Ipsofacto/ante_hoc_model.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "id": "2111825f",
      "metadata": {
        "id": "2111825f"
      },
      "source": [
        "<h1 align='center' dir='rtl' style='color:yellow'>نام طرح</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": 1,
      "id": "b97c8a8b",
      "metadata": {
        "id": "b97c8a8b"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "2024-11-25 21:52:10.162040: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
            "To enable the following instructions: SSE4.1 SSE4.2 AVX AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
          ]
        }
      ],
      "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": 2,
      "id": "511bc7e3",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "511bc7e3",
        "outputId": "b627dc6a-3c34-49c0-a89e-698f2e4ead7e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "\u001b[1m11490434/11490434\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n"
          ]
        }
      ],
      "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": 3,
      "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 get_module_1():\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": 7,
      "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 get_module_2():\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": 8,
      "id": "6a14ab0a",
      "metadata": {
        "id": "6a14ab0a"
      },
      "outputs": [],
      "source": [
        "def get_m2_input_vector(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, module_1, module_2):\n",
        "        super(InfluenceModel, self).__init__()\n",
        "        #  لایه استخراج ویژگی\n",
        "        self.module_1 = module_1\n",
        "        # لایه تمام متصل (سنجش نسبت ها)\n",
        "        self.module_2 = module_2\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",
        "        lbls = tf.one_hot(x[1], 10)\n",
        "\n",
        "        # استخراج ویژگی های تصاویر\n",
        "        features = self.module_1(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 = lbls[j:j+1,:]\n",
        "            inp = get_m2_input_vector(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.module_2(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",
        "        lbls = tf.one_hot(x[1], 10)\n",
        "\n",
        "        # استخراج ویژگی های تصاویر\n",
        "        features = self.module_1(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 = lbls[j:j+1,:]\n",
        "                inp = get_m2_input_vector(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.module_2(res_row)]\n",
        "\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.module_1.trainable_variables)\n",
        "        self.optimizer_1.apply_gradients(zip(gradient_1, self.module_1.trainable_variables))\n",
        "        gradient_2 = tape_2.gradient(loss_batch, self.module_2.trainable_variables)\n",
        "        self.optimizer_2.apply_gradients(zip(gradient_2, self.module_2.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": 9,
      "id": "f6ed77af",
      "metadata": {
        "id": "f6ed77af"
      },
      "outputs": [],
      "source": [
        "m1 = get_module_1()\n",
        "m2 = get_module_2()\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": 10,
      "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": 11,
      "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",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m127s\u001b[0m 37ms/step - loss: 1.9111 - metric: 0.6572\n",
            "Epoch 2/3\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m81s\u001b[0m 37ms/step - loss: 1.6127 - metric: 0.8748\n",
            "Epoch 3/3\n",
            "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m81s\u001b[0m 37ms/step - loss: 1.5797 - metric: 0.8886\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<keras.src.callbacks.history.History at 0x78e4b1533730>"
            ]
          },
          "execution_count": 11,
          "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": 12,
      "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": 29,
      "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": 30,
      "id": "a4761918",
      "metadata": {
        "id": "a4761918"
      },
      "outputs": [],
      "source": [
        "batch_ind = 5888\n",
        "batch_ind = 4498\n",
        "batch_ind = 1522\n",
        "batch_ind = 625\n",
        "batch_ind = 9698\n",
        "\n",
        "batch_size = 64\n",
        "\n",
        "batch_ind = batch_ind+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": 31,
      "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 0x78e4a624c4f0>"
            ]
          },
          "execution_count": 31,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
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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": 32,
      "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
}