autokeras | AutoML library for deep learning | Machine Learning library

 by   keras-team Python Version: 1.1.0 License: Apache-2.0

kandi X-RAY | autokeras Summary

kandi X-RAY | autokeras Summary

autokeras is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow, Keras applications. autokeras has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. However autokeras has 4 bugs. You can download it from GitHub.

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.

            kandi-support Support

              autokeras has a medium active ecosystem.
              It has 8809 star(s) with 1396 fork(s). There are 307 watchers for this library.
              It had no major release in the last 12 months.
              There are 121 open issues and 756 have been closed. On average issues are closed in 76 days. There are 2 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of autokeras is 1.1.0

            kandi-Quality Quality

              autokeras has 4 bugs (0 blocker, 0 critical, 4 major, 0 minor) and 51 code smells.

            kandi-Security Security

              autokeras has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              autokeras code analysis shows 0 unresolved vulnerabilities.
              There are 11 security hotspots that need review.

            kandi-License License

              autokeras is licensed under the Apache-2.0 License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              autokeras releases are available to install and integrate.
              Build file is available. You can build the component from source.
              Installation instructions, examples and code snippets are available.
              autokeras saves you 5211 person hours of effort in developing the same functionality from scratch.
              It has 10947 lines of code, 826 functions and 138 files.
              It has medium code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed autokeras and discovered the below as its top functions. This is intended to give you an instant insight into autokeras implemented functionality, and help decide if they suit your requirements.
            • 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
            Get all kandi verified functions for this library.

            autokeras Key Features

            No Key Features are available at this moment for autokeras.

            autokeras Examples and Code Snippets

            Instructions for use
            Pythondot img1Lines of Code : 7dot img1no licencesLicense : No License
            copy iconCopy
            ├── test
            │   ├── 0
            │   └── 1
            └── train
                ├── 0
                └── 1
            Installation AutoKeras
            Pythondot img2Lines of Code : 1dot img2no licencesLicense : No License
            copy iconCopy
            pip install autokeras
            Instructions for use,Next
            Pythondot img3Lines of Code : 1dot img3no licencesLicense : No License
            copy iconCopy

            Community Discussions


            No module named 'keras_tuner'
            Asked 2021-Jun-30 at 04:38

            I'm executing this code on Kaلgle, and install autokeras library on it,



            Answered 2021-Jun-30 at 04:38

            Upgrade the keras_tuner



            Issue replicating AutoKeras StructuredDataClassifier
            Asked 2021-Apr-14 at 02:07

            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:



            Answered 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).

            More importantly, 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.



            TypeError: Unsupported type for StructuredDataAdapter
            Asked 2021-Mar-13 at 13:50

            can anyone help me to resolve above error?



            Answered 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.



            How to get Reproducible Results with AutoKeras
            Asked 2021-Feb-08 at 10:03

            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



            Answered 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):



            Tensorflow Check failed: status == CUDNN_STATUS_SUCCESS (7 vs. 0)Failed to set cuDNN stream
            Asked 2020-Aug-07 at 13:58

            When I run my tensorflow keras model it will sometimes stop after an epoch and throw the following error:



            Answered 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.



            ValueError: Unable to convert NumPy array to a Tensor (Unsupported object type float)
            Asked 2020-Jul-02 at 01:03

            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:



            Answered 2020-Jul-02 at 01:03

            You need to convert your final dataset to numpy array. You can use following method:



            Error Running autokeras Image Classifier Tutorial on Google Colab
            Asked 2020-Apr-03 at 19:14


            Answered 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


            Community Discussions, Code Snippets contain sources that include Stack Exchange Network


            No vulnerabilities reported

            Install autokeras

            To install the package, please use the pip installation as follows:. Please follow the installation guide for more details. Note: Currently, AutoKeras is only compatible with Python >= 3.7 and TensorFlow >= 2.8.0.


            GitHub Discussions: Ask your questions on our GitHub Discussions. It is a forum hosted on GitHub. We will monitor and answer the questions there. Slack: Request an invitation. Use the #autokeras channel for communication. QQ Group: Join our QQ group 1150366085. Password: akqqgroup.
            Find more information at:

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