def __init__(self, source, transform, extra_params=None):
"""
:param source: The signal source to be preprocessed
:param transform: A function that takes at least a list and only kwargs beyond that
:param extra_params: Extra things the transform function needs
"""
params = extra_params or {} # Mutable kwargs 'n all that
Transformer.__init__(self, [source], {source.getName(): 'l'},
lambda l: transform(l, **params))
def __init__(self
,input_dim
,bw_source
,ph_source = Source(lambda: None)
,mode = TRAINING
,batch_size = 60
,epochs = 60
,num_sizes = 5
,encdec_optimizer = 'rmsprop'
,class_optimizer = 'adadelta'
,class_loss = 'categorical_crossentropy'
,drop_rate = 0.001
,gauss_base_sigma = 0.001
,gauss_sigma_factor = 2
,l1 = 0.0
,l2 = 0.001
,model_name = 'latest'
,encdecs_name = None):
if mode is AutoTransformer.TUNING: raise ValueError("Can't instantiate an AT in 'tuning' mode")
self.mode = mode.instantiate(self, [bw_source, ph_source]) # Source order matters!
self.batch_size = batch_size
self.epochs = epochs
self.bw_source = bw_source
self.ph_source = ph_source
self.layer_sizes = [input_dim] + [2**i for i in xrange(6, 6-num_sizes+1, -1)]
self.enc_decs = []
self.current_batch = [[] for i in range(batch_size)]
self.previous_data = {}
self.current_phase = phase_names[0]
self.batched = 0
self.model = None
self.model_name = model_name
self.encdecs_name = encdecs_name
self.maxes = self.get_from_catalog("maxes", model_name) or np.ones(input_dim)
self.enc_opt = encdec_optimizer
self.cls_opt = class_optimizer
self.cls_lss = class_loss
self.drop_rate = drop_rate
self.sigma_base = gauss_base_sigma
self.sigma_fact = gauss_sigma_factor
self.best_encdecs = ("latest",0.0)
self.enc_use_drop = False
self.enc_noise = False
self.l1 = l1
self.l2 = l2
Transformer.__init__(self
,self.mode.sources
,self.mode.t_assignments
,self.mode.transform
,no_updates=self.mode.no_updates)
if self.mode.name == 'training':
self.new_encdecs()
else:
self.load_model()
def __init__(self, source, transform, extra_params=None):
"""
:param source: The signal source to be preprocessed
:param transform: A function that takes at least a list and only kwargs beyond that
:param extra_params: Extra things the transform function needs
"""
params = extra_params or {} # Mutable kwargs 'n all that
Transformer.__init__(self
,[source]
,{source.getName(): 'l'}
,lambda l: transform(l,**params))
def __init__(self, source):
self.original_source = source
if hasattr(source, 'select'):
raw_source = source.select('raw')
else:
raw_source = source
fixlentrans = Transformer([raw_source], {raw_source.getName(): 'l'}, lambda l: fix_length(l, 512))
self.wt = Preprocessor(fixlentrans, wavelet_trans)
self.ft = Preprocessor(fixlentrans, fourier_trans)
self.et = Preprocessor(fixlentrans, extremes)
Transformer.__init__(self,
[self.wt, self.ft, self.et]
,{self.wt.getName(): 'w',
self.ft.getName(): 'f',
self.et.getName(): 'e'}
,lambda w,f,e: merge(w,f,e))
#if hasattr(source, 'initialized'):
# if not source.initialized:
# source.push()
source.push()
self.pull()
# For some reason this needs to happen at least once for the output_dim to be correct
# TODO check why ^
self.output_dim = len(self.pull())
def __init__(self, source):
self.original_source = source
if hasattr(source, 'select'):
raw_source = source.select('raw')
else:
raw_source = source
fixlentrans = Transformer([raw_source], {raw_source.getName(): 'l'},
lambda l: fix_length(l, 512))
self.wt = Preprocessor(fixlentrans, wavelet_trans)
self.ft = Preprocessor(fixlentrans, fourier_trans)
self.et = Preprocessor(fixlentrans, extremes)
Transformer.__init__(self, [self.wt, self.ft, self.et], {
self.wt.getName(): 'w',
self.ft.getName(): 'f',
self.et.getName(): 'e'
}, lambda w, f, e: merge(w, f, e))
#if hasattr(source, 'initialized'):
# if not source.initialized:
# source.push()
source.push()
self.pull()
# For some reason this needs to happen at least once for the output_dim to be correct
# TODO check why ^
self.output_dim = len(self.pull())
