def __init__(self, data, targets, connections, model_out, n_ensembles,
rng):
self.data = data
self.targets = targets
self.connections = connections
self.model_out = model_out
self.n_ensembles = n_ensembles
self.rng = rng
self.enkf = EnKF(maxit=1, online=False, n_batches=1)
self.connections = self._shape_connections(connections)
conv_loss_mnist = []
# average test losses
test_losses = []
np.random.seed(0)
torch.manual_seed(0)
batch_size = 64
model = MnistOptimizee(root=root,
batch_size=batch_size,
seed=0,
n_ensembles=n_ensembles).to(device)
conv_ens = None
gamma = np.eye(10) * 0.01
enkf = EnKF(tol=1e-5,
maxit=1,
stopping_crit='',
online=False,
shuffle=False,
n_batches=1,
converge=False)
rng = int(60000 / batch_size * 8)
for i in range(1):
model.generation = i + 1
if i == 0:
try:
out = model.load_model('')
# replace cov matrix with cov from weights (ensembles)
# m = torch.distributions.Normal(out['conv_params'].mean(),
# out['conv_params'].std())
# model.cov = m.sample((n_ensembles, model.length))
except FileNotFoundError as fe:
print(fe)
dyn_change = {
'model_reps': [],
'n_ensembles': [],
'iteration': [],
'rng': 60000,
'test_loss': []
}
# init model
model = MnistOptimizee(root=root,
batch_size=batch_size,
n_ensembles=n_ensembles).to(device)
conv_ens = None
g_scaler = config['gamma']
gamma = np.eye(10) * g_scaler
enkf = EnKF(maxit=1, online=False, n_batches=1)
# len dataset divided by batch size and repetitions
rng = int(60000 / batch_size * 8)
i = 0
while i < rng:
model.generation = i + 1
if i == 0:
try:
out = model.load_model('')
except FileNotFoundError as fe:
print(fe)
print('Model not found! Initalizaing new ensembles.')
out = model.create_individual()
conv_ens = out['conv_params']
out = model.set_parameters(conv_ens)
print('loss {} generation {}'.format(out['conv_loss'],