Hello, messaged you before via mail. Following your instructions, I tried the unit test "benchmark" and it gives the same error I got earlier when experimenting with my own model.
Debug Assertion Failed!
Program ...SCCN.exe
File:c:\program files(x86)....\msvc\14.16.27023\include\vector
Line: 122
Expression: cannot seek vector iterator after end
Because I suspected the input dimension being wrong in my model, I managed to make the conv3x3 unit test work just fine. I will run more tests and successively modify the unit test conv3x3 towards the benchmark one and see when it breaks.
EDIT: found the culprit (or at least how to trigger it). I modified the Conv3x3 test to use 1 channel input as follows:
This works. But as soon as I increase the input size to anything larger than the kernel size of the Conv2D layer and load the generated model into the Cpp project.
Hello, messaged you before via mail. Following your instructions, I tried the unit test "benchmark" and it gives the same error I got earlier when experimenting with my own model.
Debug Assertion Failed! Program ...SCCN.exe File:c:\program files(x86)....\msvc\14.16.27023\include\vector Line: 122
Expression: cannot seek vector iterator after end
Because I suspected the input dimension being wrong in my model, I managed to make the conv3x3 unit test work just fine. I will run more tests and successively modify the unit test conv3x3 towards the benchmark one and see when it breaks.
EDIT: found the culprit (or at least how to trigger it). I modified the Conv3x3 test to use 1 channel input as follows:
test_x = np.random.rand(10, 3, 3, 1).astype('f').astype('f') test_y = np.random.rand(10, 1).astype('f') model = Sequential([ Conv2D(1, (3, 3), input_shape=(3, 3, 1)), Flatten(), Dense(1) ])
This works. But as soon as I increase the input size to anything larger than the kernel size of the Conv2D layer and load the generated model into the Cpp project.
test_x = np.random.rand(10, 9, 9, 1).astype('f').astype('f') test_y = np.random.rand(10, 1).astype('f') model = Sequential([ Conv2D(1, (3, 3), input_shape=(9, 9, 1)), Flatten(), Dense(1) ])
This was sufficient for me to trigger the error.