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INSTALLATION
pip install git+https://github.com/keras-team/keras-tuner.git pipinstall autokeras
AUTOMODEL - AUTOKERAS AutoModel. A Model defined by inputs and outputs. AutoModel combines a HyperModel and a Tuner to tune the HyperModel. The user can use it in a similar way to a Keras model since it also has fit () and predict () methods. The AutoModel has two use cases. In the first case, the user only specifies the input nodes and output heads of the AutoModel. BASE CLASS - AUTOKERAS The base class for different Block. The Block can be connected together to build the search space for an AutoModel. Notably, many args in the init function are defaults to be a tunable variable when not specified by the user.UTILS - AUTOKERAS
Then calling image_dataset_from_directory(main_directory) will return a tf.data.Dataset that yields batches of images from the subdirectories class_a and class_b, together with labels 'class_a' and 'class_b'.. Supported image formats: jpeg, png, bmp, gif. Animated gifs are truncated to the first frame. Arguments. directory str: Directory where the data is located. STRUCTUREDDATAREGRESSOR Search for the best model and hyperparameters for the AutoModel. Arguments. x: String, numpy.ndarray, pandas.DataFrame or tensorflow.Dataset.Training data x. If the data is from a csv file, it should be a string specifying the path of the csv file of the trainingdata.
TIMESERIESFORECASTER TimeSeriesForecaster. View in Colab GitHub source. !pip install autokeras. import pandas as pd import tensorflow as tf import autokeras as ak. To make this tutorial easy to follow, we use the UCI Airquality dataset, and try to forecast the AH value at the different timesteps. Some basic preprocessing has also been performed on thedataset as it
NODE - AUTOKERAS
Input node for tensor data. The data should be numpy.ndarray or tf.data.Dataset. Arguments. name Optional: String.The name of the input node. If unspecified, it will STRUCTURED DATA CLASSIFICATIONLOAD DATA FROM DISK
Load Images from Disk. If the data is too large to put in memory all at once, we can load it batch by batch into memory from disk with tf.data.Dataset. This function can help you build such a tf.data.Dataset for image data. First, we download the data and extract the files. The directory should look like this. Each folder contains the images in AUTOKERASHOMEINSTALLATIONDOCKERCONTRIBUTING GUIDEABOUTOVERVIEW 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 for everyone.INSTALLATION
pip install git+https://github.com/keras-team/keras-tuner.git pipinstall autokeras
AUTOMODEL - AUTOKERAS AutoModel. A Model defined by inputs and outputs. AutoModel combines a HyperModel and a Tuner to tune the HyperModel. The user can use it in a similar way to a Keras model since it also has fit () and predict () methods. The AutoModel has two use cases. In the first case, the user only specifies the input nodes and output heads of the AutoModel. BASE CLASS - AUTOKERAS The base class for different Block. The Block can be connected together to build the search space for an AutoModel. Notably, many args in the init function are defaults to be a tunable variable when not specified by the user.UTILS - AUTOKERAS
Then calling image_dataset_from_directory(main_directory) will return a tf.data.Dataset that yields batches of images from the subdirectories class_a and class_b, together with labels 'class_a' and 'class_b'.. Supported image formats: jpeg, png, bmp, gif. Animated gifs are truncated to the first frame. Arguments. directory str: Directory where the data is located. STRUCTUREDDATAREGRESSOR Search for the best model and hyperparameters for the AutoModel. Arguments. x: String, numpy.ndarray, pandas.DataFrame or tensorflow.Dataset.Training data x. If the data is from a csv file, it should be a string specifying the path of the csv file of the trainingdata.
