AI_First_Project.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "AI First Project",
      "provenance": [],
      "collapsed_sections": [],
      "mount_file_id": "https://github.com/AlirezaAK2000/Adad_khon/blob/master/AI_First_Project.ipynb",
      "authorship_tag": "ABX9TyM0/QL5HKWAFFQTHdEVq2E0",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/AlirezaAK2000/digit_recognizer/blob/master/AI_First_Project.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lvknyedA2mVx"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import time"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PlxerNM45omk"
      },
      "source": [
        "# A function to plot images\n",
        "def show_image(img):\n",
        "    image = img.reshape((28, 28))\n",
        "    plt.imshow(image, 'gray')\n",
        "def right_shift(xs, num_shift):\n",
        "  for i in range(num_shift):\n",
        "    xs = np.c_[xs[:,-1],xs[:,:-1]]\n",
        "  return xs\n",
        "def read_data():\n",
        "  train_images_file = open('drive/MyDrive/colab samples/train-images.idx3-ubyte', 'rb')\n",
        "  train_images_file.seek(4)\n",
        "  num_of_train_images = int.from_bytes(train_images_file.read(4), 'big')\n",
        "  train_images_file.seek(16)\n",
        "\n",
        "  train_labels_file = open('drive/MyDrive/colab samples/train-labels.idx1-ubyte', 'rb')\n",
        "  train_labels_file.seek(8)\n",
        "\n",
        "  train_set = []\n",
        "  for n in range(num_of_train_images):\n",
        "    image = np.zeros((784, 1))\n",
        "    for i in range(784):\n",
        "        image[i, 0] = int.from_bytes(train_images_file.read(1), 'big') / 256\n",
        "    \n",
        "    label_value = int.from_bytes(train_labels_file.read(1), 'big')\n",
        "    label = np.zeros((10, 1))\n",
        "    label[label_value, 0] = 1\n",
        "      \n",
        "    train_set.append((image, label))\n",
        "  # Reading The Test Set\n",
        "  test_images_file = open('drive/MyDrive/colab samples/t10k-images.idx3-ubyte', 'rb')\n",
        "  test_images_file.seek(4)\n",
        "\n",
        "  test_labels_file = open('drive/MyDrive/colab samples/t10k-labels.idx1-ubyte', 'rb')\n",
        "  test_labels_file.seek(8)\n",
        "\n",
        "  num_of_test_images = int.from_bytes(test_images_file.read(4), 'big')\n",
        "  test_images_file.seek(16)\n",
        "\n",
        "  shifted_test_set = []\n",
        "  test_set = []\n",
        "  for n in range(num_of_test_images):\n",
        "    image = np.zeros((784, 1))\n",
        "    image_mat = np.zeros((28,28))\n",
        "    for i in range(784):\n",
        "        data = int.from_bytes(test_images_file.read(1), 'big') / 256\n",
        "        image[i] = data\n",
        "        image_mat[i // 28][i % 28] = data\n",
        "\n",
        "    \n",
        "    # corrupting data for adverserial attack\n",
        "    image_mat = right_shift(image_mat , 4).flatten().reshape(-1,1)\n",
        "    assert(image.shape == image_mat.shape)\n",
        "\n",
        "    shifted_test_set.append(image_mat)\n",
        "\n",
        "    label_value = int.from_bytes(test_labels_file.read(1), 'big')\n",
        "    label = np.zeros((10, 1))\n",
        "    label[label_value, 0] = 1\n",
        "    \n",
        "    test_set.append((image, label))\n",
        "\n",
        "  train_set_x_v = np.transpose(np.squeeze(np.array([rec[0] for rec in train_set])))\n",
        "  test_set_x_v = np.transpose(np.squeeze(np.array([rec[0] for rec in test_set])))\n",
        "\n",
        "  train_set_y_v = np.transpose(np.squeeze(np.array([rec[1] for rec in train_set])))\n",
        "  test_set_y_v = np.transpose(np.squeeze(np.array([rec[1] for rec in test_set])))\n",
        "\n",
        "  shifted_test_set_x_v = np.transpose(np.squeeze(np.array(shifted_test_set)))\n",
        "\n",
        "  assert(shifted_test_set_x_v.shape == test_set_x_v.shape)\n",
        "\n",
        "  return train_set_x_v , test_set_x_v , train_set_y_v , test_set_y_v , shifted_test_set_x_v"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZW2cSr7wVhA7"
      },
      "source": [
        "<div dir=rtl>\n",
        "در همان ابتدای کار نمونه های test\n",
        "و train\n",
        "را به صورت vectorize \n",
        "در می آوریم و یک کپی از داده های test را \n",
        "۴ بار به سمت \n",
        "راست شیفت می‌دهیم.\n",
        "</div>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "GM2sd3aB86ss",
        "outputId": "fc2409f7-d704-439d-e236-8590a1ed1187"
      },
      "source": [
        "train_set_x_v , test_set_x_v , train_set_y_v , test_set_y_v , shifted_test_set_x_v= read_data()\n",
        "show_image(np.transpose(train_set_x_v[:,1]))\n",
        "plt.show()\n",
        "# corrupted\n",
        "print('\\nshifted pic :\\n')\n",
        "show_image(np.transpose(shifted_test_set_x_v[:,1]))\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "shifted pic :\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l0_fGc849Wse"
      },
      "source": [
        "def sigmoid(A):\n",
        "  return 1/(1+np.exp(-1 * A))\n",
        "def sigmoid_deriv(A):\n",
        "  sig = sigmoid(A)\n",
        "  return sig * (1 - sig)\n",
        "def softmax(A):\n",
        "  e = np.exp(A)\n",
        "  return e / np.sum(e)\n",
        "def relu(A):\n",
        "  return np.maximum(A , 0)\n",
        "def relu_deriv(A):\n",
        "  return (A > 0).astype(int)\n",
        "def tanh(A):\n",
        "  return np.tanh(A)\n",
        "def tanh_deriv(A):\n",
        "  return 1 - tanh(A) ** 2\n",
        "def leaky_relu(A,alpha=0.01):\n",
        "  return np.where(A > 0, A, A * alpha)     \n",
        "def leaky_relu_deriv(A, alpha=0.01):\n",
        "  dx = np.ones_like(A)\n",
        "  dx[A < 0] = alpha\n",
        "  return dx                     "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lNrKHVbI0QSk"
      },
      "source": [
        "<div dir =rtl>\n",
        "<h1><b>\n",
        "مدل شبکه عصبی\n",
        "</b></h1>\n",
        "\n",
        "مدل خود را می توانید به صورت زیر تعریف کنید به این صورت که خط اول تعداد نورون های لایه ورودی و در خطوط بعدی تعداد نورون ها،تابع فعال سازی و مشتق آن را میدهیم.\n",
        "\n",
        "```\n",
        "(dataset_size , None)\n",
        "```\n",
        "\n",
        "\n",
        "\n",
        "```\n",
        "(neurons , activation , activation_deriv)\n",
        "```\n",
        "مدل خواسته شده در پروژه به صورت زیر است:\n",
        "\n",
        "</div>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PhboO7FiBBwx",
        "outputId": "c7cd22c6-778b-4888-d955-a6746296871d"
      },
      "source": [
        "model = [\n",
        "    (train_set_x_v.shape[0] , None),\n",
        "    (16 , relu ,relu_deriv ),\n",
        "    (16 , relu ,relu_deriv ),\n",
        "    (10 , sigmoid , sigmoid_deriv),\n",
        "]\n",
        "model"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(784, None),\n",
              " (16, <function __main__.relu>, <function __main__.relu_deriv>),\n",
              " (16, <function __main__.relu>, <function __main__.relu_deriv>),\n",
              " (10, <function __main__.sigmoid>, <function __main__.sigmoid_deriv>)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IbE0PraS6Hcw"
      },
      "source": [
        "np.random.seed(2)\n",
        "def get_dimensions(model):\n",
        "  dimensions = [(model[i+1][0], model[i][0]) for i in range(len(model)-1)]\n",
        "  return dimensions"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ros-p-vV094Y"
      },
      "source": [
        "<div dir=rtl><h1><b>\n",
        "مقدار دهی وزن ها\n",
        "</b></h1></div>\n",
        "<div dir=rtl>\n",
        "استفاده از تکنیک مناسب برای مقداردهی اولیه به یادگیری سریع تر و بهتر شبکه عصبی کمک میکند.اگر تمام وزن هارا صفر بدهیم یا اینکه مقادیر بسیار کوچک باشد آنگاه باعث می شود که cost  تغییری نکند.\n",
        "در اینجا از یک متد مقداردهی به نام \n",
        "<b>HE</b>\n",
        "استفاده می‌کنیم:\n",
        "<a href=https://arxiv.org/abs/1512.03385>\n",
        "[He et al., 2015]\n",
        "</a>\n",
        "<br>\n",
        "تابع بعدی نیز برای مقداردهی وزن های \n",
        "<b>\n",
        "Momentum\n",
        "</b>\n",
        "است که به همه وزن ها مقادیر صفر می‌دهد.\n",
        "</div>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0eZ06aVfBO90"
      },
      "source": [
        "def initializer(shape ):\n",
        "  w = np.random.randn(shape[0],shape[1]) * np.sqrt(2./shape[1])\n",
        "  # b = np.random.randn(shape[0] , 1) * np.sqrt(2./shape[1])\n",
        "  b = np.zeros((shape[0] , 1))\n",
        "\n",
        "  return (w , b)\n",
        "def V_initializer(shape ):\n",
        "  v_w = np.zeros(shape)\n",
        "  v_b = np.zeros((shape[0] , 1))\n",
        "\n",
        "  return (v_w , v_b)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rht81vZJ6LJy"
      },
      "source": [
        "def initialize(model,use_momentum = False):\n",
        "  W = []\n",
        "  B = []\n",
        "  dimensions = get_dimensions(model)\n",
        "  for shape in dimensions:\n",
        "    w , b  = initializer(shape)\n",
        "    assert(w.shape == shape)\n",
        "    assert(b.shape == (shape[0] , 1))\n",
        "    W.append(w)\n",
        "    B.append(b)\n",
        "  if use_momentum:\n",
        "    V = {'dW':[],'dB':[]}\n",
        "    for shape in dimensions:\n",
        "      v_w , v_b  = V_initializer(shape)\n",
        "      V['dW'].append(v_w)\n",
        "      V['dB'].append(v_b)\n",
        "    return W , B , V\n",
        "\n",
        "  return W , B , None"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GhRfRYHe_P8w"
      },
      "source": [
        "def mse_cost(y , y_prim):\n",
        "  return np.sum((y_prim-y)**2)/y.shape[1]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bJWuY6ISW69H"
      },
      "source": [
        "<div dir=rtl>\n",
        "کد مربوط به پیاده‌سازی \n",
        "<b>Forward Propagation</b>\n",
        "در تابع زیر آمده است.\n",
        "در آخر \n",
        "<b>\n",
        "Activation\n",
        "</b>\n",
        " های هر لایه را برای \n",
        "<b>\n",
        "Forward Propagation\n",
        "</b>\n",
        "، \n",
        "ذخیره می‌کنیم.\n",
        "</div>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eXbUoelB_ED_"
      },
      "source": [
        "def forward_propagation(X , W , B,model):\n",
        "  layers_num = len(W)\n",
        "  A = [X]\n",
        "  Z = []\n",
        "  for i in range(layers_num):\n",
        "    # assert(W[i].shape[1] == X.shape[0])\n",
        "    z = W[i] @ A[i] + B[i]\n",
        "    a = model[i + 1][1](z)\n",
        "    A.append(a)\n",
        "    Z.append(z)\n",
        "    # print(z.shape)\n",
        "  return {\n",
        "      'A':A,\n",
        "      'Z':Z\n",
        "  }"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W7nY4HCkYGgu"
      },
      "source": [
        "<div dir=rtl>\n",
        "در تابع زیر نیز \n",
        "<b>\n",
        "Forward Propagration\n",
        "</b>\n",
        "پیاده سازی شده است و در آخر گرادیان‌ها را به ما برمی‌گرداند.\n",
        "</div>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gRCffpfnSy5w"
      },
      "source": [
        "def backward_propagation(W , B , cache, Y , model):\n",
        "  A , Z = cache['A'] ,cache['Z']\n",
        "  # print(Z)\n",
        "  dW , dB= [] , []\n",
        "  layers_num = len(W)\n",
        "  tmp = (2 * (A[-1] - Y)) * model[-1][2](Z[-1])\n",
        "  db = tmp  @ np.ones((A[-1].shape[1],1))\n",
        "  dw = tmp  @ np.transpose(A[-2])\n",
        "  dA = [np.transpose(W[-1]) @ tmp]\n",
        "  assert(dw.shape == W[-1].shape)\n",
        "  assert(db.shape == B[-1].shape)\n",
        "  dW.append(dw)\n",
        "  dB.append(db)\n",
        "  for i in reversed(range(layers_num - 1)):\n",
        "    tmp = dA[0] * model[i+1][2](Z[i])\n",
        "    db = tmp @ np.ones((tmp.shape[1],1))\n",
        "    dw = tmp @ np.transpose(A[i])\n",
        "    da = np.transpose(W[i]) @ tmp\n",
        "    dA.insert(0,da)\n",
        "    dW.insert(0,dw)\n",
        "    dB.insert(0,db)\n",
        "    assert(dw.shape == W[i].shape)\n",
        "    assert(db.shape == B[i].shape)\n",
        "\n",
        "  return {\n",
        "      'dW':dW,\n",
        "      'dB':dB\n",
        "  }\n",
        "    \n",
        "def predict(W, B , X , model):\n",
        "  cache = forward_propagation(X , W , B , model)\n",
        "  Y_prim = cache['A'][-1]\n",
        "  Y_prim = np.round(Y_prim)\n",
        "  return np.where(Y_prim.reshape(Y_prim.shape[1]) == 1)[0][0]\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ewZ-JPvCYjaa"
      },
      "source": [
        "<div dir=rtl> \n",
        "در آخر نیز فرایند یادگیری در تابع \n",
        "<b>\n",
        "main_model\n",
        "</b>\n",
        "انجام می‌شود.\n",
        "به عنوان خروجی نیز وزن ها و دقت شبکه عصبی بر روی داده های \n",
        "Test \n",
        "و\n",
        "Train \n",
        "داده می‌شود.\n",
        "همانطور که می‌دانیم \n",
        "<b>\n",
        "Cost\n",
        "</b>\n",
        "در روش\n",
        "<b>\n",
        "SGD\n",
        "</b>\n",
        "نوسانات زیادی دارد و برای از بین بردن این نوسانات معمولا از تکنیک های مختلفی مانند: \n",
        "\n",
        "\n",
        "1.   Momentum\n",
        "2.   RMSprop\n",
        "3.   ADAM\n",
        "\n",
        "استفاده می‌شود که در اینجا ما از \n",
        "<b>Momentum</b>\n",
        "استفاده کرده‌ایم.\n",
        "ایده اصلی استفاده از مفهومی به نام \n",
        "<b>\n",
