2020 기초인공지능 플젝3

소개

"2020 기초인공지능 플젝3"에 대한 내용입니다.

목차

1. Question 1: AlexNet with CIFAR-10
2. Question 2: Transfer Learning
3. Conclusion

본문내용

Question 1: AlexNet with CIFAR-10
We will use the structure of AlexNet and adjust the parameter to apply CIFAR-10 dataset.
(a)
The project was implemented on Google Colab with tensorflow 2.4.0.
Several training setup is done for this question.
- Data pre-processing / Data Augmentation
- # load train and test dataset
- def load_dataset():
- # load dataset
- (x_train, y_train), (x_test, y_test) = cifar10.load_data()
-
- """
- # split into train, validation, test dataset
- x_valid, y_valid = x_train[:5000], y_train[:5000]
- x_train, y_train = x_train[5000:], y_train[5000:]
- """
-
- # one hot encode target values
- y_train = to_categorical(y_train)
- y_test = to_categorical(y_test)
-
- return x_train, y_train, x_test, y_test
▶ First, we load the dataset and convert label data into one-hot-encoding values.
# scale pixels
def prep_pixels(train, test):
# convert from integers to floats
train_norm = train.astype('float32')
test_norm = test.astype('float32')
# normalize to range 0-1
train_norm = train_norm / 255.0
test_norm = test_norm / 255.0

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