tensorflow构建网络

with slim.arg_scope([slim.conv2d, slim.fully_connected],
                         activation_fn=tf.nn.relu,
                         weights_initializer=tf.glorot_uniform_initializer(),
                         biases_initializer=tf.constant_initializer(0)):
        
        net = slim.conv2d(inputs, 64, [11, 11], 4)#步长默认是1，这里改成4了；64是输出特征图个数
        net = slim.max_pool2d(net, [3, 3])
        net = slim.conv2d(net, 192, [5, 5])
        net = slim.max_pool2d(net, [3, 3])
        net = slim.conv2d(net, 384, [3, 3])
        net = slim.conv2d(net, 384, [3, 3])
        net = slim.conv2d(net, 256, [3, 3])
        net = slim.max_pool2d(net, [3, 3])
        
        # 数据扁平化
        net = slim.flatten(net)
        net = slim.fully_connected(net, 1024)
        net = slim.dropout(net, is_training=is_training)
        
        net0 = slim.fully_connected(net, num_classes, activation_fn=tf.nn.softmax)
        net1 = slim.fully_connected(net, num_classes, activation_fn=tf.nn.softmax)
        net2 = slim.fully_connected(net, num_classes, activation_fn=tf.nn.softmax)
        net3 = slim.fully_connected(net, num_classes, activation_fn=tf.nn.softmax)