训练 Keras 模型会产生多个优化器错误

Question

所以我需要使用我自己的数据集重新训练 Tiny YOLO。 我正在使用的模型可以在这里找到： keras-yolo3 。

我开始训练并遇到多个优化器错误，添加了错误代码以防止混淆。 我注意到即使它应该使用 GPU，训练也会变慢，经过深入研究后，我发现这不是使用 GPU 进行训练。 我应该注意到，在我用于学习训练的另一个较小的网络上使用 GPU，所以从那一边一切都设置正确，当我进行训练时，它们没有这种类型的错误。

由于上述错误，这是否缓慢且有点 CPU 训练？ 有谁知道我该如何解决这个问题？

Using TensorFlow backend.
WARNING: Logging before flag parsing goes to stderr.
2019-08-19 09:45:08.057713: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library nvcuda.dll
2019-08-19 09:45:08.264577: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1640] Found device 0 with properties:
name: GeForce GTX 1060 6GB major: 6 minor: 1 memoryClockRate(GHz): 1.8475
pciBusID: 0000:01:00.0
2019-08-19 09:45:08.270723: I tensorflow/stream_executor/platform/default/dlopen_checker_stub.cc:25] GPU libraries are statically linked, skip dlopen check.
2019-08-19 09:45:08.275827: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1763] Adding visible gpu devices: 0
2019-08-19 09:45:09.214197: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1181] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-08-19 09:45:09.217605: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1187]      0
2019-08-19 09:45:09.219777: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1200] 0:   N
2019-08-19 09:45:09.222399: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1326] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 4712 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1060 6GB, pci bus id: 0000:01:00.0, compute capability: 6.1)
Create Tiny YOLOv3 model with 6 anchors and 80 classes.
Load weights model_data/tiny_yolo_weights.h5.
Freeze the first 42 layers of total 44 layers.
Train on 8298 samples, val on 922 samples, with batch size 32.
Epoch 1/50
2019-08-19 09:45:19.742610: E tensorflow/core/grappler/optimizers/meta_optimizer.cc:502] shape_optimizer failed: Invalid argument: Subshape must have computed start >= end since stride is negative, but is 0 and 2 (computed from start 0 and end 9223372036854775807 over shape with rank 2 and stride-1)
2019-08-19 09:45:19.781035: E tensorflow/core/grappler/optimizers/meta_optimizer.cc:502] remapper failed: Invalid argument: Subshape must have computed start >= end since stride is negative, but is 0 and 2 (computed from start 0 and end 9223372036854775807 over shape with rank 2 and stride-1)
2019-08-19 09:45:19.935930: E tensorflow/core/grappler/optimizers/meta_optimizer.cc:502] layout failed: Invalid argument: Subshape must have computed start >= end since stride is negative, but is 0 and 2 (computed from start 0 and end 9223372036854775807 over shape with rank 2 and stride-1)
2019-08-19 09:45:20.168936: E tensorflow/core/grappler/optimizers/meta_optimizer.cc:502] shape_optimizer failed: Invalid argument: Subshape must have computed start >= end since stride is negative, but is 0 and 2 (computed from start 0 and end 9223372036854775807 over shape with rank 2 and stride-1)
2019-08-19 09:45:20.205304: E tensorflow/core/grappler/optimizers/meta_optimizer.cc:502] remapper failed: Invalid argument: Subshape must have computed start >= end since stride is negative, but is 0 and 2 (computed from start 0 and end 9223372036854775807 over shape with rank 2 and stride-1)
258/259 [============================>.] - ETA: 3s - loss: 41.82962019-08-19 10:01:51.053474: E tensorflow/core/grappler/optimizers/meta_optimizer.cc:502] remapper failed: Invalid argument: Subshape must have computed start >= end since stride is negative, but is 0 and 2 (computed from start 0 and end 9223372036854775807 over shape with rank 2 and stride-1)
2019-08-19 10:01:51.138957: E tensorflow/core/grappler/optimizers/meta_optimizer.cc:502] layout failed: Invalid argument: Subshape must have computed start >= end since stride is negative, but is 0 and 2 (computed from start 0 and end 9223372036854775807 over shape with rank 2 and stride-1)
2019-08-19 10:01:51.243888: E tensorflow/core/grappler/optimizers/meta_optimizer.cc:502] remapper failed: Invalid argument: Subshape must have computed start >= end since stride is negative, but is 0 and 2 (computed from start 0 and end 9223372036854775807 over shape with rank 2 and stride-1)
259/259 [==============================] - 1078s 4s/step - loss: 41.8008 - val_loss: 35.7122

Answer 1

我在这里找到了解决方案： https : //github.com/tensorflow/tensorrt/issues/118

您必须在 yolo3/model.py 中更改行（140/141）：

box_xy = (K.sigmoid(feats[..., :2]) + grid) / K.cast(grid_shape[::-1], K.dtype(feats))
box_wh = K.exp(feats[..., 2:4]) * anchors_tensor / K.cast(input_shape[::-1], K.dtype(feats))

到：

box_xy = (K.sigmoid(feats[..., :2]) + grid) / K.cast(grid_shape[...,::-1], K.dtype(feats))
box_wh = K.exp(feats[..., 2:4]) * anchors_tensor / K.cast(input_shape[...,::-1], K.dtype(feats))

同样在我的情况下，有助于将批量大小从8减少到4 。

Answer 2

这是一个版本问题。 请重新安装Keras和Tensorflow。 对于Keras使用2.1.5版本，对于Tensorflow使用1.6.0版本。 请检查自述文件中的要求。 此外，请考虑根据https://github.com/qqwweee/keras-yolo3/issues/548中的修复程序更改train.py文件。 那是和你相似的问题。