import chemtorch.ml as ct_ml
import chemtorch.features as ct_ft
from pydoc import locate
from chemtorch.parameters import settings
dtype, device = locate(settings['dtype']), settings['device']
M2, M3 = settings['M2'], settings['M3']
num_feat, num_engy = M2 + M3**3, 1
mlp = [num_feat] + settings['hidden_layers'] + [num_engy]
Rc = settings['Router']
weights, biases = ct_nn.get_weights(mlp), ct_nn.get_biases(mlp)
optimizer = torch.optim.Adam(biases + weights, lr=settings['learning_rate'])
feat_a, feat_b = ct_ml.get_scalar_csv(settings["valid_feat_file"])
engy_a, engy_b = ct_ml.get_scalar_csv(settings["valid_engy_file"])
feat_a, feat_b = ct_nn.np2torch(feat_a, feat_b, dtype=dtype)
engy_a, engy_b = ct_nn.np2torch(engy_a, engy_b, dtype=dtype)
for i_epoch in range(settings['epoch']):
start = time.time()
# featFile = "tests\\data\\feat" if platform == 'win32' else "tests/data/feat"
# engyFile = "tests\\data\\engy" if platform == 'win32' else "tests/data/engy"
feat_chunk = pd.read_csv(settings['train_feat_file'],
header=None,
chunksize=settings['chunk_size'])
engy_chunk = pd.read_csv(settings['train_engy_file'],
header=None,
chunksize=settings['chunk_size'])
weights, biases = load_weights_biases(len(settings['hidden_layers'])+1) M2 = settings['M2'] M3 = settings['M3'] Rinner = settings['Rinner'] Router = settings['Router'] R2 = Router - (Router - Rinner) / M2 R3 = Router - (Router - Rinner) / M3 dcut = settings['Router'] pwmat_mmt = stream_structures(settings['input_file'], format=settings['input_format'], get_forces=True) Rcart, lattice, atom_types, F, Ei = next(pwmat_mmt) nAtoms = len(Rcart) gscalar = get_scalar_csv(settings["valid_feat_file"]) escalar = get_scalar_csv(settings["valid_engy_file"]) dtype, device = locate(settings['dtype']), settings['device'] M2, M3 = settings['M2'], settings['M3'] num_feat, num_engy = M2 + M3 ** 3, 1 mlp = [num_feat, *settings['hidden_layers'], num_engy] Rc = settings['Router'] # initialize weights and biases if they are not provided weights = list(np2torch(*weights)) biases = list(np2torch(*biases)) for (w, b) in zip(weights, biases): w.requires_grad, b.requires_grad = True, True # convert gscalar and escale to torch tensors
import chemtorch.io as ct_io
import chemtorch.ml as ct_ml
import chemtorch.features as ct_ft
dtype, device = torch.float, torch.device('cpu')
M2, M3 = 25, 5
num_feat, num_engy = M2 + M3**3, 1
mlp = [num_feat, 50, 50, num_engy]
weights, biases = ct_nn.get_weights(mlp), ct_nn.get_biases(mlp)
optimizer = torch.optim.Adam(biases + weights, lr=1e-4)
Rc = 6.2
vfeatFile = "tests\\data\\vfeat" if platform == 'win32' else "tests/data/vfeat"
vengyFile = "tests\\data\\vengy" if platform == 'win32' else "tests/data/vengy"
feat_a, feat_b = ct_ml.get_scalar_csv(vfeatFile)
engy_a, engy_b = ct_ml.get_scalar_csv(vengyFile)
torch_engy_a, torch_engy_b = ct_nn.torch2np(engy_a, engy_b, dtype=dtype)
for iter in range(5):
start = time.time()
featFile = "tests\\data\\feat" if platform == 'win32' else "tests/data/feat"
engyFile = "tests\\data\\engy" if platform == 'win32' else "tests/data/engy"
feat_chunk = pd.read_csv(featFile, header=None, chunksize=1000)
engy_chunk = pd.read_csv(engyFile, header=None, chunksize=1000)
for step, (feat, engy) in enumerate(zip(feat_chunk, engy_chunk)):
feat_scaled = ct_nn.np2torch(feat_a * feat.values + feat_b,
dtype=dtype)
engy_scaled = ct_nn.np2torch(engy_a * engy.values.reshape(
(-1, 1)) + engy_b,