python.keras - k821209/pipelines GitHub Wiki

# 이미지를 돌려서 augmentation하는 간단한 코드 
import imutils as im
data_aug = []
ixs       = []
for n,img in enumerate(data):
    for angle in list(range(-10,10)):
        aug = im.rotate_bound(img,angle)
        aug = cv2.resize(aug,(100,300))
        data_aug.append(aug)
        ixs.append(n)

y_aug = np.array(y)[ixs,:]
data_aug  = np.stack(data_aug)

# breast cancer example
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from keras.models import Sequential
from keras.layers import Dense

d = load_breast_cancer()
X = d.data
Y = d.target
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.2, random_state = 0)


classifier = Sequential() # Initialising the ANN

classifier.add(Dense(units = 16, kernel_initializer = 'uniform', activation = 'relu', input_dim = 30))
classifier.add(Dense(units = 8, kernel_initializer = 'uniform', activation = 'relu'))
classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'relu'))
classifier.add(Dense(units = 1, kernel_initializer = 'uniform', activation = 'sigmoid'))

classifier.compile(optimizer = 'rmsprop', loss = 'binary_crossentropy', metrics = ['accuracy'])

classifier.fit(X_train, Y_train, batch_size = 1, epochs = 100)