Download Built-in model parameters from ASE_ANI (#481)

* download resources from ASE_ANI

* update gitignore

* get resources with request

* fix flke8

* rm svn checkout for resources

* update

* fix flake8

torchani/resources/ani-1ccx_8x/train0/networks/ANN-O.nnf-l4.bparam

-Ri
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torchani/resources/ani-1ccx_8x/train0/rHCNO-5.2R_16-3.5A_a4-8.params

-TM = 1
-Rcr = 5.2000e+00
-Rca = 3.5000e+00
-EtaR = [1.6000000e+01]
-ShfR = [9.0000000e-01,1.1687500e+00,1.4375000e+00,1.7062500e+00,1.9750000e+00,2.2437500e+00,2.5125000e+00,2.7812500e+00,3.0500000e+00,3.3187500e+00,3.5875000e+00,3.8562500e+00,4.1250000e+00,4.3937500e+00,4.6625000e+00,4.9312500e+00]
-Zeta = [3.2000000e+01]
-ShfZ = [1.9634954e-01,5.8904862e-01,9.8174770e-01,1.3744468e+00,1.7671459e+00,2.1598449e+00,2.5525440e+00,2.9452431e+00]
-EtaA = [8.0000000e+00]
-ShfA = [9.0000000e-01,1.5500000e+00,2.2000000e+00,2.8500000e+00]
-Atyp = [H,C,N,O]

torchani/resources/ani-1ccx_8x/train0/sae_linfit.dat

-H,0=-0.5991501324919538
-C,1=-38.03750806057356
-N,2=-54.67448347695333
-O,3=-75.16043537275567

torchani/resources/ani-1ccx_8x/train1/inputtrain.ipt

-!InputFile for Force Prediction Network
-sflparamsfile=rHCNO-5.2R_16-3.5A_a4-8.params
-ntwkStoreDir=networks/
-atomEnergyFile=sae_linfit.dat
-nmax=0! Maximum number of iterations (0 = inf)
-tolr=200! Tolerance - early stopping
-emult=0.5!Multiplier by eta after tol switch
-eta=0.001! Eta -- Learning rate
-tcrit=1.0E-6! Eta termination criterion
-tmax=0! Maximum time (0 = inf)
-tbtchsz=2560
-vbtchsz=2560
-gpuid=0
-ntwshr=0
-nkde=2
-energy=1
-freezelayer=0,2
-force=0
-fmult=0.0
-pbc=0
-cmult =0.001
-runtype=ANNP_CREATE_HDNN_AND_TRAIN!Create and train a HDN network
-network_setup {
-  inputsize=384;
-  atom_net H $
-    layer [
-          nodes=160;
-          activation=9;
-          type=0;
-    ]
-    layer [
-          nodes=128;
-          activation=9;
-          type=0;
-          l2norm=1;
-          l2valu=0.00001;
-    ]
-    layer [
-          nodes=96;
-          activation=9;
-          type=0;
-          l2norm=1;
-          l2valu=0.000001;
-    ]
-    layer [
-          nodes=1;
-          activation=6;
-          type=0;
-    ]
-    $
-  atom_net C $
-    layer [
-          nodes=144;
-          activation=9;
-          type=0;
-    ]
-    layer [
-          nodes=112;
-          activation=9;
-          type=0;
-          l2norm=1;
-          l2valu=0.00001;
-    ]
-    layer [
-          nodes=96;
-          activation=9;
-          type=0;
-          l2norm=1;
-          l2valu=0.000001;
-    ]
-    layer [
-          nodes=1;
-          activation=6;
-          type=0;
-    ]
-    $
-  atom_net N $
-    layer [
-          nodes=128;
-          activation=9;
-          type=0;
-    ]
-    layer [
-          nodes=112;
-          activation=9;
-          type=0;
-          l2norm=1;
-          l2valu=0.00001;
-    ]
-    layer [
-          nodes=96;
-          activation=9;
-          type=0;
-          l2norm=1;
-          l2valu=0.000001;
-    ]
-    layer [
-          nodes=1;
-          activation=6;
-          type=0;
-    ]
-    $
-  atom_net O $
-    layer [
-          nodes=128;
-          activation=9;
-          type=0;
-    ]
-    layer [
-          nodes=112;
-          activation=9;
-          type=0;
-          l2norm=1;
-          l2valu=0.00001;
-    ]
-    layer [
-          nodes=96;
-          activation=9;
-          type=0;
-          l2norm=1;
-          l2valu=0.000001;
-    ]
-    layer [
-          nodes=1;
-          activation=6;
-          type=0;
-    ]
-    $
-
-}
-adptlrn=OFF ! Adaptive learning (OFF,RMSPROP)
-decrate=0.9 !Decay rate of RMSPROP
-moment=ADAM! Turn on momentum or nesterov momentum (OFF,CNSTTEMP,TMANNEAL,REGULAR,NESTEROV)
-mu=0.99 ! Mu factor for momentum
-

torchani/resources/ani-1ccx_8x/train1/networks/ANN-C.nnf-l2.bparam

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torchani/resources/ani-1ccx_8x/train1/networks/ANN-C.nnf-l4.bparam

-|
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