convnet_cuda128.py 文件源码

def __init__ (self, model_params, nkerns=[1,8,4,2,1,1], ckern=128*3, filter_sizes=[5,5,5,5,5,4]):
        """Initializes the architecture of the discriminator"""

        self.num_hid, num_dims, num_class, self.batch_size, self.num_channels = model_params
        self.D =  int(np.sqrt(num_dims / self.num_channels))
        numpy_rng=np.random.RandomState(1234)

        self.nkerns     = np.asarray(nkerns) * ckern # of constant gen filters in first conv layer
        self.nkerns[0] = self.num_channels
        self.filter_sizes=filter_sizes

        num_convH = self.nkerns[-1]*filter_sizes[-1]*filter_sizes[-1]

        self.W      = initialize_weight(num_convH,  self.num_hid,  'W', numpy_rng, 'uniform') 
        self.hbias  = theano.shared(np.zeros((self.num_hid,), dtype=theano.config.floatX), name='hbias')       
        self.W_y    = initialize_weight(self.num_hid, num_class,  'W_y', numpy_rng, 'uniform') 

        self.L1 = BN_Conv_layer(self.batch_size, numpy_rng, tnkern=self.nkerns[0], bnkern=self.nkerns[1] , bfilter_sz=filter_sizes[1], tfilter_sz=filter_sizes[0])
        self.L2 = BN_Conv_layer(self.batch_size, numpy_rng, tnkern=self.nkerns[1], bnkern=self.nkerns[2] , bfilter_sz=filter_sizes[2], tfilter_sz=filter_sizes[1])
        self.L3 = BN_Conv_layer(self.batch_size, numpy_rng, tnkern=self.nkerns[2], bnkern=self.nkerns[3] , bfilter_sz=filter_sizes[3], tfilter_sz=filter_sizes[2])
        self.L4 = BN_Conv_layer(self.batch_size, numpy_rng, tnkern=self.nkerns[3], bnkern=self.nkerns[4] , bfilter_sz=filter_sizes[4], tfilter_sz=filter_sizes[3])
        self.L5 = BN_Conv_layer(self.batch_size, numpy_rng, tnkern=self.nkerns[4], bnkern=self.nkerns[5] , bfilter_sz=filter_sizes[5], tfilter_sz=filter_sizes[4])

        self.num_classes = num_class
        self.params = [self.W_y, self.W] \
                + self.L1.params + self.L2.params + self.L3.params + self.L4.params + self.L5.params