Fixes Neutral Style Transfer example (#10779)

* Fixes Neutral Style Transfer example

Fixes tensorflow/models#9451 by creating image from example that can be downloaded.

* Fixes Neutral Style Transfer Adam reference

In TF2 this function moved from tf.optimizers.Adam(). Only TF2 is supported on Google colab

https://www.tensorflow.org/api_docs/python/tf/compat/v1/train/AdamOptimizer

* Update 4_Neural_Style_Transfer_with_Eager_Execution.ipynb

Updates tf.keras.preprocessing.image() function reference to TF2 equivalent to restore functionality on Google colab.

https://www.tensorflow.org/api_docs/python/tf/keras/preprocessing/image

* Fixes Neutral Style Transfer example

Eager Execution is now enabled by default in TF2

https://www.tensorflow.org/api_docs/python/tf/executing_eagerly

research/nst_blogpost/4_Neural_Style_Transfer_with_Eager_Execution.ipynb

@@ -171,7 +171,7 @@
         "%tensorflow_version 1.x\n",
         "import tensorflow as tf\n",
         "\n",
-        "from tensorflow.python.keras.preprocessing import image as kp_image\n",
+        "from tensorflow.keras.utils import image_dataset_from_directory as kp_image\n",
         "from tensorflow.python.keras import models \n",
         "from tensorflow.python.keras import losses\n",
         "from tensorflow.python.keras import layers\n",
@@ -180,30 +180,6 @@
       "execution_count": 0,
       "outputs": []
     },
-    {
-      "metadata": {
-        "id": "L7sjDODq67HQ",
-        "colab_type": "text"
-      },
-      "cell_type": "markdown",
-      "source": [
-        "We’ll begin by enabling [eager execution](https://www.tensorflow.org/guide/eager). Eager execution allows us to work through this technique in the clearest and most readable way. "
-      ]
-    },
-    {
-      "metadata": {
-        "id": "sfjsSAtNrqQx",
-        "colab_type": "code",
-        "colab": {}
-      },
-      "cell_type": "code",
-      "source": [
-        "tf.enable_eager_execution()\n",
-        "print(\"Eager execution: {}\".format(tf.executing_eagerly()))"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
     {
       "metadata": {
         "id": "IOiGrIV1iERH",
@@ -828,7 +804,7 @@
         "  init_image = load_and_process_img(content_path)\n",
         "  init_image = tf.Variable(init_image, dtype=tf.float32)\n",
         "  # Create our optimizer\n",
-        "  opt = tf.train.AdamOptimizer(learning_rate=5, beta1=0.99, epsilon=1e-1)\n",
+        "  opt = tf.optimizers.Adam(learning_rate=5, epsilon=1e-1)\n",
         "\n",
         "  # For displaying intermediate images \n",
         "  iter_count = 1\n",
@@ -945,6 +921,8 @@
       "cell_type": "code",
       "source": [
         "#from google.colab import files\n",
+        "#final_img = Image.fromarray(best)\n",
+        "#final_img.save('wave_turtle.png')\n",
         "#files.download('wave_turtle.png')"
       ],
       "execution_count": 0,