Autodiff Example

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      "source": [
        "<a href=\"https://colab.research.google.com/github/JulesAM/TensorFlow-2.x-Tutorials/blob/master/Autodiff%20Example\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "outputId": "58e42151-c89e-479d-c9cb-7070fe873a88"
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      "source": [
        "# Tensorflow==2.0\n",
        "try:\n",
        "    %tensorflow_version 2.x\n",
        "except Exception:\n",
        "  pass\n",
        "import tensorflow as tf\n",
        "print(tf.__version__)\n",
        "# init Rand Tensor of size(2,2) \n",
        "x = tf.ones((2, 2))\n",
        "#print (\"x:\", x)\n",
        "with tf.GradientTape() as t: #load tape for the operations on objects\n",
        "  t.watch(x)  # records tape object\n",
        "  y = tf.reduce_sum(x) #sum along dimensions of tensor x and computes a scalar = 4\n",
        "  print(\"\\ny:\", y)\n",
        "  z = tf.multiply(y, y) # \n",
        "  print(\"\\z:\", z)\n",
        " \n",
        "# Derivative of z with respect to the original input tensor x\n",
        "dz_dx = t.gradient(z, x)\n",
        "print(\"\\ndz_dz:\", dz_dx)\n",
        "for i in [0, 1]:\n",
        "  for j in [0, 1]:\n",
        "    assert dz_dx[i][j].numpy() == 8.0"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "TensorFlow 2.x selected.\n",
            "2.1.0\n",
            "x: tf.Tensor(\n",
            "[[1. 1.]\n",
            " [1. 1.]], shape=(2, 2), dtype=float32)\n",
            "\n",
            "y: tf.Tensor(4.0, shape=(), dtype=float32)\n",
            "\\z: tf.Tensor(16.0, shape=(), dtype=float32)\n",
            "\n",
            "dz_dz: tf.Tensor(\n",
            "[[8. 8.]\n",
            " [8. 8.]], shape=(2, 2), dtype=float32)\n"
          ],
          "name": "stdout"
        }
      ]
    }
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