerasLayer(MODULE_HANDLE, output_shape=[FV_SIZE],
4 trainable=do_fine_tuning),
5 tf.keras.layers.Dropout(rate=0.2),
AttributeError: module 'tensorflow_hub' has no attribute 'KerasLayer'
by using the tensorflow hub retrain the previous hub model by adding new dence fully connected layers.when run the code it show the above error.is any have idea about that.please help
Please check the tensorflow version. It should be a recent nightly version.
When I use a version like 1.13.1, I see the following warning before the error, no attribute 'KerasLayer':
W0423 20:04:16.453974 139707130586880 __init__.py:56] Some hub symbols are not available because TensorFlow version is less than 1.14
After, doing pip install "tf-nightly", everything works fine.
https://www.tensorflow.org/hub
For the BatchNormalizationv1 issue, you can use tf2.0 nightly which should also take care of the original issue.
pip install -U tf-nightly-2.0-preview
https://github.com/tensorflow/tfjs/issues/1255
hub.KerasLayer works with TF2 pre releases:
pip install tf-nightly-2.0-preview --quiet
pip install tensorflow==2.0.0-alpha
pre-release candidate for GPU:
pip install -U --pre tensorflow-gpu
Related
I'm developing a project based on yolov7, but I started facing this error where torch recognizes my GPU but torchvision throws an Not Implemented Error.
This is the error
NotImplementedError: Could not run 'torchvision::nms' with arguments from the 'CUDA' backend. This could be because the operator doesn't exist for this backend, or was omitted during the selective/custom build process (if using custom build). If you are a Facebook employee using PyTorch on mobile, please visit https://fburl.com/ptmfixes for possible resolutions. 'torchvision::nms' is only available for these backends: [CPU, QuantizedCPU, BackendSelect, Python, FuncTorchDynamicLayerBackMode, Functionalize, Named, Conjugate, Negative, ZeroTensor, ADInplaceOrView, AutogradOther, AutogradCPU, AutogradCUDA, AutogradXLA, AutogradMPS, AutogradXPU, AutogradHPU, AutogradLazy, Tracer, AutocastCPU, AutocastCUDA, FuncTorchBatched, FuncTorchVmapMode, Batched, VmapMode, FuncTorchGradWrapper, PythonTLSSnapshot, FuncTorchDynamicLayerFrontMode, PythonDispatcher].
I tried installing torchvision with cuda built-in but that gave me the same error, also tried reinstalling pytorch , that didn't work either
the version of torch vision installed in my env was not equipped with cuda as it was a common install with pip with pip install torchvision whereas for torchvision to function with cuda it has to be equipped with cuda in-order for it to function with an Nvidia GPU to do so install torch with the following command conda install pytorch torchvision torchaudio pytorch-cuda={CUDA version} -c pytorch -c nvidia
I am running this colab from Roboflow: https://colab.research.google.com/drive/1ByRi9d6_Yzu0nrEKArmLMLuMaZjYfygO#scrollTo=WgHANbxqWJPa
The code should be as-is, but i get errors at the end...
I suspect a TF versioning issue, but how do i know which version of TF i should install instead?
It should use TF1:
%tensorflow_version 1.x
But
!python -c 'import keras;
print(keras.version)'
returns: Using TensorFlow backend. 2.2.4
What am i doing wrong here? Unistall TF & reinstall which version?
Thanx
Fred
I use Tensorflow 2.1.0
In this code
data_augmentation = tf.keras.Sequential([
tf.keras.layers.experimental.preprocessing.RandomFlip('horizontal'),
tf.keras.layers.experimental.preprocessing.RandomRotation(0.3)
])
I find this error:
AttributeError: module 'tensorflow_core.keras.layers.experimental.preprocessing' has no attribute 'RandomFlip'
So how can I change it without changing version of tensorflow
To work your code as expected, firstly Tensorflow has to be upgrade to the latest version
! pip install tensorflow --upgrade
If you are looking for solution in TF 2.1.0, then there are two options are available
First solution: tf.image.random_flip_left_right ( horizontal flip)
tf.image.random_flip_left_right(
image, seed=None)
Second solution: tf.keras.preprocessing.image.ImageDataGenerator
tf.keras.preprocessing.image.ImageDataGenerator(
rotation_range=30, horizontal_flip=True)
! pip install tensorflow --upgrade --user
--user option can help you without the permission problem
Add this line to the importing section (of course after import tensorflow as tf)
tf.config.experimental_run_functions_eagerly(True)
Almost any tf.keras.layers.experimental.preprocessing.SomeClass in the listed classes here, should work.
But need to do sanity check with plotting results.
I notice densenet has been added to keras (https://github.com/keras-team/keras/tree/master/keras/applications)and I want to apply it in my project but when I tried to import it in jupyter anaconda, I got an error saying:
module 'keras.applications' has no attribute 'densenet'
it seems like densenet has not been incorporated into current version of keras.
Any idea how can I add it myself?
Densenet was added in keras version 2.1.3. What version of keras are you running?
Have you tried to update keras with pip install keras --upgrade since January?
I tried pip installing 0.3.1, but when I print the version it outputs 2.1.4.
!pip install keras==0.3.1
import keras
print keras._version__
I am trying to train deepmask (https://github.com/abbypa/NNProject_DeepMask/) for which I specifically need 0.3.1.
Note that if you've already loaded keras, then the second import statement has no effect.
So first !pip install keras==0.3.1, then restart your kernel (ctrl-m . or Runtime -> Restart runtime) and then things should work as expected.