연습 또 연습.

from sklearn.model_selection import GridSearchCV from keras.wrappers.scikit_learn import KerasClassifier def build_model(): X = tf.keras.layers.Input(shape=[28, 28, 1]) H = tf.keras.layers.Conv2D(64, kernel_size=5, padding='same', activation='swish')(X) H = tf.keras.layers.BatchNormalization()(H) H = tf.keras.layers.Conv2D(64, kernel_size=5, padding='same', activation='swish')(H) H = tf.keras.la..

Make model def build_model(): X = tf.keras.layers.Input(shape=[28, 28, 1]) H = tf.keras.layers.Conv2D(64, kernel_size=5, padding='same', activation='swish')(X) H = tf.keras.layers.BatchNormalization()(H) H = tf.keras.layers.Conv2D(64, kernel_size=5, padding='same', activation='swish')(H) H = tf.keras.layers.MaxPool2D()(H) H = tf.keras.layers.Conv2D(64, kernel_size=3, padding='same', activation='..

Data Augmentation from keras.preprocessing.image import ImageDataGenerator datagen = ImageDataGenerator( featurewise_center=False, # set input mean to 0 over the dataset samplewise_center=False, # set each sample mean to 0 featurewise_std_normalization=False, # divide inputs by std of the dataset samplewise_std_normalization=False, # divide each input by its std zca_whitening=False, # apply ZCA ..