[PYTHON/KERAS] 히스토리를 텐서보드와 연동하기

■ 히스토리를 텐서보드와 연동하는 방법을 보여준다.

 

▶ 예제 코드 (PY)

import keras
import keras.datasets.mnist as mnist
import keras.models as models
import keras.utils as utils
import keras.layers as layers
import numpy as np

np.random.seed(3)

print("데이터 로드를 시작합니다.")

(trainInputNDArray, trainCottectOutputNDArray), (testInputNDArray, testCorrectOutputNDArray) = mnist.load_data()

# trainInputNDArray         : (60000, 28, 28)
# trainCottectOutputNDArray : (60000,)
# testInputNDArray          : (10000, 28, 28)
# testCorrectOutputNDArray  : (10000,)

# 훈련/검증 데이터를 분리한다.
validationInputNDArray         = trainInputNDArray[50000:]
validationCorrectOutputNDArray = trainCottectOutputNDArray[50000:]
trainInputNDArray              = trainInputNDArray[:50000]
trainCottectOutputNDArray      = trainCottectOutputNDArray[:50000]

# 훈련/검증/테스트 데이터
trainInputNDArray      = trainInputNDArray.reshape(50000, 784).astype("float32") / 255.0
validationInputNDArray = validationInputNDArray.reshape(10000, 784).astype("float32") / 255.0
testInputNDArray       = testInputNDArray.reshape(10000, 784).astype("float32") / 255.0

# trainInputNDArray      : (50000, 784)
# validationInputNDArray : (10000, 784)
# testInputNDArray       : (10000, 784)

# 훈련/검증 데이터를 섞는다.
#trainRandomIndexNDArray      = np.random.choice(50000, 700)
#validationRandomIndexNDArray = np.random.choice(10000, 300)
#
#trainInputNDArray              = trainInputNDArray[trainRandomIndexNDArray]
#trainCottectOutputNDArray      = trainCottectOutputNDArray[trainRandomIndexNDArray]
#validationInputNDArray         = validationInputNDArray[validationRandomIndexNDArray]
#validationCorrectOutputNDArray = validationCorrectOutputNDArray[validationRandomIndexNDArray]
#
# trainInputNDArray              : (700, 784)
# trainCottectOutputNDArray      : (700,)
# validationInputNDArray         : (300, 784)
# validationCorrectOutputNDArray : (300,)

# 정답 데이터에 대해 원핫 인코딩 처리한다.
trainCottectOutputNDArray      = utils.np_utils.to_categorical(trainCottectOutputNDArray)
validationCorrectOutputNDArray = utils.np_utils.to_categorical(validationCorrectOutputNDArray)
testCorrectOutputNDArray       = utils.np_utils.to_categorical(testCorrectOutputNDArray)

# trainCottectOutputNDArray      : (50000, 10)
# validationCorrectOutputNDArray : (10000, 10)
# testCorrectOutputNDArray       : (10000, 10)

print("데이터 로드를 종료합니다.")

print("모델 정의를 시작합니다.")

model = models.Sequential()

model.add(layers.Dense(units = 64, input_dim = 784, activation = "relu"))
model.add(layers.Dense(units = 10, activation = "softmax"))

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

print("모델 정의를 종료합니다.")

print("모델 학습을 시작합니다.")

tensorBoardHistory = keras.callbacks.TensorBoard(log_dir = "/graph", histogram_freq = 0, write_graph = True,\
    write_images = True)

model.fit(trainInputNDArray, trainCottectOutputNDArray, epochs = 1000, batch_size = 10,\
    validation_data = (validationInputNDArray, validationCorrectOutputNDArray), callbacks = [tensorBoardHistory])

print("모델 학습을 종료합니다.")

# 명령 프롬프트에서 아래 명령을 실행합니다.
# tensorboard --logdir=/graph