def setup(self, trainers):
"""
Add tracking to all trainers.
Parameters
----------
trainers : list
List of Train objects belonging to the parent TrainCV object.
"""
for k, trainer in enumerate(trainers):
if self.save_path is not None and self.save_folds:
path, ext = os.path.splitext(self.save_path)
save_path = path + '-{}'.format(k) + ext
else:
save_path = None
if self.tag_key is not None:
tag_key = '{}-{}'.format(self.tag_key, k)
else:
tag_key = None
extension = MonitorBasedSaveBest(
self.channel_name,
save_path=save_path,
store_best_model=True,
higher_is_better=self.higher_is_better,
tag_key=tag_key)
trainer.extensions.append(extension)
def get_extensions(self, ext_array, config_id):
if ext_array is None:
return []
extensions = []
for ext_id in ext_array:
row = self.db.executeSQL(
"""
SELECT ext_class
FROM hps3.extension
WHERE ext_id = %s
""", (ext_id, ), self.db.FETCH_ONE)
if not row or row is None:
raise HPSData("No extension for ext_id=" + str(ext_id))
ext_class = row[0]
fn = getattr(self, 'get_ext_' + ext_class)
extensions.append(fn(ext_id))
# monitor based save best
if self.mbsb_channel_name is not None:
save_path = self.save_prefix + str(config_id) + "_optimum.pkl"
extensions.append(
MonitorBasedSaveBest(channel_name=self.mbsb_channel_name,
save_path=save_path))
# HPS Logger
extensions.append(HPSLog(self.log_channel_names, self.db, config_id))
return extensions
def create_training_problem(self, save_best_path):
ext1 = MonitorBasedSaveBest(channel_name=self.optimize_for,
save_path=save_best_path)
trainer = Train(dataset=self.alg_datasets['train'],
model=self.model,
algorithm=self.algorithm,
extensions=[ext1])
self.trainer = trainer
def main(job_id, requested_params, cache):
# Fix sub directory problems
sys.path.append(os.path.dirname(os.getcwd()))
os.chdir(os.path.dirname(os.path.realpath(__file__)))
# Add parameters that are not currently being tuned but could potentially be tuned.
params = additional_args
params.update(requested_params)
if params.get('rate', None) is not None:
params['log_init_learning_rate'][0] = numpy.array([params['rate']])
train_params = {
'train_start': params['start'],
'train_stop': params['stop'],
'valid_start': 20000,
'valid_stop': 24000,
'test_stop': 4000,
'batch_size': 100,
'max_epochs': 20,
'max_batches': 10,
'sgd_seed': sgd_seed_str,
'mlp_seed': mlp_seed_str,
'weight_decay_y': math.pow(10, params['l_wdecay_y'][0]),
'max_col_norm_y': params['max_norm_y'][0],
'irange_y': math.pow(10, params['l_ir_y'][0]),
'init_momentum': 0.5,
'init_learning_rate': math.pow(10,
params['log_init_learning_rate'][0]),
}
with open('slp_fooddata.yaml', 'r') as f:
trainer = f.read()
yaml_string = trainer % train_params
train_obj = yaml_parse.load(yaml_string)
pretrained_model_path = params.get('model', None)
if pretrained_model_path is not None:
print 'loading pre trained model'
pretrained_model = serial.load(pretrained_model_path)
print 'loading done'
train_obj.model.set_param_values(pretrained_model.get_param_values())
if 'converge' in params:
train_obj.algorithm.termination_criterion._criteria[
0]._max_epochs = 100
train_obj.extensions.append(
MonitorBasedSaveBest('valid_y_misclass', params['save']))
train_obj.setup()
train_obj.model.monitor.on_channel_conflict = 'ignore'
if 'converge' not in params:
train_obj.algorithm.termination_criterion._criteria[0].initialize(
train_obj.model)
train_obj.main_loop(do_setup=False)
original_misclass = read_channel(train_obj.model, misclass_channel)
return float(original_misclass)
def test_tagging():
"""Test the tagging functionality of this extension."""
