Can someone explain the output of forward() in DNN module?
The code: predicitions = net.forward(). What output the predictions contain?
The code: predicitions = net.forward(). What output the predictions contain?
This is what forward() returns.
conf = inference_results[0, 0, i, 2] # extract the confidence (i.e., probability)
idx = int(inference_results[0, 0, i,1]) # extract the index of the class label
boxPoints = inference_results[0, 0, i, 3:7]
Here is link: predicitions = net.forward(). And here
Asked: 2019-02-05 03:40:30 -0600
Seen: 6,254 times
Last updated: Feb 05 '19
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@karan07, Your question looks like
Mat img = cv::imread("example.jpg"). What is drawn on the image?
. Deep learning networks can solve different problems and prediction interpretation depends on task.For classification - the output is usually a matrix with the probabilities for each class. For regression its usually a single output value of the series of values you want to predict (i.e price of a house). As mentionend by dkurt, the output depends on what your network should do.