Hi @ptrblck,
below are the weights i tested and there is no deviation in confusion matrix and one more observation is as weight value is increasing difference between validation loss and training loss is increasing. so is that i need to test for lower weights ? and what is the weight difference i should keep in for loop ? (value of 10 ?)
[[2.5],[2.55],[2.6],[2.65],[2.7],[2.75],[2.8],[2.85],[2.9],[2.95],[3],[3.05],[3.1],[3.15],[3.2],[3.25],[3.3],[3.35],[3.4],[3.45],[3.5],[3.55],[3.6],[3.65],[3.7],[3.75],[3.8],[3.85],[3.9],[3.94999999999999],[3.99999999999999],[4.04999999999999],[4.09999999999999],[4.14999999999999],[4.19999999999999],[4.24999999999999],[4.29999999999999],[4.34999999999999],[4.39999999999999],[4.44999999999999],[4.49999999999999],[4.54999999999999],[4.59999999999999],[4.64999999999999],[4.69999999999999],[4.74999999999999],[4.79999999999999],[4.84999999999999],[4.89999999999999],[4.94999999999999],[4.99999999999999],[5.04999999999999],[5.09999999999999],[5.14999999999999],[5.19999999999999],[5.24999999999999],[5.29999999999999],[5.34999999999999],[5.39999999999999],[5.44999999999999],[5.49999999999999],[5.54999999999999],[5.59999999999999],[5.64999999999999],[5.69999999999999],[5.74999999999999],[5.79999999999999],[5.84999999999999],[5.89999999999999],[5.94999999999999],[5.99999999999999],[6.04999999999999],[6.09999999999999],[6.14999999999999],[6.19999999999999],[6.24999999999999],[6.29999999999999],[6.34999999999999],[6.39999999999999],[6.44999999999999],[6.49999999999999],[6.54999999999999],[6.59999999999999],[6.64999999999999],[6.69999999999999],[6.74999999999998],[6.79999999999998],[6.84999999999998],[6.89999999999998],[6.94999999999998],[6.99999999999998],[7.04999999999998],[7.09999999999998],[7.14999999999998],[7.19999999999998],[7.24999999999998],[7.29999999999998],[7.34999999999998],[7.39999999999998],[7.44999999999998],[7.49999999999998],[7.54999999999998],[7.59999999999998],[7.64999999999998],[7.69999999999998],[7.74999999999998],[7.79999999999998],[7.84999999999998],[7.89999999999998],[7.94999999999998],[7.99999999999998],[8.04999999999998],[8.09999999999998],[8.14999999999998],[8.19999999999998],[8.24999999999998],[8.29999999999998],[8.34999999999998],[8.39999999999998],[8.44999999999998],[8.49999999999998],[8.54999999999998],[8.59999999999998],[8.64999999999998],[8.69999999999998],[8.74999999999998],[8.79999999999998],[8.84999999999998],[8.89999999999998],[8.94999999999998],[8.99999999999998],[9.04999999999998],[9.09999999999998],[9.14999999999998],[9.19999999999998],[9.24999999999998],[9.29999999999998],[9.34999999999998],[9.39999999999998],[9.44999999999998],[9.49999999999998],[9.54999999999997],[9.59999999999997],[9.64999999999997],[9.69999999999997],[9.74999999999997],[9.79999999999997],[9.84999999999997],[9.89999999999997],[9.94999999999997],[9.99999999999997],[10.05],[10.1],[10.15],[10.2],[10.25],[10.3],[10.35],[10.4],[10.45],[10.5],[10.55],[10.6],[10.65],[10.7],[10.75],[10.8],[10.85],[10.9],[10.95],[11],[11.05],[11.1],[11.15],[11.2],[11.25],[11.3],[11.35],[11.4],[11.45],[11.5],[11.55],[11.6],[11.65],[11.7],[11.75],[11.8],[11.85],[11.9],[11.95],[12],[12.05],[12.1],[12.15]
]