        "Exponential Average \n",
        "</b>\n",
        "است.\n",
        "همانطور که قبلا دیدیم مقدارهی اولیه این وزن ها در خطوط بالا انجام شده است و در اینجا ما در خطوط ۲۸ تا ۳۱ به پیاده‌سازی این روش پرداخته‌ایم.\n",
        "درادامه نیز به تاثیر این پیاده‌سازی بر نوسانات \n",
        "<b>\n",
        "Cost\n",
        "</b>\n",
        "می‌پردازیم.\n",
        "\n",
        "</div>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ktzS-K7NZRxJ"
      },
      "source": [
        "from collections import defaultdict\n",
        "def test(W, B , X ,Y , model):\n",
        "  cache = forward_propagation(X , W , B , model)\n",
        "  Y_prim = cache['A'][-1]\n",
        "  Y_prim = np.round(Y_prim)\n",
        "  result = defaultdict(lambda : 0,dict(zip(*(np.unique((Y_prim == Y).all(0) , return_counts=True)))))\n",
        "  return result[True] / (result[True] + result[False]) * 100\n",
        "\n",
        "\n",
        "def main_model(model , X , Y ,X_test , Y_test , epoch=200 , batch_size=20 , a=1, use_momentum = False, beta=0.9):\n",
        "  costs , test_accuracies , train_accuracies = [] , [] , []\n",
        "  num_iteration = X.shape[1]//batch_size\n",
        "  W , B , V = initialize(model,use_momentum)\n",
        "  for i in range(epoch):\n",
        "    cost = None\n",
        "    for j in range(num_iteration):\n",
        "      batch_x = X[:,j*batch_size:(j+1)*batch_size]\n",
        "      batch_y = Y[:,j*batch_size:(j+1)*batch_size]\n",
        "      cache = forward_propagation(batch_x , W,B,model)\n",
        "      cost = mse_cost(batch_y , cache['A'][-1])\n",
        "      gradients = backward_propagation(W , B , cache , batch_y , model)\n",
        "      if not use_momentum:\n",
        "        for k in range(len(W)):\n",
        "          W[k] = W[k] - a * gradients['dW'][k]\n",
        "          B[k] = B[k] - a * gradients['dB'][k]\n",
        "      else:\n",
        "        for k in range(len(W)):\n",
        "          V['dW'][k] = beta * V['dW'][k] + (1 - beta) * gradients['dW'][k]\n",
        "          W[k] = W[k] - a * V['dW'][k]\n",
        "          V['dB'][k] = beta * V['dB'][k] + (1 - beta) * gradients['dB'][k]\n",
        "          B[k] = B[k] - a * V['dB'][k]\n",
        "    # if i % 10 == 0:\n",
        "    train_accuracy = test(W , B , X , Y , model)\n",
        "    test_accuracy = test(W , B , X_test , Y_test , model)\n",
        "    print(f'*** epoch : {i} ======> cost : {cost} , train accuracy : {train_accuracy} , test accuracy : {test_accuracy}')\n",
        "    costs.append(cost)\n",
        "    test_accuracies.append(test_accuracy)\n",
        "    train_accuracies.append(train_accuracy)\n",
        "\n",
        "  train_accuracy = test(W , B , X , Y , model)\n",
        "  test_accuracy = test(W , B , X_test , Y_test , model)\n",
        "  print('__________________________________________________')\n",
        "  print(f\"train accuracy: {train_accuracy}\")\n",
        "  print(f\"test accuracy: {test_accuracy}\")  \n",
        "  return costs , test_accuracies , train_accuracies , W , B\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e3zphQ6DOdb7"
      },
      "source": [
        "def plot_result(info): \n",
        "  plt.plot([i for i in range(1,len(info[0])+1)] , info[0] , label='cost')\n",
        "  plt.legend(loc=\"upper right\", frameon=False)\n",
        "  plt.show()\n",
        "  plt.plot([i for i in range(1,len(info[1])+1)] , info[1] , label='test accuracy')\n",
        "  plt.plot([i for i in range(1,len(info[2])+1)] , info[2] , label='train accuracy')\n",
        "  plt.legend(loc=\"upper left\", frameon=False)\n",
        "  plt.show()\n",
        "\n",
        "def describe_model(model):\n",
        "  print('\\n'.join([str({'activation units':model[i][0],'activation function':model[i][1].__name__ ,'parameters': model[i][0]*model[i-1][0] }) if model[i][1] else f'input layer : {model[i][0]}' for i in range(len(model))]))\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QQ6WBfIed9r9"
      },
      "source": [
        "<div dir=rtl>\n",
        "در شبکه زیر به عنوان ورودی ۱۰۰ داده اول را داده‌ایم و همانطور که از خروجی دیده می‌شود دقت بسیار پایین است با این که دقت فقط برروی همان داده های \n",
        "<b>\n",
        "Train\n",
        "</b>\n",
        "اندازه‌گیری شده‌است.\n",
        "بااین حال همانطور که توقع داشتیم هزینه روندی نزولی داشته‌است.\n",
        "<div>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PSMEv8NoycZA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 989
        },
        "outputId": "070c3358-6650-45fa-aa1d-56bf84a76e20"
      },
      "source": [
        "# using sigmoid without momentum\n",
        "# learning on first 100 samples\n",
        "model = [\n",
        "    (train_set_x_v.shape[0] , None),\n",
        "    (16 , sigmoid ,sigmoid_deriv ),\n",
        "    (16 , sigmoid ,sigmoid_deriv ),\n",
        "    (10 , sigmoid , sigmoid_deriv),\n",
        "]\n",
        "describe_model(model)\n",
        "start = time.time()\n",
        "info = main_model(model , train_set_x_v[:,:100], train_set_y_v[:,:100],train_set_x_v[:,:100], train_set_y_v[:,:100],a=0.1, epoch=20 )\n",
        "finish = round(time.time() - start , 3)\n",
        "print('min {} : sec {}'.format(finish//60 , finish % 60))\n",
        "plot_result(info)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "input layer : 784\n",
            "{'activation units': 16, 'activation function': 'sigmoid', 'parameters': 12544}\n",
            "{'activation units': 16, 'activation function': 'sigmoid', 'parameters': 256}\n",
            "{'activation units': 10, 'activation function': 'sigmoid', 'parameters': 160}\n",
            "*** epoch : 0 ======> cost : 0.9353647483291379 , train accuracy : 0.0 , test accuracy : 0.0\n",
            "*** epoch : 1 ======> cost : 0.9285564843508258 , train accuracy : 0.0 , test accuracy : 0.0\n",
            "*** epoch : 2 ======> cost : 0.9153415958714491 , train accuracy : 0.0 , test accuracy : 0.0\n",
            "*** epoch : 3 ======> cost : 0.9029976841170276 , train accuracy : 0.0 , test accuracy : 0.0\n",
            "*** epoch : 4 ======> cost : 0.8943575115242766 , train accuracy : 0.0 , test accuracy : 0.0\n",
            "*** epoch : 5 ======> cost : 0.8871290007203287 , train accuracy : 0.0 , test accuracy : 0.0\n",
            "*** epoch : 6 ======> cost : 0.8802977435610263 , train accuracy : 0.0 , test accuracy : 0.0\n",
            "*** epoch : 7 ======> cost : 0.8725994611887371 , train accuracy : 0.0 , test accuracy : 0.0\n",
            "*** epoch : 8 ======> cost : 0.8629641080827269 , train accuracy : 0.0 , test accuracy : 0.0\n",
            "*** epoch : 9 ======> cost : 0.8507698643736523 , train accuracy : 0.0 , test accuracy : 0.0\n",
            "*** epoch : 10 ======> cost : 0.8352927402873462 , train accuracy : 0.0 , test accuracy : 0.0\n",
            "*** epoch : 11 ======> cost : 0.8157276236839452 , train accuracy : 0.0 , test accuracy : 0.0\n",
            "*** epoch : 12 ======> cost : 0.7915997390938457 , train accuracy : 1.0 , test accuracy : 1.0\n",
            "*** epoch : 13 ======> cost : 0.762819070601529 , train accuracy : 12.0 , test accuracy : 12.0\n",
            "*** epoch : 14 ======> cost : 0.7296798395480356 , train accuracy : 13.0 , test accuracy : 13.0\n",
            "*** epoch : 15 ======> cost : 0.6925251105088909 , train accuracy : 21.0 , test accuracy : 21.0\n",
            "*** epoch : 16 ======> cost : 0.6520314434858674 , train accuracy : 24.0 , test accuracy : 24.0\n",
            "*** epoch : 17 ======> cost : 0.6092044543814085 , train accuracy : 28.000000000000004 , test accuracy : 28.000000000000004\n",
            "*** epoch : 18 ======> cost : 0.5651853720407723 , train accuracy : 34.0 , test accuracy : 34.0\n",
            "*** epoch : 19 ======> cost : 0.521521530571727 , train accuracy : 43.0 , test accuracy : 43.0\n",
            "__________________________________________________\n",
            "train accuracy: 43.0\n",
            "test accuracy: 43.0\n",
            "min 0.0 : sec 0.116\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E079U648fCeB"
      },
      "source": [
        "<div dir=rtl>\n",
        "در ادامه به آزمایش تاثیر استفاده از \n",
        "<b>\n",
        "Momentum\n",
        "</b>\n",
        "در کنار توابع فعالسازی مختلف می‌پردازیم.\n",
        "همانطور که در نتایج دیده می‌شود این تکنیک توانسته تا حد خوبی این نوسانات را کنترل کند.\n",
        "عملکرد این روش به مقداری که برای پارامتر \n",
        "<b><i>\n",
        "beta\n",
        "</i></b>\n",
        "ست می‌کنیم دارد.\n",
        "این مقدار معمولا روی 0.9 ست می‌شود.\n",
        "</div>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "cE7nDCpSe8oX",
        "outputId": "ff472ceb-f36f-4c53-b8b9-fdf89bbf22de"
      },
      "source": [
        "# using relu and momentum\n",
        "model = [\n",
        "    (train_set_x_v.shape[0] , None),\n",
        "    (16 , relu ,relu_deriv ),\n",
        "    (16 , relu ,relu_deriv ),\n",
        "    (10 , sigmoid , sigmoid_deriv),\n",
        "]\n",
        "describe_model(model)\n",
        "start = time.time()\n",
        "info = main_model(model , train_set_x_v, train_set_y_v,test_set_x_v , test_set_y_v,a=0.01, epoch=40 , use_momentum=True , beta=0.9)\n",
        "finish = round(time.time() - start , 3)\n",
        "print('min {} : sec {}'.format(finish//60 , finish % 60))\n",
        "plot_result(info)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "input layer : 784\n",
            "{'activation units': 16, 'activation function': 'relu', 'parameters': 12544}\n",
            "{'activation units': 16, 'activation function': 'relu', 'parameters': 256}\n",
            "{'activation units': 10, 'activation function': 'sigmoid', 'parameters': 160}\n",
            "*** epoch : 0 ======> cost : 0.05683344175404874 , train accuracy : 84.44333333333334 , test accuracy : 84.55\n",
            "*** epoch : 1 ======> cost : 0.004813840609163047 , train accuracy : 87.29166666666667 , test accuracy : 86.99\n",
            "*** epoch : 2 ======> cost : 0.003366621126146703 , train accuracy : 87.75666666666667 , test accuracy : 87.74\n",
            "*** epoch : 3 ======> cost : 0.0008431110888929814 , train accuracy : 88.765 , test accuracy : 88.85\n",
            "*** epoch : 4 ======> cost : 0.00155396746824631 , train accuracy : 89.275 , test accuracy : 88.88000000000001\n",
            "*** epoch : 5 ======> cost : 0.0003466200122428818 , train accuracy : 89.37 , test accuracy : 89.03\n",
            "*** epoch : 6 ======> cost : 0.0010370478622880731 , train accuracy : 90.31833333333333 , test accuracy : 89.71000000000001\n",
            "*** epoch : 7 ======> cost : 0.0004281226564242315 , train accuracy : 90.43333333333334 , test accuracy : 90.14\n",
            "*** epoch : 8 ======> cost : 0.0007486270936304064 , train accuracy : 90.415 , test accuracy : 89.74\n",
            "*** epoch : 9 ======> cost : 0.0014655249811995827 , train accuracy : 90.64166666666667 , test accuracy : 90.12\n",
            "*** epoch : 10 ======> cost : 0.0005819844340700656 , train accuracy : 90.26333333333334 , test accuracy : 89.62\n",
            "*** epoch : 11 ======> cost : 0.00036588317313980697 , train accuracy : 91.06833333333333 , test accuracy : 90.10000000000001\n",
            "*** epoch : 12 ======> cost : 0.000609845168748211 , train accuracy : 91.38 , test accuracy : 90.3\n",
            "*** epoch : 13 ======> cost : 0.00027574163147730017 , train accuracy : 91.47666666666666 , test accuracy : 90.57\n",
            "*** epoch : 14 ======> cost : 0.0006636575759281897 , train accuracy : 91.93666666666667 , test accuracy : 90.86\n",
            "*** epoch : 15 ======> cost : 0.0008625865052376868 , train accuracy : 91.84666666666666 , test accuracy : 90.75999999999999\n",
            "*** epoch : 16 ======> cost : 0.015682916178697836 , train accuracy : 91.37166666666666 , test accuracy : 90.34\n",
            "*** epoch : 17 ======> cost : 0.00021988833075830576 , train accuracy : 91.95666666666666 , test accuracy : 91.03999999999999\n",
            "*** epoch : 18 ======> cost : 0.0010353460420994662 , train accuracy : 92.32833333333333 , test accuracy : 91.3\n",
            "*** epoch : 19 ======> cost : 0.00013809230728484937 , train accuracy : 91.905 , test accuracy : 90.8\n",
            "*** epoch : 20 ======> cost : 0.005047868181128198 , train accuracy : 91.88 , test accuracy : 90.67\n",
            "*** epoch : 21 ======> cost : 0.0002968130885442489 , train accuracy : 92.66 , test accuracy : 91.34\n",
            "*** epoch : 22 ======> cost : 0.0005032367004198145 , train accuracy : 92.82333333333334 , test accuracy : 91.71000000000001\n",
            "*** epoch : 23 ======> cost : 0.0012371461170608803 , train accuracy : 92.4 , test accuracy : 91.3\n",
            "*** epoch : 24 ======> cost : 0.001246351045945493 , train accuracy : 92.45833333333333 , test accuracy : 91.31\n",
            "*** epoch : 25 ======> cost : 0.00037268362491874675 , train accuracy : 92.92833333333334 , test accuracy : 91.75999999999999\n",
            "*** epoch : 26 ======> cost : 0.00023619616264968892 , train accuracy : 92.54833333333333 , test accuracy : 91.44\n",
            "*** epoch : 27 ======> cost : 0.0001606579728492434 , train accuracy : 92.28666666666666 , test accuracy : 91.2\n",
            "*** epoch : 28 ======> cost : 0.00011084201712396682 , train accuracy : 93.305 , test accuracy : 92.22\n",
            "*** epoch : 29 ======> cost : 0.0031454186907819543 , train accuracy : 92.47333333333333 , test accuracy : 91.57\n",
