def finite_nin_caffe(data_opts: DataOpts,
isrelu=True,
isnormstoch=False) -> Tuple[NetOpts, OptimOpts]:
model_string = ''
isrelu = booltostr(isrelu)
isnormstoch = booltostr(isnormstoch)
nl = 'lnorm|s:{}'.format(isnormstoch)
convparam = 'param:logdirich,stoch:0,isrelu:{},coef:4'.format(isrelu)
d = '->'
finish = 'fin'
model_string += 'klconvb|r:5,f:192,pad:same,bias:1,{}'.format(
convparam) + d + nl + d
model_string += 'klconv|r:1,f:160,pad:same,bias:1,{}'.format(
convparam) + d + nl + d
model_string += 'klconv|r:1,f:96,pad:same,bias:1,{}'.format(
convparam) + d + nl + d
model_string += 'klavgpool|r:3,f:32,pad:valid,stride:2,bias:1' + d
# model_string += 'dropout|p:0.5' + d
model_string += 'klconv|r:5,f:192,pad:same,bias:1,{}'.format(
convparam) + d + nl + d
model_string += 'klconv|r:1,f:192,pad:same,bias:1,{}'.format(
convparam) + d + nl + d
model_string += 'klconv|r:1,f:192,pad:same,bias:1,{}'.format(
convparam) + d + nl + d
model_string += 'klavgpool|r:3,f:32,pad:valid,stride:2,bias:1' + d
# model_string += 'dropout|p:0.5' + d
model_string += 'klconv|r:3,f:192,pad:same,bias:1,{}'.format(
convparam) + d + nl + d
model_string += 'klconv|r:1,f:192,pad:same,bias:1,{}'.format(
convparam) + d + nl + d
model_string += 'klconv|r:1,f:' + str(
data_opts.classnum) + ',pad:valid,bias:0,{}'.format(
convparam) + d + nl + d
model_string += 'klavgpool|r:7,f:32,pad:valid,stride:2,bias:1' + d + nl + d
model_string += finish
'''Data OPTs'''
data_transforms = []
'''LR SCHED'''
lr_sched = discrete_exp_decay_lr(init_lr=1, step=30, exp_decay_perstep=1)
''' Net Options'''
opts_net = NetOpts(
model_string,
inputspatszvalidator=lambda x: x == 32,
data_transforms=data_transforms,
classicNet=False,
weightinit=lambda x: x.normal_(0, 0.05),
biasinit=lambda x: x.zero_(),
)
opts_optim = OptimOpts(lr=1,
lr_sched_lambda=lr_sched,
type='SGD',
momentum=0.9,
weight_decay=0,
dampening=0,
nestrov=False,
loss=NLLLoss(reduce=False))
'''Optimizer Options'''
return opts_net, opts_optim
def vgg(data_opts: DataOpts) -> Tuple[NetOpts, OptimOpts]:
model_string = ''
nl = 'relu'
d = '->'
finish = 'fin'
model_string += 'conv|r:3,f:64,pad:same,bias:1' + d + nl + d + 'bn' + d + 'dropout|p:0.3' + d
model_string += 'conv|r:3,f:64,pad:same,bias:1' + d + nl + d
model_string += 'maxpool|r:2,pad:valid,stride:2,bias:1' + d
model_string += 'conv|r:3,f:128,pad:same,bias:1' + d + nl + d + 'bn' + d + 'dropout|p:0.4' + d
model_string += 'conv|r:3,f:128,pad:same,bias:1' + d + nl + d
model_string += 'maxpool|r:2,pad:valid,stride:2,bias:1' + d
model_string += 'conv|r:3,f:256,pad:same,bias:1' + d + nl + d + 'bn' + d + 'dropout|p:0.4' + d
model_string += 'conv|r:3,f:256,pad:same,bias:1' + d + nl + d + 'bn' + d + 'dropout|p:0.4' + d
model_string += 'conv|r:3,f:256,pad:same,bias:1' + d + nl + d
model_string += 'maxpool|r:2,pad:valid,stride:2,bias:1' + d
model_string += 'conv|r:3,f:512,pad:same. 1 +,bias:1' + d + nl + d + 'bn' + d + 'dropout|p:0.4' + d
model_string += 'conv|r:3,f:512,pad:same,bias:1' + d + nl + d + 'bn' + d + 'dropout|p:0.4' + d
