I'm struggle with running module.
from tensorflow import Session, ConfigProto, GPUOptions
gpuoptions = GPUOptions(allow_growth=True)
session = Session(config=ConfigProto(gpu_options=gpuoptions))
K.set_session(session)
classifier = Sequential()
I don't know why it's not working.
It just shows me:
ImportError: cannot import name 'Session' from 'tensorflow' (C:\Users\hayou\AppData\Local\Programs\Python\Python37\lib\site-packages\tensorflow\__init__.py)
I guess you're using TensorFlow 2.x. In that case, use tf.compat.v1.--function--() instead.
import tensorflow as tf
gpuoptions = tf.compat.v1.GPUOptions(allow_growth=True)
session = tf.compat.v1.Session(config=tf.compat.v1.ConfigProto(gpu_options=gpuoptions))
K.set_session(session)
classifier = Sequential()
In my case I was using tensorflow v2.2 which doesn't have Session. However it has been present in previous versions, so you might want to install an older version of tensorflow like v1.15 which contains Session class.
pip install tensorflow==1.15
I'm not exactly sure this is the best solution, but it worked for me, so I hope it does work for you as well!
Related
When i try to run the efficientNetv2 model
I got this erreur Error-message
AttributeError: module'tensorflow.keras.applications ' has no attribute 'efficientnet_v2'
Tensorflow version : tensorflow-gpu:2.6
The import is incorrect, you need to update it, it might have worked in older Keras versions,but the internal per-network modules inside keras.applications are not exposed anymore, so your correct import would be:
keras.applications.EfficientNetV2S
Or if you use tf.keras:
tf.keras.applications.EfficientNetV2S
For future reference, always check the documentation, for EfficientNetV2S the link is here.
install efficientnet in you env
!pip install keras-efficientnet
then you can import model as
import efficientnet.tfkeras as efc
done...
you can use prefix 'efc' for B0-B7
Trying to convert a keras model (Thumbs.h5) into an onnx model on Google Colab, however I am getting an "AttributeError: module 'tensorflow.python.keras' has no attribute 'applications'" error when I run the code.
My code:
from tensorflow.python.keras import backend as K
from tensorflow.python.keras.models import load_model
import onnx
import keras2onnx
onnx_model_name = 'fish-resnet50.onnx'
model = load_model('model-resnet50-final.h5')
onnx_model = keras2onnx.convert_keras(model, model.name)
onnx.save_model(onnx_model, onnx_model_name)
What I've tried:
Updating keras with !pip install keras --upgrade (already updated)
Running it locally with a jupyter notebook on my M1 Mac (V12.4) to get the same error
Pointers or solutions greatly appreciated.
As per the documentation
keras2onnx has been tested on Python 3.5 - 3.8, with tensorflow
1.x/2.0 - 2.2
So install compatible version of tensorflow.
Also keras-onnx is not under active development so use tf2onnx as per the documentation
I'm taking a Microsoft PyTorch course and trying to implement on Kaggle Notebooks but I kept having the same error message over and over again: "ModuleNotFoundError: No module named 'onnxruntime'". I've checked everywhere possible if I could find a solution to it but none, I even tried installing it manually using pip in the notebook, but it's still not working. I've checked the official onnxruntime website and documentation but there's nowhere it states anything about something being outdated or any other issue. Someone help. My code won't run because it says "onnxruntime is not defined". Here are my imports:
%matplotlib inline
import torch
import onnxruntime
from torch import nn
import torch.onnx as onnx
import torchvision.models as models
from torchvision import datasets
from torchvision.transforms import ToTensor
and the code cell I'm trying to run
session = onnxruntime.InferenceSession(onnx_model, None)
input_name = session.get_inputs()[0].name
output_name = session.get_outputs()[0].name
result = session.run([output_name], {input_name: x.numpy()})
predicted, actual = classes[result[0][0].argmax(0)], classes[y]
print(f'Predicted: "{predicted}", Actual: "{actual}"')
And you can find the complete notebook here: https://www.kaggle.com/faisalalbasu/complete-model
the error occurs because "import" cannot find onnxruntime in any of the paths, check where import is searching and see if onnxruntime is in there.
check what path pip install installs to, that way in the future you won't have the same problem! :)
I cloned this repository/documentation https://huggingface.co/EleutherAI/gpt-neo-125M
I get the below error whether I run it on google collab or locally. I also installed transformers using this
pip install git+https://github.com/huggingface/transformers
and made sure the configuration file is named as config.json
5 tokenizer = AutoTokenizer.from_pretrained("gpt-neo-125M/",from_tf=True)
----> 6 model = AutoModelForCausalLM.from_pretrained("gpt-neo-125M",from_tf=True)
7
8
3 frames
/usr/local/lib/python3.7/dist-packages/transformers/file_utils.py in __getattr__(self, name)
AttributeError: module transformers has no attribute TFGPTNeoForCausalLM
Full code:
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neo-125M",from_tf=True)
model = AutoModelForCausalLM.from_pretrained("EleutherAI/gpt-neo-125M",from_tf=True)
transformers-cli env results:
transformers version: 4.10.0.dev0
Platform: Linux-4.4.0-19041-Microsoft-x86_64-with-glibc2.29
Python version: 3.8.5
PyTorch version (GPU?): 1.9.0+cpu (False)
Tensorflow version (GPU?): 2.5.0 (False)
Flax version (CPU?/GPU?/TPU?): not installed (NA)
Jax version: not installed
JaxLib version: not installed
Using GPU in script?:
Using distributed or parallel set-up in script?:
Both collab and locally have TensorFlow 2.5.0 version
Try without using from_tf=True flag like below:
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neo-125M")
model = AutoModelForCausalLM.from_pretrained("EleutherAI/gpt-neo-125M")
from_tf expects the pretrained_model_name_or_path (i.e. the first parameter) to be a path to load saved Tensorflow checkpoints from.
My solution was to first edit the source code to remove the line that adds "TF" in front of the package as the correct transformers module is GPTNeoForCausalLM
, but somewhere in the source code it manually added a "TF" in front of it.
Secondly, before cloning the repository it is a must to run
git lfs install.
This link helped me install git lfs properly https://askubuntu.com/questions/799341/how-to-install-git-lfs-on-ubuntu-16-04
Currently I am using transformers(3.0.2) and python(3.7.3) which encountered the below error:
cannot import name 'TFBertForQuestionAnswering' from 'transformers'
from transformers import BertTokenizer, TFBertForQuestionAnswering
model = TFBertForQuestionAnswering.from_pretrained('bert-base-cased')
f = open(model_path, "wb")
pickle.dump(model, f)
How do resolve this issue?
Upgrade your TensorFlow library. It works fine with 2.3.1. version.
Some TensorFlow components are only available when you have TensorFlow2 installed. Make sure you have the required version for the environment you are working on!