            "*** epoch : 30 ======> cost : 0.0007656740082857329 , train accuracy : 92.86166666666666 , test accuracy : 91.9\n",
            "*** epoch : 31 ======> cost : 0.00036196324459461537 , train accuracy : 93.02666666666667 , test accuracy : 92.0\n",
            "*** epoch : 32 ======> cost : 0.0007083886223743668 , train accuracy : 93.35333333333334 , test accuracy : 92.16\n",
            "*** epoch : 33 ======> cost : 0.00048743932050771997 , train accuracy : 93.06333333333333 , test accuracy : 91.93\n",
            "*** epoch : 34 ======> cost : 0.0007538671013857148 , train accuracy : 93.55333333333333 , test accuracy : 92.17999999999999\n",
            "*** epoch : 35 ======> cost : 0.00018758825971754744 , train accuracy : 92.84666666666666 , test accuracy : 91.88\n",
            "*** epoch : 36 ======> cost : 0.00023401565778832797 , train accuracy : 91.94166666666666 , test accuracy : 90.77\n",
            "*** epoch : 37 ======> cost : 0.0005679642251686259 , train accuracy : 93.17833333333333 , test accuracy : 92.25\n",
            "*** epoch : 38 ======> cost : 0.00045943809079258016 , train accuracy : 93.645 , test accuracy : 92.43\n",
            "*** epoch : 39 ======> cost : 0.0007486642367897245 , train accuracy : 93.26 , test accuracy : 91.72\n",
            "__________________________________________________\n",
            "train accuracy: 93.26\n",
            "test accuracy: 91.72\n",
            "min 1.0 : sec 6.873999999999995\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "STM-tOFEFfrN",
        "outputId": "3e287b95-d58c-40ed-881a-ea5795db6311"
      },
      "source": [
        "# using relu and without momentum\n",
        "model = [\n",
        "    (train_set_x_v.shape[0] , None),\n",
        "    (16 , relu ,relu_deriv ),\n",
        "    (16 , relu ,relu_deriv ),\n",
        "    (10 , sigmoid , sigmoid_deriv),\n",
        "]\n",
        "describe_model(model)\n",
        "start = time.time()\n",
        "info = main_model(model , train_set_x_v, train_set_y_v,test_set_x_v , test_set_y_v,a=0.01, epoch=40 , use_momentum=False)\n",
        "finish = round(time.time() - start , 3)\n",
        "print('min {} : sec {}'.format(finish//60 , finish % 60))\n",
        "plot_result(info)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "input layer : 784\n",
            "{'activation units': 16, 'activation function': 'relu', 'parameters': 12544}\n",
            "{'activation units': 16, 'activation function': 'relu', 'parameters': 256}\n",
            "{'activation units': 10, 'activation function': 'sigmoid', 'parameters': 160}\n",
            "*** epoch : 0 ======> cost : 0.10161006206631165 , train accuracy : 78.25166666666667 , test accuracy : 78.10000000000001\n",
            "*** epoch : 1 ======> cost : 0.10137221485600582 , train accuracy : 79.965 , test accuracy : 79.97\n",
            "*** epoch : 2 ======> cost : 0.1394841521370324 , train accuracy : 80.78999999999999 , test accuracy : 80.78999999999999\n",
            "*** epoch : 3 ======> cost : 0.14877480694329184 , train accuracy : 82.81500000000001 , test accuracy : 82.71\n",
            "*** epoch : 4 ======> cost : 0.12694508378105684 , train accuracy : 82.77333333333333 , test accuracy : 82.69\n",
            "*** epoch : 5 ======> cost : 0.1160718011453997 , train accuracy : 83.565 , test accuracy : 83.47\n",
            "*** epoch : 6 ======> cost : 0.10587546102737659 , train accuracy : 83.87 , test accuracy : 83.67\n",
            "*** epoch : 7 ======> cost : 0.11846822739319116 , train accuracy : 84.38166666666666 , test accuracy : 83.83\n",
            "*** epoch : 8 ======> cost : 0.10026804183214102 , train accuracy : 83.82333333333334 , test accuracy : 83.55\n",
            "*** epoch : 9 ======> cost : 0.14551306644885004 , train accuracy : 83.71499999999999 , test accuracy : 83.02000000000001\n",
            "*** epoch : 10 ======> cost : 0.10872516687532492 , train accuracy : 84.59833333333333 , test accuracy : 84.02\n",
            "*** epoch : 11 ======> cost : 0.13799540747958894 , train accuracy : 84.06833333333333 , test accuracy : 83.56\n",
            "*** epoch : 12 ======> cost : 0.1003251010038398 , train accuracy : 84.87166666666667 , test accuracy : 84.00999999999999\n",
            "*** epoch : 13 ======> cost : 0.10074718457814044 , train accuracy : 85.13833333333334 , test accuracy : 84.48\n",
            "*** epoch : 14 ======> cost : 0.10085056640476031 , train accuracy : 84.44666666666667 , test accuracy : 83.65\n",
            "*** epoch : 15 ======> cost : 0.1000668260576335 , train accuracy : 85.34833333333334 , test accuracy : 84.47\n",
            "*** epoch : 16 ======> cost : 0.10008854363527989 , train accuracy : 84.64166666666667 , test accuracy : 84.1\n",
            "*** epoch : 17 ======> cost : 0.10142956913418844 , train accuracy : 85.05833333333334 , test accuracy : 83.98\n",
            "*** epoch : 18 ======> cost : 0.10002384448202777 , train accuracy : 85.555 , test accuracy : 84.47\n",
            "*** epoch : 19 ======> cost : 0.10000696579983812 , train accuracy : 85.42333333333333 , test accuracy : 84.34\n",
            "*** epoch : 20 ======> cost : 0.10000886061048123 , train accuracy : 85.14333333333333 , test accuracy : 83.99\n",
            "*** epoch : 21 ======> cost : 0.10000189460663748 , train accuracy : 85.475 , test accuracy : 84.25\n",
            "*** epoch : 22 ======> cost : 0.10006341346334295 , train accuracy : 85.34333333333333 , test accuracy : 84.33\n",
            "*** epoch : 23 ======> cost : 0.10072970222533531 , train accuracy : 84.56833333333333 , test accuracy : 83.7\n",
            "*** epoch : 24 ======> cost : 0.10340301070380789 , train accuracy : 84.74166666666667 , test accuracy : 83.7\n",
            "*** epoch : 25 ======> cost : 0.11157774958330371 , train accuracy : 84.83166666666668 , test accuracy : 83.67999999999999\n",
            "*** epoch : 26 ======> cost : 0.10000652272865959 , train accuracy : 85.66666666666667 , test accuracy : 84.61\n",
            "*** epoch : 27 ======> cost : 0.10001069946558121 , train accuracy : 84.54166666666667 , test accuracy : 83.2\n",
            "*** epoch : 28 ======> cost : 0.10010660963510092 , train accuracy : 85.09166666666667 , test accuracy : 83.93\n",
            "*** epoch : 29 ======> cost : 0.10000519904689502 , train accuracy : 84.97 , test accuracy : 84.04\n",
            "*** epoch : 30 ======> cost : 0.10000160131745232 , train accuracy : 85.39166666666667 , test accuracy : 84.42\n",
            "*** epoch : 31 ======> cost : 0.10000102734921335 , train accuracy : 85.77833333333334 , test accuracy : 84.61\n",
            "*** epoch : 32 ======> cost : 0.10838703356335959 , train accuracy : 85.185 , test accuracy : 84.24000000000001\n",
            "*** epoch : 33 ======> cost : 0.10163492939804739 , train accuracy : 84.61666666666666 , test accuracy : 83.47\n",
            "*** epoch : 34 ======> cost : 0.10000758907810281 , train accuracy : 85.67166666666667 , test accuracy : 84.74000000000001\n",
            "*** epoch : 35 ======> cost : 0.10000018444647567 , train accuracy : 86.035 , test accuracy : 84.74000000000001\n",
            "*** epoch : 36 ======> cost : 0.10001162208489012 , train accuracy : 84.74833333333333 , test accuracy : 83.39\n",
            "*** epoch : 37 ======> cost : 0.1000006813790502 , train accuracy : 85.42999999999999 , test accuracy : 84.3\n",
            "*** epoch : 38 ======> cost : 0.10001053824290355 , train accuracy : 85.47833333333334 , test accuracy : 84.32\n",
            "*** epoch : 39 ======> cost : 0.1000022203089613 , train accuracy : 84.11999999999999 , test accuracy : 82.94\n",
            "__________________________________________________\n",
            "train accuracy: 84.11999999999999\n",
            "test accuracy: 82.94\n",
            "min 0.0 : sec 48.768\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "goIhy8Bou-rv",
        "outputId": "cec4b83e-a318-400b-8e4e-191bf0c86bea"
      },
      "source": [
        "# using sigmoid with momentum\n",
        "model = [\n",
        "    (train_set_x_v.shape[0] , None),\n",
        "    (16 , sigmoid ,sigmoid_deriv ),\n",
        "    (16 , sigmoid ,sigmoid_deriv ),\n",
        "    (10 , sigmoid , sigmoid_deriv),\n",
        "]\n",
        "describe_model(model)\n",
        "start = time.time()\n",
        "info = main_model(model , train_set_x_v, train_set_y_v,test_set_x_v , test_set_y_v,a=0.01, epoch=40 , use_momentum=True , beta=0.9)\n",
        "finish = round(time.time() - start , 3)\n",
        "print('min {} : sec {}'.format(finish//60 , finish % 60))\n",
        "plot_result(info)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "input layer : 784\n",
            "{'activation units': 16, 'activation function': 'sigmoid', 'parameters': 12544}\n",
            "{'activation units': 16, 'activation function': 'sigmoid', 'parameters': 256}\n",
            "{'activation units': 10, 'activation function': 'sigmoid', 'parameters': 160}\n",
            "*** epoch : 0 ======> cost : 0.22680323921975284 , train accuracy : 73.205 , test accuracy : 74.33\n",
            "*** epoch : 1 ======> cost : 0.06362146726019205 , train accuracy : 85.22333333333333 , test accuracy : 85.53\n",
            "*** epoch : 2 ======> cost : 0.03416333655926474 , train accuracy : 87.47333333333333 , test accuracy : 87.7\n",
            "*** epoch : 3 ======> cost : 0.021534653049827428 , train accuracy : 88.65666666666667 , test accuracy : 89.0\n",
            "*** epoch : 4 ======> cost : 0.017474946464590153 , train accuracy : 89.38166666666667 , test accuracy : 89.64999999999999\n",
            "*** epoch : 5 ======> cost : 0.016941855941534655 , train accuracy : 89.96 , test accuracy : 90.08\n",
            "*** epoch : 6 ======> cost : 0.018310641067381995 , train accuracy : 90.45666666666666 , test accuracy : 90.46\n",
            "*** epoch : 7 ======> cost : 0.020089314651955716 , train accuracy : 90.935 , test accuracy : 90.72\n",
            "*** epoch : 8 ======> cost : 0.02130236492449004 , train accuracy : 91.295 , test accuracy : 90.92\n",
            "*** epoch : 9 ======> cost : 0.021207974063969903 , train accuracy : 91.67 , test accuracy : 91.25999999999999\n",
            "*** epoch : 10 ======> cost : 0.019495052881138113 , train accuracy : 92.06166666666667 , test accuracy : 91.53\n",
            "*** epoch : 11 ======> cost : 0.01675822834978096 , train accuracy : 92.43833333333333 , test accuracy : 91.79\n",
            "*** epoch : 12 ======> cost : 0.014271518888736168 , train accuracy : 92.705 , test accuracy : 91.99000000000001\n",
            "*** epoch : 13 ======> cost : 0.012318064263706536 , train accuracy : 92.925 , test accuracy : 92.23\n",
            "*** epoch : 14 ======> cost : 0.010592336198257233 , train accuracy : 93.075 , test accuracy : 92.34\n",
            "*** epoch : 15 ======> cost : 0.009146683704770665 , train accuracy : 93.24 , test accuracy : 92.35\n",
            "*** epoch : 16 ======> cost : 0.00801963713000807 , train accuracy : 93.42 , test accuracy : 92.39\n",
            "*** epoch : 17 ======> cost : 0.0072728197891136935 , train accuracy : 93.56166666666667 , test accuracy : 92.43\n",
            "*** epoch : 18 ======> cost : 0.006994288702527547 , train accuracy : 93.69666666666666 , test accuracy : 92.47\n",
            "*** epoch : 19 ======> cost : 0.0067806072289859146 , train accuracy : 93.79833333333333 , test accuracy : 92.49000000000001\n",
            "*** epoch : 20 ======> cost : 0.006450306091996904 , train accuracy : 93.85 , test accuracy : 92.5\n",
            "*** epoch : 21 ======> cost : 0.006121193847081095 , train accuracy : 93.90833333333333 , test accuracy : 92.47\n",
            "*** epoch : 22 ======> cost : 0.005841574179929918 , train accuracy : 93.96833333333333 , test accuracy : 92.46\n",
            "*** epoch : 23 ======> cost : 0.005609122373291151 , train accuracy : 94.00666666666667 , test accuracy : 92.4\n",
            "*** epoch : 24 ======> cost : 0.005443602774997286 , train accuracy : 94.07833333333333 , test accuracy : 92.33\n",
            "*** epoch : 25 ======> cost : 0.0053305733615414295 , train accuracy : 94.11166666666666 , test accuracy : 92.38\n",
            "*** epoch : 26 ======> cost : 0.0052498067835080355 , train accuracy : 94.14666666666666 , test accuracy : 92.42\n",
            "*** epoch : 27 ======> cost : 0.005182249795080071 , train accuracy : 94.21000000000001 , test accuracy : 92.39\n",
            "*** epoch : 28 ======> cost : 0.005090515857021899 , train accuracy : 94.24833333333333 , test accuracy : 92.35\n",
            "*** epoch : 29 ======> cost : 0.0049507568266377155 , train accuracy : 94.30333333333333 , test accuracy : 92.36999999999999\n",
            "*** epoch : 30 ======> cost : 0.0048033269296701864 , train accuracy : 94.37166666666667 , test accuracy : 92.39\n",
            "*** epoch : 31 ======> cost : 0.0046677822720277125 , train accuracy : 94.41166666666668 , test accuracy : 92.39\n",
            "*** epoch : 32 ======> cost : 0.004551907954432991 , train accuracy : 94.43 , test accuracy : 92.41\n",
            "*** epoch : 33 ======> cost : 0.004469196646879577 , train accuracy : 94.47166666666666 , test accuracy : 92.45\n",
            "*** epoch : 34 ======> cost : 0.004429086024571613 , train accuracy : 94.49166666666666 , test accuracy : 92.44\n",
            "*** epoch : 35 ======> cost : 0.004424255801345902 , train accuracy : 94.51333333333334 , test accuracy : 92.45\n",
            "*** epoch : 36 ======> cost : 0.004442913892638944 , train accuracy : 94.55333333333333 , test accuracy : 92.45\n",
