diff --git a/TensorFlow Deployment/Course 3 - TensorFlow Datasets/Week 1/Examples/rps-exercise-answer.ipynb b/TensorFlow Deployment/Course 3 - TensorFlow Datasets/Week 1/Examples/rps-exercise-answer.ipynb
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+++ b/TensorFlow Deployment/Course 3 - TensorFlow Datasets/Week 1/Examples/rps-exercise-answer.ipynb	
@@ -0,0 +1,132 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "horse-or-human.ipynb",
+      "provenance": [],
+      "collapsed_sections": [],
+      "toc_visible": true,
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "accelerator": "GPU"
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/lmoroney/dlaicourse/blob/master/TensorFlow%20Deployment/Course%203%20-%20TensorFlow%20Datasets/Week%201/Examples/rps-exercise-answer.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "zX4Kg8DUTKWO",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "coY1OkmCnT_8",
+        "colab_type": "text"
+      },
+      "source": [
+        "Good to run this to ensure you are using TF2.x"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab_type": "code",
+        "id": "ioLbtB3uGKPX",
+        "colab": {}
+      },
+      "source": [
+        "try:\n",
+        "  # %tensorflow_version only exists in Colab.\n",
+        "  %tensorflow_version 2.x\n",
+        "except Exception:\n",
+        "  pass"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "iSq4t32ZHHpt",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "import tensorflow as tf\n",
+        "import tensorflow_datasets as tfds\n",
+        "\n",
+        "\n",
+        "def my_one_hot(feature, label):\n",
+        "    return feature, tf.one_hot(label, depth=3)\n",
+        "\n",
+        "\n",
+        "data = tfds.load('rock_paper_scissors', split='train', as_supervised=True)\n",
+        "val_data = tfds.load('rock_paper_scissors', split='test', as_supervised=True)\n",
+        "\n",
+        "data = data.map(my_one_hot)\n",
+        "val_data = val_data.map(my_one_hot)\n",
+        "\n",
+        "\n",
+        "train_batches = data.shuffle(100).batch(10)\n",
+        "validation_batches = val_data.batch(32)\n",
+        "\n",
+        "model = tf.keras.models.Sequential([\n",
+        "    tf.keras.layers.Conv2D(16, (3, 3), activation='relu', input_shape=(300, 300, 3)),\n",
+        "    tf.keras.layers.MaxPooling2D(2, 2),\n",
+        "    tf.keras.layers.Conv2D(32, (3, 3), activation='relu'),\n",
+        "    tf.keras.layers.MaxPooling2D(2, 2),\n",
+        "    tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),\n",
+        "    tf.keras.layers.MaxPooling2D(2, 2),\n",
+        "    tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),\n",
+        "    tf.keras.layers.MaxPooling2D(2, 2),\n",
+        "    tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),\n",
+        "    tf.keras.layers.MaxPooling2D(2, 2),\n",
+        "    tf.keras.layers.Flatten(),\n",
+        "    tf.keras.layers.Dense(512, activation='relu'),\n",
+        "    tf.keras.layers.Dense(3, activation='softmax')\n",
+        "])\n",
+        "\n",
+        "model.summary()\n",
+        "\n",
+        "model.compile(loss = 'categorical_crossentropy', optimizer='Adam', metrics=['accuracy'])\n",
+        "\n",
+        "history = model.fit(train_batches, epochs=10, validation_data=validation_batches, validation_steps=1)\n",
+        "model.save(\"test2.h5\")"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    }
+  ]
+}
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