def load_dataset(self):
# TODO: we might need other variables for identifying what kind of
# extra preprocessing was done such as features product and number
# of features kept based on MI.
#base_path = get_data_path(self.state)
#self.base_path = base_path
#import pdb
#pdb.set_trace()
if self.state.dataset == 'mnist':
self.test_ddm = MNIST(which_set='test', one_hot=True)
dataset = MNIST(which_set='train', shuffle=True, one_hot=True)
train_X, valid_X = np.split(dataset.X, [50000])
train_y, valid_y = np.split(dataset.y, [50000])
self.train_ddm = DenseDesignMatrix(X=train_X, y=train_y)
self.valid_ddm = DenseDesignMatrix(X=valid_X, y=valid_y)
elif self.state.dataset == 'svhn':
self.train_ddm = SVHN(which_set='splitted_train')
self.test_ddm = SVHN(which_set='test')
self.valid_ddm = SVHN(which_set='valid')
elif self.state.dataset == 'cifar10':
self.train_ddm = My_CIFAR10(which_set='train', one_hot=True)
self.test_ddm = None
self.valid_ddm = My_CIFAR10(which_set='test', one_hot=True)
elif self.state.dataset == 'faceEmo':
self.train_ddm = FaceEmo(which_set='train', one_hot=True)
self.test_ddm = None
self.valid_ddm = FaceEmo(which_set='test', one_hot=True)
if self.train_ddm is not None:
self.nvis = self.train_ddm.X.shape[1]
self.nout = self.train_ddm.y.shape[1]
print "nvis, nout :", self.nvis, self.nout
self.ntrain = self.train_ddm.X.shape[0]
print "ntrain :", self.ntrain
if self.valid_ddm is not None:
self.nvalid = self.valid_ddm.X.shape[0]
print "nvalid :", self.nvalid
if self.test_ddm is not None:
self.ntest = self.test_ddm.X.shape[0]
print "ntest :", self.ntest
class HPS:
def __init__(self,
state,
base_channel_names = ['train_objective'],
save_prefix = "model_",
cache_dataset = True):
self.cache_dataset = cache_dataset
self.dataset_cache = {}
self.state = state
self.mbsb_channel_name = self.state.term_array.early_stopping.save_best_channel
self.base_channel_names = base_channel_names
self.save_prefix = save_prefix
# TODO store this in data for each experiment or dataset
def run(self):
(model, learner, algorithm) \
= self.get_config()
# try:
print 'learning'
learner.main_loop()
# except Exception, e:
# print e
print 'End of model training'
def get_config(self):
# dataset
self.load_dataset()
# model
self.load_model()
# monitor:
self.setup_monitor()
# training algorithm
algorithm = self.get_train()
# extensions
extensions = self.get_extensions()
# channels
#self.setup_channels()
# learner
learner = Train(dataset=self.train_ddm,
model=self.model,
algorithm=algorithm,
extensions=extensions)
return (self.model, learner, algorithm)
def load_dataset(self):
# TODO: we might need other variables for identifying what kind of
# extra preprocessing was done such as features product and number
# of features kept based on MI.
#base_path = get_data_path(self.state)
#self.base_path = base_path
#import pdb
#pdb.set_trace()
if self.state.dataset == 'mnist':
self.test_ddm = MNIST(which_set='test', one_hot=True)
dataset = MNIST(which_set='train', shuffle=True, one_hot=True)
train_X, valid_X = np.split(dataset.X, [50000])
train_y, valid_y = np.split(dataset.y, [50000])
self.train_ddm = DenseDesignMatrix(X=train_X, y=train_y)
self.valid_ddm = DenseDesignMatrix(X=valid_X, y=valid_y)
elif self.state.dataset == 'svhn':
self.train_ddm = SVHN(which_set='splitted_train')
self.test_ddm = SVHN(which_set='test')
self.valid_ddm = SVHN(which_set='valid')
elif self.state.dataset == 'cifar10':
self.train_ddm = My_CIFAR10(which_set='train', one_hot=True)
self.test_ddm = None
self.valid_ddm = My_CIFAR10(which_set='test', one_hot=True)
elif self.state.dataset == 'faceEmo':
self.train_ddm = FaceEmo(which_set='train', one_hot=True)
self.test_ddm = None
self.valid_ddm = FaceEmo(which_set='test', one_hot=True)
if self.train_ddm is not None:
self.nvis = self.train_ddm.X.shape[1]
self.nout = self.train_ddm.y.shape[1]
print "nvis, nout :", self.nvis, self.nout
self.ntrain = self.train_ddm.X.shape[0]
print "ntrain :", self.ntrain
if self.valid_ddm is not None:
self.nvalid = self.valid_ddm.X.shape[0]
print "nvalid :", self.nvalid
if self.test_ddm is not None:
self.ntest = self.test_ddm.X.shape[0]
print "ntest :", self.ntest
def load_model(self):
model_class = self.state.model_class
fn = getattr(self, 'get_model_'+model_class)
self.model = fn()
return self.model
def get_model_mlp(self):