try:
# TODO: serial.save should be able to take an open file-like object so
# we can direct its output to a StringIO or something and not need to
# screw around like this in tests that don't actually need to touch
# the filesystem. /dev/null would work but the test would fail on
# Windows.
fd, fn = tempfile.mkstemp(suffix='.pkl')
os.close(fd)
# Test that the default key gets created.
def_model = MockModel()
def_model.monitor = MockMonitor()
def_ext = MonitorBasedSaveBest(channel_name='foobar', save_path=fn)
def_ext.setup(def_model, None, None)
assert 'MonitorBasedSaveBest' in def_model.tag
# Test with a custom key.
model = MockModel()
model.monitor = MockMonitor()
model.monitor.channels['foobar'] = MockChannel()
ext = MonitorBasedSaveBest(channel_name='foobar', tag_key='test123',
save_path=fn)
# Best cost is initially infinity.
ext.setup(model, None, None)
assert model.tag['test123']['best_cost'] == float("inf")
# Best cost after one iteration.
model.monitor.channels['foobar'].val_record.append(5.0)
ext.on_monitor(model, None, None)
assert model.tag['test123']['best_cost'] == 5.0
# Best cost after a second, worse iteration.
model.monitor.channels['foobar'].val_record.append(7.0)
ext.on_monitor(model, None, None)
assert model.tag['test123']['best_cost'] == 5.0
# Best cost after a third iteration better than 2 but worse than 1.
model.monitor.channels['foobar'].val_record.append(6.0)
ext.on_monitor(model, None, None)
assert model.tag['test123']['best_cost'] == 5.0
# Best cost after a fourth, better iteration.
model.monitor.channels['foobar'].val_record.append(3.0)
ext.on_monitor(model, None, None)
assert model.tag['test123']['best_cost'] == 3.0
finally:
os.remove(fn)
def create_algorithm(self, data, save_best_path=None):
self.set_dataset(data)
self.create_adjustors()
term = EpochCounter(max_epochs=self.max_epochs)
if self.valid_stop:
cost_crit = MonitorBased(channel_name='valid_objective',
prop_decrease=.0,
N=3)
term = And(criteria=[cost_crit, term])
#(layers, A_weight_decay)
coeffs = None
if self.reg_factors:
rf = self.reg_factors
lhdims = len(self.tagger.hdims)
l_inputlayer = len(self.tagger.layers[0].layers)
coeffs = ([[rf] * l_inputlayer] + ([rf] * lhdims) + [rf], rf)
cost = SeqTaggerCost(coeffs, self.dropout)
self.cost = cost
self.mbsb = MonitorBasedSaveBest(channel_name='valid_objective',
save_path=save_best_path)
mon_dataset = dict(self.dataset)
if not self.monitor_train:
del mon_dataset['train']
_learning_rule = (self.momentum_rule if self.use_momentum else None)
self.algorithm = SGD(
batch_size=1,
learning_rate=self.lr,
termination_criterion=term,
monitoring_dataset=mon_dataset,
cost=cost,
learning_rule=_learning_rule,
)
self.algorithm.setup(self, self.dataset['train'])
if self.plot_monitor:
cn = ["valid_objective", "test_objective"]
if self.monitor_train:
cn.append("train_objective")
plots = Plots(channel_names=cn, save_path=self.plot_monitor)
self.pm = PlotManager([plots], freq=1)
self.pm.setup(self, None, self.algorithm)
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_layer_trainer_sgd(model, trainset):
drop_cost = Dropout(input_include_probs={'h0': .4},
input_scales={'h0': 1.})
# configs on sgd
train_algo = SGD(train_iteration_mode='batchwise_shuffled_equential',
learning_rate=0.2,
cost=drop_cost,
monitoring_dataset=trainset,
termination_criterion=EpochCounter(max_epochs=MAX_EPOCHS),
update_callbacks=None)
extensions = [
MonitorBasedSaveBest(channel_name="y_kl",
save_path="./convnet_test_best.pkl")
]
return Train(model=model,
algorithm=train_algo,
extensions=extensions,
dataset=trainset)
