I wanted to convert conllu format for Hindi to json using spacy convert and it is generating jsonl format. When I pass on the same .jsonl file to spacy train I am getting an error. If I pass a json file to the train command, it is working. Shouldn't the output of spacy convert be compatible with spacy train?
The error message is as follows:
I am using :
Spacy version - 2.1.3
Python 3.6.8
ubuntu 18.04
I had the same problem when outputting convertions as default jsonl format. Fixed by converting with file-type : json attribute e.g.
python -m spacy convert lang_train.conllu output-folder --file-type json
Related
I want to use USAS semantic tagger in Google Colab using the instructions here
pip install https://github.com/UCREL/pymusas-models/releases/download/en_dual_none_contextual-0.3.1/en_dual_none_contextual-0.3.1-py3-none-any.whl
python -m spacy download en_core_web_sm
import spacy
I install the package using the above code. And then run the following:
# We exclude the following components as we do not need them.
nlp = spacy.load('en_core_web_sm', exclude=['parser', 'ner'])
# Load the English PyMUSAS rule based tagger in a separate spaCy pipeline
english_tagger_pipeline = spacy.load('en_dual_none_contextual')
# Adds the English PyMUSAS rule based tagger to the main spaCy pipeline
nlp.add_pipe('pymusas_rule_based_tagger', source=english_tagger_pipeline)
I encounter the following error for the second line:
TypeError: load_model_from_init_py() got an unexpected keyword
argument 'enable'
Note that this error doesn't show up when running the same code on my local machine. Only in Google Colab...
My sapCy version was too high. That's how I solved it:
!pip3 install spacy==3.2.3
I have trained one object detection model in tensorflow.
My Environment--
tf version == 1.15, network== ssd mobilnet v2
Now i want to convert my saved_model(.pb) file to tfjs(.json) format.
I followed below steps--
pip install tensorflowjs==0.8.6 # not sure if it's compatible with tf version 1.15
command==
tensorflowjs_converter --input_format=tf_saved_model --output_format=tfjs_graph_model --signature_name=serving_default --saved_model_tags=serve exported_path/saved_model exported_path/web_model_path
Error== AttributeError: module 'keras_applications' has no attribute 'set_keras_submodules'
Then i degrade keras_application version.
Now getting error as
usage: TensorFlow.js model converters. [-h]
[--input_format {keras,tf_session_bundle,keras_saved_model,tf_hub,tf_saved_model,tensorflowjs,tf_frozen_model}]
[--output_format {keras,tensorflowjs}]
[--output_node_names OUTPUT_NODE_NAMES]
[--signature_name SIGNATURE_NAME]
[--saved_model_tags SAVED_MODEL_TAGS]
[--quantization_bytes {1,2}]
[--split_weights_by_layer] [--version]
[--skip_op_check SKIP_OP_CHECK]
[--strip_debug_ops STRIP_DEBUG_OPS]
[--output_json OUTPUT_JSON]
[input_path] [output_path]
TensorFlow.js model converters.: error: argument --output_format: invalid choice: 'tfjs_graph_model' (choose from 'keras', 'tensorflowjs')
So there is no option for tf_graph_model in output_format.
Now when i am installing pip install tensorflowjs (not passing any specific version), then it installs tfjs==3.3.0, and uninstaling my current tf1.15 and installing new tf2.x version. which i need to avoid at any cost.
Can somebody please guide me , how to convert the saved_model to tf_js format in version tensorflow==1.15.
Thanks in Advance.
Can you please upgrade tensorflow to latest version tf 2.x, stable versions tf 2.6/2.7 and let us know if this issue exist as tf 1.x is not supported any more and hence these errors.
For model checkpoint files (usually consist of .meta, .data, .index) generated from TF-2.0, how can I convert it to onnx or pb file?
since I found most of the existing tools, such as tf2onnx only support TF-1.x.
tf2onnx supports tf2, saved models, and checkpoint files. I would recommend making a saved model:
model.save("path")
Then use tf2onnx command line:
tf2onnx.convert --saved-model path/to/model --output model.onnx --opset 12
https://github.com/onnx/tensorflow-onnx#--saved-model
I trained a custom CNN model using keras and tensorflow 2.2.0 as background. After that, I saved the model as .ckpt file having assets, variables, .pb file as subfolders init. After that to convert it into IR in openvino documentation it is given that we have use the following command:
**To convert such TensorFlow model:
Go to the <INSTALL_DIR>/deployment_tools/model_optimizer directory
Run the mo_tf.py script with a path to the SavedModel directory to convert a model:
python3 mo_tf.py --saved_model_dir <SAVED_MODEL_DIRECTORY>**
so, I went to the following directory as mentioned and tired the following command:
python3 mo_tf.py --saved_model_dir C:\Users\vyas\Desktop\saved_model\cp.ckpt
There is no output or anything. There is no error also.
Also, I tried the following command:
python3 mo_tf.py --saved_model_dir C:\Users\vyas\Desktop\saved_model\cp.ckpt --output_dir C:\Users\vyas\Desktop\out
Still there is no output.
Can someone please help.
I am using tensorflow 2.2.0
Can someone please help me with this
--saved_model_dir must provide a path to the SavedModel directory.
Modify your command as follows:
python3 mo_tf.py --saved_model_dir C:\Users\vyas\Desktop\saved_model
Following the tutorial on TensorFlow for Poets (Android) (https://codelabs.developers.google.com/codelabs/tensorflow-for-poets-2/#0)
Attempting to use Inception model instead of Mobilenet
Trying to strip DecodeJpeg Op from the retrained model using strip_unused.py, but encountered the following error.
Error:
/home/user/tensorflow/bin/python: Error while finding spec for 'tensorflow.python.tools.strip_unused.py' (AttributeError: module 'tensorflow.python.tools.strip_unused' has no attribute '__path__')
Command line:
python -m tensorflow.python.tools.strip_unused.py --input_graph=tf_files/retrained_graph.pb --output_graph=tf_files/stripped_graph.pb --input_node_names="Mul" --output_node_names="final_result" --input_binary=true
Machine:
Ubuntu 16.04 LTS
Python 3.5.2
TensorFlow 1.4.1
Any assistance is greatly appreciated. Thanks!
Might be due to the typo mistake as file extension .py was specified. This seemed to work:
python -m tensorflow.python.tools.strip_unused --input_graph=tf_files/retrained_graph.pb --output_graph=tf_files/stripped_graph.pb --input_node_names="Mul" --output_node_names="final_result" --input_binary=true
Result: 997 ops in the final graph.