There is a pre-trained NER model and I want to test how well it's performing so I just loaded with spacy
mdl = spacy.load("..../model.pkl")
But getting error says couldn't read meta.json. what exactly error means.
Related
I have been trying to develop an image classification model using ViT and done with it till model building phase.
when I'm trying to train the model it's showing an error of this kind this is the error
And I'm attaching an full code regarding my model Model Notebook
Thanks & Regards
Satwik Sunnam
I loaded my CNN model using
model = load_model('./model/model.h5')
This model loads well ( Though I do get warning messages: "Call initializer instance with the dtype argument instead of passing it to the constructor" )
However, when I try to predict using this model, I get the following error:
UnknownError: 2 root error(s) found.
(0) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
[[{{node conv2d/Conv2D}}]]
[[dense_2/Softmax/_273]]
(1) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
[[{{node conv2d/Conv2D}}]]
0 successful operations.
0 derived errors ignored
Any idea how to overcome this issue?
Apparently, I just didn't have CUDA installed on my system. Did that and now it's working!
I was trying to run a customized model on caffe.Unfortunately,I was only provided with a trainval.prototxt and trainval.caffemodel.
The exact error is as follows
Error parsing text-format caffe.NetParameter: 54:17: Message type “caffe.ParamSpec has no field named compression
This is followed by
[upgrade_proto.cpp :79] check failed read protofromtextfile failed to parse param file
A similar question was asked here.
So should I assume that my version of caffe that I have on my system was different from the client's version of caffe and apparently the client version of caffe has a slightly different proto definition??
Recently I've trained a model using MNIST dataset in Google colab. I've saved the weights using Model.save('model.h5').
I've downloaded the weights and tried to run in other code by loading weights offline in anaconda Model = Keras.models.load_model('model.h5).
But it throws
ValueError: Unknown initializer: GlorotUniform
The issue could be because you're using tf.keras and keras in a mixed way. There could also be a version mismatch between local and remote keras versions. Do check this discussion on stackoverflow.
I tried running the VGG16 keras script.
I get this error:
Downloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels.h5
Traceback (most recent call last):
File "test_imagenet.py", line 40, in
model = VGG16(weights="imagenet")
File "/home/nvidia/deep-learning-models/imagenet-example/vgg16.py", line 143, in VGG16
cache_subdir='models')
File "build/bdist.linux-aarch64/egg/keras/utils/data_utils.py", line 222, in get_file
Exception: URL fetch failure on https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels.h5:
I tried to download it manually from here and paste it to ~/.keras/models.
But still, I am getting the same error. Why? I don´t understand the error because the correct model already is in .keras/models.
The default value of include_top parameter in VGG16 function is True. This means if you want to use a full layer pre-trained VGG network (with fully connected parts) you need to download vgg16_weights_tf_dim_ordering_tf_kernels.h5 file, not vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5.