def __init__(self,
beta1=0.9,
beta2=0.999,
epsilon=1e-8,
max_colm_norm=False,
max_norm=15.0):
self.beta1 = sharedX_value(beta1, name='beta1', borrow=True)
self.beta2 = sharedX_value(beta2, name='beta2', borrow=True)
self.epsilon = sharedX_value(epsilon, name='epsilon', borrow=True)
self.max_colm_norm = max_colm_norm
self.max_norm = max_norm
def __init__(self,
decay=0.9,
max_scaling=1e5,
max_colm_norm=False,
max_norm=15.0):
assert 0. <= decay < 1., 'decay must be: 0. <= decay < 1'
assert max_scaling > 0., 'max_scaling must be > 0.'
self.decay = sharedX_value(decay, name='decay', borrow=True)
self.epsilon = 1. / max_scaling
self.mean_square_grads = None
self._first_time = True
self.max_colm_norm = max_colm_norm
self.max_norm = max_norm
def __init__(self,
init_momentum,
nesterov_momentum=False,
imagenet=False,
imagenetDecay=5e-4,
max_colm_norm=False,
max_norm=15.0):
assert init_momentum >= 0., 'The initial momentum should be >=0.'
assert init_momentum < 1., 'The initial momentum should be < 1.'
self.momentum = sharedX_value(value=init_momentum,
name="momentum",
borrow=True)
self.nesterov_momentum = nesterov_momentum
self._first_time = True
self.velocity = None # tracks the velocity at the previous time
self.imagenet = imagenet
self.imagenetDecay = sharedX_value(value=imagenetDecay,
name="imagenetDecay",
borrow=True)
self.max_colm_norm = max_colm_norm
self.max_norm = max_norm
aes_in = []
aes_out = []
if id_code is not None:
assert aes_out != []
# Train
# Data
tr_batch_size = 10
vl_batch_size = 8000
with open(path_valid, 'r') as f:
l_samples_vl = pkl.load(f)
list_minibatchs_vl = split_data_to_minibatchs_eval(
l_samples_vl, vl_batch_size)
max_epochs = int(1000)
lr_vl = 1e-3
lr = sharedX_value(lr_vl, name="lr")
# cost weights
separate = True
l_in = [sharedX_value(0., name="l_in"), sharedX_value(0.0, name="l_in2")]
l_out = [sharedX_value(0., name="l_out")]
l_sup = sharedX_value(1., name="l_sup")
l_code = sharedX_value(0.0, name="l_code")
if not separate:
assert l_sup.get_value() + l_in.get_value() + l_out.get_value() == 1.
if l_in[0].get_value() != 0. and aes_in == []:
raise ValueError("You setup the l_in but no aes in found.")
if l_out[0].get_value() != 0. and aes_out == []:
raise ValueError("You setup the l_out but no aes out found.")
# Train criterion
cost_type = CostType.MeanSquared # CostType.MeanSquared
# Compile the functions
"deep_conv_ae_init_" + id_deep_conv_ae + ".pkl"
if not os.path.isfile(path_ini_params_deep_conv_ae):
deep_conv_ae.save_params(path_ini_params_deep_conv_ae)
else:
deep_conv_ae.set_params_vals(path_ini_params_deep_conv_ae)
with open(path_valid, 'r') as f:
l_samples_vl = pkl.load(f)
# convert to 3D
nbr_xx = l_samples_vl["x"].shape[0]
l_samples_vl["x"] = l_samples_vl["x"].reshape((nbr_xx, 1, h, w))
list_minibatchs_vl = split_data_to_minibatchs_eval(l_samples_vl, vl_batch_size)
max_epochs = int(1000)
lr_vl = 1e-4
lr = sharedX_value(lr_vl, name="lr")
optimizer = "momentum"
if optimizer == "adadelta":
updater = AdaDelta(decay=0.95)
elif optimizer == "momentum":
updater = Momentum(0.9,
nesterov_momentum=True,
imagenet=False,
imagenetDecay=5e-4,
max_colm_norm=False)
else:
raise ValueError("Optimizer not recognized.")
# Prepare costs.
cost_train, _, _ = deep_conv_ae.get_train_cost()
# updates
