CNN - KMNIST.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "KMNIST.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yXDXyvfHF9LX",
        "colab_type": "text"
      },
      "source": [
        "# **Training Dataset**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c1ubF6SCzvlU",
        "colab_type": "text"
      },
      "source": [
        "**Library-library yang digunakan**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "djlIKB2m8aur",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Primitive function\n",
        "from __future__ import absolute_import, division, print_function, unicode_literals\n",
        "\n",
        "#Tensorflow Sensor\n",
        "try:\n",
        "  %tensorflow_version 2.x\n",
        "except Exception:\n",
        "  pass\n",
        "\n",
        "#Tensorflow Library  \n",
        "import tensorflow as tf\n",
        "import tensorflow.python.keras\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "from tensorflow.keras import datasets, layers, models\n",
        "from tensorflow.keras.models import model_from_json, load_model\n",
        "from tensorflow.keras.initializers import glorot_uniform\n",
        "from tensorflow.keras.utils import to_categorical, CustomObjectScope\n",
        "\n",
        "#Library Lain\n",
        "import glob\n",
        "import os\n",
        "import h5py\n",
        "import math\n",
        "import numpy as np\n",
        "from numpy import genfromtxt\n",
        "import matplotlib.pyplot as plt\n",
        "from PIL import Image\n",
        "import random\n",
        "import cv2"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gnXuNlA50_GG",
        "colab_type": "text"
      },
      "source": [
        "**Import direktori dari Google Drive**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G_zoKAau7n2P",
        "colab_type": "code",
        "outputId": "752cf0aa-64af-4cb5-e885-4092220e918d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RwMQtle81baz",
        "colab_type": "text"
      },
      "source": [
        "**Memasukan Dataset**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3EBFL0qE2we1",
        "colab_type": "text"
      },
      "source": [
        "***Gambar***"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mt5nhj7K18uE",
        "colab_type": "text"
      },
      "source": [
        "*Dari Numpy Array yang Telah Disave*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6VPElQ7cOgvZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data = np.load('/content/drive/My Drive/Colab Notebooks/kmnist-test-imgs.npz')\n",
        "test_images = data['arr_0']\n",
        "data = np.load('/content/drive/My Drive/Colab Notebooks/kmnist-test-labels.npz')\n",
        "test_labels = data['arr_0']\n",
        "data = np.load('/content/drive/My Drive/Colab Notebooks/kmnist-train-imgs.npz')\n",
        "train_images = data['arr_0']\n",
        "data = np.load('/content/drive/My Drive/Colab Notebooks/kmnist-train-labels.npz')\n",
        "train_labels = data['arr_0']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EL7QKib1AQ_R",
        "colab_type": "code",
        "outputId": "bd3f26e6-3438-454d-b8d4-cc54c8e23098",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#Show All Variabels\n",
        "%whos"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Interactive namespace is empty.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ckTzJl1GQMXM",
        "colab_type": "code",
        "outputId": "a6ead30a-270a-4c93-914d-afda2b7c44b2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "print(test_images.shape)\n",
        "print(test_labels.shape)\n",
        "print(train_images.shape)\n",
        "print(train_labels.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(10000, 28, 28)\n",
            "(10000,)\n",
            "(60000, 28, 28)\n",
            "(60000,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i14Go7vz4LYE",
        "colab_type": "text"
      },
      "source": [
        "**Menampilkan Gambar**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NoSv9nCC_3CO",
        "colab_type": "code",
        "outputId": "a1497784-d491-4e94-c3eb-34fc38c5e76d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# Plot random Train Images\n",
        "\n",
        "plt.figure(figsize=(20,20))\n",
        "for i in range(100):\n",
        "    plt.subplot(10,10,i+1)\n",
        "    plt.xticks([])\n",
        "    plt.yticks([])\n",
        "    num = random.randint(1,60000)\n",
        "    plt.grid(False)\n",
        "    plt.imshow(train_images[num], cmap=\"Greys\")\n",
        "    plt.xlabel(train_labels[num])\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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sWbJkSazjcM2bxOjzzz+P9X/Ha1xfcP03EfNaGVsO43VJhgwZjPvg5/Hjjz+u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cdQXtaDrwVTpW/DIiIiIiIiIiIxoh9rRERERERERERiJOvKoMJp0rVq1crMhpRiPHXsrLPOsnz3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\nERGJkawvn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157We7WrZtlrr8H/LWLuEV4uJ/z+kL8HGH7YW5NyceNWrVqeeNycU2Fwgrb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8889Vn3322aqPPDL87SWcNUfErfI0ZswY1YMGDXLahavQhZYtkX/Dky+88MKw/UlF0HZqw3TR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6ueMWOG6htvvDHF70l2Pv30U+c1WpfQAtGpUydnHF57fvrpJ9XXXnutapwzi4i8/vrrqs89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\nGVBpAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1440x1440 with 100 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QyDPnRCMJ5HC",
        "colab_type": "code",
        "outputId": "debee7cb-2a27-4de1-a920-8cc049a5c1a3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 589