            "*** epoch : 37 ======> cost : 0.004476880584170409 , train accuracy : 94.57166666666666 , test accuracy : 92.49000000000001\n",
            "*** epoch : 38 ======> cost : 0.0045138029334378296 , train accuracy : 94.61 , test accuracy : 92.58999999999999\n",
            "*** epoch : 39 ======> cost : 0.004538937152689059 , train accuracy : 94.65 , test accuracy : 92.63\n",
            "__________________________________________________\n",
            "train accuracy: 94.65\n",
            "test accuracy: 92.63\n",
            "min 1.0 : sec 1.125\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Wfikj8EYHlSH",
        "outputId": "3ecec838-f144-4646-82ae-7bf9ed3ff6cb"
      },
      "source": [
        "# using sigmoid without momentum\n",
        "model = [\n",
        "    (train_set_x_v.shape[0] , None),\n",
        "    (16 , sigmoid ,sigmoid_deriv ),\n",
        "    (16 , sigmoid ,sigmoid_deriv ),\n",
        "    (10 , sigmoid , sigmoid_deriv),\n",
        "]\n",
        "describe_model(model)\n",
        "start = time.time()\n",
        "info = main_model(model , train_set_x_v, train_set_y_v,test_set_x_v , test_set_y_v,a=0.01, epoch=40 )\n",
        "finish = round(time.time() - start , 3)\n",
        "print('min {} : sec {}'.format(finish//60 , finish % 60))\n",
        "plot_result(info)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "input layer : 784\n",
            "{'activation units': 16, 'activation function': 'sigmoid', 'parameters': 12544}\n",
            "{'activation units': 16, 'activation function': 'sigmoid', 'parameters': 256}\n",
            "{'activation units': 10, 'activation function': 'sigmoid', 'parameters': 160}\n",
            "*** epoch : 0 ======> cost : 0.21405427485599365 , train accuracy : 70.40833333333333 , test accuracy : 71.05\n",
            "*** epoch : 1 ======> cost : 0.045454383903222775 , train accuracy : 84.84166666666667 , test accuracy : 84.96000000000001\n",
            "*** epoch : 2 ======> cost : 0.021988947361763662 , train accuracy : 87.355 , test accuracy : 87.52\n",
            "*** epoch : 3 ======> cost : 0.015891156006671275 , train accuracy : 88.6 , test accuracy : 88.64999999999999\n",
            "*** epoch : 4 ======> cost : 0.013606488160372738 , train accuracy : 89.42833333333333 , test accuracy : 89.42\n",
            "*** epoch : 5 ======> cost : 0.013245056447849115 , train accuracy : 90.05666666666666 , test accuracy : 89.89\n",
            "*** epoch : 6 ======> cost : 0.013541606894351483 , train accuracy : 90.64999999999999 , test accuracy : 90.39\n",
            "*** epoch : 7 ======> cost : 0.013926656757897293 , train accuracy : 91.10333333333334 , test accuracy : 90.75999999999999\n",
            "*** epoch : 8 ======> cost : 0.014047607476585053 , train accuracy : 91.445 , test accuracy : 91.08000000000001\n",
            "*** epoch : 9 ======> cost : 0.013447244257349694 , train accuracy : 91.81 , test accuracy : 91.25\n",
            "*** epoch : 10 ======> cost : 0.012118313793666189 , train accuracy : 92.095 , test accuracy : 91.39\n",
            "*** epoch : 11 ======> cost : 0.010646833480878248 , train accuracy : 92.31 , test accuracy : 91.43\n",
            "*** epoch : 12 ======> cost : 0.009702917282731838 , train accuracy : 92.50666666666667 , test accuracy : 91.59\n",
            "*** epoch : 13 ======> cost : 0.009338415826132531 , train accuracy : 92.68833333333333 , test accuracy : 91.74\n",
            "*** epoch : 14 ======> cost : 0.009313816957179448 , train accuracy : 92.845 , test accuracy : 91.89\n",
            "*** epoch : 15 ======> cost : 0.009436700234811187 , train accuracy : 92.97166666666666 , test accuracy : 91.99000000000001\n",
            "*** epoch : 16 ======> cost : 0.009530237647347103 , train accuracy : 93.16333333333333 , test accuracy : 92.01\n",
            "*** epoch : 17 ======> cost : 0.009481782235683325 , train accuracy : 93.33666666666667 , test accuracy : 92.08\n",
            "*** epoch : 18 ======> cost : 0.009189235396368272 , train accuracy : 93.45166666666667 , test accuracy : 92.21000000000001\n",
            "*** epoch : 19 ======> cost : 0.008551236437121619 , train accuracy : 93.57833333333333 , test accuracy : 92.29\n",
            "*** epoch : 20 ======> cost : 0.007720678694996664 , train accuracy : 93.71833333333333 , test accuracy : 92.4\n",
            "*** epoch : 21 ======> cost : 0.006955074177595226 , train accuracy : 93.77166666666666 , test accuracy : 92.33\n",
            "*** epoch : 22 ======> cost : 0.006364195673785605 , train accuracy : 93.86833333333333 , test accuracy : 92.39\n",
            "*** epoch : 23 ======> cost : 0.005937673144627405 , train accuracy : 93.95166666666667 , test accuracy : 92.43\n",
            "*** epoch : 24 ======> cost : 0.005634877498935237 , train accuracy : 94.02666666666667 , test accuracy : 92.52\n",
            "*** epoch : 25 ======> cost : 0.005413186233275576 , train accuracy : 94.11 , test accuracy : 92.52\n",
            "*** epoch : 26 ======> cost : 0.005238838168811094 , train accuracy : 94.16166666666666 , test accuracy : 92.54\n",
            "*** epoch : 27 ======> cost : 0.005095931492867756 , train accuracy : 94.23666666666666 , test accuracy : 92.61\n",
            "*** epoch : 28 ======> cost : 0.004986079749427143 , train accuracy : 94.29333333333332 , test accuracy : 92.63\n",
            "*** epoch : 29 ======> cost : 0.004913517042548377 , train accuracy : 94.35 , test accuracy : 92.65\n",
            "*** epoch : 30 ======> cost : 0.004879322370648223 , train accuracy : 94.42166666666667 , test accuracy : 92.73\n",
            "*** epoch : 31 ======> cost : 0.004887938780637179 , train accuracy : 94.48333333333333 , test accuracy : 92.75\n",
            "*** epoch : 32 ======> cost : 0.004948612948999408 , train accuracy : 94.53 , test accuracy : 92.78\n",
            "*** epoch : 33 ======> cost : 0.005065969706247673 , train accuracy : 94.58666666666666 , test accuracy : 92.85\n",
            "*** epoch : 34 ======> cost : 0.0052323377607703 , train accuracy : 94.62666666666667 , test accuracy : 92.86999999999999\n",
            "*** epoch : 35 ======> cost : 0.005433366576156506 , train accuracy : 94.68333333333334 , test accuracy : 92.88\n",
            "*** epoch : 36 ======> cost : 0.005653476933192364 , train accuracy : 94.73 , test accuracy : 92.9\n",
            "*** epoch : 37 ======> cost : 0.005881126700402851 , train accuracy : 94.79166666666666 , test accuracy : 92.92\n",
            "*** epoch : 38 ======> cost : 0.006112283720494765 , train accuracy : 94.85833333333333 , test accuracy : 92.91\n",
            "*** epoch : 39 ======> cost : 0.006342954242933366 , train accuracy : 94.90333333333332 , test accuracy : 92.97\n",
            "__________________________________________________\n",
            "train accuracy: 94.90333333333332\n",
            "test accuracy: 92.97\n",
            "min 0.0 : sec 55.419\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "3OymyXD7vUEE",
        "outputId": "f3ce706c-26bc-46a6-faba-a428e4cb1fdf"
      },
      "source": [
        "# using tanh with momentum\n",
        "model = [\n",
        "    (train_set_x_v.shape[0] , None),\n",
        "    (16 , tanh ,tanh_deriv ),\n",
        "    (16 , tanh ,tanh_deriv ),\n",
        "    (10 , sigmoid , sigmoid_deriv),\n",
        "]\n",
        "describe_model(model)\n",
        "start = time.time()\n",
        "info = main_model(model , train_set_x_v, train_set_y_v,test_set_x_v , test_set_y_v,a=0.01, epoch=40, use_momentum=True , beta=0.9)\n",
        "finish = round(time.time() - start , 3)\n",
        "print('min {} : sec {}'.format(finish//60 , finish % 60))\n",
        "plot_result(info)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "input layer : 784\n",
            "{'activation units': 16, 'activation function': 'tanh', 'parameters': 12544}\n",
            "{'activation units': 16, 'activation function': 'tanh', 'parameters': 256}\n",
            "{'activation units': 10, 'activation function': 'sigmoid', 'parameters': 160}\n",
            "*** epoch : 0 ======> cost : 0.009912547609995775 , train accuracy : 87.29666666666667 , test accuracy : 87.17\n",
            "*** epoch : 1 ======> cost : 0.00492074478227055 , train accuracy : 90.795 , test accuracy : 90.48\n",
            "*** epoch : 2 ======> cost : 0.012192932578503077 , train accuracy : 91.33 , test accuracy : 90.8\n",
            "*** epoch : 3 ======> cost : 0.002937287738472487 , train accuracy : 92.37666666666667 , test accuracy : 91.71000000000001\n",
            "*** epoch : 4 ======> cost : 0.006697127965472792 , train accuracy : 92.35333333333332 , test accuracy : 91.53999999999999\n",
            "*** epoch : 5 ======> cost : 0.004971741033102149 , train accuracy : 92.14666666666666 , test accuracy : 91.43\n",
            "*** epoch : 6 ======> cost : 0.01141769369962594 , train accuracy : 92.92166666666667 , test accuracy : 91.93\n",
            "*** epoch : 7 ======> cost : 0.015300889127450552 , train accuracy : 92.44166666666666 , test accuracy : 91.69\n",
            "*** epoch : 8 ======> cost : 0.004202126811133568 , train accuracy : 92.925 , test accuracy : 91.67\n",
            "*** epoch : 9 ======> cost : 0.009060933796564969 , train accuracy : 93.075 , test accuracy : 91.78\n",
            "*** epoch : 10 ======> cost : 0.0023028541054858735 , train accuracy : 93.32666666666667 , test accuracy : 92.32000000000001\n",
            "*** epoch : 11 ======> cost : 0.017390064922510436 , train accuracy : 93.55499999999999 , test accuracy : 92.27\n",
            "*** epoch : 12 ======> cost : 0.002672929162875682 , train accuracy : 93.105 , test accuracy : 91.45\n",
            "*** epoch : 13 ======> cost : 0.015249701624126949 , train accuracy : 93.64666666666666 , test accuracy : 92.21000000000001\n",
            "*** epoch : 14 ======> cost : 0.010291352525099171 , train accuracy : 93.77 , test accuracy : 92.14\n",
            "*** epoch : 15 ======> cost : 0.009212193368802725 , train accuracy : 93.72500000000001 , test accuracy : 92.03\n",
            "*** epoch : 16 ======> cost : 0.006906438629395358 , train accuracy : 93.27833333333334 , test accuracy : 91.60000000000001\n",
            "*** epoch : 17 ======> cost : 0.0031998290824869547 , train accuracy : 94.00333333333334 , test accuracy : 92.22\n",
            "*** epoch : 18 ======> cost : 0.01640546503299229 , train accuracy : 94.07333333333334 , test accuracy : 92.25\n",
            "*** epoch : 19 ======> cost : 0.0019666903653124753 , train accuracy : 94.29666666666667 , test accuracy : 92.58\n",
            "*** epoch : 20 ======> cost : 0.009439689670735562 , train accuracy : 93.96333333333334 , test accuracy : 92.52\n",
            "*** epoch : 21 ======> cost : 0.005924819170323203 , train accuracy : 94.32333333333334 , test accuracy : 92.67\n",
            "*** epoch : 22 ======> cost : 0.008122214120383487 , train accuracy : 94.28833333333333 , test accuracy : 92.51\n",
            "*** epoch : 23 ======> cost : 0.005194895708219636 , train accuracy : 93.76 , test accuracy : 92.25\n",
            "*** epoch : 24 ======> cost : 0.02533211751139086 , train accuracy : 94.13333333333334 , test accuracy : 92.67\n",
            "*** epoch : 25 ======> cost : 0.0029202558596689455 , train accuracy : 94.39166666666667 , test accuracy : 92.60000000000001\n",
            "*** epoch : 26 ======> cost : 0.008305841867844046 , train accuracy : 93.92833333333334 , test accuracy : 92.27\n",
            "*** epoch : 27 ======> cost : 0.0014314447254410035 , train accuracy : 94.90166666666666 , test accuracy : 93.08\n",
            "*** epoch : 28 ======> cost : 0.0008031536873134062 , train accuracy : 94.71333333333334 , test accuracy : 92.9\n",
            "*** epoch : 29 ======> cost : 0.0012631998151242173 , train accuracy : 94.19833333333332 , test accuracy : 92.30000000000001\n",
            "*** epoch : 30 ======> cost : 0.0011871709653346392 , train accuracy : 94.50333333333333 , test accuracy : 92.81\n",
            "*** epoch : 31 ======> cost : 0.0176277358520121 , train accuracy : 94.34 , test accuracy : 92.34\n",
            "*** epoch : 32 ======> cost : 0.0013948737241482902 , train accuracy : 94.80833333333332 , test accuracy : 92.9\n",
            "*** epoch : 33 ======> cost : 0.0046001250609515165 , train accuracy : 95.08166666666666 , test accuracy : 93.03\n",
            "*** epoch : 34 ======> cost : 0.014280687697854785 , train accuracy : 94.105 , test accuracy : 91.99000000000001\n",
            "*** epoch : 35 ======> cost : 0.001532916647624486 , train accuracy : 95.06833333333333 , test accuracy : 93.04\n",
            "*** epoch : 36 ======> cost : 0.004085578906600751 , train accuracy : 95.03333333333333 , test accuracy : 92.97999999999999\n",
            "*** epoch : 37 ======> cost : 0.001635899451137029 , train accuracy : 95.39666666666666 , test accuracy : 93.11\n",
            "*** epoch : 38 ======> cost : 0.002298234198696574 , train accuracy : 95.21333333333334 , test accuracy : 93.11\n",
            "*** epoch : 39 ======> cost : 0.0012358884314469993 , train accuracy : 94.74166666666667 , test accuracy : 92.58999999999999\n",
            "__________________________________________________\n",
            "train accuracy: 94.74166666666667\n",
            "test accuracy: 92.58999999999999\n",
            "min 1.0 : sec 2.381999999999998\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "UxlYIwkRIqAG",
        "outputId": "f0e624c2-2463-459a-d915-43224940c863"
      },
      "source": [
        "# using tanh without momentum\n",
        "model = [\n",
        "    (train_set_x_v.shape[0] , None),\n",
