kandi X-RAY | autokeras Summary
kandi X-RAY | autokeras Summary
AutoKeras: An AutoML system based on Keras. It is developed by DATA Lab at Texas A&M University. The goal of AutoKeras is to make machine learning accessible to everyone.
Top functions reviewed by kandi - BETA
- Create an image dataset from a directory
- Index a directory
- Convert path to image
- Get training or validation
- Fit the hyperparameters
- Analyze data
- Build the hyperpipeline
- Convert input to dataset
- Initialize the data
- Evaluate the model
- Establishes the model
- Predict for the input dataset
- Load LIMDB dataset
- Process the block
- This function is used for reuter
- Index a subdirectory
- Convert a notebook to a python file
- Connects the graph
- Convert a Python file to a notebook
- Builds the graph
- Create a text dataset from a directory
- Build the graph
- Fit the model
- Connects the transform layer
- Tokenize text
- Fit and predict the model
autokeras Key Features
Trending Discussions on autokeras
I'm executing this code on Kaلgle, and install autokeras library on it,...
ANSWERAnswered 2021-Jun-30 at 04:38
I have a model that I generated using AutoKeras and I want to replicate the model so that I can construct it with keras tuner to do further hyperparameter tuning. But I am running into issues replicating the model. The model summary of the autokeras model is:...
ANSWERAnswered 2021-Apr-11 at 14:51
AutoKeras doesn't support any direct conversions - its dependencies are too inbuilt to be isolated from the package itself. The above answer indicates a lack of
softmax activation is wrong as there indeed is present:
classification_head_1 (Softm --> probably text got truncated
Next up - do you notice the lack of parameters?
858 is a pretty small number - that is because most layers have
0 parameters - Autokeras uses custom layers which constitute their custom blocks (more about their blocks from their docs)
You can see that to re-create those custom layers, you would need their exact code - which can't be isolated as of the time of writing (though @haifeng-jin is discussing it) since there are specific packages they use to process the input data and what powers their NAS (Neural Architecture search) and the optimization routines they perform.
Unless you can study their code and the implementations of the custom layers and recreate it (which in itself would be quite some work, but not much since the code is already available), it would be a futile attempt if you use
keras.clone_model that works with pre-defined keras layers. that would obviously lead to broken models (like the one you have currently).
AutoKeras does HyperParameter tuning on its own - if you want to tune your model further, just run AutoKeras for longer period of time to get better results.
tl;dr you can't clone custom layers and blocks with in-package dependencies directly. But if you want to do Hyperparameter tuning, you can run the search for much longer to get a better model.
can anyone help me to resolve above error?...
ANSWERAnswered 2021-Mar-13 at 12:58
The possible reason (due to the low readability of your code pasted above) could be using different datasets with saved models. I suggest you add
overwrite=True in the
BayesianOptimization construction code block. Reinstalling TensorFlow may also help.
I need to reproduce results with AutoKeras for the same input and configurations: I tried the following at the beginning of my notebook but still didn't got the same results.
I am using Tensorflow 2.0.4 and AutoKeras 1.0.12...
ANSWERAnswered 2021-Feb-08 at 10:03
I guess, you need to seed the generators before each call you want to be reproducable. The best option is to make such a decorator (or a context manager):
When I run my tensorflow keras model it will sometimes stop after an epoch and throw the following error:...
ANSWERAnswered 2020-Jul-06 at 14:46
So I found the cause to the error, you need to downgrade your Nvidia drivers to version 431.36. Everything above that makes tensorflow crash in above mentioned manner.
I'm having a real problem diagnosing an issue I have with Tensorflow. I get this ValueError and it's not intuitive at all. I've tried converting my floats to
np.float32 as specified in other SO questions. I've tried a basket of solutions, but I can't seem to get past it.
My data is in a Pandas dataframe. I joined a TF-IDF dataframe with some other wrangled features to get my final data set. The dimensions are
(7176, 1006). I'm using Tensorflow and my model code is:
ANSWERAnswered 2020-Jul-02 at 01:03
You need to convert your final dataset to numpy array. You can use following method:
fails on the line:...
ANSWERAnswered 2020-Mar-29 at 22:39
The problem is not with the code, I tried it myself on my local machine and it works perfectly. The real problem is this line
No vulnerabilities reported
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