        }
      },
      "source": [
        "#Plot random Test Images\n",
        "\n",
        "plt.figure(figsize=(10,10))\n",
        "for i in range(25):\n",
        "    plt.subplot(5,5,i+1)\n",
        "    plt.xticks([])\n",
        "    plt.yticks([])\n",
        "    num = random.randint(1,10000)\n",
        "    plt.grid(False)\n",
        "    plt.imshow(test_images[num], cmap=\"Greys\")\n",
        "    plt.xlabel(test_labels[num])\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Mtvr166seP3686lDP5csvv2wc4wP90EMPGW34oAaK4zjbghuZf0I9n4MHD1bd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\nsUCLHiGEEELEgv8HvEu0EA37a5UAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x720 with 25 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vwn-tIdFCHVE",
        "colab_type": "text"
      },
      "source": [
        "**Sistem Learning**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3kF5L2qQdPOm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Reshape for showing image at the above\n",
        "test_images = test_images.reshape(test_images.shape[0], 28, 28)\n",
        "train_images = train_images.reshape(train_images.shape[0], 28, 28)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aKjktvlcbmAo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Using Resolution 28x28 pixels\n",
        "\n",
        "#Reshape for training\n",
        "test_images = test_images.reshape(test_images.shape[0], 28, 28,1)\n",
        "train_images = train_images.reshape(train_images.shape[0], 28, 28,1)\n",
        "\n",
        "#Models : CNN + Maxpooling + CNN + Maxpool + Flatten + NN + NN\n",
        "model = models.Sequential()\n",
        "model.add(layers.Conv2D(6, (3, 3),use_bias=False, activation='relu', input_shape=(28, 28, 1), name ='layer_1'))\n",
        "model.add(layers.MaxPooling2D((2, 2)))\n",
        "model.add(layers.Conv2D(6, (3, 3),use_bias=False, activation='relu', name = 'layer_2'))\n",
        "model.add(layers.MaxPooling2D((2, 2)))\n",
        "model.add(layers.Flatten())\n",
        "model.add(layers.Dense(64, use_bias=False, activation='relu', name='layer_3'))\n",
        "model.add(layers.Dense(10, use_bias=False, activation='softmax', name='layer_Output'))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tdtSy-B4UVfG",
        "colab_type": "code",
        "outputId": "daf5e83e-df23-4aa1-ed4b-98204e3efb94",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        }
      },
      "source": [
        "#Showing the Summary\n",
        "model.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_26\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "layer_1 (Conv2D)             (None, 26, 26, 3)         27        \n",
            "_________________________________________________________________\n",
            "max_pooling2d_52 (MaxPooling (None, 13, 13, 3)         0         \n",
            "_________________________________________________________________\n",
            "layer_2 (Conv2D)             (None, 11, 11, 3)         81        \n",
            "_________________________________________________________________\n",
            "max_pooling2d_53 (MaxPooling (None, 5, 5, 3)           0         \n",
            "_________________________________________________________________\n",
            "flatten_26 (Flatten)         (None, 75)                0         \n",
            "_________________________________________________________________\n",
            "layer_3 (Dense)              (None, 64)                4800      \n",
            "_________________________________________________________________\n",
            "layer_Output (Dense)         (None, 10)                640       \n",
            "=================================================================\n",
            "Total params: 5,548\n",
            "Trainable params: 5,548\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z-pBVxebVTn-",
        "colab_type": "code",
        "outputId": "cd7f438a-7f1f-4c27-f288-235f7595cc88",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "source": [
        "#Compilation & Training\n",
        "model.compile(optimizer='adam',\n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "history = model.fit(train_images, train_labels, epochs =3,\n",
        "                    validation_data=(test_images,test_labels))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 60000 samples, validate on 10000 samples\n",
            "Epoch 1/3\n",
            "60000/60000 [==============================] - 25s 424us/sample - loss: 2.0573 - accuracy: 0.7235 - val_loss: 0.9424 - val_accuracy: 0.7258\n",
            "Epoch 2/3\n",
            "60000/60000 [==============================] - 25s 412us/sample - loss: 0.3664 - accuracy: 0.8891 - val_loss: 0.6224 - val_accuracy: 0.8193\n",
            "Epoch 3/3\n",
            "60000/60000 [==============================] - 25s 411us/sample - loss: 0.2469 - accuracy: 0.9258 - val_loss: 0.5119 - val_accuracy: 0.8556\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OGo184U6CpIJ",
        "colab_type": "text"
      },
      "source": [
        "**Summary Hasil Training**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xSF_obaFYkMb",
        "colab_type": "code",
        "outputId": "637f5bf4-5749-4597-d627-4ca392e7adad",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        }
      },
      "source": [
        "#Plot accuracy\n",
        "plt.plot(history.history['accuracy'], label='accuracy')\n",
        "plt.plot(history.history['val_accuracy'], label = 'val_accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.ylim([0.5, 1])\n",
