Merge pull request #60 from remicres/58-how_to_create_your_own

DOC: add new section for building nets

doc/CREATEYOUROWN.md

+# Create your own architecture
+
+This section gives a few tips to create your own models ready to be used in inference using OTBTF's `TensorflowModelServe` and `TensorflowModelTrain` applications.
+
+## Model inputs
+
+### Dimensions
+
+All networks must input **4D tensors**.
+- **dim 0** is for the batch dimension. It is used in the `TensorflowModelTrain` application during training, and in **patch-based mode** during inference: in this mode, `TensorflowModelServe` performs the inference of several patches simultaneously. In **fully-convolutional mode**, a single slice of the batch dimension is used.
+- **dim 1** and **2** are for the spatial dimensions,
+- **dim 3** is for the image channels. Even if your image have only 1 channel, you must set a shape value equals to 1 for the last dimension of the input placeholder.
+
+### Shapes
+
+For nets intended to work in **patch-based** mode, you can stick with a placeholder where you define your patch size explicitly in **dim 1** and **dim 2**.
+However, for nets intended to work in **fully-convolutional** mode, you must set `None` in **dim 1** and **dim 2** (before Tensorflow 2.X, it was possible to feed placeholders with a tensor of different size where the dims were defined, but no more after!).
+For instance, let consider an input raster with 4 spectral bands: the input shape of the model input would be like `[None, None, None, 4]` to work in fully-convolutional mode. By doing so, the use of input images of any size is enabled (`TensorflowModelServe` will automatically compute the input/output regions sizes to process, given the **receptive field** and **expression field** of your net).
+
+## Model outputs
+
+### Dimensions
+
+Supported tensors for the outputs must have **between 2 and 4 dimensions**.
+OTBTF always consider that **the size of the last dimension is the number of channels in the output**.
+For instance, you can have a model that outputs 8 channels with a tensor of shape `[None, 8]` or `[None, None, None, 8]`
+
+### Name your tensors and nodes
+
+Always name explicitly your models outputs. You will need the output tensor name for performing the inference with `TensoflowModelServe`. If you forget to name them, use the graph viewer in `tensorboard` to get the names.
+
+### Training
+
+If you want to enable your network training with the `TensorflowModelTrain` application, do not forget to name your optimizers/operators!
+You can build a single operator from multiple ones using the `tf.group` command, which also enable you to name your new operator.
+For sequential nodes trigger, you can build an operator that do what you want is the desired order using the `tf.control_dependancies` with TF <= 1.15.
+