def get_trainer(model, trainset, validset, save_path):
monitoring = dict(valid=validset, train=trainset)
termination = MonitorBased(channel_name='valid_y_misclass', prop_decrease=.001, N=100)
extensions = [MonitorBasedSaveBest(channel_name='valid_y_misclass', save_path=save_path),
#MomentumAdjustor(start=1, saturate=100, final_momentum=.9),
LinearDecayOverEpoch(start=1, saturate=200, decay_factor=0.01)]
config = {
'learning_rate': .01,
#'learning_rule': Momentum(0.5),
'learning_rule': RMSProp(),
'train_iteration_mode': 'shuffled_sequential',
'batch_size': 1200,#250,
#'batches_per_iter' : 100,
'monitoring_dataset': monitoring,
'monitor_iteration_mode' : 'shuffled_sequential',
'termination_criterion' : termination,
}
return Train(model=model,
algorithm=SGD(**config),
dataset=trainset,
extensions=extensions)
def main():
#creating layers
#2 convolutional rectified layers, border mode valid
batch_size = 48
lr = 1.0 #0.1/4
finMomentum = 0.9
maxout_units = 2000
num_pcs = 4
lay1_reg = lay2_reg = maxout_reg = None
#save_path = './models/no_maxout/titan_lr_0.1_btch_64_momFinal_0.9_maxout_2000_4.joblib'
#best_path = '/models/no_maxout/titan_bart10_gpu2_best.joblib'
#save_path = './models/'+params.host+'_'+params.device+'_'+sys.argv[1]+'.joblib'
#best_path = './models/'+params.host+'_'+params.device+'_'+sys.argv[1]+'best.joblib'
save_path = '/Tmp/zumerjer/bart10_sumcost_adadelta_drop_perturb.joblib'
best_path = '/Tmp/zumerjer/bart10_sumcost_adadelta_drop_perturb_best.joblib'
#numBatches = 400000/batch_size
'''
print 'Applying preprocessing'
ddmTrain = EmotiwKeypoints(start=0, stop =40000)
ddmValid = EmotiwKeypoints(start=40000, stop = 44000)
ddmTest = EmotiwKeypoints(start=44000)
stndrdz = preprocessing.Standardize()
stndrdz.applyLazily(ddmTrain, can_fit=True, name = 'train')
stndrdz.applyLazily(ddmValid, can_fit=False, name = 'val')
stndrdz.applyLazily(ddmTest, can_fit=False, name = 'test')
GCN = preprocessing.GlobalContrastNormalization(batch_size = 1000)
GCN.apply(ddmTrain, can_fit =True, name = 'train')
GCN.apply(ddmValid, can_fit =False, name = 'val')
GCN.apply(ddmTest, can_fit = False, name = 'test')
return
'''
ddmTrain = ComboDatasetPyTable('/Tmp/zumerjer/perturbed_', which_set='train')
ddmValid = ComboDatasetPyTable('/Tmp/zumerjer/perturbed_', which_set='valid')
#ddmSmallTrain = ComboDatasetPyTable('/Tmp/zumerjer/all_', which_set='small_train')
layer1 = ConvRectifiedLinear(layer_name = 'convRect1',
output_channels = 64,
irange = .05,
kernel_shape = [5, 5],
pool_shape = [4, 4],
pool_stride = [2, 2],
W_lr_scale = 0.1,
max_kernel_norm = lay1_reg)
layer2 = ConvRectifiedLinear(layer_name = 'convRect2',
output_channels = 128,
irange = .05,
kernel_shape = [5, 5],
pool_shape = [3, 3],
pool_stride = [2, 2],
W_lr_scale = 0.1,
max_kernel_norm = lay2_reg)
# Rectified linear units
#layer3 = RectifiedLinear(dim = 3000,
# sparse_init = 15,
# layer_name = 'RectLin3')
#Maxout layer
maxout = Maxout(layer_name= 'maxout',
irange= .005,
num_units= maxout_units,
num_pieces= num_pcs,
W_lr_scale = 0.1,
max_col_norm= maxout_reg)
#multisoftmax
n_groups = 196
n_classes = 96
layer_name = 'multisoftmax'
layerMS = MultiSoftmax(n_groups=n_groups,irange = 0.05, n_classes=n_classes, layer_name= layer_name)
#setting up MLP
MLPerc = MLP(batch_size = batch_size,
input_space = Conv2DSpace(shape = [96, 96],
num_channels = 3, axes=('b', 0, 1, 'c')),
layers = [ layer1, layer2, maxout, layerMS])
#mlp_cost
missing_target_value = -1
mlp_cost = MLPCost(cost_type='default',
missing_target_value=missing_target_value )
mlp_cost.setup_dropout(input_include_probs= { 'convRect1' : 1.0 }, input_scales= { 'convRect1': 1. })