self.dropout = False
self.input_include_probs = {}
self.input_scales = {}
self.weight_decay = False
self.weight_decays = {}
self.l1_weight_decay = False
self.l1_weight_decays = {}
nnet_layers = self.state.layers
input_space_id = self.state.input_space_id
nvis = self.nvis
self.batch_size = self.state.batch_size
# TODO: add input_space as a config option.
input_space = None
# TODO: top_view always False for the moment.
self.topo_view = False
assert nvis is not None
layers = []
for i,layer in enumerate(nnet_layers.values()):
layer = expand(layer)
layer = self.get_layer(layer, i)
layers.append(layer)
# create MLP:
print layers
model = MLP(layers=layers,input_space=input_space,nvis=nvis,
batch_size=self.batch_size)
self.mlp = model
return model
def get_layer(self, layer, layer_id):
layer_class = layer.layer_class
layer_name = layer.layer_name
dropout_scale = layer.dropout_scale
dropout_prob = layer.dropout_probability
weight_decay = layer.weight_decay
l1_weight_decay = layer.l1_weight_decay
fn = getattr(self, 'get_layer_'+layer_class)
if layer_name is None:
layer_name = layer_class+str(layer_id)
layer.layer_name = layer_name
layer = fn(layer)
# per-layer cost function parameters:
if (dropout_scale is not None):
self.dropout = True
self.input_scales[layer_name] = dropout_scale
if (dropout_prob is not None):
self.dropout = True
self.input_include_probs[layer_name] = (1. - dropout_prob)
if (weight_decay is not None):
self.weight_decay = False
self.weight_decays[layer_name] = weight_decay
if (l1_weight_decay is not None):
self.l1_weight_decay = False
self.l1_weight_decays[layer_name] = l1_weight_decay
return layer
def get_layer_sigmoid(self, layer):
return Sigmoid(layer_name=layer.layer_name,dim=layer.dim,irange=layer.irange,
istdev=layer.istdev,sparse_init=layer.sparse_init,
sparse_stdev=layer.sparse_stdev, include_prob=layer.include_prob,
init_bias=layer.init_bias,W_lr_scale=layer.W_lr_scale,
b_lr_scale=layer.b_lr_scale,max_col_norm=layer.max_col_norm,
max_row_norm=layer.max_row_norm)
def get_layer_tanh(self, layer):
return My_Tanh(layer_name=layer.layer_name,dim=layer.dim,irange=layer.irange,
istdev=layer.istdev,sparse_init=layer.sparse_init,
sparse_stdev=layer.sparse_stdev, include_prob=layer.include_prob,
init_bias=layer.init_bias,W_lr_scale=layer.W_lr_scale,
b_lr_scale=layer.b_lr_scale,max_col_norm=layer.max_col_norm,
max_row_norm=layer.max_row_norm)
def get_layer_rectifiedlinear(self, layer):
# TODO: left_slope is set to 0.0 It should be set by the user!
layer.left_slope = 0.0
return RectifiedLinear(layer_name=layer.layer_name,dim=layer.dim,irange=layer.irange,
istdev=layer.istdev,sparse_init=layer.sparse_init,
sparse_stdev=layer.sparse_stdev, include_prob=layer.include_prob,
init_bias=layer.init_bias,W_lr_scale=layer.W_lr_scale,
b_lr_scale=layer.b_lr_scale,max_col_norm=layer.max_col_norm,
max_row_norm=layer.max_row_norm,
left_slope=layer.left_slope,use_bias=layer.use_bias)
def get_layer_softmax(self, layer):
return My_Softmax(layer_name=layer.layer_name,n_classes=layer.dim,irange=layer.irange,
istdev=layer.istdev,sparse_init=layer.sparse_init,
init_bias_target_marginals=layer.init_bias, W_lr_scale=layer.W_lr_scale,
b_lr_scale=layer.b_lr_scale, max_col_norm=layer.max_col_norm,
max_row_norm=layer.max_row_norm)
def get_layer_noisyRELU(self, layer):
return NoisyRELU(
dim=layer.dim,
layer_name=layer.layer_name,
irange=layer.irange,
sparse_init=layer.sparse_init,
W_lr_scale=layer.W_lr_scale,
b_lr_scale=layer.b_lr_scale,
mask_weights = None,
max_row_norm=layer.max_row_norm,
max_col_norm=layer.max_col_norm,
use_bias=True,
noise_factor=layer.noise_factor,
desired_active_rate=layer.desired_active_rate,
adjust_threshold_factor=layer.adjust_threshold_factor
)
def get_layer_convRectifiedLinear(self, layer):
return ConvRectifiedLinear(
layer_name=layer.layer_name,
output_channels=layer.output_channels,
irange=layer.irange,