TIMESERIESFORECASTER TimeSeriesForecaster. View in Colab GitHub source. !pip install autokeras. import pandas as pd import tensorflow as tf import autokeras as ak. To make this tutorial easy to follow, we use the UCI Airquality dataset, and try to forecast the AH value at the different timesteps. Some basic preprocessing has also been performed on thedataset as it
NODE - AUTOKERAS
Input node for tensor data. The data should be numpy.ndarray or tf.data.Dataset. Arguments. name Optional: String.The name of the input node. If unspecified, it will STRUCTURED DATA CLASSIFICATIONLOAD DATA FROM DISK
Load Images from Disk. If the data is too large to put in memory all at once, we can load it batch by batch into memory from disk with tf.data.Dataset. This function can help you build such a tf.data.Dataset for image data. First, we download the data and extract the files. The directory should look like this. Each folder contains the images inINSTALLATION
pip install git+https://github.com/keras-team/keras-tuner.git pipinstall autokeras
OVERVIEW - AUTOKERAS AutoKeras 1.0 Tutorial Supported Tasks. AutoKeras supports several tasks with extremely simple interface. You can click the links below to see the detailed tutorial for each task.UTILS - AUTOKERAS
Then calling image_dataset_from_directory(main_directory) will return a tf.data.Dataset that yields batches of images from the subdirectories class_a and class_b, together with labels 'class_a' and 'class_b'.. Supported image formats: jpeg, png, bmp, gif. Animated gifs are truncated to the first frame. Arguments. directory str: Directory where the data is located.IMAGECLASSIFIER
AutoKeras image classification class. Arguments. num_classes Optional: Int. Defaults to None.If None, it will be inferred from the data. multi_label bool: Boolean.Defaults to False. lossOptional: A Keras
loss function.Defaults to use 'binary_crossentropy' or 'categorical_crossentropy' based on the number of classes.NODE - AUTOKERAS
Input node for tensor data. The data should be numpy.ndarray or tf.data.Dataset. Arguments. name Optional: String.The name of the input node. If unspecified, it willEXPORT MODEL
Export Model. You can easily export your model the best model found by AutoKeras as a Keras Model. The following example uses ImageClassifier as an example. All the tasks and the AutoModel has this export_model function. print(tf.__version__) (x_train, y_train), (x_test, y_test) = mnist.load_data() # Initialize the image classifier. clf = akBLOCK - AUTOKERAS
autokeras.ImageBlock(block_type=None, normalize=None, augment=None, **kwargs) Block for image data. The image blocks is a block choosing from ResNetBlock, XceptionBlock, ConvBlock, which is controlled by a hyperparameter, 'block_type'. Arguments. block_type Optional : String. 'resnet', 'xception', 'vanilla'.TRAINS - AUTOKERAS
Trains Integration. Allegro Trains is a full system open source ML / DL experiment manager and ML-Ops solution. It enables data scientists and data engineers to effortlessly track, manage, compare and collaborate on their experiments as well as easily manage their training workloads on remote machines. STRUCTURED DATA REGRESSION The second step is to run the StructuredDataRegressor . As a quick demo, we set epochs to 10. You can also leave the epochs unspecified for an adaptive number of epochs. # Initialize the structured data regressor. reg = ak.StructuredDataRegressor( overwrite=True, max_trials=3 ) # It tries 3 different models. # Feed the structureddata regressor
MULTI-MODAL AND MULTI-TASK Build and Train the Model. Then we initialize the multi-modal and multi-task model with AutoModel . Since this is just a demo, we use small amount of max_trials and epochs. # Initialize the multi with multiple inputs and outputs. model = ak.AutoModel(inputs=, outputs=[
ak.RegressionHead(metrics=["mae AUTOKERASHOMEINSTALLATIONDOCKERCONTRIBUTING GUIDEABOUTOVERVIEW 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 for everyone.INSTALLATION
pip install git+https://github.com/keras-team/keras-tuner.git pipinstall autokeras
AUTOMODEL - AUTOKERAS AutoModel. A Model defined by inputs and outputs. AutoModel combines a HyperModel and a Tuner to tune the HyperModel. The user can use it in a similar way to a Keras model since it also has fit () and predict () methods. The AutoModel has two use cases. In the first case, the user only specifies the input nodes and output heads of the AutoModel. BASE CLASS - AUTOKERAS The base class for different Block. The Block can be connected together to build the search space for an AutoModel. Notably, many args in the init function are defaults to be a tunable variable when not specified by the user.UTILS - AUTOKERAS
Then calling image_dataset_from_directory(main_directory) will return a tf.data.Dataset that yields batches of images from the subdirectories class_a and class_b, together with labels 'class_a' and 'class_b'.. Supported image formats: jpeg, png, bmp, gif. Animated gifs are truncated to the first frame. Arguments. directory str: Directory where the data is located. STRUCTUREDDATAREGRESSOR Search for the best model and hyperparameters for the AutoModel. Arguments. x: String, numpy.ndarray, pandas.DataFrame or tensorflow.Dataset.Training data x. If the data is from a csv file, it should be a string specifying the path of the csv file of the trainingdata.