#dropout_cost = Dropout(input_include_probs= { 'convRect1' : .8 },
# input_scales= { 'convRect1': 1. })
#algorithm
monitoring_dataset = {'validation':ddmValid}#, 'mini-train':ddmSmallTrain}
term_crit = MonitorBased(prop_decrease = 1e-7, N = 100, channel_name = 'validation_objective')
kp_ada = KeypointADADELTA(decay_factor = 0.95,
#init_momentum = 0.5,
monitoring_dataset = monitoring_dataset, batch_size = batch_size,
termination_criterion = term_crit,
cost = mlp_cost)
#train extension
#train_ext = ExponentialDecayOverEpoch(decay_factor = 0.998, min_lr_scale = 0.001)
#train_ext = LinearDecayOverEpoch(start= 1,saturate= 250,decay_factor= .01)
#train_ext = ADADELTA(0.95)
#train object
train = Train(dataset = ddmTrain,
save_path= save_path,
save_freq=10,
model = MLPerc,
algorithm= kp_ada,
extensions = [#train_ext,
MonitorBasedSaveBest(channel_name='validation_objective',
save_path= best_path)#,
# MomentumAdjustor(start = 1,#
# saturate = 25,
# final_momentum = finMomentum)
] )
train.main_loop()
train.save()
def set_extensions(self, extensions=None): self.extensions = [MonitorBasedSaveBest(channel_name='test_objective', save_path = './training/training_monitor_best.pkl')]
algorithm = SGD(learning_rate=0.01,
cost=MethodCost("cost_from_X"),
batch_size=batchSize,
monitoring_batch_size=batchSize,
monitoring_dataset={
'train': train,
'valid': valid
},
monitor_iteration_mode="even_batchwise_shuffled_sequential",
termination_criterion=EpochCounter(max_epochs=200),
learning_rule=Momentum(init_momentum=0.99),
train_iteration_mode="even_batchwise_shuffled_sequential")
train = Train(dataset=train,
model=model,
algorithm=algorithm,
save_path="ConvNet4.pkl",
save_freq=1,
extensions=[
MonitorBasedSaveBest(channel_name="valid_y_misclass",
save_path="ConvNet4_best.pkl"),
MomentumAdjustor(final_momentum=0, start=0, saturate=100)
])
print("Starting training session")
train.main_loop()
print("Done!")
def main():
#creating layers
#2 convolutional rectified layers, border mode valid
batch_size = params.batch_size
lr = params.lr
finMomentum = params.momentum
maxout_units = params.units
num_pcs = params.pieces
lay1_reg = lay2_reg = maxout_reg = params.norm_reg
#save_path = './models/no_maxout/titan_lr_0.1_btch_64_momFinal_0.9_maxout_2000_4.joblib'
#best_path = '/models/no_maxout/titan_bart10_gpu2_best.joblib'
save_path = './models/' + params.host + '_' + params.device + '_' + sys.argv[
1] + '.joblib'
best_path = './models/' + params.host + '_' + params.device + '_' + sys.argv[
1] + 'best.joblib'
numBatches = 400000 / batch_size
from emotiw.common.datasets.faces.EmotiwKeypoints import EmotiwKeypoints
'''
print 'Applying preprocessing'
ddmTrain = EmotiwKeypoints(start=0, stop =40000)
ddmValid = EmotiwKeypoints(start=40000, stop = 44000)
ddmTest = EmotiwKeypoints(start=44000)
stndrdz = preprocessing.Standardize()
stndrdz.applyLazily(ddmTrain, can_fit=True, name = 'train')
stndrdz.applyLazily(ddmValid, can_fit=False, name = 'val')
stndrdz.applyLazily(ddmTest, can_fit=False, name = 'test')
GCN = preprocessing.GlobalContrastNormalization(batch_size = 1000)
GCN.apply(ddmTrain, can_fit =True, name = 'train')
GCN.apply(ddmValid, can_fit =False, name = 'val')
GCN.apply(ddmTest, can_fit = False, name = 'test')
return
'''
ddmTrain = EmotiwKeypoints(hack='train', preproc='STD')
ddmValid = EmotiwKeypoints(hack='val', preproc='STD')
layer1 = ConvRectifiedLinear(layer_name='convRect1',
output_channels=64,
irange=.05,
kernel_shape=[5, 5],
pool_shape=[4, 4],
pool_stride=[2, 2],
W_lr_scale=0.1,
max_kernel_norm=lay1_reg)
layer2 = ConvRectifiedLinear(layer_name='convRect2',
