revised vgg finetuning example

macesarr · Sep 9, 2016 · 95cd693 · 95cd693
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diff --git a/docs/templates/examples.md b/docs/templates/examples.md
@@ -22,6 +22,7 @@
 - [Network in Network](https://github.com/tflearn/tflearn/blob/master/examples/images/network_in_network.py). 'Network in Network' implementation for classifying CIFAR-10 dataset.
 - [Alexnet](https://github.com/tflearn/tflearn/blob/master/examples/images/alexnet.py). Apply Alexnet to Oxford Flowers 17 classification task.
 - [VGGNet](https://github.com/tflearn/tflearn/blob/master/examples/images/vgg_network.py). Apply VGG Network to Oxford Flowers 17 classification task.
+- [VGGNet Finetuning (Fast Training)](https://github.com/tflearn/tflearn/blob/master/examples/images/vgg_network_finetuning.py). Use a pre-trained VGG Network and retrain it on your own data, for fast training.
 - [RNN Pixels](https://github.com/tflearn/tflearn/blob/master/examples/images/rnn_pixels.py). Use RNN (over sequence of pixels) to classify images.
 - [Highway Network](https://github.com/tflearn/tflearn/blob/master/examples/images/highway_dnn.py). Highway Network implementation for classifying MNIST dataset.
 - [Highway Convolutional Network](https://github.com/tflearn/tflearn/blob/master/examples/images/convnet_highway_mnist.py). Highway Convolutional Network implementation for classifying MNIST dataset.

diff --git a/examples/README.md b/examples/README.md
@@ -22,6 +22,7 @@
 - [Network in Network](https://github.com/tflearn/tflearn/blob/master/examples/images/network_in_network.py). 'Network in Network' implementation for classifying CIFAR-10 dataset.
 - [Alexnet](https://github.com/tflearn/tflearn/blob/master/examples/images/alexnet.py). Apply Alexnet to Oxford Flowers 17 classification task.
 - [VGGNet](https://github.com/tflearn/tflearn/blob/master/examples/images/vgg_network.py). Apply VGG Network to Oxford Flowers 17 classification task.
+- [VGGNet Finetuning (Fast Training)](https://github.com/tflearn/tflearn/blob/master/examples/images/vgg_network_finetuning.py). Use a pre-trained VGG Network and retrain it on your own data, for fast training.
 - [RNN Pixels](https://github.com/tflearn/tflearn/blob/master/examples/images/rnn_pixels.py). Use RNN (over sequence of pixels) to classify images.
 - [Highway Network](https://github.com/tflearn/tflearn/blob/master/examples/images/highway_dnn.py). Highway Network implementation for classifying MNIST dataset.
 - [Highway Convolutional Network](https://github.com/tflearn/tflearn/blob/master/examples/images/convnet_highway_mnist.py). Highway Convolutional Network implementation for classifying MNIST dataset.

diff --git a/examples/basics/gen_files_list.py b/examples/basics/gen_files_list.py
diff --git a/examples/basics/finetuning_vgg.py → examples/images/vgg_network_finetuning.py b/examples/basics/finetuning_vgg.py → examples/images/vgg_network_finetuning.py
@@ -1,6 +1,6 @@
 # -*- coding: utf-8 -*-
 '''
-Finetuning Example with vgg.tflearn. Using weights from VGG model to retrain
+Retraining (Finetuning) Example with vgg.tflearn. Using weights from VGG model to retrain
 network for a new task (your own dataset).All weights are restored except
 last layer (softmax) that will be retrained to match the new task (finetuning).
 '''