2-1-6-MimimizingLoss.ipynb

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    "# First import the functions we will need\n",
    "from __future__ import absolute_import, division, print_function, unicode_literals\n",
    "\n",
    "try:\n",
    "  # %tensorflow_version only exists in Colab.\n",
    "  %tensorflow_version 2.x\n",
    "except Exception:\n",
    "  pass\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "9O5-58WYokl3"
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   "source": [
    "# GradientTape\n",
    "\n",
    "The Calculus is managed by a TensorFlow Gradient Tape. You can learn more about the gradient tape at https://www.tensorflow.org/api_docs/python/tf/GradientTape, and we will discuss it later in the course."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    "# Define our initial guess\n",
    "INITIAL_W = 10.0\n",
    "INITIAL_B = 10.0\n",
    "\n",
    "# Define our loss function\n",
    "def loss(predicted_y, target_y):\n",
    "  return tf.reduce_mean(tf.square(predicted_y - target_y))\n",
    "\n",
    "# Define our training procedure\n",
    "def train(model, inputs, outputs, learning_rate):\n",
    "  with tf.GradientTape() as t:\n",
    "    current_loss = loss(model(inputs), outputs)\n",
    "    # Here is where you differentiate the model values with respect to the loss function\n",
    "    dw, db = t.gradient(current_loss, [model.w, model.b])\n",
    "    # And here is where you update the model values based on the learning rate chosen\n",
    "    model.w.assign_sub(learning_rate * dw)\n",
    "    model.b.assign_sub(learning_rate * db)\n",
    "    return current_loss\n",
    "\n",
    "# Define our simple linear regression model\n",
    "class Model(object):\n",
    "  def __init__(self):\n",
    "    # Initialize the weights\n",
    "    self.w = tf.Variable(INITIAL_W)\n",
    "    self.b = tf.Variable(INITIAL_B)\n",
    "\n",
    "  def __call__(self, x):\n",
    "    return self.w * x + self.b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train our model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
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   "outputs": [],
   "source": [
    "# Define our input data and learning rate\n",
    "xs = [-1.0, 0.0, 1.0, 2.0, 3.0, 4.0]\n",
    "ys = [-3.0, -1.0, 1.0, 3.0, 5.0, 7.0]\n",
    "LEARNING_RATE=0.09\n",
    "\n",
    "# Instantiate our model\n",
    "model = Model()\n",
    "\n",
    "# Collect the history of w-values and b-values to plot later\n",
    "list_w, list_b = [], []\n",
    "epochs = range(50)\n",
    "losses = []\n",
    "for epoch in epochs:\n",
    "  list_w.append(model.w.numpy())\n",
    "  list_b.append(model.b.numpy())\n",
    "  current_loss = train(model, xs, ys, learning_rate=LEARNING_RATE)\n",
    "  losses.append(current_loss)\n",
    "  print('Epoch %2d: w=%1.2f b=%1.2f, loss=%2.5f' %\n",
    "        (epoch, list_w[-1], list_b[-1], current_loss))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot our trained values over time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "HGgb5grSk8Ax"
   },
   "outputs": [],
   "source": [
    "# Plot the w-values and b-values for each training Epoch against the true values\n",
    "TRUE_w = 2.0\n",
    "TRUE_b = -1.0\n",
    "plt.plot(epochs, list_w, 'r', epochs, list_b, 'b')\n",
    "plt.plot([TRUE_w] * len(epochs), 'r--', [TRUE_b] * len(epochs), 'b--')\n",
    "plt.legend(['w', 'b', 'True w', 'True b'])\n",
    "plt.show()"
   ]
  }
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