        "    (16 , tanh ,tanh_deriv ),\n",
        "    (16 , tanh ,tanh_deriv ),\n",
        "    (10 , sigmoid , sigmoid_deriv),\n",
        "]\n",
        "describe_model(model)\n",
        "start = time.time()\n",
        "info = main_model(model , train_set_x_v, train_set_y_v,test_set_x_v , test_set_y_v,a=0.01, epoch=40, use_momentum=False )\n",
        "finish = round(time.time() - start , 3)\n",
        "print('min {} : sec {}'.format(finish//60 , finish % 60))\n",
        "plot_result(info)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "input layer : 784\n",
            "{'activation units': 16, 'activation function': 'tanh', 'parameters': 12544}\n",
            "{'activation units': 16, 'activation function': 'tanh', 'parameters': 256}\n",
            "{'activation units': 10, 'activation function': 'sigmoid', 'parameters': 160}\n",
            "*** epoch : 0 ======> cost : 0.020956825564612494 , train accuracy : 87.98166666666667 , test accuracy : 88.24\n",
            "*** epoch : 1 ======> cost : 0.022359216499997926 , train accuracy : 90.77 , test accuracy : 90.98\n",
            "*** epoch : 2 ======> cost : 0.010109170441944763 , train accuracy : 91.55 , test accuracy : 91.12\n",
            "*** epoch : 3 ======> cost : 0.008530342331046683 , train accuracy : 91.97999999999999 , test accuracy : 91.4\n",
            "*** epoch : 4 ======> cost : 0.006612732499632063 , train accuracy : 92.44 , test accuracy : 91.75999999999999\n",
            "*** epoch : 5 ======> cost : 0.0037913262888858664 , train accuracy : 92.825 , test accuracy : 92.0\n",
            "*** epoch : 6 ======> cost : 0.002386791500049685 , train accuracy : 93.17333333333333 , test accuracy : 92.22\n",
            "*** epoch : 7 ======> cost : 0.001719026517519883 , train accuracy : 93.49166666666666 , test accuracy : 92.49000000000001\n",
            "*** epoch : 8 ======> cost : 0.002047683019675737 , train accuracy : 93.20833333333334 , test accuracy : 92.17\n",
            "*** epoch : 9 ======> cost : 0.002179416826883245 , train accuracy : 93.61333333333334 , test accuracy : 92.29\n",
            "*** epoch : 10 ======> cost : 0.002267415075151204 , train accuracy : 93.72333333333333 , test accuracy : 92.53\n",
            "*** epoch : 11 ======> cost : 0.001035325492077058 , train accuracy : 94.12 , test accuracy : 92.95\n",
            "*** epoch : 12 ======> cost : 0.0011186843039981507 , train accuracy : 93.8 , test accuracy : 92.55\n",
            "*** epoch : 13 ======> cost : 0.001483627074173854 , train accuracy : 94.13333333333334 , test accuracy : 92.60000000000001\n",
            "*** epoch : 14 ======> cost : 0.0016273381686511261 , train accuracy : 94.25166666666667 , test accuracy : 92.75999999999999\n",
            "*** epoch : 15 ======> cost : 0.00633107691119876 , train accuracy : 93.66166666666666 , test accuracy : 92.61\n",
            "*** epoch : 16 ======> cost : 0.0015716440490864206 , train accuracy : 93.80333333333334 , test accuracy : 92.52\n",
            "*** epoch : 17 ======> cost : 0.0006454055948677428 , train accuracy : 94.22166666666666 , test accuracy : 92.86\n",
            "*** epoch : 18 ======> cost : 0.0016749912398258168 , train accuracy : 94.44 , test accuracy : 93.08999999999999\n",
            "*** epoch : 19 ======> cost : 0.01720822744587282 , train accuracy : 94.39333333333333 , test accuracy : 92.78999999999999\n",
            "*** epoch : 20 ======> cost : 0.0009314445825335128 , train accuracy : 94.64333333333333 , test accuracy : 93.10000000000001\n",
            "*** epoch : 21 ======> cost : 0.0004742929854759329 , train accuracy : 94.63166666666667 , test accuracy : 93.0\n",
            "*** epoch : 22 ======> cost : 0.034377204795321435 , train accuracy : 94.73333333333333 , test accuracy : 93.13\n",
            "*** epoch : 23 ======> cost : 0.04116292951842115 , train accuracy : 94.74166666666667 , test accuracy : 93.07\n",
            "*** epoch : 24 ======> cost : 0.0004388671636080605 , train accuracy : 95.13833333333334 , test accuracy : 93.47999999999999\n",
            "*** epoch : 25 ======> cost : 0.019774205595703663 , train accuracy : 95.04666666666667 , test accuracy : 93.17999999999999\n",
            "*** epoch : 26 ======> cost : 0.009242752045069962 , train accuracy : 95.35333333333334 , test accuracy : 93.34\n",
            "*** epoch : 27 ======> cost : 0.0011105304045198943 , train accuracy : 95.07 , test accuracy : 93.27\n",
            "*** epoch : 28 ======> cost : 0.002544250560312023 , train accuracy : 94.97166666666666 , test accuracy : 93.06\n",
            "*** epoch : 29 ======> cost : 0.0011844281102386793 , train accuracy : 94.63666666666667 , test accuracy : 93.12\n",
            "*** epoch : 30 ======> cost : 0.023486468568951722 , train accuracy : 95.19333333333333 , test accuracy : 93.44\n",
            "*** epoch : 31 ======> cost : 0.008235885949498803 , train accuracy : 95.135 , test accuracy : 93.38\n",
            "*** epoch : 32 ======> cost : 0.0007508727703064636 , train accuracy : 95.41666666666667 , test accuracy : 93.61\n",
            "*** epoch : 33 ======> cost : 0.03020479044447527 , train accuracy : 95.35833333333333 , test accuracy : 93.43\n",
            "*** epoch : 34 ======> cost : 0.004656562255737818 , train accuracy : 95.45 , test accuracy : 93.30000000000001\n",
            "*** epoch : 35 ======> cost : 0.002871354052313135 , train accuracy : 95.37833333333333 , test accuracy : 93.30000000000001\n",
            "*** epoch : 36 ======> cost : 0.0006357851388364957 , train accuracy : 95.48 , test accuracy : 93.35\n",
            "*** epoch : 37 ======> cost : 0.011987998110142818 , train accuracy : 95.00999999999999 , test accuracy : 93.04\n",
            "*** epoch : 38 ======> cost : 0.04219652573213153 , train accuracy : 94.79833333333333 , test accuracy : 92.97999999999999\n",
            "*** epoch : 39 ======> cost : 0.0018781069217294044 , train accuracy : 95.71666666666667 , test accuracy : 93.63\n",
            "__________________________________________________\n",
            "train accuracy: 95.71666666666667\n",
            "test accuracy: 93.63\n",
            "min 0.0 : sec 56.83\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "mu7dvS65zDQL",
        "outputId": "9390c615-c5f6-47cd-e6d6-043b227a3204"
      },
      "source": [
        "# using leaky relu with momentum\n",
        "model = [\n",
        "    (train_set_x_v.shape[0] , None),\n",
        "    (16 , leaky_relu ,leaky_relu_deriv ),\n",
        "    (16 , leaky_relu ,leaky_relu_deriv ),\n",
        "    (10 , sigmoid , sigmoid_deriv),\n",
        "]\n",
        "describe_model(model)\n",
        "start = time.time()\n",
        "info = main_model(model , train_set_x_v, train_set_y_v,test_set_x_v , test_set_y_v,a=0.01, epoch=40 , use_momentum=True , beta=0.9)\n",
        "finish = round(time.time() - start , 3)\n",
        "print('min {} : sec {}'.format(finish//60 , finish % 60))\n",
        "plot_result(info)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "input layer : 784\n",
            "{'activation units': 16, 'activation function': 'leaky_relu', 'parameters': 12544}\n",
            "{'activation units': 16, 'activation function': 'leaky_relu', 'parameters': 256}\n",
            "{'activation units': 10, 'activation function': 'sigmoid', 'parameters': 160}\n",
            "*** epoch : 0 ======> cost : 0.2070398780154962 , train accuracy : 77.84 , test accuracy : 78.33\n",
            "*** epoch : 1 ======> cost : 0.21606485943987513 , train accuracy : 80.12166666666667 , test accuracy : 79.92\n",
            "*** epoch : 2 ======> cost : 0.16387137193308315 , train accuracy : 80.24 , test accuracy : 79.63\n",
            "*** epoch : 3 ======> cost : 0.15097282987912028 , train accuracy : 79.73166666666667 , test accuracy : 79.25999999999999\n",
            "*** epoch : 4 ======> cost : 0.15104516739417279 , train accuracy : 81.70666666666668 , test accuracy : 80.89\n",
            "*** epoch : 5 ======> cost : 0.1561051496732332 , train accuracy : 82.16833333333334 , test accuracy : 81.71000000000001\n",
            "*** epoch : 6 ======> cost : 0.16407151373689982 , train accuracy : 82.49333333333333 , test accuracy : 82.06\n",
            "*** epoch : 7 ======> cost : 0.15343584475792113 , train accuracy : 82.49333333333333 , test accuracy : 81.66\n",
            "*** epoch : 8 ======> cost : 0.15053953107430065 , train accuracy : 82.38166666666666 , test accuracy : 81.64\n",
            "*** epoch : 9 ======> cost : 0.15011661115514907 , train accuracy : 83.09666666666666 , test accuracy : 82.15\n",
            "*** epoch : 10 ======> cost : 0.15049698421270802 , train accuracy : 83.185 , test accuracy : 82.34\n",
            "*** epoch : 11 ======> cost : 0.15013009255202364 , train accuracy : 83.79666666666667 , test accuracy : 82.95\n",
            "*** epoch : 12 ======> cost : 0.15083577338034587 , train accuracy : 84.23833333333334 , test accuracy : 83.07\n",
            "*** epoch : 13 ======> cost : 0.15039139809990482 , train accuracy : 84.27333333333334 , test accuracy : 83.6\n",
            "*** epoch : 14 ======> cost : 0.15034348276479254 , train accuracy : 83.57833333333333 , test accuracy : 82.45\n",
            "*** epoch : 15 ======> cost : 0.1504769556350545 , train accuracy : 84.93 , test accuracy : 83.78999999999999\n",
            "*** epoch : 16 ======> cost : 0.1507648639433613 , train accuracy : 82.75833333333334 , test accuracy : 82.12\n",
            "*** epoch : 17 ======> cost : 0.15210290466175494 , train accuracy : 84.52333333333333 , test accuracy : 83.57\n",
            "*** epoch : 18 ======> cost : 0.15013811850644718 , train accuracy : 84.23666666666666 , test accuracy : 83.28\n",
            "*** epoch : 19 ======> cost : 0.15047939139898464 , train accuracy : 84.30666666666666 , test accuracy : 83.57\n",
            "*** epoch : 20 ======> cost : 0.15073955497286376 , train accuracy : 84.475 , test accuracy : 83.25\n",
            "*** epoch : 21 ======> cost : 0.15123181648845424 , train accuracy : 84.90666666666667 , test accuracy : 83.74000000000001\n",
            "*** epoch : 22 ======> cost : 0.15090557145761435 , train accuracy : 84.47 , test accuracy : 83.28\n",
            "*** epoch : 23 ======> cost : 0.15008757837856757 , train accuracy : 84.50999999999999 , test accuracy : 83.38\n",
            "*** epoch : 24 ======> cost : 0.15148107664711308 , train accuracy : 84.94166666666668 , test accuracy : 84.00999999999999\n",
            "*** epoch : 25 ======> cost : 0.15175425269719242 , train accuracy : 84.63666666666667 , test accuracy : 83.59\n",
            "*** epoch : 26 ======> cost : 0.1503848938366568 , train accuracy : 85.02333333333333 , test accuracy : 83.73\n",
            "*** epoch : 27 ======> cost : 0.15104815429474386 , train accuracy : 85.465 , test accuracy : 84.24000000000001\n",
            "*** epoch : 28 ======> cost : 0.15030727805855532 , train accuracy : 85.46166666666667 , test accuracy : 84.31\n",
            "*** epoch : 29 ======> cost : 0.15175327598701321 , train accuracy : 85.295 , test accuracy : 84.09\n",
            "*** epoch : 30 ======> cost : 0.15161572172644205 , train accuracy : 85.08666666666667 , test accuracy : 83.89999999999999\n",
            "*** epoch : 31 ======> cost : 0.1500024866690704 , train accuracy : 84.575 , test accuracy : 83.32000000000001\n",
            "*** epoch : 32 ======> cost : 0.15017594063357792 , train accuracy : 85.43666666666667 , test accuracy : 84.05\n",
            "*** epoch : 33 ======> cost : 0.1500777458494671 , train accuracy : 85.46666666666667 , test accuracy : 84.08\n",
            "*** epoch : 34 ======> cost : 0.15024431504219235 , train accuracy : 85.3 , test accuracy : 84.15\n",
            "*** epoch : 35 ======> cost : 0.1507545988457531 , train accuracy : 85.57833333333333 , test accuracy : 84.17999999999999\n",
            "*** epoch : 36 ======> cost : 0.15076919790491022 , train accuracy : 84.83666666666667 , test accuracy : 83.78\n",
            "*** epoch : 37 ======> cost : 0.15004303102549368 , train accuracy : 85.41333333333333 , test accuracy : 84.05\n",
            "*** epoch : 38 ======> cost : 0.15154132757123626 , train accuracy : 85.425 , test accuracy : 84.37\n",
            "*** epoch : 39 ======> cost : 0.15012486410970852 , train accuracy : 85.05 , test accuracy : 83.74000000000001\n",
            "__________________________________________________\n",
            "train accuracy: 85.05\n",
            "test accuracy: 83.74000000000001\n",
            "min 0.0 : sec 57.877\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "OlyW4D9lzRDd",
        "outputId": "f547506e-506c-47a9-b1cf-3776eba46228"
      },
      "source": [
        "# using leaky relu without momentum\n",
        "model = [\n",
        "    (train_set_x_v.shape[0] , None),\n",
        "    (16 , leaky_relu ,leaky_relu_deriv ),\n",
        "    (16 , leaky_relu ,leaky_relu_deriv ),\n",
        "    (10 , sigmoid , sigmoid_deriv),\n",
        "]\n",
        "describe_model(model)\n",
        "start = time.time()\n",
        "info = main_model(model , train_set_x_v, train_set_y_v,test_set_x_v , test_set_y_v,a=0.01, epoch=40 , use_momentum=False)\n",
        "finish = round(time.time() - start , 3)\n",
        "print('min {} : sec {}'.format(finish//60 , finish % 60))\n",
        "plot_result(info)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "input layer : 784\n",
            "{'activation units': 16, 'activation function': 'leaky_relu', 'parameters': 12544}\n",
            "{'activation units': 16, 'activation function': 'leaky_relu', 'parameters': 256}\n",
