{
  "cells": [
    {
      "cell_type": "markdown",
      "id": "2111825f",
      "metadata": {
        "id": "2111825f"
      },
      "source": [
        "<h1 align='center' dir='rtl' style='color:yellow'>IpsofactoModel</h1>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1ab61eab",
      "metadata": {
        "id": "1ab61eab"
      },
      "source": [
        "<h2 align='center' dir='rtl'>فراخوانی کتابخانه های مورد نیاز</h2>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "id": "b97c8a8b",
      "metadata": {
        "id": "b97c8a8b"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras.layers import Dense, Input, Flatten, Conv2D\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.datasets import mnist"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "79ef7ea0",
      "metadata": {
        "id": "79ef7ea0"
      },
      "source": [
        "<h2 align='center' dir='rtl'> لود کردن دیتاست MNIST</h2>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "511bc7e3",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "511bc7e3",
        "outputId": "b627dc6a-3c34-49c0-a89e-698f2e4ead7e"
      },
      "outputs": [],
      "source": [
        "train_set, test_set = mnist.load_data()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8af0ed71",
      "metadata": {
        "id": "8af0ed71"
      },
      "source": [
        "<h2 align='center' dir='rtl'>طراحی لایه های استخراج ویژگی و سنجش نسبت ها</h2>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "bb994741",
      "metadata": {
        "id": "bb994741"
      },
      "source": [
        "<h3 align='center' dir='rtl'>لایه استخراج ویژگی</h3>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "8494ca6d",
      "metadata": {
        "id": "8494ca6d"
      },
      "outputs": [],
      "source": [
        "# لایه استخراج ویژگی\n",
        "kernel_initializer = 'normal'\n",
        "activation = \"relu\"\n",
        "\n",
        "dtype = tf.float32\n",
        "\n",
        "\n",
        "def feature_extractor():\n",
        "    shape=(28,28,1)\n",
        "    chs=1\n",
        "    inputs = Input(shape)\n",
        "    layer = Conv2D(filters=32*chs, kernel_size=(3,3), activation=activation, kernel_initializer=kernel_initializer)(inputs)\n",
        "    layer = Conv2D(filters=16*chs, kernel_size=(3,3), strides=(2, 2), activation=activation, kernel_initializer=kernel_initializer)(layer)\n",
        "    layer = Conv2D(filters=8*chs, kernel_size=(3,3), activation=activation, kernel_initializer=kernel_initializer)(layer)\n",
        "    layer = Conv2D(filters=4*chs, kernel_size=(3,3), strides=(2, 2), activation='linear', kernel_initializer=kernel_initializer)(layer)\n",
        "    layer = Conv2D(filters=64*2, kernel_size=(4,4), activation='softplus', kernel_initializer=kernel_initializer)(layer)\n",
        "    layer = Flatten()(layer)\n",
        "\n",
        "    model = Model(inputs, layer)\n",
        "\n",
        "    return model"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ed717db9",
      "metadata": {
        "id": "ed717db9"
      },
      "source": [
        "<h3 align='center' dir='rtl'>لایه سنجش نسبت ها</h3>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "f63a27eb",
      "metadata": {
        "id": "f63a27eb"
      },
      "outputs": [],
      "source": [
        "# لایه سنجش نسبت ها\n",
        "kernel_initializer = 'normal'\n",
        "activation = \"relu\"\n",
        "\n",
        "dtype = tf.float32\n",
        "\n",
        "\n",
        "def comparator():\n",
        "    shape=(64*2+10,)\n",
        "    chs=1\n",
        "    inputs = Input(shape)\n",
        "    layer = Flatten()(inputs)\n",
        "    layer = Dense(32, activation='relu', use_bias=True, kernel_initializer=kernel_initializer)(layer)\n",
        "    layer = Dense(16, activation='relu', use_bias=True, kernel_initializer=kernel_initializer)(layer)\n",
        "    log_prob = Dense(10, activation='softmax', use_bias=True, kernel_initializer=kernel_initializer)(layer)\n",
        "\n",
        "    model = Model(inputs, log_prob)\n",
        "\n",
        "    return model"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "614adfad",
      "metadata": {
        "id": "614adfad"
      },