        "plt.legend(loc='lower right')\n",
        "\n",
        "test_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "10000/1 - 2s - loss: 0.8121 - accuracy: 0.8556\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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wSgQiIglOiUBEJMEpEYiIJDglAhGRBKdEICKS4JQIREQSnBKBiEiCUyIQEUlw\nSgQiIglOiUBEJMEpEYiIJDglAhGRBKdEICKS4JQIREQSXKiJwMzONbPPzGyNmd3RyjaXmtkqM/vE\nzJ4OMx4REWkuOawDm1kS8ABwDlAIvGtmi919VdQ244AfAKe4+24zGxBWPCIi0rIwrwhmAWvcvcDd\nq4BngLlNtrkWeMDddwO4+44Q4xERkRaEmQiGAJuilgsj66KNB8ab2Ztm9paZndvSgczsOjPLN7P8\noqKikMIVEUlM8e4sTgbGAbOBy4CHzax3043cfZG757p7bv/+/ds5RBGRri3MRLAZGBa1PDSyLloh\nsNjdq919HfA5QWIQEZF2EmYieBcYZ2ajzKw7sABY3GSbFwmuBjCzfgRNRQUhxiQiIk2ElgjcvQa4\nEXgZWA086+6fmNk9ZnZhZLOXgWIzWwUsAb7n7sVhxSQiIs2Zu8c7hsOSm5vr+fn58Q5DRCKqq6sp\nLCykoqIi3qEIkJaWxtChQ0lJSTlgvZmtcPfclvYJbRyBiCSGwsJCMjIyGDlyJGYW73ASmrtTXFxM\nYWEho0aNinm/eN81JCKdXEVFBX379lUS6ADMjL59+x721ZkSgYgcNSWBjuNI/hZKBCIiCU6JQEQk\nwSkRiIjEqKamJt4hhEJ3DYlIm/kff/qEVVtK2/SYxw7O5K4LJh1yu6997Wts2rSJiooKbr75Zq67\n7jr++te/cuedd1JbW0u/fv34+9//Tnl5OTfddBP5+fmYGXfddReXXHIJvXr1ory8HIDnnnuOl156\niccff5wrr7yStLQ03n//fU455RQWLFjAzTffTEVFBT169OCxxx7jmGOOoba2lu9///v89a9/pVu3\nblx77bVMmjSJ+++/nxdffBGAV155hYULF/LCCy+06Tk6WkoEItIlPProo/Tp04f9+/dz/PHHM3fu\nXK699lqWLl3KqFGj2LVrFwD/9m//RlZWFh999BEAu3fvPuSxCwsLWbZsGUlJSZSWlvLGG2+QnJzM\nq6++yp133snzzz/PokWLWL9+PStXriQ5OZldu3aRnZ3NDTfcQFFREf379+exxx7jW9/6Vqjn4Ugo\nEYhIm4ml5h6W+++/v6GmvWnTJhYtWsTpp5/ecD99nz59AHj11Vd55plnGvbLzs4+5LHnzZtHUlIS\nACUlJXzzm9/kiy++wMyorq5uOO53vvMdkpOTD/i8K664gt/+9rdcddVVLF++nCeffLKNfuO2o0Qg\nIp1eXl4er776KsuXL6dnz57Mnj2b4447jk8//TTmY0Tfdtn0Pvz09PSG1z/+8Y8588wzeeGFF1i/\nfj2zZ88+6HGvuuoqLrjgAjNbhSYAAA1BSURBVNLS0pg3b15DouhI1FksIp1eSUkJ2dnZ9OzZk08/\n/ZS33nqLiooKli5dyrp16wAamobOOeccHnjggYZ965uGBg4cyOrVq6mrqztoG35JSQlDhgSPVnn8\n8ccb1p9zzjk89NBDDR3K9Z83ePBgBg8ezL333stVV13Vdr90G1IiEJFO79xzz6WmpoaJEydyxx13\ncOKJJ9K/f38WLVrExRdfzLRp05g/fz4AP/rRj9i9ezeTJ09m2rRpLFmyBICf/exnnH/++Zx88skM\nGjSo1c+6/fbb+cEPfsD06dMPuIvommuuYfjw4UydOpVp06bx9NONj2C//PLLGTZsGBMnTgzpDBwd\nTTonIkdl9erVHbaA6yhuvPFGpk+fztVXX90un9fS30STzomIxMnMmTNJT0/nl7/8ZbxDaZUSgYhI\niFasWBHvEA5JfQQiIglOiUBEJMEpEYiIJDglAhGRBKdEICKS4JQIRCSh9OrVK94hdDi6fVRE2s5f\n7oBtH7XtMXOmwFd+1rbH7ABqamo6zLxDuiIQkU7tjjvuOGDuoLvvvpt7772XOXPmMGPGDKZMmcIf\n//jHmI5VXl7e6n5PPvlkw/QRV1xxBQDbt2/noosuYtq0aUybNo1ly5axfv16Jk+e3LDfL37xC+6+\n+24AZs+ezS233EJubi733Xcff/rTnzjhhBOYPn06Z599Ntu3b2+I46qrrmLKlClMnTqV559/nkcf\nfZRbbrml4bgPP/wwt9566xGftwO4e6f6mTlzpotIx7Fq1aq4fv57773np59+esPyxIkTfePGjV5S\nUuLu7kVFRT5mzBivq6tzd/f09PRWj1VdXd3ifh9//LGPGzfOi4qK3N29uLjY3d0vvfRS/9WvfuXu\n7jU1Nb5nzx5ft26dT5o0qeGY//Ef/+F33XWXu7ufccYZfv311ze8t2vXroa4Hn74Yb/tttvc3f32\n22/3m2+++YDtysrKfPTo0V5VVeXu7ieddJJ/+OGHLf4eLf1NgHxvpVztGNclIiJHaPr06ezYsYMt\nW7ZQVFREdnY2OTk53HrrrSxdupRu3bqxefNmtm/fTk5OzkGP5e7ceeedzfZ77bXXmDdvHv369QMa\nnzXw2muvNTxfICkpiaysrEM+6KZ+8jsIHngzf/58tm7dSlVVVcOzE1p7ZsJZZ53FSy+9xMSJE6mu\nrmbKlCmHebZapkQgIp3evHnzeO6559i2bRvz58/nqaeeoqioiBUrVpCSksLIkSObPWOgJUe6X7Tk\n5GTq6uoalg/2bIObbrqJ2267jQsvvJC8vLyGJqTWXHPNNfz0pz9lwoQJbTqltfoIRKTTmz9/Ps88\n8wzPPfcc8+bNo6SkhAEDBpCSksKSJUvYsGFDTMdpbb+zzjqLP/zhDxQXFwONzxqYM2cODz74IAC1\ntbWUlJQwcOBAduzYQXFxMZWVlbz00ksH/bz6Zxs88cQTDetbe2bCCSecwKZNm3j66ae57LLLYj09\nh6REICKd3qRJkygrK2PIkCEMGjSIyy+/nPz8fKZMmcKTTz7JhAkTYjpOa/tNmjSJH/7wh5xxxhlM\nmzaN2267DYD77ruPJUuWMGXKFGbOnMmqVatISUnhJz/5CbNmzeKcc8456GfffffdzJs3j5kzZzY0\nO0Hrz0wAuPTSSznllFNiesRmrPQ8AhE5KnoeQfs6//zzufXWW5kzZ06r2xzu8wh0RSAi0gns2bOH\n8ePH06NHj4MmgSOhzmIRSTgfffRRw1iAeqmpqbz99ttxiujQevfuzeeffx7KsZUIROSouTtmFu8w\nYjZlyhRWrlwZ7zBCcSTN/WoaEpGjkpaWRnFx8REVQNK23J3i4mLS0tIOaz9dEYjIURk6dCiFhYUU\nFRXFOxQhSMxDhw49rH2UCETkqKSkpDSMiJXOKdSmITM718w+M7M1ZnZHC+9faWZFZrYy8nNNmPGI\niEhzoV0RmFkS8ABwDlAIvGtmi919VZNNf+/uN4YVh4iIHFyYVwSzgDXuXuDuVcAzwNwQP09ERI5A\nmH0EQ4BNUcuFwAktbHeJmZ0OfA7c6u6bmm5gZtcB10UWy83ssyOMqR+w8wj3DZPiOjyK6/B11NgU\n1+E5mrhGtPZGvDuL/wT8zt0rzezbwBPAWU03cvdFwKKj/TAzy29tiHU8Ka7Do7gOX0eNTXEdnrDi\nCrNpaDMwLGp5aGRdA3cvdvfKyOIjwMwQ4xERkRaEmQjeBcaZ2Sgz6w4sABZHb2Bmg6IWLwRWhxiP\niIi0ILSmIXevMbMbgZeBJOBRd//EzO4heGTaYuBfzOxCoAbYBVwZVjwRR928FBLFdXgU1+HrqLEp\nrsMTSlydbhpqERFpW5prSEQkwSkRiIgkuC6TCGKYziLVzH4fef9tMxsZ9d4PIus/M7Mvt3Nct5nZ\nKjP70Mz+bmYjot6rjZp+Y3HTfUOOq9XpP8zsm2b2ReTnm+0c16+iYvrczPZEvRfm+XrUzHaY2cet\nvG9mdn8k7g/NbEbUe6GcrxhiujwSy0dmtszMpkW9tz6yfqWZtfkj/2KIbbaZlUT9vX4S9d5BvwMh\nx/W9qJg+jnyn+kTeC+WcmdkwM1sSKQc+MbObW9gm3O+Xu3f6H4LO6LXAaKA78AFwbJNtbgB+E3m9\ngGBqC4BjI9unAqMix0lqx7jOBHpGXl9fH1dkuTyO5+tK4Nct7NsHKIj8mx15nd1ecTXZ/iaCmxBC\nPV+RY58OzAA+buX984C/AAacCLzdDufrUDGdXP9ZwFfqY4osrwf6xfF8zQZeOtrvQFvH1WTbC4DX\nwj5nwCBgRuR1BsHg2qb/H0P9fnWVK4JYprOYSzBgDeA5YI6ZWWT9M+5e6e7rgDWR47VLXO6+xN33\nRRbfIhhvEbajmf7jy8Ar7r7L3XcDrwDnximuy4DftdFnH5S7LyW4s601c4EnPfAW0Dtye3Ro5+tQ\nMbn7sshnQvt9t+o/+1DnqzWhTk1zmHG1y/fL3be6+3uR12UEt9EPabJZqN+vrpIIWprOoumJbNjG\n3WuAEqBvjPuGGVe0qwmyfr00M8s3s7fM7GttFNPhxHVJ5DL0OTOrHxzYIc5XpAltFPBa1Oqwzlcs\nWos9zPN1OJp+txz4m5mtsGAKl3g4ycw+MLO/mNmkyLoOcb7MrCdBgfp81OrQz5kFTdbTgabPzAz1\n+xXvKSYkwsy+AeQCZ0StHuHum81sNPCamX3k7mvbKaSYpv+IowXAc+5eG7UunuerwzKzMwkSwalR\nq0+NnKsBwCtm9mmkttxe3iP4e5Wb2XnAi8C4dvz8Q7kAeNPdo68eQj1nZtaLIPHc4u6lbXXcWHSV\nK4JDTmcRvY2ZJQNZQHGM+4YZF2Z2NvBD4EJvnHIDd98c+bcAyCOoKbRLXN769B9xP18RC2hy2R7i\n+YpFa7GHeb4OycymEvz95rp7cf36qHO1A3iBtmsOjYm7l7p7eeT1n4EUM+tHnM9XlIN9v9r8nJlZ\nCkESeMrd/28Lm4T7/Wrrjo94/BBc2RQQNBXUdzBNarLNdzmws/jZyOtJHNhZXEDbdRbHEtd0gs6x\ncU3WZwOpkdf9gC9oo06zGOMaFPX6IuAtb+ycWheJLzvyuk97xRXZbgJBx521x/mK+oyRtN75+VUO\n7Mx7J+zzFUNMwwn6vE5usj4dyIh6vQw4ty3PVQyx5dT//QgK1I2RcxfTdyCsuCLvZxH0I6S3xzmL\n/N5PAv95kG1C/X616R8+nj8EveqfExSqP4ysu4eglg2QBvwh8h/jHWB01L4/jOz3GfCVdo7rVWA7\nsDLysziy/mTgo8h/hI+Aq9s5rn8HPol8/hJgQtS+34qcxzXAVe0ZV2T5buBnTfYL+3z9DtgKVBO0\nw14NfAf4TuR9I3gQ09rI5+eGfb5iiOkRYHfUdys/sn505Dx9EPkb/7Atz1WMsd0Y9f16i6hk1dJ3\noL3iimxzJcENJNH7hXbOCJrsHPgw6m91Xnt+vzTFhIhIgusqfQQiInKElAhERBKcEoGISIJTIhAR\nSXBKBCIiCU6JQKSJJrOYrmzLGTDNbGRrM1+KxIummBBpbr+7HxfvIETai64IRGIUmY/+55E56d8x\ns7GR9SPN7DVrfKbE8Mj6gWb2QmRitQ/M7OTIoZLM7OHI3PN/M7MecfulRFAiEGlJjyZNQ/Oj3itx\n9ynAr4H/jKz7P8AT7j4VeAq4P7L+fuB1d59GMAf+J5H144AH3H0SsAe4JOTfR+SgNLJYpAkzK3f3\nXi2sXw+c5e4FkUnCtrl7XzPbSTA3U3Vk/VZ372dmRcBQj5pIMDLN8CvuPi6y/H0gxd3vDf83E2mZ\nrghEDo+38vpwVEa9rkV9dRJnSgQih2d+1L/LI6+XEcxoC3A58Ebk9d8JHj+KmSWZWVZ7BSlyOFQT\nEWmuh5mtjFr+q7vX30KabWYfEtTqL4usuwl4zMy+BxQBV0XW3wwsMrOrCWr+1xPMfCnSoaiPQCRG\nkT6CXHffGe9YRNqSmoZERBKcrghERBKcrghERBKcEoGISIJTIhARSXBKBCIiCU6JQEQkwf1/9Ncd\nAJuvPFYAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HgIgx-mCeH2k",