            "{'activation units': 10, 'activation function': 'sigmoid', 'parameters': 160}\n",
            "*** epoch : 0 ======> cost : 0.12324627142729938 , train accuracy : 79.00166666666667 , test accuracy : 79.12\n",
            "*** epoch : 1 ======> cost : 0.1032576255477911 , train accuracy : 81.11 , test accuracy : 80.75\n",
            "*** epoch : 2 ======> cost : 0.1102808488903492 , train accuracy : 81.91833333333334 , test accuracy : 81.5\n",
            "*** epoch : 3 ======> cost : 0.10428959874911918 , train accuracy : 82.905 , test accuracy : 82.35\n",
            "*** epoch : 4 ======> cost : 0.14259500343925344 , train accuracy : 81.03666666666666 , test accuracy : 80.84\n",
            "*** epoch : 5 ======> cost : 0.13315294813098064 , train accuracy : 83.06833333333333 , test accuracy : 82.66\n",
            "*** epoch : 6 ======> cost : 0.13220408436423706 , train accuracy : 82.83333333333334 , test accuracy : 82.52000000000001\n",
            "*** epoch : 7 ======> cost : 0.1307568904568615 , train accuracy : 83.20833333333333 , test accuracy : 82.94\n",
            "*** epoch : 8 ======> cost : 0.14066032018104585 , train accuracy : 83.34833333333333 , test accuracy : 82.80999999999999\n",
            "*** epoch : 9 ======> cost : 0.12185933444278918 , train accuracy : 83.685 , test accuracy : 83.28\n",
            "*** epoch : 10 ======> cost : 0.15652357925035756 , train accuracy : 84.90166666666667 , test accuracy : 84.19\n",
            "*** epoch : 11 ======> cost : 0.10581170889330838 , train accuracy : 84.28166666666667 , test accuracy : 83.56\n",
            "*** epoch : 12 ======> cost : 0.11369405527133691 , train accuracy : 84.58666666666666 , test accuracy : 83.88\n",
            "*** epoch : 13 ======> cost : 0.10047061174145362 , train accuracy : 84.87 , test accuracy : 83.97\n",
            "*** epoch : 14 ======> cost : 0.15374374727999462 , train accuracy : 83.84666666666666 , test accuracy : 83.03\n",
            "*** epoch : 15 ======> cost : 0.1976907721817128 , train accuracy : 84.28 , test accuracy : 83.49\n",
            "*** epoch : 16 ======> cost : 0.1459646768004976 , train accuracy : 84.20333333333333 , test accuracy : 83.21\n",
            "*** epoch : 17 ======> cost : 0.15251105425467953 , train accuracy : 84.38333333333333 , test accuracy : 83.65\n",
            "*** epoch : 18 ======> cost : 0.15133012629064885 , train accuracy : 84.36666666666667 , test accuracy : 83.58\n",
            "*** epoch : 19 ======> cost : 0.12006510392134366 , train accuracy : 84.88 , test accuracy : 84.23\n",
            "*** epoch : 20 ======> cost : 0.10009758090209317 , train accuracy : 84.69333333333333 , test accuracy : 83.75\n",
            "*** epoch : 21 ======> cost : 0.10579215436288866 , train accuracy : 84.79166666666667 , test accuracy : 83.82\n",
            "*** epoch : 22 ======> cost : 0.15887691554588051 , train accuracy : 84.355 , test accuracy : 83.62\n",
            "*** epoch : 23 ======> cost : 0.10557885192420853 , train accuracy : 85.36666666666667 , test accuracy : 84.49\n",
            "*** epoch : 24 ======> cost : 0.10553449062842038 , train accuracy : 84.66 , test accuracy : 83.64\n",
            "*** epoch : 25 ======> cost : 0.1556611473519708 , train accuracy : 84.82499999999999 , test accuracy : 83.91\n",
            "*** epoch : 26 ======> cost : 0.1556396944652481 , train accuracy : 85.2 , test accuracy : 84.14\n",
            "*** epoch : 27 ======> cost : 0.10006849031784093 , train accuracy : 85.10833333333333 , test accuracy : 83.97\n",
            "*** epoch : 28 ======> cost : 0.20228630443965095 , train accuracy : 85.16666666666667 , test accuracy : 84.07\n",
            "*** epoch : 29 ======> cost : 0.15150262533259268 , train accuracy : 84.68666666666667 , test accuracy : 83.75\n",
            "*** epoch : 30 ======> cost : 0.15010976676870405 , train accuracy : 85.43333333333332 , test accuracy : 84.34\n",
            "*** epoch : 31 ======> cost : 0.14548102376000732 , train accuracy : 85.06 , test accuracy : 83.99\n",
            "*** epoch : 32 ======> cost : 0.15194306715476763 , train accuracy : 84.18166666666667 , test accuracy : 82.98\n",
            "*** epoch : 33 ======> cost : 0.18685558843022132 , train accuracy : 85.27333333333334 , test accuracy : 84.1\n",
            "*** epoch : 34 ======> cost : 0.1016795898612356 , train accuracy : 85.505 , test accuracy : 84.52\n",
            "*** epoch : 35 ======> cost : 0.1499999786458573 , train accuracy : 85.18333333333334 , test accuracy : 84.16\n",
            "*** epoch : 36 ======> cost : 0.18506160126445612 , train accuracy : 83.43666666666667 , test accuracy : 82.61\n",
            "*** epoch : 37 ======> cost : 0.1500005209826772 , train accuracy : 84.0 , test accuracy : 82.67\n",
            "*** epoch : 38 ======> cost : 0.1500229370782923 , train accuracy : 84.74333333333334 , test accuracy : 83.47\n",
            "*** epoch : 39 ======> cost : 0.13314311569791687 , train accuracy : 85.33333333333334 , test accuracy : 84.19\n",
            "__________________________________________________\n",
            "train accuracy: 85.33333333333334\n",
            "test accuracy: 84.19\n",
            "min 0.0 : sec 52.077\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hbvMl4-ChdgL"
      },
      "source": [
        "<div dir=rtl>\n",
        "<h1><b>Adverserial Attack</h1></b>\n",
        "در این نوع حمله معمولا به داده های یک نویز وارد می‌شود.برای مثال به داده های تصویری معمولا به پیکسل های آنها مقادیری تصادفی اعمال می‌شود با این \n",
        "<b>\n",
        "shift\n",
        "</b>\n",
        "پیدا می‌کنند یا روش های دیگر.\n",
        "در اینجا داده های \n",
        "<b>\n",
        "Test\n",
        "</b>\n",
        "را به اندازه ۴ واحد به سمت راست \n",
        "<b>shift</b>\n",
        "دادیم.\n",
        "نتیجه آن تغییر زیاد بین دقت شبکه عصبی روی \n",
        "<b>\n",
        "Test Set\n",
        "</b>\n",
        "و\n",
        "<b>\n",
        "Train Set\n",
        "</b>\n",
        "است که از نشانه‌های\n",
        "<b>\n",
        "Overfit\n",
        "</b>\n",
        "است.\n",
        "دلیل آن تازه بودن داده‌های \n",
        "<b>Test</b>\n",
        "برای شبکه عصبی است.\n",
        "درواقع داده‌های \n",
        "<b>Test</b>\n",
        ",\n",
        "<b>Train</b>\n",
        "باید از یک توزیع\n",
        "(\n",
        "  <b>distribution</b>\n",
        ")\n",
        "باشند تا از این مشکل جلوگیری شود.\n",
        "</div>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_Y3J7jV59hY8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "8f0e0cfe-8e32-4e5a-a32c-0069f2ad66f7"
      },
      "source": [
        "# testing corrupted dataset\n",
        "model = [\n",
        "    (train_set_x_v.shape[0] , None),\n",
        "    (16 , sigmoid ,sigmoid_deriv ),\n",
        "    (16 , sigmoid ,sigmoid_deriv ),\n",
        "    (10 , sigmoid , sigmoid_deriv),\n",
        "]\n",
        "describe_model(model)\n",
        "start = time.time()\n",
        "info = main_model(model , train_set_x_v, train_set_y_v,shifted_test_set_x_v , test_set_y_v,a=0.01, epoch=40)\n",
        "finish = round(time.time() - start , 3)\n",
        "print('min {} : sec {}'.format(finish//60 , finish % 60))\n",
        "plot_result(info)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "input layer : 784\n",
            "{'activation units': 16, 'activation function': 'sigmoid', 'parameters': 12544}\n",
            "{'activation units': 16, 'activation function': 'sigmoid', 'parameters': 256}\n",
            "{'activation units': 10, 'activation function': 'sigmoid', 'parameters': 160}\n",
            "*** epoch : 0 ======> cost : 0.20928117310148978 , train accuracy : 72.72333333333333 , test accuracy : 12.09\n",
            "*** epoch : 1 ======> cost : 0.06632370355159434 , train accuracy : 84.245 , test accuracy : 15.25\n",
            "*** epoch : 2 ======> cost : 0.03984728456312951 , train accuracy : 86.99333333333334 , test accuracy : 17.49\n",
            "*** epoch : 3 ======> cost : 0.026390704529991836 , train accuracy : 88.53999999999999 , test accuracy : 19.09\n",
            "*** epoch : 4 ======> cost : 0.021030510273611164 , train accuracy : 89.48833333333333 , test accuracy : 19.89\n",
            "*** epoch : 5 ======> cost : 0.018327701080707248 , train accuracy : 90.10666666666667 , test accuracy : 20.51\n",
            "*** epoch : 6 ======> cost : 0.016248695033599186 , train accuracy : 90.655 , test accuracy : 20.96\n",
            "*** epoch : 7 ======> cost : 0.014027902393611475 , train accuracy : 91.12 , test accuracy : 21.33\n",
            "*** epoch : 8 ======> cost : 0.011780581306624424 , train accuracy : 91.48166666666665 , test accuracy : 21.709999999999997\n",
            "*** epoch : 9 ======> cost : 0.009569529683999014 , train accuracy : 91.78500000000001 , test accuracy : 21.85\n",
            "*** epoch : 10 ======> cost : 0.007529866059141796 , train accuracy : 91.99666666666667 , test accuracy : 22.21\n",
            "*** epoch : 11 ======> cost : 0.006039046848403245 , train accuracy : 92.21833333333333 , test accuracy : 22.28\n",
            "*** epoch : 12 ======> cost : 0.0050624855827249154 , train accuracy : 92.48833333333333 , test accuracy : 22.29\n",
            "*** epoch : 13 ======> cost : 0.004306580507346161 , train accuracy : 92.77166666666666 , test accuracy : 22.43\n",
            "*** epoch : 14 ======> cost : 0.0036728823276102308 , train accuracy : 92.97999999999999 , test accuracy : 22.31\n",
            "*** epoch : 15 ======> cost : 0.0031660747414101243 , train accuracy : 93.13166666666667 , test accuracy : 22.35\n",
            "*** epoch : 16 ======> cost : 0.0028054320387834114 , train accuracy : 93.27 , test accuracy : 22.29\n",
            "*** epoch : 17 ======> cost : 0.0026073398255785744 , train accuracy : 93.43166666666667 , test accuracy : 22.29\n",
            "*** epoch : 18 ======> cost : 0.0025467544497540426 , train accuracy : 93.56 , test accuracy : 22.43\n",
            "*** epoch : 19 ======> cost : 0.0025711846065885205 , train accuracy : 93.675 , test accuracy : 22.41\n",
            "*** epoch : 20 ======> cost : 0.002641935554678211 , train accuracy : 93.765 , test accuracy : 22.46\n",
            "*** epoch : 21 ======> cost : 0.00273085213344352 , train accuracy : 93.85666666666667 , test accuracy : 22.41\n",
            "*** epoch : 22 ======> cost : 0.0028181266785133824 , train accuracy : 93.97999999999999 , test accuracy : 22.36\n",
            "*** epoch : 23 ======> cost : 0.002895813162514736 , train accuracy : 94.07166666666666 , test accuracy : 22.400000000000002\n",
            "*** epoch : 24 ======> cost : 0.0029658061607857377 , train accuracy : 94.15666666666667 , test accuracy : 22.29\n",
            "*** epoch : 25 ======> cost : 0.0030326828270467224 , train accuracy : 94.24 , test accuracy : 22.28\n",
            "*** epoch : 26 ======> cost : 0.003099318232383803 , train accuracy : 94.325 , test accuracy : 22.35\n",
            "*** epoch : 27 ======> cost : 0.0031685929312399983 , train accuracy : 94.37333333333333 , test accuracy : 22.56\n",
            "*** epoch : 28 ======> cost : 0.0032452040421769917 , train accuracy : 94.44 , test accuracy : 22.57\n",
            "*** epoch : 29 ======> cost : 0.003332113776382034 , train accuracy : 94.49833333333333 , test accuracy : 22.75\n",
            "*** epoch : 30 ======> cost : 0.0034252333807924347 , train accuracy : 94.54 , test accuracy : 22.869999999999997\n",
            "*** epoch : 31 ======> cost : 0.0035141086563144548 , train accuracy : 94.60666666666665 , test accuracy : 23.119999999999997\n",
            "*** epoch : 32 ======> cost : 0.003589967887391822 , train accuracy : 94.66 , test accuracy : 23.13\n",
            "*** epoch : 33 ======> cost : 0.0036483655835665126 , train accuracy : 94.72 , test accuracy : 23.21\n",
            "*** epoch : 34 ======> cost : 0.003684549269264436 , train accuracy : 94.75166666666667 , test accuracy : 23.21\n",
            "*** epoch : 35 ======> cost : 0.0036905813258220426 , train accuracy : 94.8 , test accuracy : 23.11\n",
            "*** epoch : 36 ======> cost : 0.003658292296590328 , train accuracy : 94.84166666666667 , test accuracy : 23.11\n",
            "*** epoch : 37 ======> cost : 0.0035865797254577567 , train accuracy : 94.87833333333333 , test accuracy : 23.200000000000003\n",
            "*** epoch : 38 ======> cost : 0.0034831818614240685 , train accuracy : 94.89333333333333 , test accuracy : 23.13\n",
            "*** epoch : 39 ======> cost : 0.003368581154840991 , train accuracy : 94.92833333333334 , test accuracy : 23.06\n",
            "__________________________________________________\n",
            "train accuracy: 94.92833333333334\n",
            "test accuracy: 23.06\n",
            "min 0.0 : sec 55.409\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xb2IeFIrjcSq"
      },
      "source": [
        "\n",
        "<div dir=rtl>\n",
        "در آخر نیز شبکه عصبی خود را با تعداد \n",
        "<b>Epoch</b>\n",
        "بیشتر تعلیم می‌دهیم.\n",
        "بیشترین دقت های دیده شده برروی هر دو مجموعه داده به شرح زیر است:\n",
        "\n",
        "</div>\n",
        "\n",
        "*   Test Set : 94\n",
        "*   Train Set : 97\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kzBGus7yEcA0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "2c7107f4-057d-43e3-b83e-3e14ce23b2d5"
      },
      "source": [
        "# final result\n",
        "model = [\n",
        "    (train_set_x_v.shape[0] , None),\n",
        "    (16 , sigmoid ,sigmoid_deriv ),\n",
        "    (16 , sigmoid ,sigmoid_deriv ),\n",