output_channels=128,
irange=.05,
kernel_shape=[5, 5],
pool_shape=[3, 3],
pool_stride=[2, 2],
W_lr_scale=0.1,
max_kernel_norm=lay2_reg)
# Rectified linear units
#layer3 = RectifiedLinear(dim = 3000,
# sparse_init = 15,
# layer_name = 'RectLin3')
#Maxout layer
maxout = Maxout(layer_name='maxout',
irange=.005,
num_units=maxout_units,
num_pieces=num_pcs,
W_lr_scale=0.1,
max_col_norm=maxout_reg)
#multisoftmax
n_groups = 196
n_classes = 96
irange = 0
layer_name = 'multisoftmax'
layerMS = MultiSoftmax(n_groups=n_groups,
irange=0.05,
n_classes=n_classes,
layer_name=layer_name)
#setting up MLP
MLPerc = MLP(batch_size=batch_size,
input_space=Conv2DSpace(shape=[96, 96], num_channels=3),
layers=[layer1, layer2, maxout, layerMS])
#mlp_cost
missing_target_value = -1
mlp_cost = MLPCost(cost_type='default',
missing_target_value=missing_target_value)
mlp_cost.setup_dropout(input_include_probs={'convRect1': 1.0},
input_scales={'convRect1': 1.})
#dropout_cost = Dropout(input_include_probs= { 'convRect1' : .8 },
# input_scales= { 'convRect1': 1. })
#algorithm
monitoring_dataset = {'validation': ddmValid}
term_crit = MonitorBased(prop_decrease=1e-7,
N=100,
channel_name='validation_objective')
kpSGD = KeypointSGD(learning_rate=lr,
init_momentum=0.5,
monitoring_dataset=monitoring_dataset,
batch_size=batch_size,
termination_criterion=term_crit,
cost=mlp_cost)
#train extension
#train_ext = ExponentialDecayOverEpoch(decay_factor = 0.998, min_lr_scale = 0.001)
train_ext = LinearDecayOverEpoch(start=1, saturate=250, decay_factor=.01)
#train object
train = Train(dataset=ddmTrain,
save_path=save_path,
save_freq=10,
model=MLPerc,
algorithm=kpSGD,
extensions=[
train_ext,
MonitorBasedSaveBest(channel_name='validation_objective',
save_path=best_path),
MomentumAdjustor(start=1,
saturate=25,
final_momentum=finMomentum)
])
train.main_loop()
train.save()
def main(job_id, params, cache):
# Fix sub directory problems
sys.path.append(os.path.dirname(os.getcwd()))
os.chdir(os.path.dirname(os.path.realpath(__file__)))
# Add parameters that are not currently being tuned but could potentially be tuned.
params.update(additional_args)
fixed_params = (params['kernel_size_h2'][0], params['kernel_size_h3'][0])
if 'cached_trainer' + str(fixed_params) not in cache:
train_params = {
'train_stop': 20000,
'valid_stop': 24000,
'test_stop': 4000,
'batch_size': 100,
'max_epochs': 1,
'max_batches': 10,
'sgd_seed': sgd_seed_str,
'mlp_seed': mlp_seed_str,
'save_file': 'result',
'kernel_size_h2': int(params['kernel_size_h2'][0]),
'output_channels_h2': 1 * k,
'irange_h2': math.pow(10, params['l_ir_h2'][0]),
'max_kernel_norm_h2': params['max_norm_h2'][0],
'kernel_size_h3': int(params['kernel_size_h3'][0]),
'output_channels_h3': int(1.7 * k),
'irange_h3': math.pow(10, params['l_ir_h3'][0]),
'max_kernel_norm_h3': params['max_norm_h3'][0],
'kernel_size_h4': int(params['kernel_size_h4'][0]),
'output_channels_h4': int(2.5 * k),
'irange_h4': math.pow(10, params['l_ir_h4'][0]),
'max_kernel_norm_h4': params['max_norm_h4'][0],
'weight_decay_h2': math.pow(10, params['l_wdecay_h2'][0]),
'weight_decay_h3': math.pow(10, params['l_wdecay_h3'][0]),
'weight_decay_h4': math.pow(10, params['l_wdecay_h4'][0]),
'weight_decay_y': math.pow(10, params['l_wdecay_y'][0]),
'max_col_norm_y': params['max_norm_y'][0],
'irange_y': math.pow(10, params['l_ir_y'][0]),
'init_learning_rate': math.pow(10, params['log_init_learning_rate'][0]),
'init_momentum': params['init_momentum'][0],
'rectifier_left_slope': 0.2
}
with open('conv_fooddata_spearmint.yaml', 'r') as f:
trainer = f.read()
yaml_string = trainer % train_params
