Dear @ipodsky,
To answer your question 1, let’s have a look at the application’s parameters:
Parameters:
-in <string> Input image (mandatory)
-out <string> [pixel] Output Image [pixel=uint8/uint16/int16/uint32/int32/float/double/cint16/cint32/cfloat/cdouble] (default value is float) (mandatory)
-usenan <boolean> Consider NaN as no-data (mandatory, default value is false)
-mode <string> No-data handling mode [buildmask/changevalue/apply] (mandatory, default value is buildmask)
-mode.buildmask.inv <float> Inside Value (mandatory, default value is 1)
-mode.buildmask.outv <float> Outside Value (mandatory, default value is 0)
-mode.changevalue.newv <float> The new no-data value (mandatory, default value is 0)
-mode.apply.mask <string> Mask image (mandatory)
-mode.apply.ndval <float> Nodata value used (mandatory, default value is 0)
-ram <int32> Available RAM (MB) (optional, off by default, default value is 256)
-progress <boolean> Report progress
-help <string list> Display long help (empty list), or help for given parameters keys
We can see that the inside and outside values are set with respectively -mode.buildmask.inv and -mode.buildmask.outv. For example, I could run the application with this command:
otbcli_ManageNoData -in input_image.tiff -out output_image.tiff -mode buildmask -mode.buildmask.inv 42. -mode.buildmask.outv 3.14
The other parameter you are looking for is -mode.changevalue.newv to provide the new value for no_data. For example:
otbcli_ManageNoData -in input_image.tiff -out output_image.tiff -mode changevalue -mode.changevalue.newv 1.618
About question 2, you have to use the parameter -classifier.ann.sizes in order to specify the size (the number of neurons) of the intermediate layers for your network. For example, if my network has 3 intermediates layers with respectively 15, 10 and 5 neurons:
otbcli_TrainImagesClassifier -io.il input_image1.tif input_image2.tif -io.vd input_vector1 input_vector2 -io.out output_model.txt -classifier ann -classifier.ann.sizes 15 10 5
The error you describe in question 3 happens when someone sets a value with a bad format (for example, a string when expecting an integer). Your guess is probably right, try again with a correct -classifier.ann.sizes and this error should disappear.
Best regards.