monitor='val_acc',
verbose=1,
save_best_only=True)
early_stop = EarlyStopping(monitor='val_loss',
min_delta=0,
patience=20,
verbose=0,
mode='auto')
# this is the augmentation configuration we will use for training
train_datagen = ImageDataGenerator(
samplewise_center=True, # 输入数据集去中心化，按feature执行
rescale=1. / 255, # 重缩放因子
shear_range=0.1, # 剪切强度（逆时针方向的剪切变换角度）
zoom_range=0.1, # 随机缩放的幅度
rotation_range=10., # 图片随机转动的角度
width_shift_range=0.1, # 图片水平偏移的幅度
height_shift_range=0.1, # 图片竖直偏移的幅度
horizontal_flip=True, # 进行随机水平翻转
vertical_flip=True, # 进行随机竖直翻转
)
# this is the augmentation configuration we will use for validation:
# only rescaling
val_datagen = ImageDataGenerator(rescale=1. / 255)
train_generator = train_datagen.flow_from_directory(
train_data_dir,
target_size=(img_width, img_height),
batch_size=batch_size,
shuffle=True,
# 共21类(影像中所有地物的名称)
ObjectNames = ['agricultural', 'airplane', 'baseballdiamond', 'beach',
'buildings', 'chaparral', 'denseresidential', 'forest',
'freeway', 'golfcourse', 'harbor', 'intersection',
'mediumresidential', 'mobilehomepark', 'overpass', 'parkinglot',
'river', 'runway', 'sparseresidential', 'storagetanks', 'tenniscourt'
]
root_path = '/media/files/xdm/classification'
weights_path = os.path.join(root_path, 'weights/UCMerced_LandUse/InceptionV3_UCM_weights.h5')
test_data_dir = os.path.join(root_path, 'data/UCMerced_LandUse/test/')
# test data generator for prediction
test_datagen = ImageDataGenerator(rescale=1. / 255)
test_generator = test_datagen.flow_from_directory(
test_data_dir,
target_size=(img_width, img_height),
batch_size=batch_size,
shuffle=False, # Important !!!
classes=None,
class_mode=None)
test_image_list = test_generator.filenames
print('Loading model and weights from training process ...')
InceptionV3_model = load_model(weights_path)
print('Begin to predict for testing data ...')
