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本文簡(jiǎn)單整理了網(wǎng)上公布的基于 TensorFlow 實(shí)現(xiàn)圖像語(yǔ)義分析的一些經(jīng)典網(wǎng)絡(luò)，方便大家參考學(xué)習(xí)。

1. TensorFlow-Slim

TF-Slim 是 tensorflow 較新版本的擴(kuò)充包，可以簡(jiǎn)化繁雜的網(wǎng)絡(luò)定義，其中也提供了一些demo:

AlexNet
InceptionV1/V2/V3
OverFeat
ResNet
VGG

例如 VGG-16 網(wǎng)絡(luò)，寥寥數(shù)行就可以定義完畢：

def vgg16(inputs):
  with slim.arg_scope([slim.conv2d, slim.fully_connected],
                      activation_fn=tf.nn.relu,
                      weights_initializer=tf.truncated_normal_initializer(0.0, 0.01),
                      weights_regularizer=slim.l2_regularizer(0.0005)):
    net = slim.repeat(inputs, 2, slim.conv2d, 64, [3, 3], scope='conv1')
    net = slim.max_pool2d(net, [2, 2], scope='pool1')
    net = slim.repeat(net, 2, slim.conv2d, 128, [3, 3], scope='conv2')
    net = slim.max_pool2d(net, [2, 2], scope='pool2')
    net = slim.repeat(net, 3, slim.conv2d, 256, [3, 3], scope='conv3')
    net = slim.max_pool2d(net, [2, 2], scope='pool3')
    net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3], scope='conv4')
    net = slim.max_pool2d(net, [2, 2], scope='pool4')
    net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3], scope='conv5')
    net = slim.max_pool2d(net, [2, 2], scope='pool5')
    net = slim.fully_connected(net, 4096, scope='fc6')
    net = slim.dropout(net, 0.5, scope='dropout6')
    net = slim.fully_connected(net, 4096, scope='fc7')
    net = slim.dropout(net, 0.5, scope='dropout7')
    net = slim.fully_connected(net, 1000, activation_fn=None, scope='fc8')
  return net