Keras 使用自己的資料分類，並使用tensorboard記錄的簡單例項

1.使用的分類圖片按照不同類別儲存在不同資料夾子中，並且切分好訓練集和測試集，如下圖顯示

注意：檔名建議使用標籤名

from keras.models import Sequential
import keras
from keras.preprocessing.image import ImageDataGenerator
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D

from
 
 keras.callbacks import TensorBoard
import time
import os
import tensorflow as tf
#指定GPU，限制GPU記憶體
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
from keras.backend.tensorflow_backend import set_session
config = tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.7
set_session(tf.Session(
 
config=config))


BATCHSIZE = 100
IMG_SIZE = (100,100)

#訓練集，測試集檔案路徑
train_path = '../data/train'
test_path = '../data/test'


s_time = time.strftime("%Y%m%d%H%M%S", time.localtime())  #時間戳


#image_batch_generator

train_datagen = ImageDataGenerator(
         rescale=1./255)

test_datagen = ImageDataGenerator(
 
 rescale=1./255 )


#訓練集batch生成器
train_generator = train_datagen.flow_from_directory(
        train_path,
        target_size=IMG_SIZE,
        batch_size=BATCHSIZE,
        color_mode='grayscale',
        classes=['original','tampered'],
        class_mode='categorical')

#測試集batch生成器
validation_generator = test_datagen.flow_from_directory(
        test_path,
        target_size=IMG_SIZE,
        color_mode='grayscale',
        batch_size=BATCHSIZE,
        classes=['original','tampered'],
        class_mode='categorical')



#網路結構
model = Sequential()
model.add(Conv2D(16, (3, 3), activation='relu', input_shape=(IMG_SIZE[0],IMG_SIZE[1],1)))
model.add(Conv2D(16, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))

model.add(Conv2D(32, (3, 3), activation='relu'))
#model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))

model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(2, activation='softmax'))

#優化器
adam = keras.optimizers.Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08)
model.compile(loss='binary_crossentropy',
              optimizer=adam,
              metrics=['accuracy'])

#logs檔案路徑
logs_path = 'F:/zy/logs/log_%s'%(s_time)


try:
    os.makedirs(logs_path)
except:
    pass

#將loss ，acc， val_loss ,val_acc記錄tensorboard
tensorboard = TensorBoard(log_dir=logs_path, histogram_freq=1,write_graph=True,write_batch_performance=True)

#模型訓練
model.fit_generator(
        train_generator,
        steps_per_epoch=60,
        epochs=50,
        verbose=1,
        validation_data=validation_generator,
        validation_steps=60,
        callbacks=[tensorboard]
)