        "    (10 , sigmoid , sigmoid_deriv),\n",
        "]\n",
        "describe_model(model)\n",
        "start = time.time()\n",
        "info = main_model(model , train_set_x_v, train_set_y_v,test_set_x_v , test_set_y_v,a=0.01, epoch=200 , use_momentum=True , beta=0.85 , batch_size=120)\n",
        "finish = round(time.time() - start , 3)\n",
        "print('min {} : sec {}'.format(finish//60 , finish % 60))\n",
        "plot_result(info)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "input layer : 784\n",
            "{'activation units': 16, 'activation function': 'sigmoid', 'parameters': 12544}\n",
            "{'activation units': 16, 'activation function': 'sigmoid', 'parameters': 256}\n",
            "{'activation units': 10, 'activation function': 'sigmoid', 'parameters': 160}\n",
            "*** epoch : 0 ======> cost : 0.40848440515107676 , train accuracy : 57.14 , test accuracy : 58.440000000000005\n",
            "*** epoch : 1 ======> cost : 0.16463396076379813 , train accuracy : 84.28166666666667 , test accuracy : 84.58\n",
            "*** epoch : 2 ======> cost : 0.12006005115486983 , train accuracy : 87.66333333333334 , test accuracy : 87.76\n",
            "*** epoch : 3 ======> cost : 0.09730532293288417 , train accuracy : 89.21833333333333 , test accuracy : 88.9\n",
            "*** epoch : 4 ======> cost : 0.07928239193774593 , train accuracy : 90.11 , test accuracy : 89.48\n",
            "*** epoch : 5 ======> cost : 0.06691411425663125 , train accuracy : 90.725 , test accuracy : 90.06\n",
            "*** epoch : 6 ======> cost : 0.0592397472187328 , train accuracy : 91.20666666666666 , test accuracy : 90.52\n",
            "*** epoch : 7 ======> cost : 0.05430223498823634 , train accuracy : 91.56166666666667 , test accuracy : 90.73\n",
            "*** epoch : 8 ======> cost : 0.05104513397352775 , train accuracy : 91.89333333333335 , test accuracy : 91.05\n",
            "*** epoch : 9 ======> cost : 0.048603320458592815 , train accuracy : 92.17833333333333 , test accuracy : 91.36\n",
            "*** epoch : 10 ======> cost : 0.04643760805698693 , train accuracy : 92.405 , test accuracy : 91.5\n",
            "*** epoch : 11 ======> cost : 0.04443047215368525 , train accuracy : 92.58833333333332 , test accuracy : 91.66\n",
            "*** epoch : 12 ======> cost : 0.042649719328398374 , train accuracy : 92.77499999999999 , test accuracy : 91.79\n",
            "*** epoch : 13 ======> cost : 0.04122778897450665 , train accuracy : 92.94 , test accuracy : 91.89\n",
            "*** epoch : 14 ======> cost : 0.04012479065871127 , train accuracy : 93.05499999999999 , test accuracy : 91.97\n",
            "*** epoch : 15 ======> cost : 0.03918365955782934 , train accuracy : 93.15166666666667 , test accuracy : 91.96\n",
            "*** epoch : 16 ======> cost : 0.03833272034681821 , train accuracy : 93.26333333333334 , test accuracy : 91.96\n",
            "*** epoch : 17 ======> cost : 0.03758056718005965 , train accuracy : 93.37833333333333 , test accuracy : 92.02\n",
            "*** epoch : 18 ======> cost : 0.036958313936521665 , train accuracy : 93.465 , test accuracy : 92.08\n",
            "*** epoch : 19 ======> cost : 0.03646267681892484 , train accuracy : 93.55333333333333 , test accuracy : 92.13\n",
            "*** epoch : 20 ======> cost : 0.03601741674491416 , train accuracy : 93.67166666666667 , test accuracy : 92.23\n",
            "*** epoch : 21 ======> cost : 0.035633726808795875 , train accuracy : 93.77333333333333 , test accuracy : 92.31\n",
            "*** epoch : 22 ======> cost : 0.03536058550958687 , train accuracy : 93.88 , test accuracy : 92.39\n",
            "*** epoch : 23 ======> cost : 0.03517993722187472 , train accuracy : 93.96833333333333 , test accuracy : 92.38\n",
            "*** epoch : 24 ======> cost : 0.035063702201417156 , train accuracy : 94.04333333333334 , test accuracy : 92.49000000000001\n",
            "*** epoch : 25 ======> cost : 0.03498843770346071 , train accuracy : 94.13 , test accuracy : 92.5\n",
            "*** epoch : 26 ======> cost : 0.034925579340030516 , train accuracy : 94.19999999999999 , test accuracy : 92.52\n",
            "*** epoch : 27 ======> cost : 0.03485798699955191 , train accuracy : 94.25 , test accuracy : 92.47999999999999\n",
            "*** epoch : 28 ======> cost : 0.03479009669867693 , train accuracy : 94.30166666666666 , test accuracy : 92.51\n",
            "*** epoch : 29 ======> cost : 0.03473169329847243 , train accuracy : 94.32333333333334 , test accuracy : 92.46\n",
            "*** epoch : 30 ======> cost : 0.034695487322286195 , train accuracy : 94.34666666666666 , test accuracy : 92.53\n",
            "*** epoch : 31 ======> cost : 0.03467504226047648 , train accuracy : 94.345 , test accuracy : 92.53\n",
            "*** epoch : 32 ======> cost : 0.03462967478965547 , train accuracy : 94.39166666666667 , test accuracy : 92.49000000000001\n",
            "*** epoch : 33 ======> cost : 0.03451894442868226 , train accuracy : 94.43 , test accuracy : 92.47999999999999\n",
            "*** epoch : 34 ======> cost : 0.034343400059963995 , train accuracy : 94.49833333333333 , test accuracy : 92.5\n",
            "*** epoch : 35 ======> cost : 0.03415499179979228 , train accuracy : 94.55333333333333 , test accuracy : 92.57\n",
            "*** epoch : 36 ======> cost : 0.03395007215207404 , train accuracy : 94.60166666666666 , test accuracy : 92.61\n",
            "*** epoch : 37 ======> cost : 0.03365060485332303 , train accuracy : 94.645 , test accuracy : 92.58999999999999\n",
            "*** epoch : 38 ======> cost : 0.03324323333312661 , train accuracy : 94.69999999999999 , test accuracy : 92.58\n",
            "*** epoch : 39 ======> cost : 0.03274407990175928 , train accuracy : 94.77499999999999 , test accuracy : 92.60000000000001\n",
            "*** epoch : 40 ======> cost : 0.03216422696280475 , train accuracy : 94.82333333333334 , test accuracy : 92.65\n",
            "*** epoch : 41 ======> cost : 0.031505849588921345 , train accuracy : 94.86166666666666 , test accuracy : 92.7\n",
            "*** epoch : 42 ======> cost : 0.030795764682258454 , train accuracy : 94.91333333333334 , test accuracy : 92.7\n",
            "*** epoch : 43 ======> cost : 0.0301137270938387 , train accuracy : 94.95833333333333 , test accuracy : 92.73\n",
            "*** epoch : 44 ======> cost : 0.02953605317703206 , train accuracy : 95.00333333333333 , test accuracy : 92.71000000000001\n",
            "*** epoch : 45 ======> cost : 0.029087952454964858 , train accuracy : 95.04833333333333 , test accuracy : 92.78\n",
            "*** epoch : 46 ======> cost : 0.028758582748859274 , train accuracy : 95.08166666666666 , test accuracy : 92.81\n",
            "*** epoch : 47 ======> cost : 0.0285129929460826 , train accuracy : 95.12666666666667 , test accuracy : 92.84\n",
            "*** epoch : 48 ======> cost : 0.028306245749889988 , train accuracy : 95.175 , test accuracy : 92.83\n",
            "*** epoch : 49 ======> cost : 0.028106136160753917 , train accuracy : 95.19999999999999 , test accuracy : 92.84\n",
            "*** epoch : 50 ======> cost : 0.02790075266323694 , train accuracy : 95.22166666666666 , test accuracy : 92.86999999999999\n",
            "*** epoch : 51 ======> cost : 0.027689695320847387 , train accuracy : 95.24666666666667 , test accuracy : 92.89\n",
            "*** epoch : 52 ======> cost : 0.02747507487818833 , train accuracy : 95.26 , test accuracy : 92.94\n",
            "*** epoch : 53 ======> cost : 0.027262778913003702 , train accuracy : 95.285 , test accuracy : 92.96\n",
            "*** epoch : 54 ======> cost : 0.02706333188893589 , train accuracy : 95.295 , test accuracy : 92.96\n",
            "*** epoch : 55 ======> cost : 0.026890980852313947 , train accuracy : 95.32166666666667 , test accuracy : 92.95\n",
            "*** epoch : 56 ======> cost : 0.0267679761122561 , train accuracy : 95.34166666666667 , test accuracy : 92.99\n",
            "*** epoch : 57 ======> cost : 0.0267190197566622 , train accuracy : 95.37 , test accuracy : 92.99\n",
            "*** epoch : 58 ======> cost : 0.026740562133960113 , train accuracy : 95.40333333333334 , test accuracy : 92.97999999999999\n",
            "*** epoch : 59 ======> cost : 0.026797067917295154 , train accuracy : 95.41166666666666 , test accuracy : 93.02\n",
            "*** epoch : 60 ======> cost : 0.02685937136078736 , train accuracy : 95.42 , test accuracy : 93.02\n",
            "*** epoch : 61 ======> cost : 0.026910875250921708 , train accuracy : 95.45 , test accuracy : 93.01\n",
            "*** epoch : 62 ======> cost : 0.02693359677480028 , train accuracy : 95.47666666666666 , test accuracy : 93.05\n",
            "*** epoch : 63 ======> cost : 0.026914677986911963 , train accuracy : 95.45833333333333 , test accuracy : 93.05\n",
            "*** epoch : 64 ======> cost : 0.026846099700153482 , train accuracy : 95.43166666666667 , test accuracy : 93.07\n",
            "*** epoch : 65 ======> cost : 0.02672810112760411 , train accuracy : 95.43666666666667 , test accuracy : 93.08\n",
            "*** epoch : 66 ======> cost : 0.02658985940464671 , train accuracy : 95.48 , test accuracy : 93.06\n",
            "*** epoch : 67 ======> cost : 0.02644339521802515 , train accuracy : 95.505 , test accuracy : 93.07\n",
            "*** epoch : 68 ======> cost : 0.026234438929131538 , train accuracy : 95.545 , test accuracy : 93.13\n",
            "*** epoch : 69 ======> cost : 0.025885836871400066 , train accuracy : 95.57 , test accuracy : 93.14\n",
            "*** epoch : 70 ======> cost : 0.025514040364765084 , train accuracy : 95.61666666666667 , test accuracy : 93.14\n",
            "*** epoch : 71 ======> cost : 0.025215496617453117 , train accuracy : 95.66333333333333 , test accuracy : 93.14\n",
            "*** epoch : 72 ======> cost : 0.02496662183599301 , train accuracy : 95.68666666666667 , test accuracy : 93.12\n",
            "*** epoch : 73 ======> cost : 0.024724806371161083 , train accuracy : 95.71666666666667 , test accuracy : 93.12\n",
            "*** epoch : 74 ======> cost : 0.024481041302148943 , train accuracy : 95.74166666666667 , test accuracy : 93.12\n",
            "*** epoch : 75 ======> cost : 0.024262067153140932 , train accuracy : 95.77666666666667 , test accuracy : 93.14\n",
            "*** epoch : 76 ======> cost : 0.02408554186831867 , train accuracy : 95.80333333333333 , test accuracy : 93.14\n",
            "*** epoch : 77 ======> cost : 0.02394828602072367 , train accuracy : 95.80499999999999 , test accuracy : 93.11\n",
            "*** epoch : 78 ======> cost : 0.023839150876106434 , train accuracy : 95.82833333333333 , test accuracy : 93.10000000000001\n",
            "*** epoch : 79 ======> cost : 0.02375122207738884 , train accuracy : 95.83500000000001 , test accuracy : 93.08\n",
            "*** epoch : 80 ======> cost : 0.02368238683227388 , train accuracy : 95.84166666666667 , test accuracy : 93.08999999999999\n",
            "*** epoch : 81 ======> cost : 0.023631132030333363 , train accuracy : 95.84666666666666 , test accuracy : 93.11\n",
            "*** epoch : 82 ======> cost : 0.02359495641754052 , train accuracy : 95.865 , test accuracy : 93.14\n",
            "*** epoch : 83 ======> cost : 0.023568756061470842 , train accuracy : 95.89333333333333 , test accuracy : 93.13\n",
            "*** epoch : 84 ======> cost : 0.0235465800454955 , train accuracy : 95.89833333333333 , test accuracy : 93.17999999999999\n",
            "*** epoch : 85 ======> cost : 0.02352737647234038 , train accuracy : 95.92 , test accuracy : 93.2\n",
            "*** epoch : 86 ======> cost : 0.023515734755339312 , train accuracy : 95.97166666666666 , test accuracy : 93.21000000000001\n",
            "*** epoch : 87 ======> cost : 0.023507618061634705 , train accuracy : 96.00333333333333 , test accuracy : 93.24\n",
            "*** epoch : 88 ======> cost : 0.023502598214089166 , train accuracy : 96.01666666666667 , test accuracy : 93.26\n",
            "*** epoch : 89 ======> cost : 0.023498102284924365 , train accuracy : 96.045 , test accuracy : 93.32000000000001\n",
            "*** epoch : 90 ======> cost : 0.02347116485264144 , train accuracy : 96.07833333333333 , test accuracy : 93.34\n",
            "*** epoch : 91 ======> cost : 0.023435375499975472 , train accuracy : 96.10166666666666 , test accuracy : 93.38\n",
            "*** epoch : 92 ======> cost : 0.023395516340876332 , train accuracy : 96.12666666666667 , test accuracy : 93.4\n",
            "*** epoch : 93 ======> cost : 0.023348824649087815 , train accuracy : 96.14666666666668 , test accuracy : 93.41000000000001\n",
            "*** epoch : 94 ======> cost : 0.023294819780479804 , train accuracy : 96.17 , test accuracy : 93.42\n",
            "*** epoch : 95 ======> cost : 0.02323389887434025 , train accuracy : 96.17666666666666 , test accuracy : 93.42\n",
            "*** epoch : 96 ======> cost : 0.023165130036084492 , train accuracy : 96.17833333333333 , test accuracy : 93.42\n",
            "*** epoch : 97 ======> cost : 0.023083448737136955 , train accuracy : 96.19833333333332 , test accuracy : 93.41000000000001\n",
            "*** epoch : 98 ======> cost : 0.022982378445492634 , train accuracy : 96.21166666666666 , test accuracy : 93.4\n",
            "*** epoch : 99 ======> cost : 0.022859374515364617 , train accuracy : 96.22333333333334 , test accuracy : 93.37\n",
            "*** epoch : 100 ======> cost : 0.022715184217244343 , train accuracy : 96.23666666666666 , test accuracy : 93.4\n",
            "*** epoch : 101 ======> cost : 0.022550526157562745 , train accuracy : 96.24833333333333 , test accuracy : 93.39\n",
