I've got the following error: https://pastebin.com/X7146Ury
when running this script.
AttributeError: 'InputLayer' object has no attribute 'inbound_nodes'
In the latest version of Keras this was renamed to _inbound_nodes (note the added underscore). The version of coremltools you're using does not appear to be compatible with that Keras version yet.
However, the latest version on GitHub does appear to use the new _inbound_nodes name. I suggest you install that, using:
pip install -U git+https://github.com/apple/coremltools.git
I was trying to write a custom layer, the problem was the class which I was subclassing, instead of from tensorflow.keras.layers import Layer, I used
from Keras import layers
class layerName(layers.Layer):
#impelementation
and it worked.
Related
I got an error corresponding to the title when batch_first is set to True in the Pytorch transformer code. When batch_first = False, such an error does not appear.
Torch version 1.9.0+cu111 and Python version 3.7.12 are used.
The code below belongs to the TransformerEncoderLayer class, where the error corresponding to the title occurs.
A code with an error
I can't find the torch._transformer_encoder_layer_fwd function even in the pytorch official documentation.
This issue is because the function _transformer_encoder_layer_fwd is not available in PyTorch v1.9.
Switching to the latest torch v1.12 solved the issue for me.
I have problem connected tensorflow, In my script has decoder #tf.contrib.eager.defun. After running gave only error (AttributeError: module 'tensorflow' has no attribute 'contrib'). Please help me. My version tensor = 2.8.0
I change some tensorflow command but again this problem
#tf.contrib.eager.defun appears to do some sort of pre-compilation of its decorated function to improve performance and is a TF 1.x method. I couldn't find where it was moved in TF 2.0, so I got it to work by either downgrading TF or removing the decorator (the latter slows down execution speed).
nadam = torch.optim.NAdam(model.parameters())
This gives the error AttributeError: module 'torch.optim' has no attribute 'NAdam'. My PyTorch version is '1.9.1+cu102', the python version is 3.7.11. VS code does not even suggest the optimizer but the documentation clearly mentions the optimizer. I can import other optimizers like Adam
https://pytorch.org/docs/1.9.1/optim.html
From the official website, NAdam is not among the optimizers in pytorch v 1.9.1.
Try upgrading to v 1.10.0, and your code should work just fine.
According to documentation, NAdam is new in 1.10. It does not show in https://pytorch.org/docs/1.9.1/optim.html?highlight=optim#module-torch.optim
It is in https://pytorch.org/docs/1.10.0/optim.html?highlight=optim#module-torch.optim
I am using TensorFlow version=2.0.0
python version=3.7.3
I am trying to import the below statement
from tensorflow.contrib import rnn
And it gives error as
Module 'tensorflow' has no attribute 'contrib'
How can I resolve this?
from tensor flow
https://www.tensorflow.org/guide/upgrade#compatibility_modules
Because of TensorFlow 2.x module deprecations (for example, tf.flags and tf.contrib), some changes can not be worked around by switching to compat.v1. Upgrading this code may require using an additional library (for example, absl.flags) or switching to a package in tensorflow/addons.
and as describe in this thread
tensorflow.contrib doesn't exist in 2.0.
https://github.com/tensorflow/tensorflow/issues/31350#issuecomment-518749548
I haven't used older versions of tensorflow. Is this what you're looking for?
from tensorflow.keras.layers import RNN
Info on contrib:
https://www.tensorflow.org/guide/migrate#a_note_on_slim_contriblayers
I am new to Tensorflow and I am running the tutorial of word2vec embedding code (https://github.com/tensorflow/models/tree/master/tutorials/embedding) on Tensorflow (cpu-only), OS X: 10.11.6. I installed tensorflow via pip install.
Running word2vec_basic.py can reach the expected result, but when it turns to word2vec.py and word2vec_optimized.py, the following error is displayed:
You'll need to use bazel to build the directory, since the op 'skipgram_word2vec' is defined in C++ and not in Python.