我是一名学习机器学习几个月的学生。
目标
我目前正在尝试创建一个多标签图像分类模型。
问题
随着学习的进行，会消耗大量的内存并出现错误。 尽管我只使用了大约 500MB 的数据（总共大约 50,000 张图像），但内存使用量超过了 32GB。 我怀疑我准备的数据集有问题。
我的尝试
我更改了批量大小并切换到使用数据生成器的实现。
关于批量大小 → 学习到总步数的 1/10 左右后。但我认为即使我尝试将其设置为 1 也不会完成学习过程。
关于数据生成器 → 我收到错误：[ 完成 GeneratorDataset 迭代器时发生错误：前提条件失败：Python 解释器状态未初始化。进程可能会终止。]
代码 ※这是一部分。
import ~~~
AUTOTUNE = tf.data.experimental.AUTOTUNE
def macro_f1(y, y_hat, thresh=0.5):
y_pred = tf.cast(tf.greater(y_hat, thresh), tf.float32)
tp = tf.cast(tf.math.count_nonzero(y_pred * y, axis=0), tf.float32)
fp = tf.cast(tf.math.count_nonzero(y_pred * (1 - y), axis=0), tf.float32)
fn = tf.cast(tf.math.count_nonzero((1 - y_pred) * y, axis=0), tf.float32)
f1 = 2*tp / (2*tp + fn + fp + 1e-16)
macro_f1 = tf.reduce_mean(f1)
return macro_f1
def create_dataset(filenames, labels, is_training=True, total_cnt=0, batch_size=8, train_flag=False):
dataset = tf.data.Dataset.from_tensor_slices((filenames, labels))
if train_flag:
dataset = dataset.map(parse_function, num_parallel_calls=AUTOTUNE)
else:
dataset = dataset.map(parse_function_val, num_parallel_calls=AUTOTUNE)
dataset = dataset.cache()
if train_flag:
dataset = dataset.shuffle(buffer_size=total_cnt)
dataset = dataset.repeat().batch(batch_size)
dataset = dataset.prefetch(buffer_size=AUTOTUNE)
return dataset
def parse_function(filename, label):
image_string = tf.io.read_file(filename)
image_decoded = tf.image.decode_jpeg(image_string, channels=3)
image_resized = tf.image.resize(image_decoded, [224, 224])
image_normalized = (image_resized / 255) -1
image_aug = tf.image.random_flip_left_right(image=image_normalized)
image_aug = tf.image.random_flip_up_down(image=image_aug)
return image_aug, label
def parse_function_val(filename, label):
image_string = tf.io.read_file(filename)
image_decoded = tf.image.decode_jpeg(image_string, channels=3)
image_resized = tf.image.resize(image_decoded, [224, 224])
image_normalized = (image_resized / 255) -1
return image_normalized, label
def main():
~Abbreviation~
# list for dataset
a_train = []
b_train = []
c_train = []
image_list_train = []
a_val = []
b_val = []
c_val = []
asphalt_val = []
image_list_val = []
DATA_DIR = os.path.join(DIR, 'data')
modes = ['train', 'val']
classes = ['a', 'b', 'c', 'a_b', 'b_c', 'c_a', 'a_b_c'] #dir-name
#make dataset
for mode in modes:
for class in classes:
dir_path = os.path.join(DATA_DIR, mode, class)
file_list = glob.glob(os.path.join(dir_path, '*'))
file_list = sorted(file_list)
for file_name in file_list:
# Split the folder name (ex: a_b) by _ and
# label it according to whether it contains the letters a, b, c
cls_list = cls_name.split('_')
if 'a' in cls_list: a = 1
else: a = 0
if 'b' in cls_list: b = 1
else: b = 0
if 'c' in cls_list: c = 1
else: c = 0
if mode == 'train':
a_train.append(a)
b_train.append(b)
c_train.append(c)
image_list_train.append(file_name)
elif mode == 'val':
a_val.append(a)
b_val.append(b)
c_val.append(c)
image_list_val.append(file_name)
else:
print('ERROR')
num_train = len(image_list_train)
num_val = len(image_list_val)
# make label ex) [[0,0,1],[1,1,0],[0,1,0]]
cls_id_train = []
cls_id_val = []
for i in range(num_train):
cls_id_train.append([a_train[i], b_train[i], c_train[i]])
for i in range(num_val):
cls_id_val.append([a_val[i], b_val[i], c_val[i]])
checkpoint_path = ckpt_dir + '/ckpt-{epoch}-{val_macro_f1:.2f}-{val_loss:.2f}'
# make dataset
train_ds = create_dataset(image_list_train,
cls_id_train,
total_cnt=num_train,
batch_size=batch_size,
train_flag=True,
)
val_ds = create_dataset(image_list_val,
cls_id_val,
total_cnt=num_val,
batch_size=batch_size,
train_flag=False,
)
# train
IMG_SHAPE = (img_size, img_size, channels)
base_model = tf.keras.applications.MobileNetV2(input_shape=IMG_SHAPE,
include_top=False,
weights='imagenet')
global_average_layer = tf.keras.layers.GlobalAveragePooling2D()
prediction_layer = tf.keras.layers.Dense(n_classes, activation='sigmoid')
model = tf.keras.Sequential([base_model,global_average_layer,prediction_layer])
model.compile(optimizer=optimizers.SGD(lr=lr, momentum=momentum, nesterov=nesterov),loss='binary_crossentropy', metrics=[macro_f1])
ckpt_cb = tf.keras.callbacks.ModelCheckpoint(checkpoint_path,
save_weights_only=True,
monitor='val_macro_f1',
mode='max',
verbose=1)
csv_logger = tf.keras.callbacks.CSVLogger(ckpt_dir+'/training.csv', separator=',')
history = model.fit(train_ds,
steps_per_epoch= int(num_train//batch_size),
validation_data=val_ds,
validation_steps= int(num_val//batch_size),
shuffle=True,
epochs=epochs,
callbacks=[ckpt_cb, csv_logger],)
model.save_weights(ckpt_dir + '/my_checkpoint')``
答案 0 :(得分：0)
在您的代码之前添加此代码片段，如果您使用的是 Tensorflow-GPU 版本，这将限制内存增长。
gpus = tf.config.list_physical_devices('GPU')
tf.config.experimental.set_memory_growth(gpu[0], True)