Klasifikasi Varietas Beras MobileNet_nf.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Klasifikasi Varietas Beras MobileNet_nf.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "machine_shape": "hm",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Soedirman-Machine-Learning/rice-varieties-classification/blob/master/Klasifikasi%20Varietas%20Beras%20MobileNet_nf.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x6JoE4rdE9ae",
        "colab_type": "text"
      },
      "source": [
        "# **Klasifikasi 3 Varietas Beras**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r9hD24Q7uaMn",
        "colab_type": "text"
      },
      "source": [
        "# Mengambil *Dataset* dari *Github*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f_aICNeDuobz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!apt-get install subversion > /dev/null\n",
        "\n",
        "!svn export https://github.com/Soedirman-Machine-Learning/rice-varieties-classification/trunk/data_nf > /dev/null"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hxlu9J0UFEbb",
        "colab_type": "text"
      },
      "source": [
        "## Persiapan"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kNqEeYHZIj7W",
        "colab_type": "text"
      },
      "source": [
        "Program ini dibuat untuk membedakan 3 varietas beras yaitu :\n",
        "\n",
        "1.   Varietas Beras Basmathi\n",
        "2.   Varietas Beras IR 64\n",
        "3.   Varietas Beras Ketan\n",
        "\n",
        "Pada program ini menggunakan metode *Convolutional Neural Network* (CNN) untuk melakukan klasifikasinya."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DdqTISDXFI9q",
        "colab_type": "text"
      },
      "source": [
        "## Memasukan Fungsi *Library*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vv_CFDFPE2BJ",
        "colab_type": "code",
        "outputId": "d09a2f61-7798-4ff9-c882-1a85651734ca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from __future__ import absolute_import, division, print_function, unicode_literals\n",
        "\n",
        "try:\n",
        "  # The %tensorflow_version magic only works in colab.\n",
        "  %tensorflow_version 2.x\n",
        "except Exception:\n",
        "  pass\n",
        "import numpy as np \n",
        "import math, os, sys\n",
        "import itertools\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "plt.style.use('default')\n",
        "from scipy import ndimage\n",
        "\n",
        "from skimage import measure, morphology\n",
        "from skimage.io import imsave, imread\n",
        "from skimage.filters import threshold_otsu\n",
        "from skimage.transform import resize\n",
        "\n",
        "import tensorflow as tf\n",
        "from sklearn import svm, datasets\n",
        "from sklearn.metrics import confusion_matrix\n",
        "import pandas as pd"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "TensorFlow 2.x selected.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p_I6ZZ6HKvsV",
        "colab_type": "text"
      },
      "source": [
        "## Mengimpor Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W_oxMl_MLoQw",
        "colab_type": "code",
        "outputId": "c4f0e75d-74f2-46b7-f2a7-da63eb77a274",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 714
        }
      },
      "source": [
        "#Perintah di bawah ini untuk melihat list bagian dari file yang sudah diunduh dari Github\n",
        "!ls data_nf/image\n",
