def train(auto_keras_config_id):
print('auto_keras_config object: ' + str(auto_keras_config_id))
auto_keras_config = AutoKerasConfig.objects.get(id=auto_keras_config_id)
auto_keras_config.status = 'in_progress'
auto_keras_config.save()
# Storing save location for models
try:
dump_file = os.path.join(AUTO_ML_MODELS_PATH, 'auto_keras' + str(datetime.datetime.now()) + '.h5')
print('Files to load: ' + auto_keras_config.training_data_filename)
x = numpy.load(os.path.join(AUTO_ML_DATA_PATH, auto_keras_config.training_data_filename))
y = numpy.load(os.path.join(AUTO_ML_DATA_PATH, auto_keras_config.training_labels_filename))
# x, y = load_ml_data(auto_keras_config.training_data_filename, auto_keras_config.training_labels_filename, False, auto_keras_config.make_one_hot_encoding_task_binary)
# TODO this might not work on low ram machines work, but array has to be 3d
if auto_keras_config.preprocessing_object.input_data_type == 'wav':
array4d = []
i=0
for datapoint in x:
print(i)
x_3d = datapoint.reshape((172,128,10)).transpose()
array4d.append(x_3d)
i+=1
x = numpy.array(array4d)
clf = ImageClassifier(verbose=auto_keras_config.verbose)
start = time.time()
clf.fit(x, y, time_limit=auto_keras_config.time_limit)
end = time.time()
#clf.final_fit(x_train, y_train, x_test, y_test, retrain=True)
#y = clf.evaluate(x_test, y_test)
print("Fitting Success!!!")
# storing the best performer
clf.export_autokeras_model(dump_file)
print('saved!')
#
auto_keras_config.training_time = round(end-start, 2)
auto_keras_config.status = 'success'
auto_keras_config.model_path = dump_file
auto_keras_config.save()
print('Status final ' + auto_keras_config.status)
except Exception as e:
end = time.time()
if 'start' in locals():
print('failed after:' + str(end-start))
auto_keras_config.training_time = round(end-start, 2)
auto_keras_config.status = 'fail'
auto_keras_config.additional_remarks = e
auto_keras_config.save()
def run_autokeras():
#x_train, y_train,x_test = load_plant_seedlings()
x_train, y_train, x_test = load_dog_breed()
#x_train,y_train,x_text = load_invasive_species()
# After loading train and evaluate classifier.
clf = ImageClassifier(verbose=True, augment=False)
clf.fit(x_train, y_train, time_limit=12 * 60 * 60)
clf.save("keras_model_dog")
clf.export_autokeras_model('best_auto_keras_model_dog_v2.h5')
predictions = clf.predict(x_test)
print(predictions)
df = pd.DataFrame(clf.predict_proba(x_test))
df.to_csv("dog_breed.csv", index=False)
import os
from keras.datasets import mnist
from autokeras import ImageClassifier
from autokeras import pickle_from_file
# Customer temp dir by your own
TEMP_DIR = '/tmp/autokeras_U8KEOQ'
model_file_name = os.path.join(TEMP_DIR, 'test_autokeras_model.pkl')
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,
augment=False,
path=TEMP_DIR,
resume=True)
clf.fit(x_train, y_train, time_limit=30 * 60)
clf.final_fit(x_train, y_train, x_test, y_test)
clf.export_autokeras_model(model_file_name)
model = pickle_from_file(model_file_name)
results = model.evaluate(x_test, y_test)
print(results)
import os
from keras.datasets import mnist
from autokeras import ImageClassifier
from autokeras.utils import pickle_from_file
# Customer temp dir by your own
TEMP_DIR = '/tmp/autokeras_U8KEOQ'
model_file_name = os.path.join(TEMP_DIR, 'test_autokeras_model.pkl')
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, augment=False, path=TEMP_DIR, resume=True)
clf.fit(x_train, y_train, time_limit=30 * 60)
clf.final_fit(x_train, y_train, x_test, y_test)
clf.export_autokeras_model(model_file_name)
model = pickle_from_file(model_file_name)
results = model.evaluate(x_test, y_test)
print(results)
from keras.datasets import mnist
from autokeras import ImageClassifier
from autokeras.constant import Constant
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, augment=False)
clf.fit(x_train, y_train, time_limit=30 * 60)
clf.final_fit(x_train, y_train, x_test, y_test, retrain=True)
y = clf.evaluate(x_test, y_test)
print(y * 100)
clf.export_autokeras_model('my_model-1.h5')