
=====================
Starting a PyAMFF job
=====================

    0.01s: Reading inputs
             Fingerprints used:
             Type:  BP
             Li:   2 G1s   2 G2s
    0.04s: Processing training data
             Number of training images: 69
    0.23s: Calculating fingerprints
   15.89s: Defining machine-learning model
             Model type: neural_network
             Model structure: 2
             Creating model from saved potential
             Energy coefficient: 1.000000
             Force coefficent:   0.050000
             Energy tolerance:   0.000100
             Force tolerance:    0.010000
             Loss tolerance:     0.023853
             Parallelization setup:
               Number of processes:           1
               Total number of batches:       1
               Number of batches per process: 1
   15.90s: Partitioning data
   16.21s: Training started
             Epoch    LossValue   EnergyRMSE    ForceRMSE
                 1     0.561799     0.560296     0.183639
                 2     0.526641     0.524834     0.194940
                 3     0.483837     0.481514     0.211763
                 4     0.431874     0.428638     0.236002
                 5     0.369285     0.364329     0.269666
                 6     0.295353     0.286857     0.314527
                 7     0.212819     0.195936     0.371508
                 8     0.141462     0.101713     0.439681
                 9     0.139488     0.089408     0.478811
                10     0.130508     0.081115     0.457223
                11     0.117960     0.072993     0.414404
                12     0.114514     0.073840     0.391435
                13     0.106116     0.064205     0.377847
                14     0.099930     0.057394     0.365837
                15     0.093830     0.053039     0.346147
                16     0.084505     0.047385     0.312913
                17     0.074102     0.043843     0.267167
                18     0.065653     0.044096     0.217524
                19     0.055791     0.032150     0.203913
                20     0.047076     0.019477     0.191664
                21     0.043936     0.014948     0.184765
                22     0.048380     0.021108     0.194684
                23     0.044021     0.014364     0.186094
                24     0.046273     0.022195     0.181579
                25     0.044048     0.017395     0.180976
                26     0.043623     0.016582     0.180445
                27     0.043623     0.016582     0.180445
                28     0.042885     0.014676     0.180209
                29     0.043031     0.015198     0.180037
                30     0.042745     0.014416     0.179961
                31     0.042730     0.014247     0.180160
                32     0.042664     0.014257     0.179832
                33     0.042472     0.014121     0.179135
                34     0.042379     0.014328     0.178362
                35     0.042233     0.014269     0.177766
                36     0.042059     0.014101     0.177206
                37     0.041878     0.014079     0.176381
                38     0.041650     0.014068     0.175317
                39     0.041381     0.014057     0.174058
                40     0.041066     0.014047     0.172576
                41     0.040698     0.014039     0.170836
                42     0.040272     0.014037     0.168807
                43     0.039783     0.013988     0.166553
                44     0.039227     0.013941     0.163978
                45     0.038601     0.013886     0.161075
                46     0.037911     0.013844     0.157832
                47     0.037173     0.013840     0.154292
                48     0.036561     0.013780     0.151449
                49     0.036380     0.014396     0.149415
                50     0.035922     0.013547     0.148787
             Max Epoch Reached
   26.34s: Training done, time used: 10.14s