        "!ls data_nf/image/train\n",
        "!ls data_nf/image/train/Basmathi\n",
        "!ls data_nf/image/train/IR64\n",
        "!ls data_nf/image/train/Ketan"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "test  train\n",
            "Basmathi  IR64\tKetan\n",
            "B_nf001.jpg  B_nf014.jpg  B_nf027.jpg  B_nf040.jpg  B_nf053.jpg  B_nf066.jpg\n",
            "B_nf002.jpg  B_nf015.jpg  B_nf028.jpg  B_nf041.jpg  B_nf054.jpg  B_nf067.jpg\n",
            "B_nf003.jpg  B_nf016.jpg  B_nf029.jpg  B_nf042.jpg  B_nf055.jpg  B_nf068.jpg\n",
            "B_nf004.jpg  B_nf017.jpg  B_nf030.jpg  B_nf043.jpg  B_nf056.jpg  B_nf069.jpg\n",
            "B_nf005.jpg  B_nf018.jpg  B_nf031.jpg  B_nf044.jpg  B_nf057.jpg  B_nf070.jpg\n",
            "B_nf006.jpg  B_nf019.jpg  B_nf032.jpg  B_nf045.jpg  B_nf058.jpg  B_nf071.jpg\n",
            "B_nf007.jpg  B_nf020.jpg  B_nf033.jpg  B_nf046.jpg  B_nf059.jpg  B_nf072.jpg\n",
            "B_nf008.jpg  B_nf021.jpg  B_nf034.jpg  B_nf047.jpg  B_nf060.jpg  B_nf073.jpg\n",
            "B_nf009.jpg  B_nf022.jpg  B_nf035.jpg  B_nf048.jpg  B_nf061.jpg  B_nf074.jpg\n",
            "B_nf010.jpg  B_nf023.jpg  B_nf036.jpg  B_nf049.jpg  B_nf062.jpg  B_nf075.jpg\n",
            "B_nf011.jpg  B_nf024.jpg  B_nf037.jpg  B_nf050.jpg  B_nf063.jpg\n",
            "B_nf012.jpg  B_nf025.jpg  B_nf038.jpg  B_nf051.jpg  B_nf064.jpg\n",
            "B_nf013.jpg  B_nf026.jpg  B_nf039.jpg  B_nf052.jpg  B_nf065.jpg\n",
            "I_nf001.jpg  I_nf014.jpg  I_nf027.jpg  I_nf040.jpg  I_nf053.jpg  I_nf066.jpg\n",
            "I_nf002.jpg  I_nf015.jpg  I_nf028.jpg  I_nf041.jpg  I_nf054.jpg  I_nf067.jpg\n",
            "I_nf003.jpg  I_nf016.jpg  I_nf029.jpg  I_nf042.jpg  I_nf055.jpg  I_nf068.jpg\n",
            "I_nf004.jpg  I_nf017.jpg  I_nf030.jpg  I_nf043.jpg  I_nf056.jpg  I_nf069.jpg\n",
            "I_nf005.jpg  I_nf018.jpg  I_nf031.jpg  I_nf044.jpg  I_nf057.jpg  I_nf070.jpg\n",
            "I_nf006.jpg  I_nf019.jpg  I_nf032.jpg  I_nf045.jpg  I_nf058.jpg  I_nf071.jpg\n",
            "I_nf007.jpg  I_nf020.jpg  I_nf033.jpg  I_nf046.jpg  I_nf059.jpg  I_nf072.jpg\n",
            "I_nf008.jpg  I_nf021.jpg  I_nf034.jpg  I_nf047.jpg  I_nf060.jpg  I_nf073.jpg\n",
            "I_nf009.jpg  I_nf022.jpg  I_nf035.jpg  I_nf048.jpg  I_nf061.jpg  I_nf074.jpg\n",
            "I_nf010.jpg  I_nf023.jpg  I_nf036.jpg  I_nf049.jpg  I_nf062.jpg  I_nf075.jpg\n",
            "I_nf011.jpg  I_nf024.jpg  I_nf037.jpg  I_nf050.jpg  I_nf063.jpg\n",
            "I_nf012.jpg  I_nf025.jpg  I_nf038.jpg  I_nf051.jpg  I_nf064.jpg\n",
            "I_nf013.jpg  I_nf026.jpg  I_nf039.jpg  I_nf052.jpg  I_nf065.jpg\n",
            "K_nf001.jpg  K_nf014.jpg  K_nf027.jpg  K_nf040.jpg  K_nf053.jpg  K_nf066.jpg\n",
            "K_nf002.jpg  K_nf015.jpg  K_nf028.jpg  K_nf041.jpg  K_nf054.jpg  K_nf067.jpg\n",
            "K_nf003.jpg  K_nf016.jpg  K_nf029.jpg  K_nf042.jpg  K_nf055.jpg  K_nf068.jpg\n",
            "K_nf004.jpg  K_nf017.jpg  K_nf030.jpg  K_nf043.jpg  K_nf056.jpg  K_nf069.jpg\n",
            "K_nf005.jpg  K_nf018.jpg  K_nf031.jpg  K_nf044.jpg  K_nf057.jpg  K_nf070.jpg\n",
            "K_nf006.jpg  K_nf019.jpg  K_nf032.jpg  K_nf045.jpg  K_nf058.jpg  K_nf071.jpg\n",
            "K_nf007.jpg  K_nf020.jpg  K_nf033.jpg  K_nf046.jpg  K_nf059.jpg  K_nf072.jpg\n",
            "K_nf008.jpg  K_nf021.jpg  K_nf034.jpg  K_nf047.jpg  K_nf060.jpg  K_nf073.jpg\n",
            "K_nf009.jpg  K_nf022.jpg  K_nf035.jpg  K_nf048.jpg  K_nf061.jpg  K_nf074.jpg\n",
