Commit b4d7738f authored by remi cresson's avatar remi cresson
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parent 517af350
......@@ -63,6 +63,7 @@ otbcli_PatchesExtraction -vec points.sqlite -source1.il $s2_list -source1.patchs
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## Build your Tensorflow model
You can build your Tensorflow model as shown in the `otb/Modules/Remote/otbtensorflow/python` directory. The high-level Python API of Tensorflow is used here to explort a *SavedModel* that applications of this remote module can read.
Python purists can even train their own models, thank to Python bindings of OTB: to get patches as 4D numpy arrays, just read the patches images with OTB (**ExtractROI** application for instance) and get the output float vector image as numpy array. Then, simply do a np.reshape to the dimensions that you want !
However, you can use any deep net available on the web, or use an existing gui application to create your own Tensorflow models.
The important thing here is to know the following parameters for your **placeholders** (the inputs of your model) and **output tensors** (the outputs of your model).
- For each **input placeholder**:
......@@ -78,8 +79,6 @@ The important thing here is to know the following parameters for your **placehol
Here the scale factor is related to one of the model inputs. It tells if your model perform a physical change of spacing of the output (e.g. introduced by non unitary strides in pooling or convolution operators). For each output, it must be expressed relatively to one single input called the reference input.
Additionally, you will need to remember the **target nodes** (e.g. optimizers, ...) used for training and every other placeholder that are important, especially user placeholders that are used only for training without default value (e.g. "dropout value").
## Create a new model
You can use the tool of your choice to build a tensorflow model. This is out of scope of this remote module. However, you can take a look in the `otb/Modules/Remote/otbtensorflow/python` to check how its done typically using the high level python API of Tensorflow. Python purists can even train their own models, thank to Python bindings of OTB: to get patches as 4D numpy arrays, just read the patches images with OTB (**ExtractROI** application for instance) and get the output float vector image as numpy array. Then, simply do a np.reshape to the dimensions that you want !
## Train your Tensorflow model
Here we assume that you have produced patches using the **PatchesExtraction** application, and that you have a model stored in a directory somewhere on your filesystem. The **TensorflowModelTrain** 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, ...).
Set you input data for training and for validation. The validation against test data is performed on the same data as for training, and the validation against the validation data, well, is performed on the dataset that you give to the application. You can set also batches sizes, and custom placeholders for single valued tensors for both training and validation. The last is useful if you have a model that behaves differently depending 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. Hence you will pass a placeholder with dropout=0.3 for training and dropout=0.0 for validation.
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