Я пытаюсь предсказать особенности в изображениях, используя keras с поддержкой TensorFlow. В частности, я ImageDataGenerator использовать keras ImageDataGenerator. Модель рассчитана на 4 эпохи и работает до 4-й эпохи, когда она терпит неудачу с MemoryError.
Я запускаю эту модель на экземпляре AWS g2.2xlarge, работающем под управлением Ubuntu Server 16.04 LTS (HVM), SSD Volume Type.
Образовательные изображения представляют собой 256x256 RGB пиксельные плитки (8 бит без знака), а обучающая маска - 256x256 однополосных (8-битных неподписанных) данных, где 255 == интересующая функция и 0 == все остальное.
Следующие три функции относятся к этой ошибке.
Как я могу устранить этот MemoryError?
def train_model():
batch_size = 1
training_imgs = np.lib.format.open_memmap(filename=os.path.join(data_path, 'data.npy'),mode='r+')
training_masks = np.lib.format.open_memmap(filename=os.path.join(data_path, 'mask.npy'),mode='r+')
dl_model = create_model()
print(dl_model.summary())
model_checkpoint = ModelCheckpoint(os.path.join(data_path,'mod_weight.hdf5'), monitor='loss',verbose=1, save_best_only=True)
dl_model.fit_generator(generator(training_imgs, training_masks, batch_size), steps_per_epoch=(len(training_imgs)/batch_size), epochs=4,verbose=1,callbacks=[model_checkpoint])
def generator(train_imgs, train_masks=None, batch_size=None):
# Create empty arrays to contain batch of features and labels#
if train_masks is not None:
train_imgs_batch = np.zeros((batch_size,y_to_res,x_to_res,bands))
train_masks_batch = np.zeros((batch_size,y_to_res,x_to_res,1))
while True:
for i in range(batch_size):
# choose random index in features
index= random.choice(range(len(train_imgs)))
train_imgs_batch[i] = train_imgs[index]
train_masks_batch[i] = train_masks[index]
yield train_imgs_batch, train_masks_batch
else:
rec_imgs_batch = np.zeros((batch_size,y_to_res,x_to_res,bands))
while True:
for i in range(batch_size):
# choose random index in features
index= random.choice(range(len(train_imgs)))
rec_imgs_batch[i] = train_imgs[index]
yield rec_imgs_batch
def train_generator(train_images,train_masks,batch_size):
data_gen_args=dict(rotation_range=90.,horizontal_flip=True,vertical_flip=True,rescale=1./255)
image_datagen = ImageDataGenerator()
mask_datagen = ImageDataGenerator()
# # Provide the same seed and keyword arguments to the fit and flow methods
seed = 1
image_datagen.fit(train_images, augment=True, seed=seed)
mask_datagen.fit(train_masks, augment=True, seed=seed)
image_generator = image_datagen.flow(train_images,batch_size=batch_size)
mask_generator = mask_datagen.flow(train_masks,batch_size=batch_size)
return zip(image_generator, mask_generator)
Следующие данные выводятся из модели, детализирующей эпохи, и сообщения об ошибке:
Epoch 00001: loss improved from inf to 0.01683, saving model to /home/ubuntu/deep_learn/client_data/mod_weight.hdf5
Epoch 2/4
7569/7569 [==============================] - 3394s 448ms/step - loss: 0.0049 - binary_crossentropy: 0.0027 - jaccard_coef_int: 0.9983
Epoch 00002: loss improved from 0.01683 to 0.00492, saving model to /home/ubuntu/deep_learn/client_data/mod_weight.hdf5
Epoch 3/4
7569/7569 [==============================] - 3394s 448ms/step - loss: 0.0049 - binary_crossentropy: 0.0026 - jaccard_coef_int: 0.9982
Epoch 00003: loss improved from 0.00492 to 0.00488, saving model to /home/ubuntu/deep_learn/client_data/mod_weight.hdf5
Epoch 4/4
7569/7569 [==============================] - 3394s 448ms/step - loss: 0.0074 - binary_crossentropy: 0.0042 - jaccard_coef_int: 0.9975
Epoch 00004: loss did not improve
Traceback (most recent call last):
File "image_rec.py", line 291, in <module>
train_model()
File "image_rec.py", line 208, in train_model
dl_model.fit_generator(train_generator(training_imgs,training_masks,batch_size),steps_per_epoch=1,epochs=1,workers=1)
File "image_rec.py", line 274, in train_generator
image_datagen.fit(train_images, augment=True, seed=seed)
File "/home/ubuntu/pyvirt_test/local/lib/python2.7/site-packages/keras/preprocessing/image.py", line 753, in fit
x = np.copy(x)
File "/home/ubuntu/pyvirt_test/local/lib/python2.7/site-packages/numpy/lib/function_base.py", line 1505, in copy
return array(a, order=order, copy=True)
MemoryError