STRUCTUREDDATACLASSIFIER AutoKeras structured data classification class. Arguments. column_names Optional: A list of strings specifying the names of the columns.The length of the list should be equal to the number of columns of the data excluding the target column.NODE - AUTOKERAS
Input node for tensor data. The data should be numpy.ndarray or tf.data.Dataset. Arguments. name Optional: String.The name of the input node. If unspecified, it willLOAD DATA FROM DISK
Load Images from Disk. If the data is too large to put in memory all at once, we can load it batch by batch into memory from disk with tf.data.Dataset. This function can help you build such a tf.data.Dataset for image data. First, we download the data and extract the files. The directory should look like this. Each folder contains the images in STRUCTURED DATA CLASSIFICATION AUTOKERASHOMEINSTALLATIONDOCKERCONTRIBUTING GUIDEABOUTOVERVIEW 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 for everyone.INSTALLATION
pip install git+https://github.com/keras-team/keras-tuner.git pipinstall autokeras
AUTOMODEL - AUTOKERAS AutoModel. A Model defined by inputs and outputs. AutoModel combines a HyperModel and a Tuner to tune the HyperModel. The user can use it in a similar way to a Keras model since it also has fit () and predict () methods. The AutoModel has two use cases. In the first case, the user only specifies the input nodes and output heads of the AutoModel. BASE CLASS - AUTOKERAS The base class for different Block. The Block can be connected together to build the search space for an AutoModel. Notably, many args in the init function are defaults to be a tunable variable when not specified by the user.UTILS - AUTOKERAS
Then calling image_dataset_from_directory(main_directory) will return a tf.data.Dataset that yields batches of images from the subdirectories class_a and class_b, together with labels 'class_a' and 'class_b'.. Supported image formats: jpeg, png, bmp, gif. Animated gifs are truncated to the first frame. Arguments. directory str: Directory where the data is located. STRUCTUREDDATAREGRESSOR Search for the best model and hyperparameters for the AutoModel. Arguments. x: String, numpy.ndarray, pandas.DataFrame or tensorflow.Dataset.Training data x. If the data is from a csv file, it should be a string specifying the path of the csv file of the trainingdata.
STRUCTUREDDATACLASSIFIER AutoKeras structured data classification class. Arguments. column_names Optional: A list of strings specifying the names of the columns.The length of the list should be equal to the number of columns of the data excluding the target column.NODE - AUTOKERAS
Input node for tensor data. The data should be numpy.ndarray or tf.data.Dataset. Arguments. name Optional: String.The name of the input node. If unspecified, it willLOAD DATA FROM DISK
Load Images from Disk. If the data is too large to put in memory all at once, we can load it batch by batch into memory from disk with tf.data.Dataset. This function can help you build such a tf.data.Dataset for image data. First, we download the data and extract the files. The directory should look like this. Each folder contains the images in STRUCTURED DATA CLASSIFICATION BASE CLASS - AUTOKERAS The base class for different Block. The Block can be connected together to build the search space for an AutoModel. Notably, many args in the init function are defaults to be a tunable variable when not specified by the user. OVERVIEW - AUTOKERAS AutoKeras 1.0 Tutorial Supported Tasks. AutoKeras supports several tasks with extremely simple interface. You can click the links below to see the detailed tutorial for each task.UTILS - AUTOKERAS
Then calling image_dataset_from_directory(main_directory) will return a tf.data.Dataset that yields batches of images from the subdirectories class_a and class_b, together with labels 'class_a' and 'class_b'.. Supported image formats: jpeg, png, bmp, gif. Animated gifs are truncated to the first frame. Arguments. directory str: Directory where the data is located.IMAGECLASSIFIER
AutoKeras image classification class. Arguments. num_classes Optional: Int. Defaults to None.If None, it will be inferred from the data. multi_label bool: Boolean.Defaults to False. lossOptional: A Keras
loss function.Defaults to use 'binary_crossentropy' or 'categorical_crossentropy' based on the number of classes.EXPORT MODEL
