My goal is to classify MNIST handwritten digits using keras. I am trying to reproduce the results from this website.
When creating the model ("model = baseline_model()"), I get the error message
AssertionError: Keyword argument not understood: kernel_initializer
Do you know how to solve this issue ? I am using keras 1.1.1 with theano back-end
As it's mentioned in a comment at the beginning of an article - this is a version for keras 2.0.2, so in order to make this example working you need to use this version of Keras.
Related
I have an EfficientNet model (tensorflow.keras==2.4) and would like to use innvestigate to inspect the results, but it requires keras==2.2.4
Training code:
tensorflow.keras.__version__ # 2.4
model = tf.keras.applications.EfficientNetB1(**params)
# do training
model.save('testModel')
I have the model saved as file but can not load it into Keras 2.2.4. This is the point where I'm stuck, I couldn't figure out what to do to convert the model.
Use Innvestigate:
keras.__version__ # 2.2.4
keras.model.load_model('testModel') # Error
# some more stuff...
I also found this thread, might try it, but since efficient net has > 350 layers it is not really applicable
How to load tf.keras models with keras
I don't know if it's actually possible to convert models between tensorflow.keras and keras, I appreciate all help I can get.
Due to version incompatibility between tensorflow as keras, you were not able to load model.
Your issue will be resolved, once you upgrade keras and tensorflow to 2.5.
I have h5 weights from a Keras model.
I want to rewrite the Keras model into a tf.keras model (using TF2.x).
I know that only the high level API changed, but do you know if I still can use the h5 weights?
Most likely they can be loaded, but is the structure different between Keras and tf.keras weights?
Thanks
It seems that they are the same
cudos to Mohsin hasan answer
In the past, when I had to convert tf.keras model to keras model, I
did following:
Train model in tf.keras
Save only the weights tf_model.save_weights("tf_model.hdf5")
Make Keras model architecture using all layers in keras (same as the tf keras one)
load weights by layer names in keras: keras_model.load_weights(by_name=True)
This seemed to work for me. Since, I was using out of box architecture
(DenseNet169), I had to very less work to replicate tf.keras network
to keras.
And the answer from Alex Cohn
tf.keras HDF5 model and Keras HDF5 models are not different things,
except for inevitable software version update synchronicity. This is
what the official docs say:
tf.keras is TensorFlow's implementation of the Keras API specification. This is a high-level API to build and train models that
includes first-class support for TensorFlow-specific functionality
If the convertor can convert a keras model to tf.lite, it will deliver
same results. But tf.lite functionality is more limited than tf.keras.
If this feature set is not enough for you, you can still work with
tensorflow, and enjoy its other advantages.
I am trying to implement a DQN agent using Keras-rl. The problem is that when I define my model I need to use an LSTM layer in the architecture:
model = Sequential()
model.add(Flatten(input_shape=(1, 8000)))
model.add(Reshape(target_shape=(200, 40)))
model.add(LSTM(20))
model.add(Dense(3, activation='softmax'))
return model
Executing the rl-agent I obtain the following error:
RuntimeError: Attempting to capture an EagerTensor without building a function.
Which is related to the use of the LSTM and to the following line of code:
tf.compat.v1.disable_eager_execution()
Using a Dense layer instead of an LSTM:
model = Sequential()
model.add(Flatten(input_shape=(1, 8000)))
model.add(Dense(20))
model.add(Dense(3, activation='softmax'))
return model
and maintaining eager execution disabled I don't have the previously reported error. If I delete the disabling of the eager execution with the LSTM layer I have other errors.
Can anyone help me to understand the reason of the error?
The keras-rl library does not have explicit support for TensorFlow 2.0, so it will not work with such version of TensorFlow. The library is sparsely updated and the last release is around 2 years old (from 2018), so if you want to use it you should use TensorFlow 1.x
Install keras-rl2 from github support tensorflow 2.x
Although it is possible to migrate the code for keras-rl to use eager execution and therefore an LSTM. LSTMs need to be updated with a whole episode of learning to prove accurate something which keras-rl does not support. See more here: https://github.com/keras-rl/keras-rl/issues/41
I am using Pytorch for image classification. I am looking for CNN models pretrained on a dataset other than ImageNet, I have found a link to a ".ckpt" file. I also found tutorials on loading this file with Tenserflow, but not using pytorch.
How can I load pretrained model using Pytorch from ".ckpt" file ?
I agree with #jodag that in general, PyTorch and Tensorflow are not interoperable. There are some special cases in which you may be able to do this. For example, HuggingFace provides support for converting the transformer model from TensorFlow to PyTorch.
There is a related (though closed) question on DataScience StackExchange where the idea is to rewrite the Tensorflow model into PyTorch and then loads the weights from the checkpoint file. Note that this can get tricky at times.
Need Suggestion
I am trying to design a model to guess Facial-Points. Its a part of Kaggle Competition (https://www.kaggle.com/c/facial-keypoints-detection).
In this solution, I am trying to design a CNN model (using Keras Library), as a Multi-variable regression model to Predict the co-ordinates of Facial-points.
Issue Faced --> I am getting loss as "nan"
Solutions tried --
1. Tried optimizers - Adam, SGD
2. tested with Learning rate 0.01 to 0.00001
3. Tried with various batch sizes
Can anyone suggest, if I am missing something. The code is present in below link -
https://www.kaggle.com/saurabhrathor/facialpoints-practice