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())