I download and run the file from the link below
https://github.com/keunwoochoi/keras_callbacks_example
But the it has the error "Sequential has no attribute "validation_data"". Can anyone explain for me?
Try using self.model.predict(self.validation_data[0]). That is what worked for me.
You can always check what is in the object with dir().
I had the same problem using self.model.validation_data. Checking with dir(self.model) showed me that there was indeed no attribute validation_data for my particular problem. But then checking dir(self) I could find it.
i had the same issue.
here is the solution:
use self.validation_data in your custom callback class
provide validation_data = (x,y) in your fit method.
if point 2 is not done, self.validation_data will be empty.
hope this helps
This would work on the object of type keras.engine.training.Model.
Try self.model.validation_data
Try self.validation_data instead of self.model.validation_data for Keras 2.0 and after.
You'll also have to define validation_data within fit(). Using train_test_split, validation_data=(X_test, y_test).
Example: https://www.kaggle.com/yassinealouini/f2-score-per-epoch-in-keras
Related
I try to use WanDB but when i use wandb.init() there is nothing.!
I am waiting a lot of time.
However, there is nothing in window.
This is working well in Kernel.
please.. help me guys
I work at Weights & Biases. If you're in a notebook the quickest thing you can do to get going with wandb is simply:
wandb.init(project=MY_PROJECT, entity=MY_ENTITY)
No !wandb login, wandb.login() or %%wandb needed. If you're not already logged in then wandb.init will ask you for you API key.
(curious where you found %%wandb by the way?)
I need to use a custom kernel with the Smooth function, but attempting this throws an error even though I specify cutoff:
Error: The argument ‘cutoff’ is required when a non-Gaussian kernel is specified and scalekernel=FALSE
n=4; PPP=ppp(rep(1:n,n),rep(1:n,each=n), c(1,n),c(1,n), marks=1:n^2);
Smooth.ppp(PPP,cutoff=50,kernel=gaussian,at="points")
Here is a toy example that makes the same error. I'm not 100% sure whether this is an error or something that's unintuitive to me. Thank you!!
This is a bug. Thank you for bringing it to our attention.
It has been fixed in the development version of spatstat.core (2.4-2.003) which is available at the GitHub repository
The bug only affects the case where kernel is a function, at="points", and scalekernel=FALSE. In the meantime you can get the desired result by either
Adding scalekernel=TRUE and sigma=1, or
Deleting at="points" so that the result is a pixel image Z say, and extracting the desired values by Z[PPP].
I am taking an Ethical Hacking class, and my lab for the week is trying to crack passwords created by our professor. For this specific challenge, I used a 64 bit decoder to translate his string in his code, and have found the way to solve the problem.
The issue is when I run the equation found in the code, I run into an error in Python. I'll attach an image for clarification. I do know that length is typically found using len() but I don't know how to use that in this context. The part giving me issues is the [chunk2.length-1].
I was informed to add the errors I am receiving so here it is:
you = chunk1[2+1]+d[1+1]+h[3]+chunk2[chunk2.length-1] AttributeError: 'str'
object has no attribute 'length'
Any help provided would be great, thank you guys.
I changed chunk2[chunk2.length-1] to chunk2[-1] and that solved my issue.
I'm trying to train my model with new intent and entities but I see an error called "string indices must be integers" as you can see:
Please help with a quick fix. Thanks
what you need to do is this.
you need to disable the gazette in the pipeline.
Go to settings up! in NLU pipeline, remove name: rasa_addons.nlu.components.gazette.Gazette
Thanks
I'm following the AWS Sagemaker tutorial, but I think there's an error in the step 4a. Particularly, at line 3 I'm instructed to type:
s3_input_train = sagemaker.s3_input(s3_data='s3://{}/{}/train'.format(bucket_name, prefix), content_type='csv')
and I get the error
----> 3 s3_input_train = sagemaker.s3_input(s3_data='s3://{}/{}/train'.format(bucket_name, prefix), content_type='csv')
AttributeError: module 'sagemaker' has no attribute 's3_input'
Indeed, using dir shows that sagemaker has no attribute called s3_input. How can fix this so that I can keep advancing in the tutorial? I tried using session.inputs, but this redirects me to a page saying that session is deprecated and suggesting that I use sagemaker.inputs.TrainingInput instead of sagemaker.s3_inputs. Is this a good way of going forward?
Thanks everyone for the help and patience!
Using sagemaker.inputs.TrainingInput instead of sagemaker.s3_inputs worked to get that code cell functioning. It is an appropriate solution, though there may be another approach.
Step 4.b also had code which needed updating
sess = sagemaker.Session()
xgb = sagemaker.estimator.Estimator(containers[my_region],role, train_instance_count=1, train_instance_type='ml.m4.xlarge',output_path='s3://{}/{}/output'.format(bucket_name, prefix),sagemaker_session=sess)
xgb.set_hyperparameters(max_depth=5,eta=0.2,gamma=4,min_child_weight=6,subsample=0.8,silent=0,objective='binary:logistic',num_round=100)
uses parameters train_instance_count and train_instance_type which have been changed in a later version (https://sagemaker.readthedocs.io/en/stable/v2.html#parameter-and-class-name-changes).
Making these changes resolved the errors for the tutorial using conda_python3 kernel.