train_obj = yaml_parse.load(yaml_string)
if 'converge' in params:
del train_obj.algorithm.termination_criterion._criteria[:]
train_obj.extensions.append(MonitorBasedSaveBest('valid_y_misclass', 'best_model.pkl'))
train_obj.setup()
train_obj.model.monitor.on_channel_conflict = 'ignore'
cache['cached_trainer' + str(fixed_params)] = train_obj
else:
train_obj = cache['cached_trainer' + str(fixed_params)]
train_obj.model.monitor.set_state([0, 0, 0])
train_obj.model.training_succeeded = False
# train_obj.algorithm.update_callbacks[0].reinit_from_monitor()
model = train_obj.model
model_params = dict([(param.name, param) for param in model.get_params()])
rng = model.rng
update_conv_layer(model.layers[0], params['l_ir_h2'][0], params['max_norm_h2'][0], model_params, rng)
update_conv_layer(model.layers[1], params['l_ir_h3'][0], params['max_norm_h3'][0], model_params, rng)
update_conv_layer(model.layers[2], params['l_ir_h4'][0], params['max_norm_h4'][0], model_params, rng)
update_softmax_layer(model.layers[3], params['l_ir_y'][0], params['max_norm_y'][0], model_params, rng)
train_obj.algorithm.learning_rate.set_value(
math.pow(10, params['log_init_learning_rate'][0].astype(numpy.float32)))
train_obj.algorithm.learning_rule.momentum.set_value(params['init_momentum'][0].astype(numpy.float32))
pass
if 'converge' not in params:
train_obj.algorithm.termination_criterion._criteria[0].initialize(train_obj.model)
train_obj.main_loop(do_setup=False)
original_misclass = read_channel(train_obj.model, misclass_channel)
return float(original_misclass) * 50
model = MLP(layers = [h0, h1, y],
batch_size = batchSize,
input_space = inputSpace)
algorithm = SGD(learning_rate = 1e-3,
cost = MethodCost("cost_from_X"),
batch_size = batchSize,
monitoring_batch_size = batchSize,
monitoring_dataset = {'train': train,
'valid':valid},
monitor_iteration_mode = "even_batchwise_shuffled_sequential",
termination_criterion = EpochCounter(max_epochs = 100),
learning_rule = Momentum(init_momentum = 0.0),
train_iteration_mode = "even_batchwise_shuffled_sequential")
train = Train(dataset = train,
model = model,
algorithm = algorithm,
save_path = "2_layer_conv.pkl",
save_freq = 1,
extensions = [
MonitorBasedSaveBest(channel_name = "valid_y_misclass",
save_path = "2_layer_conv_best.pkl")
])
print("Starting training session")
train.main_loop()
print("Done!")
pdb.set_trace()
algorithm = SGD(
batch_size=batch_size,
learning_rate=learning_rate,
init_momentum=.5,
monitoring_dataset={'valid': val_ds},
cost=Dropout(input_include_probs={'h0': .8}, input_scales={'h0': 1.}),
termination_criterion=MonitorBased(channel_name="valid_y_misclass",
prop_decrease=0.,
N=100),
#termination_criterion: !obj:pylearn2.termination_criteria.EpochCounter {max_epochs: 1},
update_callbacks=ExponentialDecay(decay_factor=1.00004, min_lr=.000001))
extensions = [
MonitorBasedSaveBest(channel_name='valid_y_misclass',
save_path=save_best_path),
MomentumAdjustor(start=1, saturate=250, final_momentum=.7)
]
model = MLP(batch_size=batch_size,
input_space=Conv2DSpace(shape=[48, 48],
num_channels=num_chan,
axes=['c', 0, 1, 'b']),
layers=[
MaxoutConvC01B(layer_name='h0',
pad=0,
num_channels=64,
num_pieces=2,
kernel_shape=[8, 8],
pool_shape=[4, 4],
pool_stride=[2, 2],
def test_works():
load = True
if load == False:
ddmTrain = FacialKeypoint(which_set='train', start=0, stop=6000)
ddmValid = FacialKeypoint(which_set='train', start=6000, stop=7049)
# valid can_fit = false
pipeline = preprocessing.Pipeline()
stndrdz = preprocessing.Standardize()
stndrdz.apply(ddmTrain, can_fit=True)
#doubt, how about can_fit = False?
