TensorFlow Cloud
TensorFlow Cloud allows you to run your TensorFlow program leveraging the computing power on Google Cloud easily. Please follow the instructions to setup your account.
AutoKeras has successfully integrated with this service. Now you can run your program on Google Cloud only by inserting a few more lines of code. Please see the example below.
import argparse
import os
import autokeras as ak
import tensorflow_cloud as tfc
from tensorflow.keras.datasets import mnist
parser = argparse.ArgumentParser(description="Model save path arguments.")
parser.add_argument("--path", required=True, type=str, help="Keras model save path")
args = parser.parse_args()
tfc.run(
chief_config=tfc.COMMON_MACHINE_CONFIGS["V100_1X"],
docker_base_image="haifengjin/autokeras:1.0.3",
)
# Prepare the dataset.
(x_train, y_train), (x_test, y_test) = mnist.load_data()
print(x_train.shape) # (60000, 28, 28)
print(y_train.shape) # (60000,)
print(y_train[:3]) # array([7, 2, 1], dtype=uint8)
# Initialize the ImageClassifier.
clf = ak.ImageClassifier(max_trials=2)
# Search for the best model.
clf.fit(x_train, y_train, epochs=10)
# Evaluate on the testing data.
print("Accuracy: {accuracy}".format(accuracy=clf.evaluate(x_test, y_test)[1]))
clf.export_model().save(os.path.join(args.path, "model.h5"))