            "K_nf010.jpg  K_nf023.jpg  K_nf036.jpg  K_nf049.jpg  K_nf062.jpg  K_nf075.jpg\n",
            "K_nf011.jpg  K_nf024.jpg  K_nf037.jpg  K_nf050.jpg  K_nf063.jpg\n",
            "K_nf012.jpg  K_nf025.jpg  K_nf038.jpg  K_nf051.jpg  K_nf064.jpg\n",
            "K_nf013.jpg  K_nf026.jpg  K_nf039.jpg  K_nf052.jpg  K_nf065.jpg\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9AXVWZgTPGYx",
        "colab_type": "code",
        "outputId": "f5008210-4558-4522-8e98-4c446fb9fc93",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        }
      },
      "source": [
        "#Perintah untuk memvisualisasikan satu gambar dari data train\n",
        "image = imread(\"data_nf/image/train/IR64/I_nf017.jpg\")\n",
        "plt.figure(figsize=(3,3))\n",
        "plt.imshow(image)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fc2adb2f908>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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pXDhXTgokGh38uj7ApnGE5AVrAXSKJL3J+CybuoQy01RmwSym+jTjvzXaGpyx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CMuC4hShmDlqzuS3lBLoCXCwcidlonI+OXxhjcm2bzJNYmgArxlECY4qFvXXiRIWPKvckqPAI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\nvDhHAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 300x300 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iHNV81ptRQD1",
        "colab_type": "code",
        "outputId": "45de2390-3e10-40a7-c757-5699b8fc35d5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "source": [
        "#Memuat semua gambar ke memori untuk pertama kali\n",
        "\n",
        "#Memuat dataset pelatihan\n",
        "IMAGE_SIZE = 224\n",
        "BATCH_SIZE = 128\n",
        "base_dir = os.path.join('data_nf/image/train')\n",
        "\n",
        "datagen = tf.keras.preprocessing.image.ImageDataGenerator(\n",
        "    rescale=1./255, \n",
        "    validation_split=0.2)\n",
        "\n",
        "train_generator = datagen.flow_from_directory(\n",
        "    base_dir,\n",
        "    target_size=(IMAGE_SIZE, IMAGE_SIZE),\n",
        "    batch_size=BATCH_SIZE, \n",
        "    subset='training')\n",
        "\n",
        "val_generator = datagen.flow_from_directory(\n",
        "    base_dir,\n",
        "    target_size=(IMAGE_SIZE, IMAGE_SIZE),\n",
        "    batch_size=BATCH_SIZE, \n",
        "    subset='validation')\n",
        "\n",
        "#Memuat dataset pengujian\n",
        "X_test = []\n",
        "y_test = []\n",
        "labels = ['Basmathi', 'IR64', 'Ketan']\n",
        "\n",
        "for i,label in enumerate(labels):\n",
        "    folder = os.path.join(\"data_nf/image/test\",label)\n",
        "    files = sorted(os.listdir(folder))\n",
        "    files = [x for x in files if x.endswith(\".jpg\")]\n",
        "    for k,file in enumerate(files):\n",
        "        image_path = os.path.join(folder, file)\n",
        "        \n",
        "        image = imread(image_path)/255.\n",
        "        image = resize(image,(224,224))\n",
        "        X_test.append(image)\n",
        "        category = os.path.split(folder)[-1]\n",
        "        y_test.append(i)\n",
        "\n",
        "X_test = np.array(X_test)\n",
        "y_test = np.array(y_test)\n",
        "\n",
        "#Menampilkan bentuk dari masing-masing dataset\n",
        "for image_batch, label_batch in train_generator:\n",
        "  break\n",
        "print(\"Bentuk array dari dataset train (pelatihan) adalah:\", image_batch.shape,label_batch.shape)\n",
        "for image_batch, label_batch in val_generator:\n",
