caffe: probability distribution for regression / expanding classification (softmax layer) to allow 3D output -

i have working network pixel-wise image segmentation, i.e. 2 images input 1 data 1 label (ground_truth). therefore, using softmaxwithloss layer shown below:

layer {   name: "conv"   type: "convolution"   bottom: "bottom"   top: "conv"   convolution_param {     num_output: 256 # <-- "256 classes"     ...   } } layer {   name: "loss"   type: "softmaxwithloss"   bottom: "data"   bottom: "label"   top: "loss" } 

my input image values range [0-255], why have 256 classes. want transform segmentation / classification task regression task. assumed things have change loss layer , num_output in convolution layer this:

layer {   name: "conv"   type: "convolution"   bottom: "bottom"   top: "conv"   convolution_param {     num_output: 1 # <-- "regression"     ...   } }  layer {   name: "loss"   type: "euclideanloss"   bottom: "data"   bottom: "label"   top: "loss" } 

my regression task not produce satisfying results @ all.

i think problem regression task compared classification task classification task have distribution on depth values --> num_output = 256 --> in end have result of 256 x 128 x x128 means 1 probability each depth class. regression task output num_output = 1 returns one depth value per pixel rather 256 classification task.

1. classification: know how adjust softmax layer in order produce results like: 256 x a_number_greater_1 x width x depth rather 256 x width x depth.

or

2. regression: there other approach achieve results distribution of probabilities softmax layer does?