使用ResNet50预训练的权重我正在尝试构建一个分类器.代码库完全在Keras高级Tensorflow API中实现.完整的代码发布在下面的GitHub链接中.

源代码：Classification Using RestNet50 Architecture

预训练模型的文件大小为94.7mb.

我加载了预先训练好的文件

new_model = Sequential()

new_model.add(ResNet50(include_top=False,
                pooling='avg',
                weights=resnet_weight_paths))

并适合模型

train_generator = data_generator.flow_from_directory(
    'path_to_the_training_set',
    target_size = (IMG_SIZE,IMG_SIZE),
    batch_size = 12,
    class_mode = 'categorical'
    )

validation_generator = data_generator.flow_from_directory(
    'path_to_the_validation_set',
    target_size = (IMG_SIZE,IMG_SIZE),
    class_mode = 'categorical'
    )

#compile the model

new_model.fit_generator(
    train_generator,
    steps_per_epoch = 3,
    validation_data = validation_generator,
    validation_steps = 1
)

在训练数据集中,我有两个文件夹狗和猫,每个持有近10,000张图像.当我编译脚本时,我收到以下错误

Epoch 1/1 2018-05-12 13:04:45.847298: W
tensorflow/core/framework/allocator.cc:101] Allocation of 38535168
exceeds 10% of system memory. 2018-05-12 13:04:46.845021: W
tensorflow/core/framework/allocator.cc:101] Allocation of 37171200
exceeds 10% of system memory. 2018-05-12 13:04:47.552176: W
tensorflow/core/framework/allocator.cc:101] Allocation of 37171200
exceeds 10% of system memory. 2018-05-12 13:04:48.199240: W
tensorflow/core/framework/allocator.cc:101] Allocation of 37171200
exceeds 10% of system memory. 2018-05-12 13:04:48.918930: W
tensorflow/core/framework/allocator.cc:101] Allocation of 37171200
exceeds 10% of system memory. 2018-05-12 13:04:49.274137: W
tensorflow/core/framework/allocator.cc:101] Allocation of 19267584
exceeds 10% of system memory. 2018-05-12 13:04:49.647061: W
tensorflow/core/framework/allocator.cc:101] Allocation of 19267584
exceeds 10% of system memory. 2018-05-12 13:04:50.028839: W
tensorflow/core/framework/allocator.cc:101] Allocation of 19267584
exceeds 10% of system memory. 2018-05-12 13:04:50.413735: W
tensorflow/core/framework/allocator.cc:101] Allocation of 19267584
exceeds 10% of system memory.

任何想法来优化加载预训练模型(或)的方法摆脱这个警告信息？

谢谢！

解决方法:

尝试将batch_size属性减少为较小的数字(如1,2或3).
例：

train_generator = data_generator.flow_from_directory(
    'path_to_the_training_set',
    target_size = (IMG_SIZE,IMG_SIZE),
    batch_size = 2,
    class_mode = 'categorical'
    )