『TensorFlow』项目资源分享

Posted 2020-09-21 叠加态的猫

来源：

经典网络的 TensorFlow 实现资源汇总

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

 

2.开源代码：

• 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