Tutorial 3.1 Solution_KL_divergence.ipynb

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        "**ML for LFT in a Nutshell**\n",
        "----\n",
        "\n",
        "Recall the following map from Prof. Cheng's lecture this morning.\n",
        "![picture](https://drive.google.com/uc?export=view&id=14eZSrjzaRHuz4Rtf3hAqyJ0uu6QW2dTS)\n",
        "Our endgoal is to generate samples from a target distribution $p(\\phi) = \\frac{e^{-S[\\phi]}}{Z}$.\n",
        "* Action $S[\\phi]$ in exponent is determined by the physics problem at hand. E.g. the action for interacting scalar field theory on lattice is  $$S[\\phi] = \\sum\\limits_{x,y \\in V_L} \\phi_x \\, \\Delta_{x,y} \\, \\phi_y + \\sum\\limits_{x \\in V_L} (m^2 \\phi_x^2 + \\lambda \\phi^4_x) ,$$ at leading order.\n",
        "* $Z$ is an appropriate normalization constant.\n",
        "\n",
        "Let us consider the simplest ML approch to generate target distribution $p(\\phi)$.\n",
        "* Initialize a Neural Network with parameters $\\theta$ sampled from some initial distribution $Q_{\\theta}$, and inputs are $x, y \\in V_L$. Then outputs are drawn from initial distribution $Q(x)$.\n",
        "* In general, the target distribution $p(\\phi)$ requires entirely different choice for network parameter distributions. Let us label this intended distribution as $P_{\\theta}$.\n",
        "* Network outputs for parameters sampled from $P_{\\theta}$, and inputs $x, y \\in V_L$, are labelled as $P(x)$.\n",
        "* Training updates distribution $Q_{\\theta}$ until the difference between distributions $P(x)$ and $Q(x)$ is minimized.\n",
        "\n",
        "**We need a good loss function**\n",
        "----\n",
        "\n",
        "* An ideal loss function will minimize differences between two probability distributions, of the forms $P(x) \\sim e^{- S_P(x)}$ and $Q(x) \\sim e^{- S_Q(x)}$.\n",
        "* If the loss function involves logarithms of $P(x)$ and $Q(x)$, then evaluating derivatives of the loss function becomes easier.\n",
        "\n",
        "KL or Kullback-Leibler divergence makes a good loss function, for problems of this nature.\n",
        "\n",
        "\n"
      ],
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      "source": [
        "**Kullback-Leibler diveregnce:**\n",
        "---\n",
        "\n",
        "It's a statistical measure of how much the probability distributions of two events differ from each other, or how much entropy is gained or lost in changing one probability distributon to the other.\n",
        "\n",
        "\n",
        "> Suppose, $Q(x)$ and $P(x)$ denote the probability distributions of *approximate* and *target* distributions $Q$ and $P$ on some space $x$. Then the KL divergence loss from $Q$ to $P$ is given by\n",
        "$$D_{KL}(P || Q) = - \\sum_{x \\in X} P(x) \\log\\bigg(\\frac{Q(x)}{P(x)} \\bigg) .$$\n",
        "Note that $D_{KL}(P||Q) \\neq D_{KL}(Q || P)$, so KL divergence does not play the role of actual distance."
      ],
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      "source": [
        "\n",
        "---\n",
        "**Example 0:**\n",
        "---\n",
        "\n",
        "\n",
        "Let us demonstrate how the KL divergence between two distributions are calculated numerically. To keep life simple, we will call the function [*losses.KLDivergence*](https://www.tensorflow.org/api_docs/python/tf/keras/losses/KLDivergence) from \"*keras*\" library in \"*Tensorflow*\", and use it to directly compute the KL divergence between two data sets.  \n",
        "\n",
        "We break this down into the following steps:"
      ],
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    {
      "cell_type": "markdown",
      "source": [
        "*   Import libraries necessary to calculate KL divergence.\n",
        "\n"
      ],
      "metadata": {
        "id": "cJ9MU10ylXOu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n",
        "import tensorflow as tf"
      ],
      "metadata": {
        "id": "JPh2SqB7lnPN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "*   Define any two distributions. I am defining target distribution $P$ and approximate distribution $Q$ as two arrays. If you want to try something different, choose any $P$ and $Q$ from the same 2D or 3D Gaussian distribution, and compute the KL divergence between them.\n",
        "\n"
      ],
      "metadata": {
        "id": "IxzUbRJMo3Yg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# True distribution P_x and approximate distribution Q_x\n",
        "P_x = [[-0.4, 1], [3, 0.2]]\n",
        "Q_x = [[0.16, 0.4], [0.14, 2.1]]"
      ],
      "metadata": {
        "id": "helIaHqhAKA4"
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      "outputs": []
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      "source": [
        "* Next, the function [*losses.KLDivergence*](https://www.tensorflow.org/api_docs/python/tf/keras/losses/KLDivergence) takes in two arguments - 1) reduction, and 2) name. We need to specify \"*name=kl_divergence*\", and for reduction, we want something that will sum over the batch size (to keep our ML spirit up). The choice for summing over batch size is \"*reduction=auto*\", used here."
      ],
      "metadata": {
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    {
      "cell_type": "code",
      "source": [
        "tf.keras.losses.KLDivergence(reduction=\"auto\", name=\"kl_divergence\")\n",
        "kl = tf.keras.losses.KLDivergence()\n",
        "kl(P_x, Q_x).numpy()"
      ],
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        "colab": {
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        "outputId": "0542452f-c918-4063-96ff-9fecedd23884"
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      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "**Example 1: Let us use KL divergence loss function for CIFAR10 image classification.**\n",
        "---\n",
        "\n",
        "First, walk through the following steps to define train data and test data from CIFAR10, and then initialize a simple CNN. We will utilize Keras library in Tensorflow for this example, as its prebuilt functions are helpful for beginners.\n",
        "\n",
        "\n"
      ],
      "metadata": {
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    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "*   First, we import the libraries that we need.\n",
        "\n"
      ],
      "metadata": {
        "id": "pJnNb326p2nk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import keras\n",
        "from keras.datasets import cifar10\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D\n",
        "from keras import backend as K"
      ],
      "metadata": {
        "id": "YNBftyn4p090"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "*   Given below are model configurations. One can choose differently, I encourage you to play with these attributes at a later time.\n",
        "\n"
      ],
      "metadata": {
        "id": "LEmXD4ivqO4-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "img_width, img_height         = 32, 32\n",
        "batch_size                    = 250\n",
        "no_epochs                     = 25\n",
        "no_classes                    = 10\n",
        "validation_split              = 0.4\n",
        "verbosity                     = 1"
      ],
      "metadata": {
        "id": "zzMMjSBjqNKL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Load CIFAR10 dataset as train and test data."
      ],
      "metadata": {
        "id": "GlPNVrSYqmy6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "(i_train, o_train), (i_test, o_test) = cifar10.load_data()"
      ],
      "metadata": {
        "id": "KosMUY_pqipq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9d010120-9de8-4368-c556-df9e04b0e230"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\n",
            "170498071/170498071 [==============================] - 3s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* We need to reshape data based on channels first / channels last strategy."
      ],
      "metadata": {
        "id": "8roXAtdxqfCd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "if K.image_data_format() == 'channels_first':\n",
        "    i_train = i_train.reshape(i_train.shape[0],3, img_width, img_height)\n",
        "    i_test = i_test.reshape(i_test.shape[0], 3, img_width, img_height)\n",
        "    i_shape = (3, img_width, img_height)\n",
        "else:\n",
        "    i_train = i_train.reshape(i_train.shape[0], img_width, img_height, 3)\n",
        "    i_test = i_test.reshape(i_test.shape[0], img_width, img_height, 3)\n",
        "    i_shape = (img_width  , img_height, 3)\n"
      ],
      "metadata": {
        "id": "uSLPq487rCoW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "* We want all numbers as floats."
      ],
      "metadata": {
        "id": "iMjKVWotrb5G"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "i_train = i_train.astype('float32')\n",
        "i_test = i_test.astype('float32')"
      ],
      "metadata": {
        "id": "Kb62IZunrVqT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "* The test and train data require an appropriate normalization."
      ],
      "metadata": {
        "id": "oEPmcTXPrw06"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "i_train = i_train / 255\n",
        "i_test = i_test / 255"
      ],
      "metadata": {
        "id": "mz2e7h52rr7t"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Next, we need to convert target vectors to categorical targets."
      ],
      "metadata": {
        "id": "yNZINhkDry9f"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "o_train = keras.utils.to_categorical(o_train, no_classes)\n",
        "o_test = keras.utils.to_categorical(o_test, no_classes)"
      ],
      "metadata": {
        "id": "kAV1XQSjr_Gx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "* A CNN model is defined below. You can add or remove layers, or change layer sizes, or activation functions, if you want."
      ],
      "metadata": {
        "id": "Ff47tA-_tJVv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "model.add(Conv2D(32, kernel_size=(3, 3), activation='tanh', input_shape=i_shape))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "model.add(Dropout(0.40))\n",
        "model.add(Conv2D(128, kernel_size=(3, 3), activation='relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "model.add(Dropout(0.70))\n",
        "model.add(Flatten())\n",
        "model.add(Dense(64, activation='exponential'))\n",
        "model.add(Dense(no_classes, activation='softmax'))"
      ],
      "metadata": {
        "id": "f86o38MatDpn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Next, it is time to train the CNN! There are two steps to this.\n",
        " * **First**, we need to \"*compile*\" the model, where we specify the **loss function** and the optimizer. These choices are used for training. We will use the function, given below, for compiling the CNN.\n",
        "```\n",
        "model.compile(loss= ,optimizer= ,metrics= )  \n",
        "```\n",
        "  Here, we will define a custom KL divergence loss function, instead of calling the inbuilt KL divergence loss ```keras.losses.kullback_leibler_divergence```. To keep life simple, we will choose ```optimizer=keras.optimizers.Adam()``` and ```metrics=['accuracy']```, respectively.\n",
        "\n",
        " * **Next**, we need to train the CNN. For this, we will use\n",
        "```\n",
        "model.fit(X, Y, epochs= , batch_size= , verbose= , validation_split= )\n",
        "```\n",
        "where ```X``` and ```Y``` are model inputs and outputs.\n",
        "\n",
        "  One can obtain training history using *.History()*. Moreover, the train and test loss and accuracies can be obtained using *.history['loss']*, *.history['val_loss']*, *.history['accuracy']*, and *.history['val_accuracy']*, respectively."
      ],
      "metadata": {
        "id": "PfNbtkpPvUZ3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Exercise 1: Define a KL loss function to compile this CNN, and then train it. Plot the loss and accuracies for train and test data sets.**\n",
        "---"
      ],
      "metadata": {
        "id": "mTikxsQxNixZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Solution (To be removed before GitHUb upload)**\n",
        "---"
      ],
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        "id": "VPwlb26L2A4w"
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    },
    {
      "cell_type": "code",
      "source": [
        "# define the custom KL divergence loss\n",
        "def my_KL_loss_fn(y_true, y_pred):\n",
        "    return tf.reduce_sum(-tf.math.multiply_no_nan(x=y_true,y=(tf.math.log(y_pred  + 1e-3) - tf.math.log(y_true + 1e-3) )), axis=-1) # an upper cap of 1e-3 is added in argument of log(y_true) to ensure we don't get NANs from log(0)\n",
        "\n",
        "# compile the CNN\n",
        "model.compile(loss=my_KL_loss_fn,\n",
        "              optimizer=keras.optimizers.Adam(),\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "# train the CNN\n",
        "model_history = model.fit(i_train, o_train,\n",
        "      batch_size=batch_size,\n",
        "      epochs=no_epochs,\n",
        "      verbose=verbosity,\n",
        "      validation_data=(i_test, o_test),\n",
        "      validation_split=validation_split\n",
        ")\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# plot accuracy\n",
        "plt.plot(model_history.history['accuracy'])\n",
        "plt.plot(model_history.history['val_accuracy'])\n",
        "plt.title('model accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()\n",
        "# plot loss\n",
        "plt.plot(model_history.history['loss'])\n",
        "plt.plot(model_history.history['val_loss'])\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()\n"
      ],
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          "height": 1000
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      "execution_count": null,
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          "text": [
            "Epoch 1/25\n",
            "200/200 [==============================] - 122s 599ms/step - loss: 1.7392 - accuracy: 0.3693 - val_loss: 1.4109 - val_accuracy: 0.5029\n",
            "Epoch 2/25\n",
            "200/200 [==============================] - 117s 583ms/step - loss: 1.3888 - accuracy: 0.5032 - val_loss: 1.2258 - val_accuracy: 0.5784\n",
            "Epoch 3/25\n",
            "200/200 [==============================] - 115s 575ms/step - loss: 1.2698 - accuracy: 0.5490 - val_loss: 1.1447 - val_accuracy: 0.5978\n",
            "Epoch 4/25\n",
            "200/200 [==============================] - 118s 590ms/step - loss: 1.1900 - accuracy: 0.5777 - val_loss: 1.0889 - val_accuracy: 0.6196\n",
            "Epoch 5/25\n",
            "200/200 [==============================] - 133s 666ms/step - loss: 1.1328 - accuracy: 0.6001 - val_loss: 1.0235 - val_accuracy: 0.6438\n",
            "Epoch 6/25\n",
            "200/200 [==============================] - 128s 639ms/step - loss: 1.0926 - accuracy: 0.6124 - val_loss: 0.9973 - val_accuracy: 0.6517\n",
            "Epoch 7/25\n",
            "200/200 [==============================] - 132s 655ms/step - loss: 1.0562 - accuracy: 0.6257 - val_loss: 0.9660 - val_accuracy: 0.6632\n",
            "Epoch 8/25\n",
            "200/200 [==============================] - 116s 578ms/step - loss: 1.0257 - accuracy: 0.6407 - val_loss: 0.9976 - val_accuracy: 0.6432\n",
            "Epoch 9/25\n",
            "200/200 [==============================] - 112s 559ms/step - loss: 1.0033 - accuracy: 0.6473 - val_loss: 0.9134 - val_accuracy: 0.6827\n",
            "Epoch 10/25\n",
            "200/200 [==============================] - 107s 535ms/step - loss: 0.9813 - accuracy: 0.6538 - val_loss: 0.8826 - val_accuracy: 0.6942\n",
            "Epoch 11/25\n",
            "200/200 [==============================] - 113s 564ms/step - loss: 0.9611 - accuracy: 0.6623 - val_loss: 0.9025 - val_accuracy: 0.6831\n",
            "Epoch 12/25\n",
            "200/200 [==============================] - 115s 573ms/step - loss: 0.9423 - accuracy: 0.6687 - val_loss: 0.8712 - val_accuracy: 0.6954\n",
            "Epoch 13/25\n",
            "200/200 [==============================] - 111s 553ms/step - loss: 0.9304 - accuracy: 0.6718 - val_loss: 0.8739 - val_accuracy: 0.6968\n",
            "Epoch 14/25\n",
            "200/200 [==============================] - 114s 572ms/step - loss: 0.9104 - accuracy: 0.6783 - val_loss: 0.8680 - val_accuracy: 0.7000\n",
            "Epoch 15/25\n",
            "200/200 [==============================] - 114s 569ms/step - loss: 0.9009 - accuracy: 0.6853 - val_loss: 0.8970 - val_accuracy: 0.6837\n",
            "Epoch 16/25\n",
            "200/200 [==============================] - 119s 597ms/step - loss: 0.8834 - accuracy: 0.6886 - val_loss: 0.8401 - val_accuracy: 0.7089\n",
            "Epoch 17/25\n",
            "200/200 [==============================] - 108s 542ms/step - loss: 0.8712 - accuracy: 0.6932 - val_loss: 0.8630 - val_accuracy: 0.7000\n",
            "Epoch 18/25\n",
            "200/200 [==============================] - 114s 571ms/step - loss: 0.8640 - accuracy: 0.6971 - val_loss: 0.8131 - val_accuracy: 0.7166\n",
            "Epoch 19/25\n",
            "200/200 [==============================] - 122s 609ms/step - loss: 0.8504 - accuracy: 0.7035 - val_loss: 0.7974 - val_accuracy: 0.7239\n",
            "Epoch 20/25\n",
            "200/200 [==============================] - 118s 592ms/step - loss: 0.8479 - accuracy: 0.7020 - val_loss: 0.8371 - val_accuracy: 0.7056\n",
            "Epoch 21/25\n",
            "200/200 [==============================] - 122s 612ms/step - loss: 0.8320 - accuracy: 0.7063 - val_loss: 0.7989 - val_accuracy: 0.7239\n",
            "Epoch 22/25\n",
            "200/200 [==============================] - 120s 599ms/step - loss: 0.8277 - accuracy: 0.7079 - val_loss: 0.8118 - val_accuracy: 0.7182\n",
            "Epoch 23/25\n",
            "200/200 [==============================] - 115s 577ms/step - loss: 0.8213 - accuracy: 0.7119 - val_loss: 0.7999 - val_accuracy: 0.7199\n",
            "Epoch 24/25\n",
            "200/200 [==============================] - 112s 556ms/step - loss: 0.8193 - accuracy: 0.7137 - val_loss: 0.7992 - val_accuracy: 0.7231\n",
            "Epoch 25/25\n",
            "200/200 [==============================] - 109s 546ms/step - loss: 0.8098 - accuracy: 0.7162 - val_loss: 0.7716 - val_accuracy: 0.7368\n"
          ]
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          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Forward and Reverse KL divergences:**\n",
        "---\n",
        "\n",
        "$D_{KL}(P||Q)$ and $D_{KL}(Q||P)$ are respectively known as the Forward and Reverse KL divergences for true and approximate distributions $P(x)$ and $Q(x)$.\n",
        "\n",
        "In the case of Forward KL divergence, $P(x) = 0$ does not contribute towards minimizing the loss, and $Q(x)$ is updated to approximate those data points for which $P(x)>0$. Thus, Forward KL divergence may not result in the best approximate distribution $Q(x)$, at the end of training.\n",
        "\n",
        "> Let us demonstrate this pictorially --\n",
        "![picture](https://drive.google.com/uc?export=view&id=15HnW-cd2QInRNP5LapvvPlTyGYCq-XiI)\n",
        "* P(x) is our target distribution.\n",
        "* Q(x) is the initial distribution, before we start training.\n",
        "* Using Forward KL divergence, the NN model learns to approximate for any $P(x)>0$, such approximation is bad here.\n",
        "* We obtain the following Q(x).\n",
        "![picture](https://drive.google.com/uc?export=view&id=1j-hZ-Co_R_eaECTUJPWmrtts31WjePiB)\n",
        "* Not the final approximate distribution we would like.\n",
        "\n",
        "\n",
        "On the other hand, the *Reverse KL divergence* $$D_{KL}(Q || P) = - \\sum_{x \\in X} Q(x) \\log\\bigg(\\frac{P(x)}{Q(x)} \\bigg)$$ ensures that any point $Q(x)=0$ does not contribute to optimization. Thus, $P(x)$ can not be learned starting from $Q(x)=0$; however, whenever $Q(x)>0$, reverse KL divergence contributes to learning, for both $P(x)=0$ and $P(x)>0$ cases.\n",
        "\n",
        "Finally, the choice of Forward vs. Reverse KL divergence depends entirely on true and initial approximate distributions $P(x)$ and $Q(x)$.\n"
      ],
      "metadata": {
        "id": "BmX67hP8a9ki"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Exercise 2: Define a Reverse KL loss, then plot and compare the train and test losses and accuracies of the same CNN using this on same test, train datasets. Any improvement?**\n",
        "---\n"
      ],
      "metadata": {
        "id": "VIgxkxw0N8Lf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Solution (To be removed before GitHub upload)**\n",
        "---"
      ],
      "metadata": {
        "id": "5pyrW52HCdlN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# redefine the loss function as reverse KL divergence\n",
        "def my_reverse_KL_loss_fn(y_true, y_pred):\n",
        "    return tf.reduce_sum(-tf.math.multiply_no_nan(x=y_pred,y=( - tf.math.log(y_pred  + 1e-3) + tf.math.log(y_true + 1e-3) )), axis=-1) # an upper cap of 1e-3 is added in argument of log(y_true) to ensure we don't get NANs from log(0)\n",
        "\n",
        "# compile the same model with new loss function\n",
        "model.compile(loss=my_reverse_KL_loss_fn,\n",
        "              optimizer=keras.optimizers.Adam(),\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "# train using new loss function\n",
        "model_history_reverse_KL = model.fit(i_train, o_train,\n",
        "      batch_size=batch_size,\n",
        "      epochs=no_epochs,\n",
        "      verbose=verbosity,\n",
        "      validation_data=(i_test, o_test),\n",
        "      validation_split=validation_split\n",
        ")\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# plot old and new accuracies (both forward and reverse KL div.)\n",
        "plt.plot(model_history.history['accuracy'])\n",
        "plt.plot(model_history.history['val_accuracy'])\n",
        "plt.plot(model_history_reverse_KL.history['accuracy'])\n",
        "plt.plot(model_history_reverse_KL.history['val_accuracy'])\n",
        "plt.title('model accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train_FKL', 'test_FKL', 'train_RKL', 'test_RKL'], loc='upper left')\n",
        "plt.show()\n",
        "# plot old and new losses (both forward and reverse KL div.)\n",
        "plt.plot(model_history.history['loss'])\n",
        "plt.plot(model_history.history['val_loss'])\n",
        "plt.plot(model_history_reverse_KL.history['loss'])\n",
        "plt.plot(model_history_reverse_KL.history['val_loss'])\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train_FKL', 'test_FKL', 'train_RKL', 'test_RKL'], loc='upper left')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "UIzyZVHWCjX7",
        "outputId": "6a13e3ae-5dfc-4e96-80bf-a48072246bbd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/25\n",
            "200/200 [==============================] - 104s 518ms/step - loss: 0.7277 - accuracy: 0.7476 - val_loss: 0.7460 - val_accuracy: 0.7425\n",
            "Epoch 2/25\n",
            "200/200 [==============================] - 105s 527ms/step - loss: 0.7211 - accuracy: 0.7464 - val_loss: 0.7104 - val_accuracy: 0.7541\n",
            "Epoch 3/25\n",
            "200/200 [==============================] - 110s 548ms/step - loss: 0.7169 - accuracy: 0.7501 - val_loss: 0.7256 - val_accuracy: 0.7473\n",
            "Epoch 4/25\n",
            "200/200 [==============================] - 110s 551ms/step - loss: 0.7131 - accuracy: 0.7504 - val_loss: 0.7226 - val_accuracy: 0.7499\n",
            "Epoch 5/25\n",
            "200/200 [==============================] - 105s 528ms/step - loss: 0.7123 - accuracy: 0.7519 - val_loss: 0.7120 - val_accuracy: 0.7515\n",
            "Epoch 6/25\n",
            "200/200 [==============================] - 105s 527ms/step - loss: 0.7070 - accuracy: 0.7526 - val_loss: 0.7047 - val_accuracy: 0.7553\n",
            "Epoch 7/25\n",
            "200/200 [==============================] - 105s 526ms/step - loss: 0.7101 - accuracy: 0.7521 - val_loss: 0.7157 - val_accuracy: 0.7521\n",