kernel_shape=layer.kernel_shape,
pool_shape=layer.pool_shape,
pool_stride=layer.pool_stride,
max_kernel_norm=layer.max_kernel_norm)
def get_layer_gaussianRELU(self, layer):
return GaussianRELU(
dim=layer.dim,
layer_name=layer.layer_name,
irange=layer.irange,
sparse_init=layer.sparse_init,
W_lr_scale=layer.W_lr_scale,
b_lr_scale=layer.b_lr_scale,
mask_weights = None,
max_row_norm=layer.max_row_norm,
max_col_norm=layer.max_col_norm,
use_bias=True,
desired_active_rate=layer.desired_active_rate,
adjust_threshold_factor=layer.adjust_threshold_factor,
noise_std=layer.noise_std
)
def setup_monitor(self):
if self.topo_view:
print "topo view"
self.minibatch = T.as_tensor_variable(
self.valid_ddm.get_batch_topo(self.batch_size),
name='minibatch'
)
else:
print "design view"
batch = self.valid_ddm.get_batch_design(self.batch_size)
if isinstance(batch, spp.csr_matrix):
print "sparse2"
self.minibatch = self.model.get_input_space().make_batch_theano()
print type(self.minibatch)
else:
self.minibatch = T.as_tensor_variable(
self.valid_ddm.get_batch_design(self.batch_size),
name='minibatch'
)
self.target = T.matrix('target')
self.monitor = Monitor.get_monitor(self.model)
self.log_channel_names = []
self.log_channel_names.extend(self.base_channel_names)
# self.monitor.add_dataset(self.valid_ddm, self.state.train_iteration_mode,
# self.batch_size)
# if self.test_ddm is not None:
# self.monitor.add_dataset(self.test_ddm, self.state.train_iteration_mode,
# self.batch_size)
def get_train(self):
train_class = self.state.train_class
fn = getattr(self, 'get_train_'+train_class)
return fn()
def get_train_sgd(self):
cost = MethodCost('cost_from_X')
#cost = self.get_costs()
num_train_batch = (self.ntrain/self.batch_size)
print "num training batches:", num_train_batch
termination_criterion = self.get_terminations()
monitoring_dataset = {}
for dataset_id in self.state.monitoring_dataset:
if dataset_id == 'test' and self.test_ddm is not None:
monitoring_dataset['test'] = self.test_ddm
elif dataset_id == 'valid' and self.valid_ddm is not None:
monitoring_dataset['valid'] = self.valid_ddm
else:
monitoring_dataset = None
return SGD( learning_rate=self.state.learning_rate,
batch_size=self.state.batch_size,
cost=cost,
batches_per_iter=num_train_batch,
monitoring_dataset=monitoring_dataset,
termination_criterion=termination_criterion,
init_momentum=self.state.init_momentum,
train_iteration_mode=self.state.train_iteration_mode)
def get_terminations(self):
if 'term_array' not in self.state:
return None
terminations = []
for term_obj in self.state.term_array.values():
fn = getattr(self, 'get_term_' + term_obj.term_class)
terminations.append(fn(term_obj))
if len(terminations) > 1:
return And(terminations)
return terminations[0]
def get_term_epochcounter(self, term_obj):
return EpochCounter(term_obj.max_epochs)
def get_term_monitorbased(self, term_obj):
print 'monitor_based'
return MonitorBased(
prop_decrease=term_obj.proportional_decrease,
N=term_obj.max_epochs,
channel_name=term_obj.channel_name
)
def get_extensions(self):
if 'ext_array' not in self.state:
return []
extensions = []
for ext_obj in self.state.ext_array.values():
fn = getattr(self, 'get_ext_' + ext_obj.ext_class)
extensions.append(fn(ext_obj))
# monitor based save best
print 'save best channel', self.mbsb_channel_name
if self.mbsb_channel_name is not None:
self.save_path = self.save_prefix + str(self.state.config_id) + "_optimum.pkl"
extensions.append(MonitorBasedSaveBest(
channel_name = self.mbsb_channel_name,
save_path = self.save_path
)
)
return extensions
def get_ext_exponentialdecayoverepoch(self, ext_obj):
return ExponentialDecayOverEpoch(
decay_factor=ext_obj.decay_factor,
min_lr_scale=ext_obj.min_lr_scale
)
def get_ext_momentumadjustor(self, ext_obj):
return MomentumAdjustor(
final_momentum=ext_obj.final_momentum,
start=ext_obj.start_epoch,
saturate=ext_obj.saturate_epoch
)