      "source": [
        "<h2 align='center' dir='rtl'>طراحی‌ و ساختن شبکه عصبی</h2>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "6a14ab0a",
      "metadata": {
        "id": "6a14ab0a"
      },
      "outputs": [],
      "source": [
        "def combiner(f0, f1, lbl1):\n",
        "    # ترکیب ویژگی های کلاس مورد مطالعه با کلاس های دیگر\n",
        "\n",
        "    inp = f0 * f1\n",
        "    return tf.concat([inp, lbl1], axis=1)\n",
        "\n",
        "\n",
        "class InfluenceModel(keras.Model):\n",
        "    def __init__(self, feature_extractor, comparator):\n",
        "        super(InfluenceModel, self).__init__()\n",
        "        #  لایه استخراج ویژگی\n",
        "        self.feature_extractor = feature_extractor\n",
        "        # لایه تمام متصل (سنجش نسبت ها)\n",
        "        self.comparator = comparator\n",
        "\n",
        "        self.loss = keras.losses.SparseCategoricalCrossentropy(from_logits=False)\n",
        "        self.metric = keras.metrics.SparseCategoricalAccuracy()\n",
        "\n",
        "\n",
        "    def compile(self, optimizer_1, optimizer_2):\n",
        "        super(InfluenceModel, self).compile()\n",
        "        self.optimizer_1 = optimizer_1\n",
        "        self.optimizer_2 = optimizer_2\n",
        "\n",
        "    def call_at_zero(self, x):\n",
        "        # جدا کردن تصاویر\n",
        "        imgs = x[0]\n",
        "        # جدا کردن برچسب\n",
        "        labels = tf.one_hot(x[1], 10)\n",
        "\n",
        "        # استخراج ویژگی های تصاویر\n",
        "        features = self.feature_extractor(imgs)\n",
        "\n",
        "        n_feat = features.shape[0]\n",
        "        # جدا سازی ویژگی های هر تصویر\n",
        "        features = tf.split(features, n_feat, axis=0)\n",
        "\n",
        "        # ویژگی های استحراج شده تصویر مورد مطالعه\n",
        "        f0 = features[0]\n",
        "\n",
        "        res_mtx = []\n",
        "        res_row = []\n",
        "        for j in range(1, n_feat):\n",
        "            # ترکیب هر نمونه با تصویر مورد مطالعه\n",
        "            f1 = features[j]\n",
        "            lbl1 = labels[j:j+1,:]\n",
        "            inp = combiner(f0, f1, lbl1)\n",
        "\n",
        "            res_row += [inp]\n",
        "\n",
        "        # محاسبه نسبت هر نمونه با تصویر مورد مطالعه\n",
        "        res_row = tf.concat(res_row, axis=0)\n",
        "        res_mtx = self.comparator(res_row)\n",
        "\n",
        "        # میانگین بر روی نمونه ها\n",
        "        res = tf.reduce_sum(res_mtx, axis=0) / (n_feat-1)\n",
        "        return res, res_mtx\n",
        "\n",
        "    # Feed - Forward\n",
        "    def call(self, x):\n",
        "\n",
        "        # جدا کردن تصاویر\n",
        "        imgs = x[0]\n",
        "        # جدا کردن برچسب\n",
        "        labels = tf.one_hot(x[1], 10)\n",
        "\n",
        "        # استخراج ویژگی های تصاویر\n",
        "        features = self.feature_extractor(imgs)\n",
        "\n",
        "        # جدا سازی ویژگی های هر تصویر\n",
        "        n_feat = features.shape[0]\n",
        "        features = tf.split(features, n_feat, axis=0)\n",
        "\n",
        "\n",
        "        res_mtx = []\n",
        "        # ساخت جفت های ترکیب شده به ازای هر تصویر برای اموزش مدل\n",
        "        for i in range(n_feat):\n",
        "            res_row = []\n",
        "            for j in range(n_feat):\n",
        "                f0 = features[i]\n",
        "                f1 = features[j]\n",
        "                lbl1 = labels[j:j+1,:]\n",
        "                inp = combiner(f0, f1, lbl1)\n",
        "                res_row += [inp]\n",
        "            res_row = tf.concat(res_row, axis=0)\n",
        "\n",
        "            # محاسبه سنجش تاثیر حضور تصاویر دیگر در پیش بینی تصویر مورد مطالعه\n",
        "            res_mtx += [self.comparator(res_row)]\n",
        "\n",
        "        # میانگین گیری بر روی نتایج حاصل\n",
        "        res_mtx = tf.stack(res_mtx)\n",
        "\n",
        "        res_mtx = tf.transpose(res_mtx, [2, 1, 0])\n",
        "\n",
