inputs, targets = inputs.to(device), targets.to(device)
# It is important to permute the dimensions of the input!!
inputs = Variable(inputs.permute(0, 2, 1)).contiguous()
output = model(inputs)
# %%
# ---------------------------------------------------------------------
# TRAING THE MODEL
# ---------------------------------------------------------------------
# Make sure the model archiecture loaded in train_cgm matches the hyper configuration'
pre_train_model = model_path / 'model-folder-name'
train_cgm(config,
max_epochs=30,
grace_period=5,
n_epochs_stop=10,
data_obj=data_obj,
useRayTune=False,
checkpoint_dir=pre_train_model)
# Load best model
model_state, optimizer_state = torch.load(code_path / 'src' /
'model_state_tmp' / 'checkpoint')
model.load_state_dict(model_state, strict=False)
# Copy the trained model to model path
copyfile(code_path / 'src' / 'model_state_tmp' / 'checkpoint',
model_path_id / 'checkpoint')
with open(code_path / 'src' / 'model_state_tmp' / 'hyperPars.json', 'w') as fp:
json.dump(config, fp)
# Build model
with open(par_file) as json_file:
optHyps = json.load(open(par_file))
model = DilatedNet(h1=optHyps["h1"], h2=optHyps["h2"])
data_obj = dataLoader(data_pars,
features,
n_steps_past=16,
n_steps_future=6,
allowed_gap=10,
scaler=StandardScaler())
train_cgm(optHyps,
max_epochs=MAX_NUM_EPOCHS_FINAL,
grace_period=GRACE_PERIOD_FINAL,
n_epochs_stop=N_EPOCHS_STOP_FINAL,
data_obj=data_obj_hyperOpt,
useRayTune=False)
# train_cgm(optHyps, max_epochs= 3, grace_period=1, n_epochs_stop=2, data_obj=data_obj, useRayTune=False)
# Load best model state
model_state, optimizer_state = torch.load(code_path / 'src' /
'model_state_tmp' / 'checkpoint')
model.load_state_dict(model_state)
current_time = datetime.datetime.now().strftime('%Y-%m-%d_%H%M%S')
user = getpass.getuser()
model_id = f'id_{current_time}_{user}'
model_id = experiment_id
model_figure_path = figure_path / model_id
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
inputs, targets = data
inputs, targets = inputs.to(device), targets.to(device)
# It is important to permute the dimensions of the input!!
inputs = Variable(inputs.permute(0, 2, 1)).contiguous()
output = model(inputs)
# %%
# ---------------------------------------------------------------------
# TRAING THE MODEL
# ---------------------------------------------------------------------
# Make sure the model archiecture loaded in train_cgm matches the hyper configuration
train_cgm(config,
max_epochs=1,
grace_period=5,
n_epochs_stop=20,
data_obj=data_obj,
useRayTune=False)
model_parameters = filter(lambda p: p.requires_grad, model.parameters())
params = [np.prod(p.size()) for p in model_parameters]
print(params)
# Load best model
model_state, optimizer_state = torch.load(code_path / 'src' /
'model_state_tmp' / 'checkpoint')
# model_state, optimizer_state = torch.load(best_epoch_checkpoint_file_name)
model.load_state_dict(model_state, strict=False)
# Copy the trained model to model path
copyfile(code_path / 'src' / 'model_state_tmp' / 'checkpoint',
model_path_id / 'checkpoint')