Revert "pix2pix_guide_update (#963)" (#965)

This reverts commit e907fa5.

Author: priyankatuteja

guide/14-deep-learning/how_pix2pix_works.ipynb

@@ -174,7 +174,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Pix2Pix model architecture shown in figure 4 is translating from simple styled map to target styled map domain. [[4](https://arxiv.org/pdf/1905.02200.pdf)]"
+    "Pix2Pix model architecture shown in figure 4 is translating from image to map domain. [[4](https://arxiv.org/pdf/1905.02200.pdf)]"
    ]
   },
   {
@@ -202,9 +202,23 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Initially, we have to export the image chips in `Export tiles` format using `Export Training data for deep learning` tool avialable in ArcGIS Pro by providing two domains of imagery in `Input Raster` and `Additional Input Raster`, then the path is provided to `prepare_data` function in `arcgis.learn` to create a databunch, than we have to create a databunch with `prepare_data` function in `arcgis.learn`\n",
+    "First, we have to create a databunch with `prepare_data` function in `arcgis.learn`\n",
     "\n",
-    "`data = arcgis.learn.prepare_data(path=r\"path/to/exported/data\")`"
+    "`data = arcgis.learn.prepare_data(path=r\"path/to/exported/data\", dataset_type='Pix2Pix')`\n",
+    "\n",
+    "The important parameters to be passed are: \n",
+    "\n",
+    "- The path to the Data directory. We need to follow the directory structure shown in figure 2. Here, 'train_a' and 'train_b' folders contain the images of domain A and B.\n",
+    "- The dataset_type as 'Pix2Pix'."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<img src=\"data:image/PNG; base64, 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\">\n",
+    "<br>\n",
+    "<center>Figure 3. Directory structure</center>"
    ]
   },
   {
@@ -215,11 +229,11 @@
     "\n",
     "`model = arcgis.learn.Pix2Pix(data=data)`\n",
     "\n",
-    "Here data is a fastai databunch, object returned from `prepare_data` function. As `arcgis.learn` is built upon fast.ai, more explanation can be found at fast.ai's docs [[6](https://fastai1.fast.ai/index.html)]\n",
+    "Here data is a fastai databunch, object returned from `prepare_data` function. As arcgis.learn is built upon fast.ai, more explanation can be found at fast.ai's docs [[6](https://fastai1.fast.ai/index.html)]\n",
     "\n",
     "Than we can continue with basic `arcgis.learn` workflow.\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.html) and [sample notebook](https://developers.arcgis.com/python/sample-notebooks/generating-rgb-imagery-from-digital-surface-model-using-pix2pix/). "
+    "For more information about the API & model, please go to the [API reference](https://developers.arcgis.com/python/api-reference/arcgis.learn.html). "
    ]
   },
   {