        "colab_type": "code",
        "outputId": "7af6e9ee-3d8d-48fd-bead-191668be5928",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "#Plot All\n",
        "fig = plt.figure()\n",
        "plt.subplot(2,1,1)\n",
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['val_accuracy'])\n",
        "plt.title('model accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='lower right')\n",
        "\n",
        "plt.subplot(2,1,2)\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper right')\n",
        "\n",
        "plt.tight_layout()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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vr5DtnoJ/D008l2sG9yQ5waYb6lCqyhpvxfhLQmVFUF3eyMmkYbJJHwB9LvQkGD8tnMSu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\nSUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gK_wVhKlaTAB",
        "colab_type": "code",
        "outputId": "9088a527-40c0-4b3e-8299-61271be06838",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "#Print the Evaluation Variabels\n",
        "print(test_acc)\n",
        "print(test_loss)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.8556\n",
            "0.5119460369110107\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LQKEAgmOF5Bm",
        "colab_type": "text"
      },
      "source": [
        "# **Pembuatan File Weights**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2JPV2mDvDfWE",
        "colab_type": "text"
      },
      "source": [
        "**Simpan Model ke File h5 dan json**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wTVSn3iDXhSq",
        "colab_type": "code",
        "outputId": "1787b104-fc0e-4d51-f102-173ff68947a6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#Serialize model to JSON\n",
        "model_json = model.to_json()\n",
        "with open(\"model.json\", \"w\") as json_file:\n",
        "    json_file.write(model_json)\n",
        "#Serialize weights to HDF5\n",
        "model.save_weights(\"CNN_Model.h5\")\n",
        "print(\"Saved model to disk\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Saved model to disk\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-KTM9GyHD3Zo",
        "colab_type": "text"
      },
      "source": [
        "# **Verifikasi**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YFfMp2V1lZdI",
        "colab_type": "code",
        "outputId": "c239b0cf-1a94-4ff8-ed94-327163c07841",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#Open File\n",
        "json_file = open('/content/model.json', 'r')\n",
        "loaded_model_json = json_file.read()\n",
        "json_file.close()\n",
        "loaded_model = model_from_json(loaded_model_json)\n",
        "#Load weights into new model\n",
        "loaded_model.load_weights(\"/content/CNN_Model.h5\")\n",
        "print(\"Loaded model from disk\")"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Loaded model from disk\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1qMszsqoXtJr",
        "colab_type": "code",
        "outputId": "99675aca-28fe-4bbd-eda8-1b570daba139",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        }
      },
      "source": [
        "#List all the h5 directory\n",
        "f = h5py.File(\"/content/CNN_Model.h5\")\n",
        "list(f)\n",
        "list(f['layer_1'])"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: H5pyDeprecationWarning: The default file mode will change to 'r' (read-only) in h5py 3.0. To suppress this warning, pass the mode you need to h5py.File(), or set the global default h5.get_config().default_file_mode, or set the environment variable H5PY_DEFAULT_READONLY=1. Available modes are: 'r', 'r+', 'w', 'w-'/'x', 'a'. See the docs for details.\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['layer_1']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3nLEjQf7Xpey",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Get weight to local variabels\n",
        "with h5py.File('/content/CNN_Model.h5','r') as hdf:\n",
        "    layer_1_data = np.array(hdf.get('layer_1/layer_1/kernel:0'))\n",
        "    layer_2_data = np.array(hdf.get('layer_2/layer_2/kernel:0'))\n",
        "    layer_3_data = np.array(hdf.get('layer_3/layer_3/kernel:0'))\n",
        "    layer_out_data = np.array(hdf.get('layer_Output/layer_Output/kernel:0'))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ChpgX2VvGZuA",
        "colab_type": "text"
      },
      "source": [
        "**Verifikasi Weight**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IRWrxUersZ6N",
        "colab_type": "code",
        "outputId": "173884c9-ff19-42f8-e02d-52959208a6df",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "#Print all shape weights\n",
        "print(layer_1_data.shape)\n",
        "print(layer_2_data.shape)\n",
        "print(layer_3_data.shape)\n",
        "print(layer_out_data.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(3, 3, 1, 6)\n",
            "(3, 3, 6, 6)\n",
            "(150, 64)\n",
            "(64, 10)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uDQrAasvhUMS",
        "colab_type": "code",
        "outputId": "2435b7fd-db25-49b0-908d-779f2d65cf86",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 231
        }
      },
      "source": [
        "#Verification\n",
        "weights = np.array(model.get_weights())\n",
        "print(np.array_equal(weights[0],layer_1_data))\n",
        "print(np.array_equal(weights[1],layer_2_data))\n",
        "print(np.array_equal(weights[2],layer_3_data))\n",
        "print(np.array_equal(weights[3],layer_out_data))"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-9-df52357896d9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mweights\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_weights\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray_equal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mweights\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlayer_1_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray_equal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mweights\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlayer_2_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray_equal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mweights\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlayer_3_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray_equal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mweights\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlayer_out_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'model' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rMNW3_nIHgma",
        "colab_type": "text"
      },
      "source": [
        "# **Kuantisasi Weights**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sGy-DkG0Hkp_",
        "colab_type": "text"
      },
      "source": [
        "**Buka File h5 dan json**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LSdzd4HivQsY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Model reconstruction from JSON file\n",
        "with open('/content/model.json', 'r') as f:\n",
        "    loaded_model = model_from_json(f.read())\n",
        "\n",
        "# Load weights into the new model\n",
        "loaded_model.load_weights('/content/CNN_Model.h5')\n",
        "loaded_model.compile(loss=\"sparse_categorical_crossentropy\", optimizer=\"adam\")\n",
        "with h5py.File('/content/CNN_Model.h5','r') as hdf:\n",
        "    layer_1_data = np.array(hdf.get('layer_1/layer_1/kernel:0'))\n",
        "    layer_2_data = np.array(hdf.get('layer_2/layer_2/kernel:0'))\n",
        "    layer_3_data = np.array(hdf.get('layer_3/layer_3/kernel:0'))\n",
        "    layer_out_data = np.array(hdf.get('layer_Output/layer_Output/kernel:0'))\n",
        "    \n",
        "#load dataset SUDAH"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qFyJZGqbHtzb",
        "colab_type": "text"
      },
      "source": [
        "**Verifikasi kedua**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WmTIVxj4HwQT",
        "colab_type": "code",
        "outputId": "5a26a386-7d51-4157-828a-931f9cc0ef0e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "#Print all shape data weights\n",
        "print(layer_1_data.shape)\n",
        "print(layer_2_data.shape)\n",
        "print(layer_3_data.shape)\n",
        "print(layer_out_data.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(3, 3, 1, 6)\n",
            "(3, 3, 6, 6)\n",
            "(150, 64)\n",
            "(64, 10)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bsNRahFIH3cK",
        "colab_type": "text"
      },
      "source": [
        "**Fungsi-fungsi yang digunakan**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8-Tkbwikvj7r",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def pool2d(mat,ksize,method='max',pad=False):\n",
        "    m, n = mat.shape[1:]\n",
        "    ky,kx=ksize\n",
        "\n",
        "    _ceil=lambda x,y: int(numpy.ceil(x/float(y)))\n",
        "\n",
        "    if pad:\n",
        "        ny=_ceil(m,ky)\n",
        "        nx=_ceil(n,kx)\n",
        "        size=(mat.shape[0],ny*ky, nx*kx)\n",
        "        mat_pad=numpy.full(size,numpy.nan)\n",
        "        mat_pad[:m,:n,...]=mat\n",
        "    else:\n",
        "        ny=m//ky\n",
        "        nx=n//kx\n",
        "        mat_pad=mat[..., :ny*ky, :nx*kx]\n",
        "\n",
        "    new_shape=(mat.shape[0],ny,ky,nx,kx)\n",
        "\n",
        "    if method=='max':\n",
        "        result=np.nanmax(mat_pad.reshape(new_shape),axis=(2,4))\n",
        "    else:\n",
        "        result=np.nanmean(mat_pad.reshape(new_shape),axis=(2,4))\n",
        "\n",
        "    return result\n",
        "\n",
        "def channel_im2col(A):\n",
        "    B = [3,3]\n",
        "    skip=[1,1]\n",
        "    # Parameters \n",
        "    D,M,N = A.shape\n",
        "    col_extent = N - B[1] + 1\n",
        "    row_extent = M - B[0] + 1\n",
        "\n",
        "    # Get Starting block indices\n",
        "    start_idx = np.arange(B[0])[:,None]*N + np.arange(B[1])\n",
        "\n",
        "    # Generate Depth indeces\n",
        "    didx=M*N*np.arange(D)\n",
        "    start_idx=(didx[:,None]+start_idx.ravel()).reshape((-1,B[0],B[1]))\n",
        "\n",
        "    # Get offsetted indices across the height and width of input array\n",
        "    offset_idx = np.arange(row_extent)[:,None]*N + np.arange(col_extent)\n",
        "\n",
        "    # Get all actual indices & index into input array for final output\n",
        "    out = np.take (A,start_idx.ravel()[:,None] + offset_idx[::skip[0],::skip[1]].ravel())\n",
        "    return out\n",
        "\n",
        "\n",
        "def conv2d(filt,act):\n",
        "    '''\n",
        "    conv 2d function\n",
        "    act = widht, height,depth (w1,h1,d1)\n",
        "    default = 3,3 filter, stride = 1, p = 0\n",
        "    im2col reshape\n",
        "    \n",
        "    W2=(W1−F+2P)/S+1\n",
        "    H2=(H1−F+2P)/S+1\n",
        "    D2=K\n",
        "    '''\n",
        "    P = 0\n",
        "    S = 1\n",
        "    D1,H1,W1 = act.shape\n",
        "    n_f,d_f,h_f,w_f = filt.shape\n",
        "    window_size = d_f*h_f*w_f\n",
        "    filter_reshape = n_f,window_size\n",
        "    W2=(W1-h_f +2*P)//S+1\n",
        "    H2=(H1- h_f+2*P)//S+1\n",
        "    D2= n_f\n",
        "    m = channel_im2col(act)\n",
        "    filt = filt.reshape(filter_reshape)\n",
        "    res = np.matmul(filt, m)\n",
        "    res = res.reshape(D2,W2,H2)\n",
        "    return res\n",
        "\n",
        "def softmax(X):\n",
        "    expo = np.exp(X)\n",
        "    expo_sum = np.sum(np.exp(X))\n",
        "    return expo/expo_sum\n",
        "\n",
        "def relu(X):\n",
        "    return np.maximum(0,X)\n",
        "\n",
        "def prequant(data,bits):\n",
        "    MaxValue = np.amax(data)\n",
        "    MinValue = np.amin(data)\n",
        "    Range_Real = MaxValue - MinValue\n",
        "    if(Range_Real==0):\n",
        "        Range_Real = 1\n",
        "    Scale = (Range_Real/(pow(2,bits)-1))\n",
        "    return Scale, Range_Real\n",
        "\n",
        "def quantize(Val, Range_Real, Scale, zero_point):  \n",
        "    temp = round(Val * (1/Scale) + zero_point)\n",
        "    return temp\n",
        "quantizefunc = np.vectorize(quantize)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gd7-F_QRvwhj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def Model2(img):\n",
        "    global q_weight1\n",
        "    global q_weight2\n",
        "    global q_weight3\n",
        "    global q_weightout\n",
        "    Scale_w1,Range_Real_w1 = prequant(layer_1_data,8)\n",
        "    Scale_d1,Range_Real_d1 = prequant(img.reshape(1,28,28),8)\n",
        "    quantized_w1 = quantizefunc(layer_1_data,Range_Real_w1,Scale_w1,0)\n",
        "    quantized_d1 = quantizefunc(img.reshape(1,28,28),Range_Real_d1,Scale_d1,0)\n",
        "    q_weight1 = quantized_w1\n",
        "\n",
        "    layer_1_out = relu(conv2d(quantized_w1,quantized_d1))\n",
        "    layer_1_out = Scale_d1*Scale_w1*pool2d(layer_1_out, (2, 2))\n",
        "    \n",
        "    Scale_w2,Range_Real_w2 = prequant(layer_2_data,8)\n",
        "    Scale_d2,Range_Real_d2 = prequant(layer_1_out,8)\n",
        "    quantized_w2 = quantizefunc(layer_2_data,Range_Real_w2,Scale_w2,0)\n",
        "    quantized_d2 = quantizefunc(layer_1_out,Range_Real_d2,Scale_d2,0)\n",
        "    q_weight2 = quantized_w2\n",
        "    \n",
        "    layer_2_out = relu(conv2d(quantized_w2,quantized_d2))\n",
        "    layer_2_out = Scale_d2*Scale_w2*pool2d(layer_2_out, (2, 2))\n",
        "    layer_2_out = layer_2_out.transpose(1,2,0) ### ubah format data dari CHW ke WHC\n",
        "    layer_2_out = layer_2_out.reshape(150)\n",
        "    \n",
        "    Scale_w3,Range_Real_w3 = prequant(layer_3_data,8)\n",
        "    Scale_d3,Range_Real_d3 = prequant(layer_2_out,8)\n",
        "    quantized_w3 = quantizefunc(layer_3_data,Range_Real_w3,Scale_w3,0)\n",
        "    quantized_d3 = quantizefunc(layer_2_out,Range_Real_d3,Scale_d3,0)\n",
        "    q_weight3 = quantized_w3\n",
        "\n",
        "    layer_3_out = np.matmul(quantized_d3,quantized_w3)\n",
        "    layer_3_out = Scale_d3*Scale_w3*relu(layer_3_out)\n",
        "    \n",
        "    Scale_w4,Range_Real_w4 = prequant(layer_out_data,8)\n",
        "    Scale_d4,Range_Real_d4 = prequant(layer_3_out,8)\n",
        "    quantized_w4 = quantizefunc(layer_out_data,Range_Real_w4,Scale_w4,0)\n",
        "    quantized_d4 = quantizefunc(layer_3_out,Range_Real_d4,Scale_d4,0)\n",
        "    q_weightout = quantized_w4\n",
        "    \n",
        "    layer_last_out = Scale_d4*Scale_w4*np.matmul(quantized_d4,quantized_w4)\n",
        "    layer_last_out = softmax(layer_last_out)\n",
        "    return layer_last_out,"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LCaH0pNhJKAO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Transpose Layer 1 dan Layer 2\n",
        "layer_1_data = layer_1_data.transpose(3,2,0,1)\n",
        "layer_2_data= layer_2_data.transpose(3,2,0,1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LZAQRwqmJAFL",
        "colab_type": "text"
      },
      "source": [
        "**Training & Verifikasi dari weights yang telah ada**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F9oHA5fpv4lD",
        "colab_type": "code",
        "outputId": "b458afcd-aba4-4a4b-9d9b-f19d0dff3c5b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "#Train from the weights\n",
        "\n",
        "val_num_uq = 0\n",
        "val_num_q = 0\n",
        "correct_q = np.empty(10000)\n",
        "correct_uq = np.empty(10000)\n",
        "val_num_q_2 = 0\n",
        "correct_q_2 = np.empty(10000)\n",
        "\n",
        "for i in range(10000):\n",
        "    indata = test_images[i]/255\n",
        "    label = test_labels[i]\n",
        "    \n",
        "    #data = indata.ravel()\n",
        "    \n",
        "    keras_res = loaded_model.predict_classes(indata.reshape(1,28,28,1))\n",