        "        res_mtx -= tf.linalg.diag(tf.linalg.diag_part(res_mtx))\n",
        "\n",
        "        res_mtx = tf.transpose(res_mtx, [2, 1, 0])\n",
        "\n",
        "        res = tf.reduce_sum(res_mtx, axis=1) / (n_feat-1)\n",
        "\n",
        "        return res, res_mtx\n",
        "\n",
        "    def train_step(self, x):\n",
        "        # تعریف تابع ضرر\n",
        "        loss = keras.losses.SparseCategoricalCrossentropy(from_logits=True)\n",
        "\n",
        "        # محاسبه گرادیان و اعمال گرادیان\n",
        "        with tf.GradientTape() as tape_1, tf.GradientTape() as tape_2:\n",
        "            output, _ = self.call(x)\n",
        "            loss_batch = loss(x[1], output)\n",
        "        gradient_1 = tape_1.gradient(loss_batch, self.feature_extractor.trainable_variables)\n",
        "        self.optimizer_1.apply_gradients(zip(gradient_1, self.feature_extractor.trainable_variables))\n",
        "        gradient_2 = tape_2.gradient(loss_batch, self.comparator.trainable_variables)\n",
        "        self.optimizer_2.apply_gradients(zip(gradient_2, self.comparator.trainable_variables))\n",
        "\n",
        "        # اعمال معیار ارزیابی\n",
        "        metric_batch = self.metric(x[1], output)\n",
        "\n",
        "        return {\"loss\": loss_batch, \"metric\": metric_batch}\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c1a0891a",
      "metadata": {
        "id": "c1a0891a"
      },
      "source": [
        "<h2 align='center' dir='rtl'>اموزش مدل</h2>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "67d4458d",
      "metadata": {
        "id": "67d4458d"
      },
      "source": [
        "<h3 align='center' dir='rtl'>اتصال لایه ها</h3>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "f6ed77af",
      "metadata": {
        "id": "f6ed77af"
      },
      "outputs": [],
      "source": [
        "m1 = feature_extractor()\n",
        "m2 = comparator()\n",
        "\n",
        "model = InfluenceModel(m1, m2)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "16f4ec08",
      "metadata": {
        "id": "16f4ec08"
      },
      "source": [
        "<h3 align='center' dir='rtl'>کامپایل کردن مدل</h3>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "0bbddc92",
      "metadata": {
        "id": "0bbddc92"
      },
      "outputs": [],
      "source": [
        "optimizer_1 = tf.optimizers.Adam(learning_rate=1e-3, beta_1=0.9)\n",
        "optimizer_2 = tf.optimizers.Adam(learning_rate=1e-3, beta_1=0.9)\n",
        "\n",
        "m1.compile(optimizer_1)\n",
        "m2.compile(optimizer_2)\n",
        "model.compile(optimizer_1, optimizer_2)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ac325eb7",
      "metadata": {
        "id": "ac325eb7"
      },
      "source": [
        "<h3 align='center' dir='rtl'>اموزش مدل</h3>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "c5e948ab",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c5e948ab",
        "outputId": "137ff429-1ccf-4e2d-dc78-6247b58e893f",
        "scrolled": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/3\n",
            "1875/1875 [==============================] - 274s 119ms/step - loss: 1.9457 - metric: 0.5935\n",
            "Epoch 2/3\n",
            "1875/1875 [==============================] - 232s 124ms/step - loss: 1.6846 - metric: 0.7640\n",
            "Epoch 3/3\n",
            "1875/1875 [==============================] - 236s 126ms/step - loss: 1.5131 - metric: 0.9450\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fd574be5550>"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model.fit(train_set[0], train_set[1], epochs=3, batch_size=32)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a42879cd",
      "metadata": {
        "id": "a42879cd"
      },
      "source": [
        "<h2 align='center' dir='rtl'>اماده سازی نمونه ها و محاسبه اهمیت هر نمونه نسبت به حضور دیگر نمونه ها</h2>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "2a669978",
      "metadata": {
        "id": "2a669978"
      },
      "outputs": [],
      "source": [
        "def get_samples(sample_ind, batch_size=64):\n",
        "\n",
        "    # اماده سازی نمونه ها\n",
        "    sample_image = test_set[0][sample_ind:sample_ind+batch_size,...]\n",
