Question

我正在尝试将SqueezeDet model的Keras实现适合新的数据集。在对配置文件进行适当的更改之后，我尝试运行Train脚本，但是在调用fit_generator()之后，它似乎挂起了。当我得到以下输出时：

/anaconda/envs/py35/lib/python3.5/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
  from ._conv import register_converters as _register_converters
Using TensorFlow backend.
Number of images: 536
Number of epochs: 100
Number of batches: 53
Batch size: 10
2018-07-04 14:18:49.711606: I tensorflow/core/platform/cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2018-07-04 14:18:54.080912: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1356] Found device 0 with properties:
name: Tesla K80 major: 3 minor: 7 memoryClockRate(GHz): 0.8235
pciBusID: 52a9:00:00.0
totalMemory: 11.17GiB freeMemory: 11.10GiB
2018-07-04 14:18:54.080958: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1435] Adding visible gpu devices: 0
2018-07-04 14:18:54.333214: I tensorflow/core/common_runtime/gpu/gpu_device.cc:923] Device interconnect StreamExecutor with strength 1 edge matrix:
2018-07-04 14:18:54.333270: I tensorflow/core/common_runtime/gpu/gpu_device.cc:929]      0
2018-07-04 14:18:54.333290: I tensorflow/core/common_runtime/gpu/gpu_device.cc:942] 0:   N
2018-07-04 14:18:54.333559: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1053] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10764 MB memory) -> physical GPU (device: 0, name: Tesla K80, pci bus id: 52a9:00:00.0, compute capability: 3.7)
Learning rate: 0.01
Weights initialized by name from ../main/model/imagenet.h5
Using single GPU
Backend Qt5Agg is interactive backend. Turning interactive mode on.
Epoch 1/100

然后，即使将其搁置一天也没有任何反应。似乎冻结的呼叫是：

squeeze.model.fit_generator(train_generator, epochs=EPOCHS, verbose=1,
                            steps_per_epoch=nbatches_train, callbacks=cb)

其中的参数是：

train_generator = generator_from_data_path(img_names, gt_names, config=cfg)
EPOCHS = 100
nbatches_train  = 53
callbacks = [# TensorBoard object, ReduceLROnPlateau object, ModelCheckpoint object #]

我的版本：

Python 3.5.4 :: Anaconda custom (64-bit)
tensorflow-gpu : 1.8.0
tensorflow : 1.8.0
Keras : 2.2.0

Answer 1

格式化评论中的对话以回答问题。

罪魁祸首是train_generator。

前一段时间，我在Keras中研究了model.fit_generator的来源。它只是从生成器中检索一些数据，并将其提交到后端，没有什么神奇的：）

因此，我的假设是由于生成器未生成任何内容，因此无法从生成器检索数据。

@Barker已确认，表明对next(train_generator)的呼叫已挂起。

我个人已经迁移到支持索引和长度的keras.utils.Sequence，并且比普通生成器方便得多。尽管此注释与当前问题无关。

致电fit_generator

1 个答案: