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+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "colab": {
+ "name": "TFE Workshop: control flow",
+ "version": "0.3.2",
+ "provenance": [],
+ "include_colab_link": true
+ }
+ },
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "view-in-github",
+ "colab_type": "text"
+ },
+ "source": [
+ "[View in Colaboratory](https://colab.research.google.com/gist/alextp/664b2f8700485ff6801f4d26293bd567/tfe-workshop-control-flow.ipynb)"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "9BpQzh9BvJlj",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 37
+ },
+ "outputId": "0b336886-8204-4815-89fa-5291a49d5784"
+ },
+ "cell_type": "code",
+ "source": [
+ "import tensorflow as tf\n",
+ "import numpy as np\n",
+ "tf.enable_eager_execution()"
+ ],
+ "execution_count": 1,
+ "outputs": []
+ },
+ {
+ "metadata": {
+ "id": "0roIB19GvOjI",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "# Eager execution basics\n",
+ "\n",
+ "When eager execution is enabled TensorFlow immediately executes operations, and Tensors are always available. "
+ ]
+ },
+ {
+ "metadata": {
+ "id": "jeO8F-V-vN24",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 68
+ },
+ "outputId": "aeb3bdec-50b7-440d-93d8-5a171f091081"
+ },
+ "cell_type": "code",
+ "source": [
+ "t = tf.constant([[1, 2], [3, 4]])\n",
+ "t"
+ ],
+ "execution_count": 2,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "<tf.Tensor: id=0, shape=(2, 2), dtype=int32, numpy=\n",
+ "array([[1, 2],\n",
+ " [3, 4]], dtype=int32)>"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 2
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "Y17RwSFxvlDL",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 68
+ },
+ "outputId": "cfcc10c7-707b-4997-99b3-a5f382c5166b"
+ },
+ "cell_type": "code",
+ "source": [
+ "tf.matmul(t, t)"
+ ],
+ "execution_count": 3,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "<tf.Tensor: id=2, shape=(2, 2), dtype=int32, numpy=\n",
+ "array([[ 7, 10],\n",
+ " [15, 22]], dtype=int32)>"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 3
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "Dab1bS3TvmRE",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ },
+ "outputId": "8a624f3d-a658-4359-c586-1c5f6bf4c8b7"
+ },
+ "cell_type": "code",
+ "source": [
+ "# It's also possible to have Python control flow which depends on the value of tensors.\n",
+ "if t[0, 0] > 0.5:\n",
+ " print(\"T is bigger\")\n",
+ "else:\n",
+ " print(\"T is smaller\")"
+ ],
+ "execution_count": 4,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "T is bigger\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "dPgptJcGwIon",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ },
+ "outputId": "c4f27f2b-0848-4475-dde5-2534dac65a5c"
+ },
+ "cell_type": "code",
+ "source": [
+ "# Tensors are also usable as numpy arrays\n",
+ "np.prod(t)"
+ ],
+ "execution_count": 6,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "24"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 6
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "p3DTfQXnwXzj",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "# Exercise\n",
+ "\n",
+ "The algorithm for bisecting line search is a pretty simple way to find a zero of a continuous scalar function in an interval [a,b] where f(a) and f(b) have different signs. Simply evaluate f((a+b)/2), and narrow the interval by replacing either a or b with (a+b)/2 such that the function when applied on the boundary of the interval still has different signs.\n",
+ "\n",
+ "Implement a python function `bisecting_line_search(f, a, b, epsilon)` which returns a value such that `tf.abs(f(value)) < epsilon`.\n",
+ "\n",
+ "One thing to keep in mind: python's `==` opertor is not overloaded on Tensors, so you need to use `tf.equal` to compare for equality."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "6eq0YuI6ykm5",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "# Example test harness to get you going\n",
+ "\n",
+ "def test_f(x):\n",
+ " return x - 0.1234\n",
+ "def bisecting_line_search(f, a, b, epsilon):\n",
+ " # Return x such that f(x) <= epsilon.\n",
+ " pass\n",
+ "a = tf.constant(0.0)\n",
+ "b = tf.constant(1.0)\n",
+ "epsilon = tf.constant(0.001)\n",
+ "x = bisecting_line_search(test_f, a, b, epsilon)\n"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "metadata": {
+ "id": "LcMmEfd_xvej",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 170
+ },
+ "outputId": "f402aa50-8ce3-4416-f755-8bbcd1af7809"
+ },
+ "cell_type": "code",
+ "source": [
+ "#@title Double-click to see the solution\n",
+ "\n",
+ "def bisecting_line_search(f, a, b, epsilon):\n",
+ " f_a = f(a)\n",
+ " f_b = f(b)\n",
+ " probe = (a + b) / 2\n",
+ " f_probe = f(probe)\n",
+ " while tf.abs(f_probe) > epsilon:\n",
+ " if tf.equal(tf.sign(f_probe), tf.sign(f_a)):\n",
+ " a = probe\n",
+ " f_a = f_probe\n",
+ " else:\n",
+ " b = probe\n",
+ " f_b = f_probe\n",
+ " probe = (a + b) / 2\n",
+ " f_probe = f(probe)\n",
+ " print(\"new probe\", probe)\n",
+ " return probe\n",
+ "\n",
+ "bisecting_line_search(test_f, 0., 1., 0.001)"
+ ],
+ "execution_count": 8,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "('new probe', 0.25)\n",
+ "('new probe', 0.125)\n",
+ "('new probe', 0.0625)\n",
+ "('new probe', 0.09375)\n",
+ "('new probe', 0.109375)\n",
+ "('new probe', 0.1171875)\n",
+ "('new probe', 0.12109375)\n",
+ "('new probe', 0.123046875)\n"
+ ],
+ "name": "stdout"
+ },
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "0.123046875"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 8
+ }
+ ]
+ }
+ ]
+}
generated by cgit v1.2.3 (git 2.39.1) at 2024-06-04 18:25:04 +0000