ThomasWolf0701
commented
3 years ago Was a general problem with my keras installation
Closed ThomasWolf0701 closed 3 years ago
ThomasWolf0701
commented
3 years ago Was a general problem with my keras installation
Describe the bug
I ran the cell sample from the github page and till the compile step everything is fine. When running the compile step I get:
Error in py_call_impl(callable, dots$args, dots$keywords) : RuntimeError: Evaluation error: ValueError: None values not supported..
A clear and concise description of what the bug is as Detailed traceback:
Error in py_call_impl(callable, dots$args, dots$keywords) : RuntimeError: Evaluation error: ValueError: None values not supported..
File "C:\Users\THOMAS~1\DOCUME~1\CONDA~1\envs\R-RETI~1\lib\site-packages\tensorflow_core\python\ops\math_ops.py", line 1422, in range limit = ops.convert_to_tensor(limit, name="limit") File "C:\Users\THOMAS~1\DOCUME~1\CONDA~1\envs\R-RETI~1\lib\site-packages\tensorflow_core\python\framework\ops.py", line 1184, in convert_to_tensor return convert_to_tensor_v2(value, dtype, preferred_dtype, name) File "C:\Users\THOMAS~1\DOCUME~1\CONDA~1\envs\R-RETI~1\lib\site-packages\tensorflow_core\python\framework\ops.py", line 1242, in convert_to_tensor_v2 as_ref=False) File "C:\Users\THOMAS~1\DOCUME~1\CONDA~1\envs\R-RETI~1\lib\site-packages\tensorflow_core\python\framework\ops.py", line 1296, in internal_convert_to_tensor ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref) File "C:\Users\THOMAS~1\DOCUME~1\CONDA~1\envs\R-RETI~1\lib\site-packages\tensorflow_c
The output after running yolo3
2020-10-17 19:37:03.572520: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_100.dll 2020-10-17 19:37:06.629388: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library nvcuda.dll 2020-10-17 19:37:06.667244: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 0 with properties: name: GeForce GTX 1070 major: 6 minor: 1 memoryClockRate(GHz): 1.645 pciBusID: 0000:01:00.0 2020-10-17 19:37:06.667676: I tensorflow/stream_executor/platform/default/dlopen_checker_stub.cc:25] GPU libraries are statically linked, skip dlopen check. 2020-10-17 19:37:06.668985: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1746] Adding visible gpu devices: 0 2020-10-17 19:37:06.669443: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 2020-10-17 19:37:06.672431: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 0 with properties: name: GeForce GTX 1070 major: 6 minor: 1 memoryClockRate(GHz): 1.645 pciBusID: 0000:01:00.0 2020-10-17 19:37:06.672801: I tensorflow/stream_executor/platform/default/dlopen_checker_stub.cc:25] GPU libraries are statically linked, skip dlopen check. 2020-10-17 19:37:06.674102: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1746] Adding visible gpu devices: 0 2020-10-17 19:37:07.358505: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1159] Device interconnect StreamExecutor with strength 1 edge matrix: 2020-10-17 19:37:07.358893: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1165] 0 2020-10-17 19:37:07.359069: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1178] 0: N 2020-10-17 19:37:07.360054: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1304] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 6794 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1070, pci bus id: 0000:01:00.0, compute capability: 6.1)
The code i ran library(tidyverse) library(platypus) library(abind)
BCCD_path <- "BCCD/" annot_path <- file.path(BCCD_path, "Annotations/") blood_labels <- c("Platelets", "RBC", "WBC") n_class <- length(blood_labels) net_h <- 416 # Must be divisible by 32 net_w <- 416 # Must be divisible by 32 anchors_per_grid <- 3
blood_anchors <- generate_anchors( anchors_per_grid = anchors_per_grid, # Number of anchors (per one grid) to generate annot_path = annot_path, # Annotations directory labels = blood_labels, # Class labels n_iter = 10, # Number of k-means++ iterations annot_format = "pascal_voc", # Annotations format seed = 55, # Random seed centroid_fun = mean # Centroid function )
blood_yolo <- yolo3( net_h = net_h, # Input image height net_w = net_w, # Input image width grayscale = FALSE, # Should images be loaded as grayscale or RGB n_class = n_class, # Number of object classes (80 for COCO dataset) anchors = blood_anchors # Anchor boxes ) blood_yolo %>% load_darknet_weights("yolov3.weights") # Optional
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