Skip to content

Latest commit

 

History

History
275 lines (199 loc) · 12.2 KB

start.md

File metadata and controls

275 lines (199 loc) · 12.2 KB
Raw

Getting Started

Installation

The installation of Auto-Keras is the same as other python packages.

Note: currently, Auto-Keras is only compatible with: Python 3.6.

Latest Stable Version (pip installation):

You can run the following pip installation command in your terminal to install the latest stable version.

pip install autokeras

Bleeding Edge Version (manual installation):

If you want to install the latest development version. You need to download the code from the GitHub repo and run the following commands in the project directory.

pip install -r requirements.txt
python setup.py install

Example

We show an example of image classification on the MNIST dataset, which is a famous benchmark image dataset for hand-written digits classification. Auto-Keras supports different types of data inputs.

Data with numpy array (.npy) format.

If the images and the labels are already formatted into numpy arrays, you can

from keras.datasets import mnist
from autokeras.image.image_supervised import ImageClassifier

if __name__ == '__main__':
    (x_train, y_train), (x_test, y_test) = mnist.load_data()
    x_train = x_train.reshape(x_train.shape + (1,))
    x_test = x_test.reshape(x_test.shape + (1,))

    clf = ImageClassifier(verbose=True)
    clf.fit(x_train, y_train, time_limit=12 * 60 * 60)
    clf.final_fit(x_train, y_train, x_test, y_test, retrain=True)
    y = clf.evaluate(x_test, y_test)
    print(y)

In the example above, the images and the labels are already formatted into numpy arrays.

What if your data are raw image files (e.g. .jpg, .png, .bmp)?

You can use our load_image_dataset function to load the images and their labels as follows.

from autokeras.image.image_supervised import load_image_dataset

x_train, y_train = load_image_dataset(csv_file_path="train/label.csv",
                                      images_path="train")
print(x_train.shape)
print(y_train.shape)

x_test, y_test = load_image_dataset(csv_file_path="test/label.csv",
                                    images_path="test")
print(x_test.shape)
print(y_test.shape)

The argument csv_file_path is the path to the CSV file containing the image file names and their corresponding labels. Here is an example of the csv file.

File Name,Label
00000.jpg,5
00001.jpg,0
00002.jpg,4
00003.jpg,1
00004.jpg,9
00005.jpg,2
00006.jpg,1
...

The second argument images_path is the path to the directory containing all the images with those file names listed in the CSV file. The returned values x_train and y_train are the numpy arrays, which can be directly feed into the fit function of ImageClassifier.

How to export keras models?

clf.load_searcher().load_best_model().produce_keras_model().save('my_model.h5')

This uses the keras function model.save() to export a single HDF5 file containing the architecture of the model, the weights of the model, the training configuration, and the state of the optimizer. See https://keras.io/getting-started/faq/#how-can-i-save-a-keras-model

Note: This is being built into AutoKeras as ImageClassifier().export_keras_model()

how to export Portable model

from autokeras import ImageClassifier
clf = ImageClassifier(verbose=True, augment=False)
clf.export_autokeras_model(model_file_name)

The model will be stored into the path model_file_name.

How to load exported Portable model?

from autokeras.utils import pickle_from_file
model = pickle_from_file(model_file_name)
results = model.evaluate(x_test, y_test)
print(results)

The model will be loaded from the path model_file_name and then you can use the functions listed in PortableImageSupervised.

How to visualize keras models?

This is not specific to AutoKeras, however, the following will generate a .PNG visualization of the best model found by AutoKeras:

from keras.models import load_model
model = load_model('my_model.h5') #See 'How to export keras models?' to generate this file before loading it.
from keras.utils import plot_model
plot_model(model, to_file='my_model.png')

How to visualize the best selected architecture ?

While trying to create a model, let's say an Image classifier on MNIST, there is a facility for the user to visualize a .PDF depiction of the best architecture that was chosen by autokeras, after model training is complete.

Prerequisites :

  1. graphviz must be installed in your system. Refer Installation Guide
  2. Additionally, also install "graphviz" python package using pip / conda

pip : pip install graphviz

conda : conda install -c conda-forge python-graphviz

If the above installations are complete, proceed with the following steps :

Step 1 : Specify a path before starting your model training

clf = ImageClassifier(path="~/automodels/",verbose=True, augment=False) # Give a custom path of your choice
clf.fit(x_train, y_train, time_limit=30 * 60)
clf.final_fit(x_train, y_train, x_test, y_test, retrain=True)

Step 2 : After the model training is complete, run examples/visualize.py, whilst passing the same path as parameter

if __name__ == '__main__':
    visualize('~/automodels/')

CnnModule tutorial

CnnGenerator in net_module.py is a child class of Networkmodule. It can generates neural architecture with basic cnn modules and the ResNet module.

Examples

Normally, there's two place to call the CnnGenerator, one is call CnnGenerator.fit while the other is CnnGenerator.final_fit.