stndrdz.apply(ddmValid, can_fit=False)
GCN = preprocessing.GlobalContrastNormalization()
GCN.apply(ddmTrain, can_fit=True)
GCN.apply(ddmValid, can_fit=False)
pcklFile = open('kpd.pkl', 'wb')
obj = (ddmTrain, ddmValid)
pickle.dump(obj, pcklFile)
pcklFile.close()
return
else:
pcklFile = open('kpd.pkl', 'rb')
(ddmTrain, ddmValid) = pickle.load(pcklFile)
pcklFile.close()
#creating layers
#2 convolutional rectified layers, border mode valid
layer1 = ConvRectifiedLinear(layer_name='convRect1',
output_channels=64,
irange=.05,
kernel_shape=[5, 5],
pool_shape=[3, 3],
pool_stride=[2, 2],
max_kernel_norm=1.9365)
layer2 = ConvRectifiedLinear(layer_name='convRect2',
output_channels=64,
irange=.05,
kernel_shape=[5, 5],
pool_shape=[3, 3],
pool_stride=[2, 2],
max_kernel_norm=1.9365)
# Rectified linear units
layer3 = RectifiedLinear(dim=3000, sparse_init=15, layer_name='RectLin3')
#multisoftmax
n_groups = 30
n_classes = 98
irange = 0
layer_name = 'multisoftmax'
layerMS = MultiSoftmax(n_groups=n_groups,
irange=0.05,
n_classes=n_classes,
layer_name=layer_name)
#setting up MLP
MLPerc = MLP(batch_size=8,
input_space=Conv2DSpace(shape=[96, 96], num_channels=1),
layers=[layer1, layer2, layer3, layerMS])
#mlp_cost
missing_target_value = -1
mlp_cost = MLPCost(cost_type='default',
missing_target_value=missing_target_value)
#algorithm
# learning rate, momentum, batch size, monitoring dataset, cost, termination criteria
term_crit = MonitorBased(prop_decrease=0.00001,
N=30,
channel_name='validation_objective')
kpSGD = KeypointSGD(learning_rate=0.001,
init_momentum=0.5,
monitoring_dataset={
'validation': ddmValid,
'training': ddmTrain
},
batch_size=8,
batches_per_iter=750,
termination_criterion=term_crit,
train_iteration_mode='random_uniform',
cost=mlp_cost)
#train extension
train_ext = ExponentialDecayOverEpoch(decay_factor=0.998,
min_lr_scale=0.01)
#train object
train = Train(dataset=ddmTrain,
save_path='kpd_model2.pkl',
save_freq=1,
model=MLPerc,
algorithm=kpSGD,
extensions=[
train_ext,
MonitorBasedSaveBest(channel_name='validation_objective',
save_path='kpd_best.pkl'),
MomentumAdjustor(start=1, saturate=20, final_momentum=.9)
])
train.main_loop()
train.save()
def main(job_id, requested_params, cache):
# Fix sub directory problems
sys.path.append(os.path.dirname(os.getcwd()))
os.chdir(os.path.dirname(os.path.realpath(__file__)))
# Add parameters that are not currently being tuned but could potentially be tuned.
params = additional_args
params.update(requested_params)
output_channels_h2 = int(1.00 * 50)
output_channels_h3 = int(3.42 * 50)
output_channels_h4 = int(11.67 * 50)
dropout_h2 = float(params['dropout_h2'][0]) / 10
dropout_h3 = float(params['dropout_h3'][0]) / 10
dropout_h4 = float(params['dropout_h4'][0]) / 10
dropout_y = float(params['dropout_y'][0]) / 10
if params.get('rate', None) is not None:
params['log_init_learning_rate'][0] += numpy.array([params['rate']])
fixed_params = (params['kernel_size_h2'][0], params['kernel_size_h3'][0], params['dropout_h2'][0],
params['dropout_h3'][0], params['dropout_h4'][0], params['dropout_y'][0])
if 'cached_trainer' + str(fixed_params) not in cache:
train_params = {
'train_start': params['start'],
'train_stop': params['stop'],
'valid_start': 20000,
'valid_stop': 24000,
'test_stop': 4000,
'batch_size': 100,
'max_epochs': params.get('epochs', 1),
'max_batches': 50,
'sgd_seed': sgd_seed_str,
'mlp_seed': mlp_seed_str,
'kernel_size_h2': int(params['kernel_size_h2'][0]),
'output_channels_h2': output_channels_h2,
'irange_h2': math.pow(10, params['l_ir_h2'][0]),
'max_kernel_norm_h2': params['max_norm_h2'][0],
'dropout_h2': dropout_h2,
'dscale_h2': params['dfac_h2'][0] * 1.0 / dropout_h2,
'w_lr_sc_h2': math.pow(dropout_h2, 2),
'weight_decay_h2': math.pow(10, params['l_wdecay_h2'][0]),
'kernel_size_h3': int(params['kernel_size_h3'][0]),
'output_channels_h3': output_channels_h3,