            "*** epoch : 102 ======> cost : 0.022366462526883322 , train accuracy : 96.26666666666667 , test accuracy : 93.38\n",
            "*** epoch : 103 ======> cost : 0.022167500022138107 , train accuracy : 96.28 , test accuracy : 93.41000000000001\n",
            "*** epoch : 104 ======> cost : 0.021963053989070887 , train accuracy : 96.29166666666666 , test accuracy : 93.41000000000001\n",
            "*** epoch : 105 ======> cost : 0.021764972010903876 , train accuracy : 96.30166666666666 , test accuracy : 93.39\n",
            "*** epoch : 106 ======> cost : 0.021585709133371395 , train accuracy : 96.30499999999999 , test accuracy : 93.38\n",
            "*** epoch : 107 ======> cost : 0.021437630172794965 , train accuracy : 96.32 , test accuracy : 93.36\n",
            "*** epoch : 108 ======> cost : 0.021319732587838577 , train accuracy : 96.33833333333334 , test accuracy : 93.36\n",
            "*** epoch : 109 ======> cost : 0.021215885501751366 , train accuracy : 96.35833333333333 , test accuracy : 93.37\n",
            "*** epoch : 110 ======> cost : 0.021106775037296986 , train accuracy : 96.35833333333333 , test accuracy : 93.37\n",
            "*** epoch : 111 ======> cost : 0.0209777569411644 , train accuracy : 96.37833333333333 , test accuracy : 93.4\n",
            "*** epoch : 112 ======> cost : 0.020836120701953138 , train accuracy : 96.40333333333334 , test accuracy : 93.41000000000001\n",
            "*** epoch : 113 ======> cost : 0.020715549511493677 , train accuracy : 96.42166666666667 , test accuracy : 93.39\n",
            "*** epoch : 114 ======> cost : 0.02062407765464151 , train accuracy : 96.45 , test accuracy : 93.42\n",
            "*** epoch : 115 ======> cost : 0.02055357764018968 , train accuracy : 96.45166666666667 , test accuracy : 93.41000000000001\n",
            "*** epoch : 116 ======> cost : 0.02050461030523969 , train accuracy : 96.44833333333334 , test accuracy : 93.4\n",
            "*** epoch : 117 ======> cost : 0.020474155029369587 , train accuracy : 96.44333333333334 , test accuracy : 93.39\n",
            "*** epoch : 118 ======> cost : 0.02045522359608442 , train accuracy : 96.44166666666666 , test accuracy : 93.39\n",
            "*** epoch : 119 ======> cost : 0.020444509893314918 , train accuracy : 96.44500000000001 , test accuracy : 93.36\n",
            "*** epoch : 120 ======> cost : 0.02044718479177906 , train accuracy : 96.45 , test accuracy : 93.35\n",
            "*** epoch : 121 ======> cost : 0.020470068215211377 , train accuracy : 96.44833333333334 , test accuracy : 93.4\n",
            "*** epoch : 122 ======> cost : 0.020511563870483123 , train accuracy : 96.43666666666667 , test accuracy : 93.41000000000001\n",
            "*** epoch : 123 ======> cost : 0.020540910987611603 , train accuracy : 96.44833333333334 , test accuracy : 93.41000000000001\n",
            "*** epoch : 124 ======> cost : 0.020515766510644774 , train accuracy : 96.45 , test accuracy : 93.43\n",
            "*** epoch : 125 ======> cost : 0.02048299781255558 , train accuracy : 96.46833333333333 , test accuracy : 93.42\n",
            "*** epoch : 126 ======> cost : 0.020481126653615313 , train accuracy : 96.47166666666666 , test accuracy : 93.42\n",
            "*** epoch : 127 ======> cost : 0.02049535047606142 , train accuracy : 96.47166666666666 , test accuracy : 93.42\n",
            "*** epoch : 128 ======> cost : 0.020529817170049588 , train accuracy : 96.47666666666666 , test accuracy : 93.41000000000001\n",
            "*** epoch : 129 ======> cost : 0.020611937845519086 , train accuracy : 96.47666666666666 , test accuracy : 93.41000000000001\n",
            "*** epoch : 130 ======> cost : 0.020740286753205096 , train accuracy : 96.48833333333333 , test accuracy : 93.41000000000001\n",
            "*** epoch : 131 ======> cost : 0.020896243338429786 , train accuracy : 96.49166666666666 , test accuracy : 93.43\n",
            "*** epoch : 132 ======> cost : 0.021032543677840514 , train accuracy : 96.48666666666666 , test accuracy : 93.38\n",
            "*** epoch : 133 ======> cost : 0.02121885690805305 , train accuracy : 96.49666666666667 , test accuracy : 93.41000000000001\n",
            "*** epoch : 134 ======> cost : 0.021360965940524775 , train accuracy : 96.51166666666666 , test accuracy : 93.46\n",
            "*** epoch : 135 ======> cost : 0.021464294025208174 , train accuracy : 96.515 , test accuracy : 93.45\n",
            "*** epoch : 136 ======> cost : 0.021368817953407386 , train accuracy : 96.54166666666667 , test accuracy : 93.41000000000001\n",
            "*** epoch : 137 ======> cost : 0.02126155279611351 , train accuracy : 96.55166666666666 , test accuracy : 93.41000000000001\n",
            "*** epoch : 138 ======> cost : 0.021360167154643218 , train accuracy : 96.53500000000001 , test accuracy : 93.39\n",
            "*** epoch : 139 ======> cost : 0.021617697627753014 , train accuracy : 96.51833333333333 , test accuracy : 93.37\n",
            "*** epoch : 140 ======> cost : 0.021617416482072098 , train accuracy : 96.55833333333334 , test accuracy : 93.39\n",
            "*** epoch : 141 ======> cost : 0.021914818145089596 , train accuracy : 96.60833333333333 , test accuracy : 93.35\n",
            "*** epoch : 142 ======> cost : 0.02166325166782901 , train accuracy : 96.62166666666666 , test accuracy : 93.36\n",
            "*** epoch : 143 ======> cost : 0.02193051032118888 , train accuracy : 96.65 , test accuracy : 93.36\n",
            "*** epoch : 144 ======> cost : 0.022120275121725517 , train accuracy : 96.65333333333334 , test accuracy : 93.32000000000001\n",
            "*** epoch : 145 ======> cost : 0.02174106691870753 , train accuracy : 96.67833333333333 , test accuracy : 93.41000000000001\n",
            "*** epoch : 146 ======> cost : 0.021979439592695306 , train accuracy : 96.66166666666666 , test accuracy : 93.4\n",
            "*** epoch : 147 ======> cost : 0.021676912807292684 , train accuracy : 96.685 , test accuracy : 93.41000000000001\n",
            "*** epoch : 148 ======> cost : 0.022041649737770747 , train accuracy : 96.675 , test accuracy : 93.4\n",
            "*** epoch : 149 ======> cost : 0.021042287791609764 , train accuracy : 96.69166666666666 , test accuracy : 93.38\n",
            "*** epoch : 150 ======> cost : 0.02148048669254076 , train accuracy : 96.72833333333334 , test accuracy : 93.35\n",
            "*** epoch : 151 ======> cost : 0.020968651891550238 , train accuracy : 96.68166666666667 , test accuracy : 93.37\n",
            "*** epoch : 152 ======> cost : 0.022022612812773245 , train accuracy : 96.71666666666667 , test accuracy : 93.37\n",
            "*** epoch : 153 ======> cost : 0.021104678367329804 , train accuracy : 96.675 , test accuracy : 93.4\n",
            "*** epoch : 154 ======> cost : 0.022568081039112235 , train accuracy : 96.71333333333332 , test accuracy : 93.38\n",
            "*** epoch : 155 ======> cost : 0.021242948412779947 , train accuracy : 96.64833333333334 , test accuracy : 93.37\n",
            "*** epoch : 156 ======> cost : 0.02210025924949838 , train accuracy : 96.70333333333333 , test accuracy : 93.39\n",
            "*** epoch : 157 ======> cost : 0.02285542819222249 , train accuracy : 96.56166666666667 , test accuracy : 93.31\n",
            "*** epoch : 158 ======> cost : 0.0214412077731251 , train accuracy : 96.74833333333333 , test accuracy : 93.42\n",
            "*** epoch : 159 ======> cost : 0.023305679408257902 , train accuracy : 96.58666666666666 , test accuracy : 93.24\n",
            "*** epoch : 160 ======> cost : 0.021772003183905396 , train accuracy : 96.66333333333334 , test accuracy : 93.38\n",
            "*** epoch : 161 ======> cost : 0.022506734597920814 , train accuracy : 96.60166666666666 , test accuracy : 93.37\n",
            "*** epoch : 162 ======> cost : 0.021165506371738026 , train accuracy : 96.545 , test accuracy : 93.28999999999999\n",
            "*** epoch : 163 ======> cost : 0.022600464423540866 , train accuracy : 96.41666666666666 , test accuracy : 93.22\n",
            "*** epoch : 164 ======> cost : 0.022082894615407658 , train accuracy : 96.575 , test accuracy : 93.30000000000001\n",
            "*** epoch : 165 ======> cost : 0.0222937201507546 , train accuracy : 96.45166666666667 , test accuracy : 93.24\n",
            "*** epoch : 166 ======> cost : 0.022490866805405716 , train accuracy : 96.53666666666668 , test accuracy : 93.17999999999999\n",
            "*** epoch : 167 ======> cost : 0.021657028038213278 , train accuracy : 96.545 , test accuracy : 93.19\n",
            "*** epoch : 168 ======> cost : 0.02197399331179931 , train accuracy : 96.56333333333333 , test accuracy : 93.21000000000001\n",
            "*** epoch : 169 ======> cost : 0.01984185025604283 , train accuracy : 96.64833333333334 , test accuracy : 93.31\n",
            "*** epoch : 170 ======> cost : 0.020970986862655396 , train accuracy : 96.71499999999999 , test accuracy : 93.28\n",
            "*** epoch : 171 ======> cost : 0.021639602571540392 , train accuracy : 96.61 , test accuracy : 93.28\n",
            "*** epoch : 172 ======> cost : 0.02166535535485212 , train accuracy : 96.62666666666667 , test accuracy : 93.28999999999999\n",
            "*** epoch : 173 ======> cost : 0.020482704203302653 , train accuracy : 96.76666666666667 , test accuracy : 93.37\n",
            "*** epoch : 174 ======> cost : 0.020773033841649836 , train accuracy : 96.68833333333333 , test accuracy : 93.28999999999999\n",
            "*** epoch : 175 ======> cost : 0.020206760450588775 , train accuracy : 96.82333333333332 , test accuracy : 93.30000000000001\n",
            "*** epoch : 176 ======> cost : 0.02143946486568639 , train accuracy : 96.76666666666667 , test accuracy : 93.33\n",
            "*** epoch : 177 ======> cost : 0.02036954845293123 , train accuracy : 96.775 , test accuracy : 93.27\n",
            "*** epoch : 178 ======> cost : 0.02323527357955047 , train accuracy : 96.75166666666667 , test accuracy : 93.28999999999999\n",
            "*** epoch : 179 ======> cost : 0.02013669770039021 , train accuracy : 96.73666666666666 , test accuracy : 93.35\n",
            "*** epoch : 180 ======> cost : 0.02138230868893655 , train accuracy : 96.79166666666667 , test accuracy : 93.4\n",
            "*** epoch : 181 ======> cost : 0.020245146058778477 , train accuracy : 96.825 , test accuracy : 93.32000000000001\n",
            "*** epoch : 182 ======> cost : 0.02098348903197712 , train accuracy : 96.83 , test accuracy : 93.33\n",
            "*** epoch : 183 ======> cost : 0.02180838116718457 , train accuracy : 96.83666666666667 , test accuracy : 93.35\n",
            "*** epoch : 184 ======> cost : 0.02094965221358682 , train accuracy : 96.82833333333333 , test accuracy : 93.32000000000001\n",
            "*** epoch : 185 ======> cost : 0.02041058463254108 , train accuracy : 96.90166666666667 , test accuracy : 93.41000000000001\n",
            "*** epoch : 186 ======> cost : 0.01977738172640722 , train accuracy : 96.93166666666667 , test accuracy : 93.35\n",
            "*** epoch : 187 ======> cost : 0.021443631113297803 , train accuracy : 96.85333333333334 , test accuracy : 93.41000000000001\n",
            "*** epoch : 188 ======> cost : 0.020550815829772348 , train accuracy : 96.76666666666667 , test accuracy : 93.30000000000001\n",
            "*** epoch : 189 ======> cost : 0.021956077590737217 , train accuracy : 96.83 , test accuracy : 93.44\n",
            "*** epoch : 190 ======> cost : 0.021229251443973664 , train accuracy : 96.81 , test accuracy : 93.24\n",
            "*** epoch : 191 ======> cost : 0.02126351838168595 , train accuracy : 96.84833333333333 , test accuracy : 93.4\n",
            "*** epoch : 192 ======> cost : 0.02092961504568108 , train accuracy : 96.875 , test accuracy : 93.4\n",
            "*** epoch : 193 ======> cost : 0.02058839625137116 , train accuracy : 96.92333333333333 , test accuracy : 93.42\n",
            "*** epoch : 194 ======> cost : 0.021757990458290785 , train accuracy : 96.78 , test accuracy : 93.28999999999999\n",
            "*** epoch : 195 ======> cost : 0.021321977238674683 , train accuracy : 96.90833333333333 , test accuracy : 93.41000000000001\n",
            "*** epoch : 196 ======> cost : 0.023503485851381904 , train accuracy : 96.99 , test accuracy : 93.5\n",
            "*** epoch : 197 ======> cost : 0.02049501326028295 , train accuracy : 96.92833333333334 , test accuracy : 93.47\n",
            "*** epoch : 198 ======> cost : 0.020902837256227602 , train accuracy : 96.85000000000001 , test accuracy : 93.51\n",
            "*** epoch : 199 ======> cost : 0.021423080112952027 , train accuracy : 96.98333333333333 , test accuracy : 93.5\n",
            "__________________________________________________\n",
            "train accuracy: 96.98333333333333\n",
            "test accuracy: 93.5\n",
            "min 4.0 : sec 24.759000000000015\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ytwN2YnEkfyh"
      },
      "source": [
        "<div dir=rtl>\n",
        "مدل خود را بر روی یکی از تصاویر امتحان می‌کنیم!!!\n",
        "</div>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "OIwrn7KTIuq-",
        "outputId": "5ef5b132-d803-4ac3-bd72-dc225ce8d99b"
      },
      "source": [
        "show_image(np.transpose(train_set_x_v[:,3]))\n",
        "pic = train_set_x_v[:,3].reshape((1,-1,1))\n",
        "W , B = info[3], info[4]\n",
        "plt.show()\n",
        "print(f\"predicted value: {predict(W,B,pic, model )}\")\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "predicted value: 1\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}