[PYTHON/KERAS] 컨볼루션 신경망 만들기 (손글씨) : 데이터 확장

■ 컨볼루션 신경망을 만드는 방법을 보여준다.

 

▶ 예제 코드 (PY)

import keras.layers as layers
import keras.layers.convolutional as convolutional
import keras.models as models
import keras.preprocessing.image as image
import numpy as np

np.random.seed(3)

print("데이터 로드를 시작합니다.")

trainImageDataGenerator = image.ImageDataGenerator(rescale = 1. / 255,
    rotation_range = 10,
    width_shift_range = 0.2,
    height_shift_range = 0.2,
    shear_range = 0.7,
    zoom_range = [0.9, 2.2],
    horizontal_flip = True,
    vertical_flip = True,
    fill_mode = "nearest")

trainDirectoryIterator = trainImageDataGenerator.flow_from_directory("handwriting_shape/train", target_size = (24, 24), batch_size = 3, class_mode = "categorical")

testImageDataGenerator = image.ImageDataGenerator(rescale = 1. / 255)

testDirectoryIterator = testImageDataGenerator.flow_from_directory("handwriting_shape/test", target_size = (24, 24), batch_size = 3, class_mode = "categorical")

print("데이터 로드를 종료합니다.")

print("모델 정의를 시작합니다.")

model = models.Sequential()

model.add(convolutional.Conv2D(32, kernel_size = (3, 3), activation = "relu", input_shape = (24, 24, 3)))
model.add(convolutional.Conv2D(64, (3, 3), activation = "relu"))
model.add(convolutional.MaxPooling2D(pool_size = (2, 2)))
model.add(layers.Flatten())
model.add(layers.Dense(128, activation = "relu"))
model.add(layers.Dense(3, activation = "softmax"))

model.compile(loss = "categorical_crossentropy", optimizer = "adam", metrics = ["accuracy"])

print("모델 정의를 종료합니다.")

print("모델 학습을 시작합니다.")

model.fit_generator(trainDirectoryIterator, steps_per_epoch = 15 * 100, epochs = 200, validation_data = testDirectoryIterator, validation_steps = 5)

print("모델 학습을 종료합니다.")

print("모델 평가를 시작합니다.")

evaluationList = model.evaluate_generator(testDirectoryIterator, steps = 5)

print("%s : %.2f%%" % (model.metrics_names[1], evaluationList[1] * 100))

print("모델 평가를 종료합니다.")

print("모델 사용을 시작합니다.")

outputNDArray = model.predict_generator(testDirectoryIterator, steps = 5)

np.set_printoptions(formatter = {"float": lambda x: "{0:0.3f}".format(x)})

classDictionary = testDirectoryIterator.class_indices

print(classDictionary)
print(outputNDArray)

print("모델 사용을 종료합니다.")