        "    sample_label = test_set[1][sample_ind:sample_ind+batch_size,...]\n",
        "    return sample_image, sample_label"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "c910c017",
      "metadata": {
        "id": "c910c017"
      },
      "outputs": [],
      "source": [
        "def get_imporance_function(sample_ind):\n",
        "    loss_fnc = tf.keras.losses.SparseCategoricalCrossentropy(reduction='none', from_logits=False)\n",
        "    img, lbl = get_samples(sample_ind, 1)\n",
        "\n",
        "    def get_batch_importance(batch_ind, batch_size=64):\n",
        "        sample_images, sample_labels = get_samples(batch_ind, batch_size)\n",
        "        sample_images = np.concatenate([img, sample_images])\n",
        "        sample_labels = np.concatenate([lbl, sample_labels])\n",
        "\n",
        "        probs_vec, probs_mtx = model.call_at_zero([sample_images, sample_labels])\n",
        "        samples = probs_mtx.numpy()\n",
        "        probs_vec = probs_vec.numpy()\n",
        "\n",
        "        m = samples.mean(axis=0)\n",
        "\n",
        "        # حذف نمونه مورد مطالعه و مشاهده تاثیر حضور دیگر نمونه و عدم حضور نمونه بر پیس بینی\n",
        "        sample_output = samples.sum(axis=0, keepdims=True) - samples\n",
        "        sample_output = sample_output/(batch_size-1)\n",
        "        loss_with_sample = loss_fnc(lbl, m)\n",
        "        loss_without_sample = loss_fnc(np.array([lbl]*batch_size), sample_output)\n",
        "        sample_importance = loss_with_sample-loss_without_sample\n",
        "        return sample_images[1:,...], sample_importance, probs_vec, samples\n",
        "    return get_batch_importance, img, lbl"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 83,
      "id": "a4761918",
      "metadata": {
        "id": "a4761918"
      },
      "outputs": [],
      "source": [
        "batch_size = 64\n",
        "\n",
        "batch_ind = batch_size+1\n",
        "\n",
        "sample_ind = batch_ind-1\n",
        "importance_function_batch, img, lbl = get_imporance_function(sample_ind)\n",
        "sample_images, importance, probs_vec, sample_output = importance_function_batch(batch_ind)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "52cfc350",
      "metadata": {
        "id": "52cfc350"
      },
      "source": [
        "<h2 align='center' dir='rtl'>مشاهده تاثیر دیگر نمونه ها بر پیش بینی مدل</h2>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 84,
      "id": "7b17b3c2",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 486
        },
        "id": "7b17b3c2",
        "outputId": "de0f988f-0e8d-4572-a28e-e3acecca9d1b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fd521902bd0>"
            ]
          },
          "execution_count": 84,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1500x500 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(15,5))\n",
        "plt.subplot(121)\n",
        "plt.imshow(img[0])\n",
        "plt.title(lbl[0])\n",
        "plt.axis('off')\n",
        "\n",
        "plt.subplot(122)\n",
        "plt.plot(probs_vec, label='model probabilities')\n",
        "plt.plot(lbl, 1, 'rx', label='correct label')\n",
        "plt.legend()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 85,
      "id": "e1124039",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "e1124039",
        "outputId": "a9579fb9-09e7-467a-f897-73eec99eb1ec"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1500x1500 with 64 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from matplotlib import pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "inds = np.argsort(importance)\n",
        "plt.figure()\n",
        "plt.plot(sample_output[inds[:5]].mean(axis=0))\n",
        "\n",
        "\n",
        "plt.figure(figsize=(15,15))\n",
        "for i in range(len(inds)):\n",
        "    plt.subplot(batch_size//8+1, 8, i+1)\n",
        "    plt.imshow(sample_images[inds[i],...])\n",
        "    plt.title(\"%.7f\"%(importance[inds[i]]))\n",
        "    plt.axis('off')"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "CV",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.11.5"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}