Python 为什么我使用Tenserflow和Keras GPU的模型会出现OOM错误？

我试图运行我的模型，但我运行的介绍错误

2021-06-03 01:20:42.015864: W tensorflow/core/common_runtime/bfc_allocator.cc:467] **************************************************************************__________________________
2021-06-03 01:20:42.015984: W tensorflow/core/framework/op_kernel.cc:1767] OP_REQUIRES failed at concat_op.cc:158 : Resource exhausted: OOM when allocating tensor with shape[8938,46080] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc

tensorflow.python.framework.errors_impl.ResourceExhaustedError: OOM when allocating tensor with shape[8938,46080] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc [Op:ConcatV2] name: concat

我的代码：

import numpy as np
import tensorflow as tf
from cv2 import cv2
from keras.applications.densenet import preprocess_input
from tensorflow import keras
from tensorflow.keras.layers import Dense, Activation
from tensorflow.keras.optimizers import Adam, SGD, RMSprop
from tensorflow.keras.metrics import categorical_crossentropy
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import matplotlib.pyplot as plt
from tensorflow.keras.preprocessing import image
from tensorflow.keras.models import Model
from tensorflow.keras.regularizers import l2
from tensorflow.keras.layers import MaxPool2D, MaxPool3D, GlobalAveragePooling2D, Reshape, GlobalMaxPooling2D, MaxPooling2D, Flatten, AveragePooling2D

# physical_devices = tf.config.experimental.list_physical_devices('GPU')
# print("Num GPU Available", len(physical_devices))
# tf.config.experimental.set_memory_growth(physical_devices[0], True)

train_path = 'data/train'
test_path = 'data/test'
batch_size = 16
image_size = (360, 360)

train_batches = ImageDataGenerator(
    preprocessing_function=preprocess_input,
    # rescale=1./255,
    horizontal_flip=True,
    rotation_range=.3,
    width_shift_range=.2,
    height_shift_range=.2,
    zoom_range=.2
).flow_from_directory(directory=train_path,
                      target_size=image_size,
                      color_mode='rgb',
                      batch_size=batch_size,
                      shuffle=True)

test_batches = ImageDataGenerator(
    preprocessing_function=preprocess_input
    # rescale=1./255
).flow_from_directory(directory=test_path,
                      target_size=image_size,
                      color_mode='rgb',
                      batch_size=batch_size,
                      shuffle=True)

# mobile = tf.keras.applications.mobilenet.MobileNet()
mobile = tf.keras.applications.mobilenet_v2.MobileNetV2(include_top=False, weights='imagenet', input_shape=(360, 360, 3))

x = MaxPool2D()(mobile.layers[-1].output)
x = Flatten()(x)
model = Model(inputs=mobile.input, outputs=x)

train_features = model.predict(train_batches, train_batches.labels)
test_features = model.predict(test_batches, test_batches.labels)

from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()

train_scaled = scaler.fit_transform(train_features)
test_scaled = scaler.fit_transform(test_features)

from sklearn.svm import SVC
svm = SVC()

svm.fit(train_scaled, train_batches.labels)

print('train accuracy:')
print(svm.score(train_scaled, train_batches.labels))
print('test accuracy:')
print(svm.score(test_scaled, test_batches.labels))

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此错误内存不足。

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如果减小批量大小值不能解决问题，请尝试减小批量大小的值。您可以启用cuda统一内存

此链接可能有助于我尝试通过对这些行进行装饰来启用cuda非固定内存

#physical_devices=tf.config.experimental.list_physical_devices（'GPU'）#print（“Num GPU Available”，len（physical_devices））#tf.config.experimental.set_memory_growth（physical_devices[0]，True）

，但这并没有解决问题，因为它在cpu上持续崩溃运行也不起作用。将“批处理大小”设置为8也不起作用。@bSwizzle能否尝试与预先训练的模型匹配的输入形状。我没有试过。请看一看。要指定输入形状，输入形状应为（224224，224，3）而不是输入形状=（360，360，3）。我已经试过了，但似乎不起作用。我真的不明白这个问题，但是一个朋友尝试在他们的笔记本电脑上运行这个模型，并且它工作正常，尽管他们的规格比我小。我只是希望我的GPU不是有问题或者什么的。