@@ -313,16 +313,16 @@ Here we will try to provide a simple example of doing a classification using a d
Our data set consists in one Spot-7 image, *spot7.tif*, and a training vector data, *terrain_truth.shp* that qualifies two classes that are forest / non-forest.
First, we **compute statistics** of the vector data : how many points can we sample inside objects, and how many objects in each class.
That's it. Now we have two images for patches and labels. If we wanna, we can split them to distinguish test/validation groups (with the **ExtractROI** application for instance). But here, we will just perform some fine tuning of our model, located in the `outmodel` directory. Our model is quite basic. It has two input placeholders, **x1** and **y1** respectively for input patches (with size 16x16) and input reference labels (with size 1x1). We named **prediction** the tensor that predict the labels and the optimizer that perform the stochastic gradient descent is an operator named **optimizer**. We perform the fine tuning and we export the new model variables in the `newvars` folder.
Let's use our **TensorflowModelTrain** application to perform the training of this existing model.