info_update

multi-dimensional info update

Author: sdash77

guide/14-deep-learning/how_PSETAE_works.ipynb

@@ -70,9 +70,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The `Export Training Data for Deep Learning` is used to export training data for the model. The input satellite time-series is a `composite` of rasters from the required time periods or time steps. \n",
+    "The [Export Training Data for Deep Learning](https://pro.arcgis.com/en/pro-app/latest/tool-reference/image-analyst/export-training-data-for-deep-learning.htm) is used to export training data for the model. The input satellite time-series is a [composite](https://pro.arcgis.com/en/pro-app/latest/tool-reference/data-management/composite-bands.htm) of rasters or [multi-dimensional raster](https://pro.arcgis.com/en/pro-app/latest/help/data/imagery/an-overview-of-multidimensional-raster-data.htm) from the required time periods or time steps. Here are the [steps](https://www.youtube.com/watch?v=HFbTFTnsMWM), to create multi-dimensional raster from collection of images. \n",
     "\n",
-    "Training labels can be created using the `Label objects for deep learning` tool available inside `Classification Tools`. Pixels are labelled with class based on the available information. As shown in figure are labelling of different crop types. "
+    "Training labels can be created using the [Label objects for deep learning](https://pro.arcgis.com/en/pro-app/latest/help/analysis/image-analyst/label-objects-for-deep-learning.htm#:~:text=The%20Label%20Objects%20for%20Deep,is%20divided%20into%20two%20parts.) tool available inside `Classification Tools`. Pixels are labelled with class based on the available information. As shown in figure are labelling of different crop types. "
    ]
   },
   {
@@ -102,7 +102,7 @@
    "source": [
     "PSETAE architecure is based on transfomers, originally developed for sequence-to-sequence modeling. The proposed architecture encodes time-series of multi-spectral images. The pixels under each class label is given by spectro-temporal tensor of size T x C x N, where T the number of temporal observation, C the number of spectral channels, and N the number of pixels.  \n",
     "\n",
-    "The architecture of pse-tae consists of a pixel-set encoder, temporal attention encoder and, classifier. The components are briefly described in following sections"
+    "The architecture of pse-tae consists of a pixel-set encoder, temporal attention encoder and, classifier. The components are briefly described in following sections."
    ]
   },
   {
@@ -153,7 +153,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This component is based on the state of the art transformer used originally for dealing with sequential data from [Vaswani et al.](https://proceedings.neurips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf). After multi-channel spectral embedding at time t by pse, the temporal attention encoder (tae) tries to find embedding for each class time-series. The authors made the following changes to original transformers:\n",
+    "This component is based on the state-of-the-art transformer used originally for dealing with sequential data from [Vaswani et al.](https://proceedings.neurips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf). After multi-channel spectral embedding at time t by pse, the temporal attention encoder (tae) tries to find embedding for each class time-series. The authors made the following changes to original transformers:\n",
     "\n",
     "- The pre-trained word embedding model used in the original mode is replaced by PSE's spectral embedding.\n",
     "- The positional encoder uses the first obervation or date to calculate number of days to other dates. This helps the model to take in account the variance in temporal observations.\n",
@@ -187,7 +187,7 @@
    "source": [
     "Shared PSE embeds all input time-series rasters in parallel and resulting embedded sequence is processed by temporal encoder. The resulting embedding is processed by an mlp to produice class logits. Spectro-temporal classifier combines PSE and TAE with a final MLP layer to produce class logits. \n",
     "\n",
-    "For further information on the model's architecture, refer to paper"
+    "For further information on the model's architecture, refer to paper."
    ]
   },
   {
@@ -201,20 +201,21 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Input Raster - Initially, time-series raster is created using `Composite Bands` tool \n",
+    "Input Raster - time-series raster is a [composite](https://pro.arcgis.com/en/pro-app/latest/tool-reference/data-management/composite-bands.htm) or [multi-dimensional](https://pro.arcgis.com/en/pro-app/latest/help/data/imagery/an-overview-of-multidimensional-raster-data.htm) raster from the required time periods or time steps. \n",
     "\n",
-    "Export - use the composited raster as input raster to export the raster chips in `RCNN Masks` metadata format using `Export Training data for deep learning` tool available in `ArcGIS Pro`.\n",
+    "Export - use the input raster to export the raster chips in `RCNN Masks` metadata format using [Export Training Data for Deep Learning](https://pro.arcgis.com/en/pro-app/latest/tool-reference/image-analyst/export-training-data-for-deep-learning.htm) tool available in `ArcGIS Pro`.\n",
     "\n",
-    "The resulting path is provided to `prepare_data` function in `arcgis.learn` to create a databunch.\n",
+    "The resulting path from from export tool is provided to [prepare_data](https://developers.arcgis.com/python/api-reference/arcgis.learn.toc.html#prepare-data) function in `arcgis.learn` to create a databunch.\n",
     "\n",
     "`data = prepare_data(path=r\"path/to/exported/data\", n_temporal, min_points, batch_size, n_temporal_dates, dataset_type=\"PSETAE\")`\n",
     "                    \n",
     "where,\n",
     "\n",
-    "* n_temporal - *Number of temporal observations or time steps or number of composited rasters*.\n",
-    "* min_points - *Number of pixels equal to or multiples of 64 to sample from the each labelled region of training data i.e. 64, 128 etc*\n",
-    "* batch_size - *suggested batch size for this model is around 128*\n",
-    "* n_temporal_dates - *The dates of the observations will be used for the positional encoding and should be stored as a list of dates strings in YYYY-MM-DD format. For example, If we have stacked imagery of n bands each from two dates then, ['YYYY-MM-DD','YYYY-MM-DD'].*"
+    "* `n_temporal` - optional for multi-dimensional, required for composite raster. *Number of temporal observations or time steps or number of composited rasters*. \n",
+    "* `min_points` - optional. *Number of pixels equal to or multiples of 64 to sample from the each labelled region of training data i.e. 64, 128 etc*\n",
+    "* `batch_size` - optional. *suggested batch size for this model is around 128*\n",
+    "* `n_temporal_dates` - optional for multi-dimensional, required for composite raster. *The dates of the observations will be used for the positional encoding and should be stored as a list of dates strings in YYYY-MM-DD format. For example, If we have stacked imagery of n bands each from two dates then, ['YYYY-MM-DD','YYYY-MM-DD'].*\n",
+    "* `dataset_type` - required. *type of dataset in-sync with the model*"
    ]
   },
   {
@@ -225,11 +226,13 @@
     "\n",
     "`model = arcgis.learn.PSETAE(data=data)`\n",
     "\n",
+    "Default values for optimal performance are set for model's hyperparmeters. \n",
+    "\n",
     "Here data is the object returned from `prepare_data` function.\n",
     "\n",
     "Than, the basic `arcgis.learn` workflow can be followed.\n",
     "\n",
-    "For more information about the API & model applications, please go to the [API reference](https://developers.arcgis.com/python/api-reference/arcgis.learn.toc.html)."
+    "For more information about the API & modify model's keyword arguments, please go to the [API reference](https://developers.arcgis.com/python/api-reference/arcgis.learn.toc.html)."
    ]
   },
   {
@@ -243,7 +246,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In this guide, we learned about the various details of the psetae model, its working and how we can initialize the model in `arcgis.learn`"
+    "In this guide, we learned about the various details of the psetae model, its working and how we can initialize the model in `arcgis.learn`."
    ]
   },
   {
@@ -278,7 +281,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.13"
+   "version": "3.7.11"
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   "toc": {
    "base_numbering": 1,