2-1-13-AssignmentQuestion.ipynb

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 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Your first assignment\n",
    "\n",
    "In this assignment you will design your own linear regression model using TensorFlow. If you are taking the course for a certificate during the end of section test you will be asked questions about this assignment.\n",
    "\n",
    "We start by setting up the problem for you."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# DO NOT CHANGE THIS CELL\n",
    "# We first import TensorFlow and other libraries\n",
    "import sys\n",
    "\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "from tensorflow.keras import Sequential\n",
    "from tensorflow.keras.layers import Dense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# DO NOT CHANGE THIS CELL\n",
    "# This script requires TensorFlow 2 and Python 3.\n",
    "if tf.__version__.split('.')[0] != '2':\n",
    "    raise Exception((f\"The script is developed and tested for tensorflow 2. \"\n",
    "                     f\"Current version: {tf.__version__}\"))\n",
    "\n",
    "if sys.version_info.major < 3:\n",
    "    raise Exception((f\"The script is developed and tested for Python 3. \"\n",
    "                     f\"Current version: {sys.version_info.major}\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "eJEpdsvsGaqD"
   },
   "outputs": [],
   "source": [
    "# DO NOT CHANGE THIS CELL\n",
    "# We then set up some functions and local variables\n",
    "predictions = []\n",
    "class myCallback(tf.keras.callbacks.Callback):\n",
    "  def on_epoch_end(self, epoch, logs={}):\n",
    "    predictions.append(model.predict(xs))\n",
    "callbacks = myCallback()\n",
    "\n",
    "# We then define the xs (inputs) and ys (outputs)\n",
    "xs = np.array([-1.0, 0.0, 1.0, 2.0, 3.0, 4.0], dtype=float)\n",
    "ys = np.array([-3.0, -1.0, 1.0, 3.0, 5.0, 7.0], dtype=float)"
   ]
  },
  {
   "attachments": {},
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   "metadata": {},
   "source": [
    "Please fill out the cell below to define your model and compile it.\n",
    "\n",
    "*A hint: your model may want to learn the average error over the dataset.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# PLEASE COMPELTE THIS CELL\n",
    "\n",
    "SHAPE = #YOUR CODE HERE#\n",
    "LOSS = #YOUR CODE HERE#\n",
    "\n",
    "# Define your model type\n",
    "model = Sequential([Dense(units=1, input_shape=SHAPE)])\n",
    "    \n",
    "# Compile your model with choice of optimizer and loss function\n",
    "model.compile(optimizer='sgd', loss=LOSS)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then include the call to fit your model to the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# DO NOT CHANGE THIS CELL\n",
    "\n",
    "# We then fit the model\n",
    "model.fit(xs, ys, epochs=300, callbacks=[callbacks], verbose=2)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then plot the resulting prediction at EPOCH_NUMBERS = 1,25,50,150,300. If you'd like to see other Epochs simply update the EPOCH_NUMBERS variable and re-run the cell!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "9NSFY8oQbNlD"
   },
   "outputs": [],
   "source": [
    "EPOCH_NUMBERS=[1,25,50,150,300] # Update me to see other Epochs\n",
    "plt.plot(xs,ys,label = \"Ys\")\n",
    "for EPOCH in EPOCH_NUMBERS:\n",
    "    plt.plot(xs,predictions[EPOCH-1],label = \"Epoch = \" + str(EPOCH))\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
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