        "    res = Model2(indata.reshape(1,28,28))\n",
        "    \n",
        "    if(label==keras_res[0]):        \n",
        "        correct_q[val_num_q] = i\n",
        "        val_num_q += 1\n",
        "\n",
        "    if(label==np.argmax(res)):\n",
        "        correct_uq[val_num_uq] = i\n",
        "        val_num_uq += 1\n",
        "        \n",
        "print('val_num_keras_model :', val_num_q)\n",
        "print('val_num_quantized_model :', val_num_uq)\n",
        "\n",
        "print('val val_num_keras_model :', (val_num_q/10000)*100,'%')\n",
        "print('val quantized  :', (val_num_uq/10000)*100,'%')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "val_num_keras_model : 8638\n",
            "val_num_quantized_model : 8630\n",
            "val val_num_keras_model : 86.38 %\n",
            "val quantized  : 86.3 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kIV_C0ZO2h5l",
        "colab_type": "text"
      },
      "source": [
        "**Training & Verifikasi dari test images**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OPKL_lae1lxy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "timg3=cv2.imread(\"/content/3.jpg\",cv2.IMREAD_GRAYSCALE)\n",
        "timg3 =cv2.bitwise_not(timg3)\n",
        "timg3 = cv2.resize(timg3, (28, 28))\n",
        "\n",
        "timg2=cv2.imread(\"/content/2.jpg\",cv2.IMREAD_GRAYSCALE)\n",
        "timg2 =cv2.bitwise_not(timg2)\n",
        "timg2 = cv2.resize(timg2, (28, 28))\n",
        "\n",
        "timg5=cv2.imread(\"/content/5.jpg\",cv2.IMREAD_GRAYSCALE)\n",
        "timg5 =cv2.bitwise_not(timg5)\n",
        "timg5 = cv2.resize(timg5, (28, 28))\n",
        "\n",
        "timg7=cv2.imread(\"/content/7.jpg\",cv2.IMREAD_GRAYSCALE)\n",
        "timg7 =cv2.bitwise_not(timg7)\n",
        "timg7 = cv2.resize(timg7, (28, 28))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "27LatbJR_Obp",
        "colab_type": "code",
        "outputId": "0340e1fc-11b0-4376-d82d-5f9cfcaf1b74",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        }
      },
      "source": [
        "#Train from file Label 2\n",
        "\n",
        "indata = timg2/255\n",
        "res = Model2(indata.reshape(1,28,28))\n",
        "plt.imshow(timg2)\n",
        "print(\"Gambar ini diklasifikasikan sebagai :\")\n",
        "print(np.argmax(res))"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Gambar ini diklasifikasikan sebagai :\n",
            "4\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPsAAAD4CAYAAAAq5pAIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nHUji/wHmzM9D0nI+SQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W2i4NLmenwMJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "outputId": "8b0afff2-35d8-41f8-bdd5-f3746977f560"
      },
      "source": [
        "#Train from file Label 3\n",
        "\n",
        "indata = timg3/255\n",
        "res = Model2(indata.reshape(1,28,28))\n",
        "plt.imshow(timg3)\n",
        "print(\"Gambar ini diklasifikasikan sebagai :\")\n",
        "print(np.argmax(res))"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Gambar ini diklasifikasikan sebagai :\n",
            "3\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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BOiAJwg4kQdiBJAg7kARhB5Ig7EAShB1I4v8BbCUXYsCjraIAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5RkesHaXnxUq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "outputId": "84bde85d-b4b9-4a63-ef37-f590fdc1fe5b"
      },
      "source": [
        "#Train from file Label 5\n",
        "\n",
        "indata = timg5/255\n",
        "res = Model2(indata.reshape(1,28,28))\n",
        "plt.imshow(timg5)\n",
        "print(\"Gambar ini diklasifikasikan sebagai :\")\n",
        "print(np.argmax(res))"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Gambar ini diklasifikasikan sebagai :\n",
            "2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KyxzMA-Anykw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "outputId": "220e4051-7f73-4ec5-8c4d-7b5b3ab1243b"
      },
      "source": [
        "#Train from file Label 7\n",
        "\n",
        "indata = timg7/255\n",
        "res = Model2(indata.reshape(1,28,28))\n",
        "plt.imshow(timg7)\n",
        "print(\"Gambar ini diklasifikasikan sebagai :\")\n",
        "print(np.argmax(res))"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Gambar ini diklasifikasikan sebagai :\n",
            "5\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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8JQ8AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uyF2Po-0Rs4O",
        "colab_type": "code",
        "outputId": "360687b6-5621-46e5-8d61-a08b0f3c10bb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        }
      },
      "source": [
        "#Print all shape weights\n",
        "print(q_weight1.shape)\n",
        "print(q_weight2.shape)\n",
        "print(q_weight3.shape)\n",
        "print(q_weightout.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(12, 1, 3, 3)\n",
            "(6, 12, 3, 3)\n",
            "(150, 10)\n",
            "(10, 6)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D4P4-1L9Pdti",
        "colab_type": "code",
        "outputId": "10c7920d-0847-4b05-e484-110b367a67f6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "#Data Weights sebelum kuantisasi\n",
        "print(\"Weights Layer 1 :\\n\")\n",
        "print(layer_1_data)\n",
        "print(np.max(layer_1_data))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Weights Layer 1 :\n",
            "\n",
            "[[[[-0.01329492 -0.04109416  0.18368442]\n",
            "   [-0.08848076 -0.23120067  0.2300477 ]\n",
