159 lines
5.0 KiB
Python
159 lines
5.0 KiB
Python
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import numpy as np
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import _pickle as cPickle
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import traceback
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#--------------------
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class storage_layer:
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def __init__(self, inputshape = (1,1,1)):
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self.inputshape = inputshape
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self.outputshape = inputshape
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self.mean = np.zeros(inputshape + (8,))
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self.var = np.zeros(inputshape + (8,8))
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self.count = 0
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def update_datum(self,z):
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self.mean = (self.count*self.mean + z)/(self.count+1)
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delta = z - self.mean
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for i in range(8):
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for j in range(8):
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self.var[:,:,:,i,j] = (self.count*self.var[:,:,:,i,j] + delta[:,:,:,i]*delta[:,:,:,j])/(self.count+1)
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self.count+=1
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return 0
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#--------------------
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class base_storage_layer:
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def __init__(self, inputshape = (1,1,1)):
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self.inputshape = inputshape
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self.outputshape = inputshape
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self.mean = np.zeros(inputshape)
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self.var = np.zeros(inputshape)
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self.count = 0
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def update_datum(self,z):
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self.mean = (self.count*self.mean + z)/(self.count+1)
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delta = z - self.mean
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self.var = (self.count*self.var + delta*delta)/(self.count+1)
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self.count+=1
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return 0
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#--------------------
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class trainer:
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def __init__(self, gen_model):
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self.gen_model = gen_model
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self.nlevel = gen_model.nlevel
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self.model = self.construct(nlevel=self.nlevel)
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#set name from variable name. http://stackoverflow.com/questions/1690400/getting-an-instance-name-inside-class-init
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(filename,line_number,function_name,text)=traceback.extract_stack()[-2]
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def_name = text[:text.find('=')].strip()
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self.name = def_name
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print('Trainer model:',self.name)
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try:
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print('load initial state')
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self.load()
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except:
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print('save initial state')
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self.save()
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def construct(self,nlevel=2):
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trainer_model = []
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inputshape = (1,1,1)
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trainer_model.append(base_storage_layer(inputshape))
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for i in np.arange(nlevel):
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trainer_model.append(storage_layer(inputshape))
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inputshape = tuple([(l * 2) for l in inputshape])
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return trainer_model
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def transfer(self, silent=False):
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if(not silent):
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print('Train model...')
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for m, t in zip(self.gen_model.model[::-1][:-1], self.model[::-1][:-1]):
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M_z_inv = self.gen_model.get_matrix_inv(t.var)
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R_inv = M_z_inv[:,:,:,0:7,0:7]
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m.Minv = np.copy(R_inv)
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R = self.gen_model.get_matrix_inv(R_inv)
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for n in range(7):
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for l in range(7):
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m.a[:,:,:,n] += -M_z_inv[:,:,:,7,l]*R[:,:,:,l,n]
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m.w = self.gen_model.get_matrix_sqrt(R)
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m.b[:,:,:,0] = t.mean[:,:,:,0] - m.a[:,:,:,0]*t.mean[:,:,:,7]
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m.b[:,:,:,1] = t.mean[:,:,:,1] - m.a[:,:,:,1]*t.mean[:,:,:,7]
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m.b[:,:,:,2] = t.mean[:,:,:,2] - m.a[:,:,:,2]*t.mean[:,:,:,7]
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m.b[:,:,:,3] = t.mean[:,:,:,3] - m.a[:,:,:,3]*t.mean[:,:,:,7]
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m.b[:,:,:,4] = t.mean[:,:,:,4] - m.a[:,:,:,4]*t.mean[:,:,:,7]
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m.b[:,:,:,5] = t.mean[:,:,:,5] - m.a[:,:,:,5]*t.mean[:,:,:,7]
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m.b[:,:,:,6] = t.mean[:,:,:,6] - m.a[:,:,:,6]*t.mean[:,:,:,7]
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#update model parameters of base layer
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self.gen_model.model[0].b = self.model[0].mean
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self.gen_model.model[0].w = np.sqrt(self.model[0].var)
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print('test',self.gen_model.model[0].b)
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if( not silent):
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print('Training done')
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return 0
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def train_single(self,x_train, silent=False):
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assert isinstance(self.gen_model.model, list), "gen_model must be a list"
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assert isinstance(self.model, list), "trainer_model must be a list"
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#work through reversed model list and ignore base layer
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if(not silent):
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print('Train model...')
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for m, t in zip(self.gen_model.model[::-1][:-1], self.model[::-1][:-1]):
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z = m.kernel.apply(x_train, direction='bwd')
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t.update_datum(z)
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x_train = z[:,:,:,7]
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self.model[0].update_datum(x_train)
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if( not silent):
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print('Training done')
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def save(self):
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"""save class as self.name.txt"""
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file = open(self.name+'.txt','wb')
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file.write(cPickle.dumps(self.__dict__))
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file.close()
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def load(self):
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"""try load self.name.txt"""
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file = open(self.name+'.txt','rb')
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dataPickle = file.read()
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file.close()
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self.__dict__ = cPickle.loads(dataPickle)
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