Tensorflow Model output weights have different values

Question

I am developing an Android application which requires an ML model integration.For it I am using TensorFlow lite for deployment.I am using Custom Model based Siamese Network for output and the output shape is [1 128].When I infer the tf lite model in python on Google Colab the output [1 128] numbers are different from the one being produced on my Android device.THe input image is same on both inferences and also the input and output shapes but still I am getting different output vectors on my Android Phone and Python TFlite model.I am using Firebase Machine Learning.

Android Code

  val interpreter=Interpreter(model)
                  val imageBitmap= Bitmap.createScaledBitmap(BitmapFactory.decodeFileDescriptor(contentResolver.openFileDescriptor(fileUri,"r")?.fileDescriptor),256,256,true)
                  val inputImage=ByteBuffer.allocateDirect(256*256*3*4).order(ByteOrder.nativeOrder())
                  for(ycord in 0 until 256){
                      for(xcord in 0 until 256){
                          val pixel=imageBitmap.getPixel(xcord,ycord)
                          inputImage.putFloat(Color.red(pixel)/1.0f)
                          inputImage.putFloat(Color.green(pixel)/1.0f)
                          inputImage.putFloat(Color.blue(pixel)/1.0f)
                      }
                  }
                  imageBitmap.recycle()
                  val modelOutput=ByteBuffer.allocateDirect(outputSize).order(ByteOrder.nativeOrder())
                  interpreter.run(inputImage,modelOutput)
                  modelOutput.rewind()
                  val probs=modelOutput.asFloatBuffer()
                  success(ImageProcessResult.Success(probs))

Kindly help me.I need it soon.Any help is appreciated

can you confirm that the numeric values that you are feeding the model are the same in both platforms? Do you resize the image, crop it, or something else? — Marzi Heidari, Feb 03 '21 at 10:22
On Android I am actually resizing the Bitmap to [256 256] but the input image is having the same dimension as before so i guess that must not be mattering that much — Vedant Gandhi, Feb 03 '21 at 11:09
Even the slightest change in input vectors would change the output vector. When you resize the bitmaps, you change the input vector. However, the final result which would be argmax of the output vector (in classification) would be the same. — Marzi Heidari, Feb 03 '21 at 11:16
I believe it won't affect the final result in a meaningful way if the model is not overfitted. — Marzi Heidari, Feb 03 '21 at 12:31

score 1 · Accepted Answer · answered Feb 03 '21 at 12:35

You are resizing the bitmap to [256,256] in the Android platform.

Even the slightest change in input vectors would change the output vector. When you resize the bitmaps, you change the input vector. However, if the model is general enough the final result which would be argmax of the output vector (in classification) would be the same.

In the case of Siamese, I believe it won't affect the final result (similarity score) in a meaningful way if the model is not overfitted.

Tensorflow Model output weights have different values

1 Answers1