I am getting this error in the Week 3 assignment 'Fine-tuning InceptionV3 for flowers classification' in 'Google Colab'.
Error: TypeError: get_updates() takes 3 positional arguments but 4 were given
This happens at the Training step, after compiling.
[# fine tune for 2 epochs (full passes through all training data)]
I can't really find a bug in the code. The previous sections have run without errors.
Could it be a version issue? Can someone please suggest the snippet from the compilation step? Any help is much appreciated.
If you are using TensorFlow version 2.* then just try degrading it to version 1.* by using the command:
pip install tensorflow==1.*
Related
I am trying to use spacy's 'pre-train' feature for a NER task, so here is what I tried doing(I am still trying to use it),
Step 1: I started by initializing the model with 'en_core_web_lg' next I saved this model to disk and tested its NER capability on few lines to see if it recognizes the tags in those test lines. (Made a note of ignored tags)
Step 2: Next I created a .jsonl file with new data to train on (about 20 new lines, I wanted to see the model's capability given new data around an entity(ignored tags found earlier) will it be able to correctly identify tags after doing transfer learning). So using this .jsonl and the model I saved earlier file I used 'spacy pre-train' command to train, this created a token2vec .bin file for me (model999.bin).
Step 3: Next I created a function that takes the location of an earlier saved model(model saved in step 1) and location of token2vec (model999.bin file obtained in step 2). Inside the function it loads the model>creates/gets pipe>disables rest of the files>uses (pipe_name).model.tok2vec.from_bytes(file_.read()) to read from model999.bin and broadcast the learned vectors to base model.
But when I run this function, I get this error:
ValueError: could not broadcast input array from shape (96,3,384) into shape (96,3,480)
(I have uploaded the entire notebook here: [https://github.com/pratikdk/ner_test/blob/master/base_model_contextual_TF.ipynb ]).
In order to pre-train I used this function
python -m spacy pre-train ub.jsonl model_saves w2s
Here are the 20 lines I tried training on top of the base model
[ https://github.com/pratikdk/ner_test/blob/master/ub.jsonl ]
What am I doing wrong here exactly? Please can you also point the fix, I am sure many would need insight on this.
Environment
Operating System: CentOS
Python Version Used: 3.7.3
spaCy Version Used: 2.1.3
Environment Information: Anaconda Jupyter Lab
So I was able to fix this, the developer(on github) answered my question.
Here is the answer:
https://github.com/explosion/spaCy/issues/3616
I am running a variational auto-encoder in TensorFlow, which could take a long time. Thus I want to report the time the algorithm has been running for as a scalar on TensorBoard.
One dirty way is to hard-code the start time of the compilation into a global variable, or pass it as an argument to the model function and compute the difference with current time.
Does Tensorflow have a native way to do it?
There is the tf.train.ProfilerHook. Comes with release 1.14.
Example usage:
estimator = tf.estimator.LinearClassifier(...)
hooks = [tf.train.ProfilerHook(output_dir=model_dir, save_secs=600, show_memory=False)]
estimator.train(input_fn=train_input_fn, hooks=hooks)
Executing the hook will generate files timeline-xx.json in output_dir.
Then open chrome://tracing/ in chrome browser and load the file. You will get a time usage timeline like below.
The tensorflow documentation does not provide any example of how to perform a periodic evaluation of the model on an evaluation set.
Some people suggested the use of an Experiment, which sounds great but unfortunately does not work (depreciation and triggers an error).
Others suggested the use of SummarySaverHook, but I don't see how you can use that with an evaluation set (as opposed to the training set).
A solution would be to do the following
for i in range(number_of_epoch):
estimator.train(...) // on training set
estimator.evaluate(...) // on evaluation set
This architecture is explicitly discouraged in this paper (page 4 top right).
Any other idea/implementation?
EDIT:
The error message when running the experiment is the following:
File ".../anaconda2/lib/python2.7/site-packages/tensorflow/contrib/learn/python/learn/experiment.py", line 253, in train if (config.environment != run_config.Environment.LOCAL and
AttributeError: 'RunConfig' object has no attribute 'environment'
Tensorflow version 1.3
Only a few parameters/options of Experiment are deprecated (what specific errors are you seeing?). If you create an Estimator that will do periodic checkpoints (using options in RunConfig) and an Experiment using it, you will get evaluation for each checkpoint by default when using train_and_evaluate method.
EDIT: As Maxime pointed out in the comments. He needed to add the following lines to get rid of his error:
os.environ['TF_CONFIG'] = json.dumps({'environment': 'local'})
config = tf.contrib.learn.RunConfig()
When I try to use mxnet to build a feedforward model it appeared the following error:
Error in mx.io.internal.arrayiter(as.array(data), as.array(label), unif.rnds, :
basic_string::_M_replace_aux
I follow the R regression example on mxnet website but I change the data into my own data which contains 109 examples and 1876 variables. The first several steps can run without error until ran the model building step. I just can't understand the error information mean. I wonder that it is because of my dataset or the way I deal with the data.
Can you provide the code snippet you are using? That gives more details on the issue. Also, any stacktrace will be useful.
You get this error message mainly due to invalid column/row access and shape (dimension) mismatch. Can you verify if you are using correct "index" values in creating matrix. Let me know if this fixes the issue.
However, MXNet can be better at printing details about error in the stacktrace. I have created a issue to follow up on this - https://github.com/dmlc/mxnet/issues/4206
I was training a neural network overnight and it crashed. I have 2 questions:
What causes this error?
How can I prevent it from happening again?
The 2 main errors are:
ERROR:tensorflow:Exception in QueueRunner: corrupted record at 52284962154
DataLossError (see above for traceback): corrupted record at 52284962154
EDIT
The same code was used on another machine and it crashed with the same error after about 6 hours. The number 52284962154 was identical.
The problem was a write error. After converting the data to TFRecords again, the error disappeared. It can go beyond step 30747 now.