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
Related
Following this example to start https://machinelearningmastery.com/tutorial-first-neural-network-python-keras/
# first neural network with keras tutorial
from numpy import loadtxt
from keras.models import Sequential
from keras.layers import Dense
Installed numpy and keras, numpy is ok, but there are red lines under "Sequential" and "Dense".
Here's the Error messages:
Cannot find reference 'Sequential' in 'models.py'
Cannot find reference 'Dense' in 'init.py'
Wonder how I can fix this? I had a look here but think it might be a different problem?
Also, on a completely different note, I can not install tensorflow for some reason? ...
As of October 2020 Tensorflow only supports the 64-bit version of Python and Tensorflow only supports Python 3.5 to 3.8.
Update your project in PyCharm to run Python 64-bit and this should solve your problem.
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 have the following error when running a CNN made in keras
File
"venv/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py",
line 4185, in truncated_normal
return tf.truncated_normal(shape, mean, stddev, dtype=dtype, seed=seed) AttributeError: module 'tensorflow' has no attribute
'truncated_normal'
I have already installed and reinstalled Tensorflow 2.0 several times. What could be happening?
In Tensorflow v2.0 and above, "tf.truncated_normal" replaced with "tf.random.truncated_normal"
For TewnsorFlow 2.x and keras 2.3 or the above, the following usage is more adaptable. Users do not need to downgrade both TensorFlow and Keras.
# Add the import statements
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
# Change it to the sample expression as follows.
init = tf.compat.v1.random.truncated_normal()
Cheers!
Keras 2.2.4 does not support TensorFlow 2.0 (it was released much before TF 2.0), so you can either downgrade TensorFlow to version 1.x, or upgrade Keras to version 2.3, which does support TensorFlow 2.0.
I want to run code for try neural network with python but
No module named 'tensorflow.python._impl
part of my code:
import tensorflow as tf
from tensorflow import keras
from tensorflow.python._impl import activations
I did all solutions that some sites said such as
- Uninstall and install package like tensorflow,..
also:
- Upgrade tensorflow
- Upgrade keras
but it doesn't solve yet. I have this error on co lab too.
please help me if you have any solution
thanks
from keras.layers.recurrent import ...
Official TensorFlow Support discourages use of tf.python.* as it is private and intended for development purposes only. While it may work in some cases, it will "break unannounced" in many others.
Instead, try importing tensorflow with the .python portion removed, e.g.:
from tensorflow._impl import activations
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.