经典网络的 TensorFlow 实现资源汇总

来源：互联网发布：名片设计软件哪个好编辑：程序博客网时间：2024/06/07 05:23

本系列文章由 @yhl_leo 出品，转载请注明出处。

文章链接： http://blog.csdn.net/yhl_leo/article/details/57412234

本文简单整理了网上公布的基于 TensorFlow 实现图像语义分析的一些经典网络，方便大家参考学习。

1. TensorFlow-Slim

TF-Slim 是 tensorflow 较新版本的扩充包，可以简化繁杂的网络定义，其中也提供了一些demo:

AlexNet
InceptionV1/V2/V3
OverFeat
ResNet
VGG

例如 VGG-16 网络，寥寥数行就可以定义完毕：

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
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2. tensorpack

tensorpack 是一个比较全面的工具包：

tensorpack

GitHub 地址： ppwwyyxx/tensorpack

3. TF-Tutorials

TF-Tutorials 是一个简短的教程，包括如下内容：

tf-tutorials

GitHub 地址： awjuliani/TF-Tutorials

4. tflearn

tflearn

GitHub 地址：tflearn/tflearn

5. Others

还有一些单一网络实现的工程，例如：

VGG: machrisaa/tensorflow-vgg
Faster RCNN: smallcorgi/Faster-RCNN_TF
SSD: seann999/ssd_tensorflow
YOLO: gliese581gg/YOLO_tensorflow
FCN: MarvinTeichmann/tensorflow-fcn
SegNet: tkuanlun350/Tensorflow-SegNet
DeepLab: DrSleep/tensorflow-deeplab-lfov, DrSleep/tensorflow-deeplab-resnet
Neural Style: anishathalye/neural-style
Pix2Pix: affinelayer/pix2pix-tensorflow
Colorization: shekkizh/Colorization.tensorflow
Depth Prediction: iro-cp/FCRN-DepthPrediction
Chessbot: Elucidation/tensorflow_chessbot
DCGAN: carpedm20/DCGAN-tensorflow
VAE-GAN: ikostrikov/TensorFlow-VAE-GAN-DRAW, timsainb/Tensorflow-MultiGPU-VAE-GAN
Mask RCNN: CharlesShang/FastMaskRCNN
……

持续更新。。。。。。

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