For example, in a image classification class ImageClassifier, one can initialize the cnn module as:

from autokeras import CnnModule
from autokeras.nn.loss_function import classification_loss
from autokeras.nn.metric import Accuracy

TEST_FOLDER = "test"
cnnModule = CnnModule(loss=classification_loss, metric=Accuracy, searcher_args={}, path=TEST_FOLDER, verbose=False)

Where:

  • loss and metric determines by the type of training model(classification or regression or others)
  • search_args can be referred in search.py
  • path is the path to store the whole searching process and generated model.
  • verbose is a boolean. Setting it to true prints to stdout.

Then, for the searching part, one can call:

cnnModule.fit(n_output_node, input_shape, train_data, test_data, time_limit=24 * 60 * 60)

where:

  • n_output_node: A integer value represent the number of output node in the final layer.
  • input_shape: A tuple to express the shape of every train entry. For example, MNIST dataset would be (28,28,1).
  • train_data: A PyTorch DataLoader instance representing the training data.
  • test_data: A PyTorch DataLoader instance representing the testing data.
  • time_limit: A integer value represents the time limit on searching for models.

And for final testing(testing the best searched model), one can call:

cnnModule.final_fit(train_data, test_data, trainer_args=None, retrain=False)

where:

  • train_data: A DataLoader instance representing the training data.
  • test_data: A DataLoader instance representing the testing data.
  • trainer_args: A dictionary containing the parameters of the ModelTrainer constructor.
  • retrain: A boolean of whether reinitialize the weights of the model.

Automated text classifier tutorial

Introduction

Class TextClassifier and TextRegressor is designed for automated generate best performance cnn neural architecture for a given text dataset.

Example

    clf = TextClassifier(verbose=True)
    clf.fit(x=x_train, y=y_train, batch_size=10, time_limit=12 * 60 * 60)

After searching the best model, one can call clf.final_fit to test the best model found in searching.

Arguments

  • x_train: string format text data
  • y_train: int format text label

Notes:

Preprocessing of the text data:

  • Class TextClassifier and TextRegressor contains a pre-process of the text data. Which means the input data should be in string format.
  • The default pre-process model uses the glove6B model from Stanford NLP.
  • To change the default setting of the pre-process model, one need to change the corresponding variable: EMBEDDING_DIM, PRE_TRAIN_FILE_LINK, PRE_TRAIN_FILE_LINK, PRE_TRAIN_FILE_NAME in constant.py.

MlpModule tutorial

MlpGenerator in net_module.py is a child class of Networkmodule. It can generates neural architecture with MLP modules

Examples

Normally, there's two place to call the MlpGenerator, one is call MlpGenerator.fit while the other is MlpGenerator.final_fit.

For example, in a image classification class ImageClassifier, one can initialize the cnn module as:

mlpModule = MlpModule(loss, metric, searcher_args, path, verbose)

Where:

  • loss and metric determines by the type of training model(classification or regression or others)
  • search_args can be referred in search.py
  • path is the path to store the whole searching process and generated model.
  • verbose is a boolean. Setting it to true prints to stdout.

Then, for the searching part, one can call:

mlpModule.fit(n_output_node, input_shape, train_data, test_data, time_limit=24 * 60 * 60)

where:

  • n_output_node: A integer value represent the number of output node in the final layer.
  • input_shape: A tuple to express the shape of every train entry. For example, MNIST dataset would be (28,28,1).
  • train_data: A PyTorch DataLoader instance representing the training data.
  • test_data: A PyTorch DataLoader instance representing the testing data.
  • time_limit: A integer value represents the time limit on searching for models.

And for final testing(testing the best searched model), one can call:

mlpModule.final_fit(train_data, test_data, trainer_args=None, retrain=False)

where:

  • train_data: A DataLoader instance representing the training data.
  • test_data: A DataLoader instance representing the testing data.
  • trainer_args: A dictionary containing the parameters of the ModelTrainer constructor.
  • retrain: A boolean of whether reinitialize the weights of the model.

Object Detection tutorial

ObjectDetector in object_detector.py is a child class of Pretrained. Currently it can load a pretrained SSD model (Liu, Wei, et al. "Ssd: Single shot multibox detector." European conference on computer vision. Springer, Cham, 2016.) and find object(s) in a given image.

Let's first import the ObjectDetector and create a detection model (detector) with

from autokeras.pretrained.object_detector import ObjectDetector
detector = ObjectDetector()

Note that the ObjectDetector class can automatically detect the existance of available cuda device(s), and use the device if exists.

Second, you will want to load the pretrained weights for your model:

detector.load()

This line will automatically download and load the weights into detector. Finally you can make predictions against an image:

    results = detector.predict("/path/to/images/000001.jpg", output_file_path="/path/to/images/")

Function detector.predict() requires the path to the image. If the output_file_path is not given, the detector will just return the numerical results as a list of dictionaries. Each dictionary is like {"left": int, "top": int, "width": int, "height": int: "category": str, "confidence": float}, where left and top is the (left, top) coordinates of the bounding box of the object and width and height are width and height of the box. category is a string representing the class the object belongs to, and the confidence can be regarded as the probability that the model believes its prediction is correct. If the output_file_path is given, then the results mentioned above will be plotted and saved in a new image file with suffix "_prediction" into the given output_file_path. If you run the example/object_detection/object_detection_example.py, you will get result [{'category': 'person', 'width': 331, 'height': 500, 'left': 17, 'confidence': 0.9741123914718628, 'top': 0}]