model_string += 'conv|r:3,f:512,pad:same,bias:1' + d + nl + d
model_string += 'maxpool|r:2,pad:valid,stride:2,bias:1' + d
model_string += 'conv|r:3,f:512,pad:same,bias:1' + d + nl + d + 'bn' + d + 'dropout|p:0.4' + d
model_string += 'conv|r:3,f:512,pad:same,bias:1' + d + nl + d + 'bn' + d + 'dropout|p:0.4' + d
model_string += 'conv|r:3,f:512,pad:same,bias:1' + d + nl + d + 'bn' + d
model_string += 'maxpool|r:2,pad:valid,stride:2,bias:1' + d
model_string += 'conv|r:1,f:512,pad:same,bias:1' + d + nl + d + 'dropout|p:0.5' + d
model_string += 'conv|r:1,f:' + str(
data_opts.classnum) + ',pad:valid,bias:1' + d
model_string += 'lnorm|s:0' + d
model_string += finish
'''Data OPTs'''
data_transforms = [transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]
'''LR SCHED'''
lr_sched = vgg_lr
''' Net Options'''
opts_net = NetOpts(model_string,
inputspatszvalidator=lambda x: x == 32,
data_transforms=data_transforms,
classicNet=True,
weightinit=lambda x: x.normal_(0, 0.01),
biasinit=lambda x: x.zero_)
'''Optimizer Options'''
opts_optim = OptimOpts(lr=.001,
lr_sched_lambda=lr_sched,
type='SGD',
momentum=0.9,
weight_decay=1e-4,
dampening=0,
nestrov=False,
loss=NLLLoss(reduce=True))
return opts_net, opts_optim
def __init__(self, char_embeddings: TextFieldEmbedder,
encoder: Seq2VecEncoder, vocab: Vocabulary) -> None:
super().__init__(vocab)
# Initialize embedding vector.
self.char_embeddings = char_embeddings
# Initialize encode
self.encoder = encoder
# Initialize hidden-tag layer.
# It outputs score for wach label.
self.hidden2tag = torch.nn.Linear(
in_features=encoder.get_output_dim(),
out_features=vocab.get_vocab_size('labels'))
# Initialize the average metric.
self.accuracy = Average()
# it’s faster and has better numerical properties compared to Softmax
self.m = LogSoftmax()
# The negative log likelihood loss. It is useful to train a
# classification problem with `C` classes
self.loss = NLLLoss()
def quick_cifar(data_opts: DataOpts) -> Tuple[NetOpts, OptimOpts]:
model_string = ''
nl = 'relu'
d = '->'
finish = 'fin'
model_string += 'conv|r:5,f:32,pad:same,bias:1' + d
model_string += 'maxpool|r:3,f:32,pad:same,stride:2,bias:1' + d
model_string += 'conv|r:5,f:64,pad:same,bias:1' + d + nl + d
model_string += 'avgpool|r:3,f:32,pad:same,stride:2,bias:1' + d
model_string += 'conv|r:4,f:64,pad:same,bias:1' + d + nl + d
model_string += 'conv|r:7,f:' + str(
data_opts.classnum) + ',pad:valid,bias:1' + d + 'lnorm|s:0' + d
model_string += finish
'''Data OPTs'''
data_transforms = [transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]
'''LR SCHED'''
initlr = .01
lr_sched = exp_decay_lr(init_lr=initlr, step=20, exp_decay_perstep=1)
''' Net Options'''
opts_net = NetOpts(
model_string,
inputspatszvalidator=lambda x: x == 32,
data_transforms=data_transforms,
classicNet=True,
weightinit=lambda x: x.normal_(0, 0.05),
biasinit=lambda x: x.zero_(),
)
'''Optimizer Options'''
opts_optim = OptimOpts(lr=initlr,
lr_sched_lambda=lr_sched,
type='SGD',
momentum=0.9,
weight_decay=1e-5,
dampening=0,
nestrov=False,
loss=NLLLoss(reduce=True))
return opts_net, opts_optim