            "   [ 0.02856025 -0.14770463  0.07292501]]]\n",
            "\n",
            "\n",
            " [[[ 0.13481796  0.04253075  0.00621174]\n",
            "   [ 0.22631559 -0.05169058 -0.20278576]\n",
            "   [-0.2508549  -0.0616309   0.1516854 ]]]\n",
            "\n",
            "\n",
            " [[[ 0.03414535  0.08352276  0.26121908]\n",
            "   [-0.0428066  -0.10331271  0.33986697]\n",
            "   [-0.2800591  -0.32983336  0.04384084]]]\n",
            "\n",
            "\n",
            " [[[ 0.40146092  0.18687972  0.07025485]\n",
            "   [ 0.4553984   0.2555486   0.02328041]\n",
            "   [ 0.15735473  0.28871608  0.01448023]]]\n",
            "\n",
            "\n",
            " [[[ 0.11272542  0.19433409 -0.15595715]\n",
            "   [-0.07399441 -0.07087922 -0.19032665]\n",
            "   [ 0.0129567  -0.06671017  0.23103958]]]\n",
            "\n",
            "\n",
            " [[[ 0.2650003   0.27509287  0.13812749]\n",
            "   [ 0.3159532   0.34829774  0.34993526]\n",
            "   [ 0.30125812  0.04737267 -0.00420555]]]\n",
            "\n",
            "\n",
            " [[[ 0.28099698 -0.29697728 -0.3330829 ]\n",
            "   [-0.08445876  0.1538653   0.08260127]\n",
            "   [ 0.18151839  0.02573946  0.00424811]]]\n",
            "\n",
            "\n",
            " [[[ 0.18709491 -0.25164706 -0.24790719]\n",
            "   [ 0.33706054  0.24250107  0.0437585 ]\n",
            "   [-0.01087505 -0.0493945  -0.19486661]]]\n",
            "\n",
            "\n",
            " [[[-0.03725029  0.29468343  0.17226678]\n",
            "   [-0.09874611 -0.05724847  0.22630662]\n",
            "   [-0.2741177  -0.02483139 -0.20161541]]]\n",
            "\n",
            "\n",
            " [[[ 0.16826835  0.29932734 -0.3021026 ]\n",
            "   [ 0.28126338  0.09466112 -0.24541166]\n",
            "   [-0.18136658  0.05735021 -0.10658365]]]\n",
            "\n",
            "\n",
            " [[[ 0.02989182  0.36440453 -0.12204948]\n",
            "   [-0.19019279  0.05127015 -0.02460621]\n",
            "   [-0.2562922  -0.08408331  0.18263496]]]\n",
            "\n",
            "\n",
            " [[[-0.29430994  0.11713341  0.02333318]\n",
            "   [ 0.12089333 -0.07337353 -0.35171774]\n",
            "   [ 0.01728172  0.202858    0.29173547]]]]\n",
            "0.4553984\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F2L466OzQhsm",
        "colab_type": "code",
        "outputId": "2abd14bf-d18e-4a02-87f2-d93e7e8fb2c3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 544
        }
      },
      "source": [
        "#Weights sesudah kuantisasi\n",
        "print(\"Weights Layer 1 :\\n\")\n",
        "print(q_weight1)\n",
        "print(np.max(q_weight1))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Weights Layer 1 :\n",
            "\n",
            "[[[[  -3.   27.  -73.]\n",
            "   [  15.   24. -108.]\n",
            "   [   8.  102.    3.]]]\n",
            "\n",
            "\n",
            " [[[  37.   52.   88.]\n",
            "   [  54.   -5.  -63.]\n",
            "   [   7.   33.   50.]]]\n",
            "\n",
            "\n",
            " [[[  -3.  -47. -141.]\n",
            "   [-139.   49.  -40.]\n",
            "   [ 105. -138.   -7.]]]\n",
            "\n",
            "\n",
            " [[[   8.   75.  -67.]\n",
            "   [  58.   19.  -88.]\n",
            "   [ -59.   75.  -24.]]]\n",
            "\n",
            "\n",
            " [[[ -41. -150.  -34.]\n",
            "   [  28.  -71.   51.]\n",
            "   [ -25.    1.   35.]]]\n",
            "\n",
            "\n",
            " [[[  51.  -40.  -38.]\n",
            "   [ -19.   62.  -41.]\n",
            "   [  50.   -1.   20.]]]]\n",
            "105.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7kB67DG5SkOK",
        "colab_type": "code",
        "outputId": "e0b088af-db54-48b1-bb15-2c7bc24bbc20",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        }
      },
      "source": [
        "print(\"Weights Layer 2 :\\n\")\n",
        "print(q_weight2[1])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Weights Layer 2 :\n",
            "\n",
            "[[[ -47.  -44.  -46.]\n",
            "  [ -10.  -52.  -85.]\n",
            "  [  59.   59.   62.]]\n",
            "\n",
            " [[  47.  -66.   72.]\n",
            "  [ -25.  -46.   -6.]\n",
            "  [ -40.  -79.  -67.]]\n",
            "\n",
            " [[  64.  -36.   72.]\n",
            "  [ -82.  -37.  -58.]\n",
            "  [  -4.   34.   37.]]\n",
            "\n",
            " [[  39.   10.   -5.]\n",
            "  [  -1.   78.   36.]\n",
            "  [ -71.   -9.   -8.]]\n",
            "\n",
            " [[  90.   35.  -20.]\n",
            "  [ -62.   -6.   21.]\n",
            "  [ -13.  -21.   12.]]\n",
            "\n",
            " [[  40.   29.   74.]\n",
            "  [   4.  -10. -107.]\n",
            "  [  -5.  104.  104.]]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "geTRauWSQUA8",
        "colab_type": "code",
        "outputId": "62d291df-99e5-4abe-b840-8e626d024758",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#Output to .pny file (numpy array)\n",
        "np.save('q_weight1.npy', q_weight1)\n",
        "np.save('q_weight2.npy', q_weight2)\n",
        "np.save('q_weight3.npy', q_weight3)\n",
        "np.save('q_weightout.npy', q_weightout)\n",
        "print(\"DONE\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "DONE\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}