我正在处理一个图像识别问题。我的模型需要训练200个周期。我希望在每个周期结束时，如果模型的验证准确率是迄今为止最好的，就保存该模型。这是我的代码，

from keras.models import Sequentialfrom keras.models import Modelfrom keras.callbacks import ModelCheckpoint, LearningRateScheduler, EarlyStopping, ReduceLROnPlateau, TensorBoardfrom keras import optimizers, losses, activations, modelsfrom keras.layers import Convolution2D, Dense, Input, Flatten, Dropout, MaxPooling2D, BatchNormalization, GlobalAveragePooling2D, Concatenatefrom keras import applicationsfrom keras import backend as Kfrom keras import callbacksfrom keras.preprocessing.image import ImageDataGeneratorROWS,COLS = 669,1026input_shape = (ROWS, COLS, 3)base_model = applications.VGG19(weights='imagenet',                                 include_top=False,                                 input_shape=(ROWS, COLS,3))l = 0for layer in base_model.layers:    layer.trainable = False    l += 1c = 0for layer in base_model.layers:    c += 1    if c > l-5:        layer.trainable = True for layer in base_model.layers:    print(layer,layer.trainable)base_model.summary()add_model = Sequential()add_model.add(base_model)add_model.add(GlobalAveragePooling2D())add_model.add(Dense(514, activation='relu'))add_model.add(Dense(128, activation='relu'))add_model.add(Dense(64, activation='relu'))add_model.add(Dropout(0.5))add_model.add(Dense(8, activation='relu'))add_model.add(Dropout(0.5))add_model.add(Dense(1, activation='sigmoid'))model = add_model# model.compile(loss='binary_crossentropy', #               optimizer=optimizers.SGD(lr=1e-, #                                        momentum=0.9),#               metrics=['accuracy'])model.compile(loss='binary_crossentropy',               optimizer='adam',              metrics=['accuracy'])model.summary()train_data_dir = '/home/spectrograms/train'validation_data_dir = '/home/spectrograms/test'nb_train_samples = 791nb_validation_samples = 198epochs = 200batch_size = 3if K.image_data_format() == 'channels_first':    input_shape = (3, ROWS, COLS)else:    input_shape = (ROWS, COLS,3)# this is the augmentation configuration we will use for trainingtrain_datagen = ImageDataGenerator(    rescale=1. / 255,    shear_range=0,    zoom_range=0,    horizontal_flip=False)test_datagen = ImageDataGenerator(rescale=1. / 255)train_generator = train_datagen.flow_from_directory(    train_data_dir,    target_size=(ROWS, COLS),    batch_size=batch_size,    class_mode='binary')validation_generator = test_datagen.flow_from_directory(    validation_data_dir,    target_size=(ROWS, COLS),    batch_size=batch_size,    class_mode='binary')checkpoint_filepath = '/home/CNN/saved_model/checkpoints/checkpoint-{epoch:02d}-{val_loss:.2f}.h5'model_checkpoint_callback = callbacks.ModelCheckpoint(    filepath=checkpoint_filepath,    save_weights_only=False,    monitor='val_accuracy',    mode='max',    save_best_only=True)model.fit_generator(    train_generator,    steps_per_epoch=nb_train_samples // batch_size,    epochs=epochs,    callbacks = [model_checkpoint_callback],    validation_data=validation_generator,    validation_steps=nb_validation_samples // batch_size)

但是我收到了错误OSError: Unable to create file (unable to open file: name = '/home/CNN/saved_model/checkpoints/checkpoint-01-0.69.h5', errno = 2, error message = 'No such file or directory', flags = 13, o_flags = 242)

但实际上该文件目录是存在的。我不明白这里的问题出在哪里。

回答：

在您的代码中添加以下内容

save_dir = '/home/CNN/saved_model/checkpoints/'if not os.path.exists(save_dir):   os.makedirs(save_dir)checkpoint_filepath = os.path.join(save_dir, "checkpoint-{epoch:02d}-{val_loss:.2f}.h5")model_checkpoint_callback = callbacks.ModelCheckpoint(filepath=checkpoint_filepath,save_weights_only=False,monitor='val_accuracy',mode='max',save_best_only=True)