def train_nn(model, train_smiles, train_raw_targets, num_epoch=1000, batch_size=128, seed=0,
validation_smiles=None, validation_raw_targets=None):
train_targets, undo_norm = normalize_array(train_raw_targets)
training_curve = []
optimizer = optimizers.Adam()
optimizer.setup(model)
optimizer.add_hook(chainer.optimizer.WeightDecay(0.0001))
num_data = len(train_smiles)
x = train_smiles
y = train_targets
sff_idx = npr.permutation(num_data)
for epoch in range(num_epoch):
for idx in range(0,num_data, batch_size):
batched_x = x[sff_idx[idx:idx+batch_size
if idx + batch_size < num_data else num_data]]
batched_y = y[sff_idx[idx:idx+batch_size
if idx + batch_size < num_data else num_data]]
model.zerograds()
loss = model(batched_x, batched_y)
loss.backward()
optimizer.update()
if epoch % 100 == 0:
train_preds = model.mse(train_smiles, train_raw_targets, undo_norm)
cur_loss = loss._data[0]
training_curve.append(cur_loss)
print("Iteration", epoch, "loss", math.sqrt(cur_loss), \
"train RMSE", math.sqrt((train_preds._data[0])))
if validation_smiles is not None:
validation_preds = model.mse(validation_smiles, validation_raw_targets, undo_norm)
print("Validation RMSE", epoch, ":", math.sqrt((validation_preds._data[0])))
return model, training_curve, undo_norm
def load_model_experiment():
'''Initialize model'''
trained_NNFP = Main(model_params)
serializers.load_npz(args.load_npz, trained_NNFP)
_, undo_norm = normalize_array(y_tests)
mse, input_attention = trained_NNFP.mse(x_tests, y_tests, undo_norm)
return math.sqrt(mse._data[0]), input_attention
def train_nn(model,
train_smiles,
train_raw_targets,
seed=0,
validation_smiles=None,
validation_raw_targets=None):
num_print_examples = N_train
train_targets, undo_norm = normalize_array(train_raw_targets)
training_curve = []
optimizer = optimizers.Adam()
optimizer.setup(model)
optimizer.add_hook(chainer.optimizer.WeightDecay(0.0001))
num_epoch = 1000
num_data = len(train_smiles)
batch_size = 64
x = train_smiles
y = train_targets
sff_idx = npr.permutation(num_data)
#TIME = time.time()
for epoch in range(num_epoch):
epoch_time = time.time()
for idx in range(0, num_data, batch_size):
batched_x = x[sff_idx[idx:idx + batch_size if idx +
batch_size < num_data else num_data]]
batched_y = y[sff_idx[idx:idx + batch_size if idx +
batch_size < num_data else num_data]]
#update_time = time.time()
model.zerograds()
loss = model(batched_x, batched_y)
loss.backward()
optimizer.update()
#print("UPDATE TIME : ", time.time() - update_time)
#print "epoch ", epoch, "loss", loss._data[0]
if epoch % 10 == 0:
#print_time = time.time()
train_preds = model.mse(train_smiles, train_raw_targets, undo_norm)
cur_loss = loss._data[0]
training_curve.append(cur_loss)
print("Iteration", epoch, "loss", math.sqrt(cur_loss), \
"train RMSE", math.sqrt((train_preds._data[0])))
if validation_smiles is not None:
validation_preds = model.mse(validation_smiles,
validation_raw_targets, undo_norm)
print("Validation RMSE", epoch, ":",
math.sqrt((validation_preds._data[0])))
#print("PRINT TIME : ", time.time() - print_time)
print("1 EPOCH TIME : ", time.time() - epoch_time)
#print loss
return model, training_curve, undo_norm
