model.py 文件源码

def build_encoder(self,input_shape):
        last_convolution = np.array(input_shape) // 8
        self.parameters['clayer'] = 8
        self.parameters['N'] = int(np.prod(last_convolution)*self.parameters['clayer'] // self.parameters['M'])
        return [Reshape((*input_shape,1)),
                GaussianNoise(0.1),
                BN(),
                Convolution2D(16,(3,3),
                              activation=self.parameters['activation'],padding='same', use_bias=False),
                Dropout(self.parameters['dropout']),
                BN(),
                MaxPooling2D((2,2)),

                Convolution2D(64,(3,3),
                              activation=self.parameters['activation'],padding='same', use_bias=False),
                SpatialDropout2D(self.parameters['dropout']),
                BN(),
                MaxPooling2D((2,2)),

                Convolution2D(64,(3,3),
                              activation=self.parameters['activation'],padding='same', use_bias=False),
                SpatialDropout2D(self.parameters['dropout']),
                BN(),
                MaxPooling2D((2,2)),

                Convolution2D(64,(1,1),
                              activation=self.parameters['activation'],padding='same', use_bias=False),
                SpatialDropout2D(self.parameters['dropout']),
                BN(),

                Convolution2D(self.parameters['clayer'],(1,1),
                              padding='same'),
                flatten,
        ]

# mixin classes ###############################################################
# Now effectively 3 subclasses; GumbelSoftmax in the output, Convolution, Gaussian.
# there are 4 more results of mixins: