Training

Info

This section is for educational purposes. No coding skills are required, and it's easy to train an existing model built with the Tensorflow API v1. If you are interested in more similar examples, please read the Tensorflow v1 models examples However, to have a full control over the model implementation and training process, the Tensorflow API v2 with Keras is the recommended approach (see also distributed training).

TensorflowModelTrain

Here we assume that you have produced patches using the PatchesExtraction application, and that you have a SavedModel stored in a directory somewhere on your filesystem. The TensorflowModelTrain application performs the training, validation ( against test dataset, and against validation dataset) providing the usual metrics that machine learning frameworks provide (confusion matrix, recall, precision, f-score, ...). You must provide the path of the SavedModel to the model.dir parameter. The model.restorefrom and model.saveto corresponds to the variables of the SavedModel used respectively for restoring and saving them. Set you input sources for training (training parameter group) and for validation (validation parameter group): the evaluation is performed against training data, and optionally also against the validation data (only if you set validation.mode to "class"). For each input sources, the patch size and the placeholder name must be provided. Regarding validation, if a different name is found in a particular input source of the validation parameter group, the application knows that the input source is not fed to the model at inference, but is used as reference to compute evaluation metrics of the validation dataset. Batch size (training.batchsize) and number of epochs (training.epochs) can be set. User placeholders can be set separately for training (training.userplaceholders) and validation (validation.userplaceholders). The validation.userplaceholders can be useful if you have a model that behaves differently depending on the given placeholder. Let's take the example of dropout: it's nice for training, but you have to disable it to use the model at inference time. Hence you will pass a placeholder with "dropout_rate=0.3" for training and " dropout_rate=0.0" for validation. Of course, one can train models from handmade python code: to import the patches images, a convenient method consist in reading patches images as numpy arrays using OTB applications (e.g. ExtractROI) or GDAL, then do a numpy.reshape to the dimensions wanted.

The application description can be displayed using:

otbcli_TensorflowModelTrain --help

As you can note, there is $OTB_TF_NSOURCES + 1 sources because we often need at least one more source for the reference data (e.g. terrain truth for land cover mapping).

Composite applications for classification

Who has never dreamed to use classic classifiers performing on deep learning features? This is possible thank to two new applications that uses the existing training/classification applications of OTB:

TrainClassifierFromDeepFeatures is a composite application that wire TensorflowModelServe application output into the existing official TrainImagesClassifier application.

The application description can be displayed using:

otbcli_TrainClassifierFromDeepFeatures --help

Example

We assume that we have already followed the sampling section. We start from the files generated at the end of the patches extraction.

Now we have two images for patches and labels. 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. The SavedModel is 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 directly in the outmodel/variables folder, overwriting the existing variables of the model. We use the TensorflowModelTrain application to perform the training of this existing model.

otbcli_TensorflowModelTrain -model.dir /path/to/oursavedmodel \
-training.targetnodesnames optimizer -training.source1.il samp_patches.tif \
-training.source1.patchsizex 16 -training.source1.patchsizey 16 \
-training.source1.placeholder x1 -training.source2.il samp_labels.tif \
-training.source2.patchsizex 1 -training.source2.patchsizey 1 \
-training.source2.placeholder y1 \
-model.saveto /path/to/oursavedmodel/variables/variables

Note that we could also have performed validation in this step. In this case, the validation.source2.placeholder would be different than the training.source2.placeholder, and would be prediction. This way, the program know what is the target tensor to evaluate.