lenet.py 文件源码

def construct_net(self,is_trained = True):
        with slim.arg_scope([slim.conv2d], padding='VALID',
                            weights_initializer=tf.truncated_normal_initializer(stddev=0.01),
                            weights_regularizer=slim.l2_regularizer(0.0005)):
            net = slim.conv2d(self.input_images,6,[5,5],1,padding='SAME',scope='conv1')
            net = slim.max_pool2d(net, [2, 2], scope='pool2')
            net = slim.conv2d(net,16,[5,5],1,scope='conv3')
            net = slim.max_pool2d(net, [2, 2], scope='pool4')
            net = slim.conv2d(net,120,[5,5],1,scope='conv5')
            net = slim.flatten(net, scope='flat6')
            net = slim.fully_connected(net, 84, scope='fc7')
            net = slim.dropout(net, self.dropout,is_training=is_trained, scope='dropout8')
            digits = slim.fully_connected(net, 10, scope='fc9')
        return digits