            "Epoch 8/25\n",
            "200/200 [==============================] - 115s 575ms/step - loss: 0.7158 - accuracy: 0.7500 - val_loss: 0.7583 - val_accuracy: 0.7379\n",
            "Epoch 9/25\n",
            "200/200 [==============================] - 106s 529ms/step - loss: 0.7017 - accuracy: 0.7558 - val_loss: 0.7603 - val_accuracy: 0.7358\n",
            "Epoch 10/25\n",
            "200/200 [==============================] - 109s 548ms/step - loss: 0.7007 - accuracy: 0.7531 - val_loss: 0.7134 - val_accuracy: 0.7520\n",
            "Epoch 11/25\n",
            "200/200 [==============================] - 105s 524ms/step - loss: 0.6979 - accuracy: 0.7574 - val_loss: 0.7320 - val_accuracy: 0.7487\n",
            "Epoch 12/25\n",
            "200/200 [==============================] - 105s 525ms/step - loss: 0.7088 - accuracy: 0.7518 - val_loss: 0.7251 - val_accuracy: 0.7495\n",
            "Epoch 13/25\n",
            "200/200 [==============================] - 105s 526ms/step - loss: 0.7088 - accuracy: 0.7538 - val_loss: 0.7462 - val_accuracy: 0.7414\n",
            "Epoch 14/25\n",
            "200/200 [==============================] - 108s 542ms/step - loss: 0.7041 - accuracy: 0.7549 - val_loss: 0.7129 - val_accuracy: 0.7568\n",
            "Epoch 15/25\n",
            "200/200 [==============================] - 104s 520ms/step - loss: 0.6941 - accuracy: 0.7603 - val_loss: 0.7345 - val_accuracy: 0.7440\n",
            "Epoch 16/25\n",
            "200/200 [==============================] - 105s 526ms/step - loss: 0.6991 - accuracy: 0.7556 - val_loss: 0.7164 - val_accuracy: 0.7541\n",
            "Epoch 17/25\n",
            "200/200 [==============================] - 105s 528ms/step - loss: 0.6911 - accuracy: 0.7566 - val_loss: 0.7744 - val_accuracy: 0.7311\n",
            "Epoch 18/25\n",
            "200/200 [==============================] - 105s 525ms/step - loss: 0.6939 - accuracy: 0.7571 - val_loss: 0.7071 - val_accuracy: 0.7533\n",
            "Epoch 19/25\n",
            "200/200 [==============================] - 105s 525ms/step - loss: 0.7045 - accuracy: 0.7537 - val_loss: 0.7292 - val_accuracy: 0.7459\n",
            "Epoch 20/25\n",
            "200/200 [==============================] - 106s 530ms/step - loss: 0.6918 - accuracy: 0.7580 - val_loss: 0.7058 - val_accuracy: 0.7529\n",
            "Epoch 21/25\n",
            "200/200 [==============================] - 106s 530ms/step - loss: 0.6967 - accuracy: 0.7564 - val_loss: 0.7193 - val_accuracy: 0.7506\n",
            "Epoch 22/25\n",
            "200/200 [==============================] - 105s 525ms/step - loss: 0.6961 - accuracy: 0.7571 - val_loss: 0.7217 - val_accuracy: 0.7469\n",
            "Epoch 23/25\n",
            "200/200 [==============================] - 104s 520ms/step - loss: 0.6934 - accuracy: 0.7584 - val_loss: 0.7071 - val_accuracy: 0.7550\n",
            "Epoch 24/25\n",
            "200/200 [==============================] - 104s 522ms/step - loss: 0.6890 - accuracy: 0.7593 - val_loss: 0.7040 - val_accuracy: 0.7595\n",
            "Epoch 25/25\n",
            "200/200 [==============================] - 107s 533ms/step - loss: 0.6892 - accuracy: 0.7602 - val_loss: 0.7178 - val_accuracy: 0.7507\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "References\n",
        "* https://github.com/christianversloot/machine-learning-articles/blob/main/how-to-use-kullback-leibler-divergence-kl-divergence-with-keras.md\n",
        "* https://keras.io/api/losses/probabilistic_losses/#kldivergence-function\n",
        "* https://www.analyticsvidhya.com/blog/2022/09/dummies-guide-to-writing-a-custom-loss-function-in-tensorflow/\n",
        "* https://dibyaghosh.com/blog/probability/kldivergence.html"
      ],
      "metadata": {
        "id": "ts1SIiNZz0Xt"
      }
    }
  ]
}