Export Model. You can easily export your model the best model found by AutoKeras as a Keras Model. The following example uses ImageClassifier as an example. All the tasks and the AutoModel has this export_model function. print(tf.__version__) (x_train, y_train), (x_test, y_test) = mnist.load_data() # Initialize the image classifier. clf = akDOCKER - AUTOKERAS
In case you need more memory to run the container, change the value of shm-size.(Docker run reference)Run application : To run a local script file.py using Auto-Keras within the container, mount the host directory -v hostDir:/app. STRUCTURED DATA REGRESSION The second step is to run the StructuredDataRegressor . As a quick demo, we set epochs to 10. You can also leave the epochs unspecified for an adaptive number of epochs. # Initialize the structured data regressor. reg = ak.StructuredDataRegressor( overwrite=True, max_trials=3 ) # It tries 3 different models. # Feed the structureddata regressor
NODE - AUTOKERAS
Input node for tensor data. The data should be numpy.ndarray or tf.data.Dataset. Arguments. name Optional: String.The name of the input node. If unspecified, it willBLOCK - AUTOKERAS
autokeras.ImageBlock(block_type=None, normalize=None, augment=None, **kwargs) Block for image data. The image blocks is a block choosing from ResNetBlock, XceptionBlock, ConvBlock, which is controlled by a hyperparameter, 'block_type'. Arguments. block_type Optional : String. 'resnet', 'xception', 'vanilla'. MULTI-MODAL AND MULTI-TASK Build and Train the Model. Then we initialize the multi-modal and multi-task model with AutoModel . Since this is just a demo, we use small amount of max_trials and epochs. # Initialize the multi with multiple inputs and outputs. model = ak.AutoModel(inputs=, outputs=[
ak.RegressionHead(metrics=["maeSkip to content
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Table of contents
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HOME
AutoKeras: An AutoML system based on Keras. It is developed by DATALab at Texas A&M
University. The goal of AutoKeras is to make machine learning accessible for everyone.EXAMPLE
Here is a short example of using the package. import autokeras as ak clf = ak.ImageClassifier() clf.fit(x_train, y_train) results = clf.predict(x_test) For detailed tutorial, please check here.
INSTALLATION
To install the package, please use the pip installation as follows: pip3 install autokeras Please follow the installation guide for more details. NOTE: Currently, AutoKeras is only compatible withPYTHON 3.
CITE THIS WORK
Haifeng Jin, Qingquan Song, and Xia Hu. "Auto-keras: An efficient neural architecture search system." Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM,2019. (Download
)
Biblatex entry:
@inproceedings{jin2019auto, title={Auto-Keras: An Efficient Neural Architecture Search System}, author={Jin, Haifeng and Song, Qingquan and Hu, Xia}, booktitle={Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining},pages={1946--1956},
year={2019},
organization={ACM}
}
COMMUNITY
You can use Gitter to communicate with people who are also interestedin AutoKeras.
You can also follow us on Twitter @autokeras for the latest news.CONTRIBUTING CODE
You can follow the Contributing Guide for details. The easist way to contribute is to resolve the issues with the "call for contributors"
tag. They are friendly to beginners.SUPPORT AUTOKERAS
We accept donations on Open Collective . Thank every backer forsupporting us!
DISCLAIMER
Please note that this is a PRE-RELEASE version of the AutoKeras which is still undergoing final testing before its official release. The website, its software and all content found on it are provided on an "as is" and "as available" basis. AutoKeras does NOT give any warranties, whether express or implied, as to the suitability or usability of the website, its software or any of its content. AutoKeras will NOT be liable for any loss, whether such loss is direct, indirect, special or consequential, suffered by any party as a result of their use of the libraries or content. Any usage of the libraries is done at the user's own risk and the user will be solely responsible for any damage to any computer system or loss of data that results from such activities. Should you encounter any bugs, glitches, lack of functionality or other problems on the website, please let us know immediately so we can rectify these accordingly. Your help in this regard is greatly appreciated.ACKNOWLEDGEMENTS
The authors gratefully acknowledge the D3M program of the Defense Advanced Research Projects Agency (DARPA) administered through AFRL contract FA8750-17-2-0116; the Texas A&M College of Engineering, andTexas A&M.
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