'irange_h3': math.pow(10, params['l_ir_h3'][0]),
'max_kernel_norm_h3': params['max_norm_h3'][0],
'dropout_h3': dropout_h3,
'dscale_h3': params['dfac_h3'][0] * 1.0 / dropout_h3,
'w_lr_sc_h3': math.pow(dropout_h3, 2),
'weight_decay_h3': math.pow(10, params['l_wdecay_h3'][0]),
'kernel_size_h4': int(params['kernel_size_h4'][0]),
'output_channels_h4': output_channels_h4,
'irange_h4': math.pow(10, params['l_ir_h4'][0]),
'max_kernel_norm_h4': params['max_norm_h4'][0],
'dropout_h4': dropout_h4,
'dscale_h4': params['dfac_h4'][0] * 1.0 / dropout_h4,
'w_lr_sc_h4': math.pow(dropout_h4, 2),
'weight_decay_h4': math.pow(10, params['l_wdecay_h4'][0]),
'weight_decay_y': math.pow(10, params['l_wdecay_y'][0]),
'max_col_norm_y': params['max_norm_y'][0],
'irange_y': math.pow(10, params['l_ir_y'][0]),
'dropout_y': dropout_y,
'dscale_y': 1.0 / dropout_y,
'w_lr_sc_y': math.pow(dropout_y, 2),
'init_learning_rate': math.pow(10, params['log_init_learning_rate'][0]),
'init_momentum': params['init_momentum'][0],
'rectifier_left_slope': 0.2
}
with open('conv_fooddata_spearmint.yaml', 'r') as f:
trainer = f.read()
yaml_string = trainer % train_params
train_obj = yaml_parse.load(yaml_string)
if 'converge' in params:
train_obj.algorithm.termination_criterion._criteria[0]._max_epochs = params.get('epochs', 100)
train_obj.extensions.append(MonitorBasedSaveBest('valid_y_misclass', params['save']))
train_obj.setup()
train_obj.model.monitor.on_channel_conflict = 'ignore'
# cache['cached_trainer' + str(fixed_params)] = train_obj
else:
train_obj = cache['cached_trainer' + str(fixed_params)]
train_obj.model.monitor.set_state([0, 0, 0])
train_obj.model.training_succeeded = False
# train_obj.algorithm.update_callbacks[0].reinit_from_monitor()
model = train_obj.model
model_params = dict([(param.name, param) for param in model.get_params()])
rng = model.rng
update_conv_layer(model.layers[0], params['l_ir_h2'][0], params['max_norm_h2'][0], model_params, rng)
update_conv_layer(model.layers[1], params['l_ir_h3'][0], params['max_norm_h3'][0], model_params, rng)
update_conv_layer(model.layers[2], params['l_ir_h4'][0], params['max_norm_h4'][0], model_params, rng)
update_softmax_layer(model.layers[3], params['l_ir_y'][0], params['max_norm_y'][0], model_params, rng)
train_obj.algorithm.learning_rate.set_value(
math.pow(10, params['log_init_learning_rate'][0].astype(numpy.float32)))
train_obj.algorithm.learning_rule.momentum.set_value(params['init_momentum'][0].astype(numpy.float32))
pass
pretrained_model_path = params.get('model', None)
if pretrained_model_path is not None:
print 'loading pre trained model'
pretrained_model = serial.load(pretrained_model_path)
print 'loading done'
train_obj.model.set_param_values(pretrained_model.get_param_values())
if 'converge' not in params:
train_obj.algorithm.termination_criterion._criteria[0].initialize(train_obj.model)
train_obj.main_loop(do_setup=False)
if params.get('savelast', False):
serial.save(params['save'] + 'f', train_obj.model, on_overwrite='backup')
original_misclass = read_channel(train_obj.model, misclass_channel)
return float(original_misclass) * 50
train = Train(dataset,
mlp,
SGD(0.1,
batch_size=128,
monitoring_dataset={
'train': dataset,
'valid': dataset_valid,
'test': dataset_test
},
termination_criterion=EpochCounter(100),
train_iteration_mode='even_shuffled_sequential',
monitor_iteration_mode='even_sequential'),
save_path="pkl/multicolumn.pkl",
save_freq=5,
extensions=[
MonitorBasedSaveBest(channel_name='test_y_misclass',
save_path="pkl/multicolumn_best.pkl")
])
# # Load the saved model
# model = serial.load(saved_model_path)
#
# # Remove last layer
# del model.layers[-1]
#
# # Add new layer
# new_output_layer = <make your new layer here>
# model.add_layers([new_output_layer])
# mlp.layers.extend(pretrained_layers[start_layer:])
# , cost=Dropout(input_include_probs={'composite':1.})))