python类batch_norm()的实例源码

def _initialize_params(self):
        all_weights = {}
        batch_norms = {}
        gen_vars = []
        disc_vars = []

        # init generator weights
        prev_layer_dim = self.z_dim
        for layer_i in xrange(len(self.generator_params['dim'])):
            name = 'gen_w' + str(layer_i)
            all_weights[name] = ops.variable(name, 
                  [self.generator_params['ksize'][layer_i], 
                   self.generator_params['ksize'][layer_i], 
                   self.generator_params['dim'][layer_i], 
                   prev_layer_dim], 
                self.initializer)
            gen_vars.append(all_weights[name])

            if layer_i+1==len(self.generator_params['dim']):
                name = 'gen_b' + str(layer_i)
                all_weights[name] = ops.variable(name, 
                    [self.generator_params['dim'][layer_i]], 
                    )
                gen_vars.append(all_weights[name])
            else:
                name = 'gen_bn' + str(layer_i)
                batch_norms[name] = ops.batch_norm(self.generator_params['dim'][layer_i], name=name)

            prev_layer_dim = self.generator_params['dim'][layer_i]

        # init discriminator weights
        prev_layer_dim = self.image_dim
        for layer_i in xrange(len(self.discriminator_params['dim'])):
            name = 'disc_w' + str(layer_i)
            all_weights[name] = ops.variable(name, 
                  [self.discriminator_params['ksize'][layer_i], 
                   self.discriminator_params['ksize'][layer_i], 
                   prev_layer_dim,
                   self.discriminator_params['dim'][layer_i]],
                self.initializer)

            disc_vars.append(all_weights[name])

            if layer_i+1==len(self.discriminator_params['dim']):
                name = 'disc_b' + str(layer_i)
                all_weights[name] = ops.variable(name, 
                        [self.discriminator_params['dim'][layer_i]], 
                        tf.constant_initializer(0.0))
                disc_vars.append(all_weights[name])
            else:
                name = 'disc_bn' + str(layer_i)
                batch_norms[name] = ops.batch_norm(self.discriminator_params['dim'][layer_i], name=name)

            prev_layer_dim = self.discriminator_params['dim'][layer_i]

        return all_weights, batch_norms, gen_vars, disc_vars