Reads images with digits. Converts each pixel into RGB value to be fed into the Neural Network. The cells are trained and fired to recognize successfully a number with a specific target.
Example of an output:
Reading images... Done.
Neural Network initialized.
Total INPUT cells: 1024
Total HIDDEN cells #1: 614
Total HIDDEN cells #2: 307
Total OUTPUT cells: 5
Epochs: 5
Batches: 5
Iterations: 250
Epoch #1
Learning... Done.
Firing...
Target: 1
Output:
(0.91352206) 91% likely to be a 1
(0.08315082) 8% likely to be a 2
(0.10422682) 10% likely to be a 3
(0.1668892) 17% likely to be a 4
(0.25973925) 26% likely to be a 5
PASSED.
Target: 2
Output:
(0.002700394) 0% likely to be a 1
(0.67905897) 68% likely to be a 2
(0.031628378) 3% likely to be a 3
(0.02233936) 2% likely to be a 4
(0.30469373) 30% likely to be a 5
PASSED.
Target: 3
Output:
(0.008017112) 1% likely to be a 1
(0.114655584) 11% likely to be a 2
(0.87434596) 87% likely to be a 3
(0.063642256) 6% likely to be a 4
(0.13389836) 13% likely to be a 5
PASSED.
Target: 3
Output:
(0.039496824) 4% likely to be a 1
(0.88973904) 89% likely to be a 2
(0.20663506) 21% likely to be a 3
(0.060800858) 6% likely to be a 4
(0.23931819) 24% likely to be a 5
FAILED.
Target: 4
Output:
(0.0035491886) 0% likely to be a 1
(0.3064328) 31% likely to be a 2
(0.07893467) 8% likely to be a 3
(0.605019) 61% likely to be a 4
(0.08867973) 9% likely to be a 5
PASSED.
Target: 5
Output:
(0.006214881) 1% likely to be a 1
(0.07108693) 7% likely to be a 2
(0.07451407) 7% likely to be a 3
(0.3341005) 33% likely to be a 4
(0.6131259) 61% likely to be a 5
PASSED.