benchmark/performance.py
# Copyright 2020 The AutoKeras Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import keras
import numpy as np
from keras.datasets import cifar10
from keras.datasets import mnist
from sklearn.datasets import load_files
import autokeras as ak
def imdb_raw(num_instances=100):
dataset = keras.utils.get_file(
fname="aclImdb.tar.gz",
origin="http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz",
extract=True,
)
# set path to dataset
IMDB_DATADIR = os.path.join(os.path.dirname(dataset), "aclImdb")
classes = ["pos", "neg"]
train_data = load_files(
os.path.join(IMDB_DATADIR, "train"), shuffle=True, categories=classes
)
test_data = load_files(
os.path.join(IMDB_DATADIR, "test"), shuffle=False, categories=classes
)
x_train = np.array(train_data.data)
y_train = np.array(train_data.target)
x_test = np.array(test_data.data)
y_test = np.array(test_data.target)
if num_instances is not None:
x_train = x_train[:num_instances]
y_train = y_train[:num_instances]
x_test = x_test[:num_instances]
y_test = y_test[:num_instances]
return (x_train, y_train), (x_test, y_test)
def test_mnist_accuracy_over_98(tmp_path):
(x_train, y_train), (x_test, y_test) = mnist.load_data()
clf = ak.ImageClassifier(max_trials=1, directory=tmp_path)
clf.fit(x_train, y_train, epochs=10)
accuracy = clf.evaluate(x_test, y_test)[1]
assert accuracy >= 0.98
def test_cifar10_accuracy_over_93(tmp_path):
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
clf = ak.ImageClassifier(max_trials=3, directory=tmp_path)
clf.fit(x_train, y_train, epochs=5)
accuracy = clf.evaluate(x_test, y_test)[1]
assert accuracy >= 0.93
def test_imdb_accuracy_over_92(tmp_path):
(x_train, y_train), (x_test, y_test) = imdb_raw(num_instances=None)
clf = ak.TextClassifier(max_trials=3, directory=tmp_path)
clf.fit(x_train, y_train, batch_size=6, epochs=1)
accuracy = clf.evaluate(x_test, y_test)[1]
assert accuracy >= 0.92