        "  break\n",
        "print(\"Bentuk array dari dataset validation (validasi) adalah:\", image_batch.shape,label_batch.shape)\n",
        "print(\"Bentuk array dari dataset test (pengujian) adalah:\", X_test.shape,y_test.shape)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 180 images belonging to 3 classes.\n",
            "Found 45 images belonging to 3 classes.\n",
            "Bentuk array dari dataset train (pelatihan) adalah: (128, 224, 224, 3) (128, 3)\n",
            "Bentuk array dari dataset validation (validasi) adalah: (45, 224, 224, 3) (45, 3)\n",
            "Bentuk array dari dataset test (pengujian) adalah: (45, 224, 224, 3) (45,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OdBB1dFija2W",
        "colab_type": "text"
      },
      "source": [
        "## Menyimpan Label"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rd1EDl97T7tE",
        "colab_type": "code",
        "outputId": "ca56bb2a-a86e-4950-aacc-daa1b89e7ec8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print (train_generator.class_indices)\n",
        "\n",
        "labels_txt = '\\n'.join(sorted(train_generator.class_indices.keys()))\n",
        "\n",
        "with open('labels.txt', 'w') as f:\n",
        "  f.write(labels_txt)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'Basmathi': 0, 'IR64': 1, 'Ketan': 2}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-QgRw5XsRKzl",
        "colab_type": "code",
        "outputId": "31c510bc-cb93-4cb7-ec08-864652dc2d33",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "!cat labels.txt"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Basmathi\n",
            "IR64\n",
            "Ketan"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s2QUH924ypzT",
        "colab_type": "text"
      },
      "source": [
        "# Membuat Model dari Jaringan CNN yang Sudah dipelajari Sebelumnya (*pre-trained convnets*)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b-pI1d12yoI1",
        "colab_type": "code",
        "outputId": "17a67a96-70cd-4e96-997d-af9df48752ad",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        }
      },
      "source": [
        "IMG_SHAPE = (224, 224, 3)\n",
        "# Membuat model dasar (base model) dari pre-trained model MobileNet\n",
        "base_model = tf.keras.applications.MobileNet(input_shape=IMG_SHAPE,\n",
        "                                              include_top=False, \n",
        "                                              weights='imagenet')"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.6/mobilenet_1_0_224_tf_no_top.h5\n",
            "17227776/17225924 [==============================] - 0s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zo1tkiE5y1YO",
        "colab_type": "text"
      },
      "source": [
        "## *Feature Extraction*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5SGS_F7Af9HH",
        "colab_type": "text"
      },
      "source": [
        "Menggunakan model *pre-trained* untuk ekstraksi fitur (*feature extraction*) : Ketika bekerja dengan dataset kecil, adalah umum untuk mengambil keuntungan dari fitur yang dipelajari oleh model yang dilatih pada dataset yang lebih besar dalam domain yang sama. Ini dilakukan dengan membuat contoh model *pre-trained* dan menambahkan classifier yang sepenuhnya terhubung di atas. Model *pre-trained* adalah \"beku\" dan hanya bobot pengklasifikasi yang diperbarui selama pelatihan. Dalam hal ini, basis konvolusional mengekstraksi semua fitur yang terkait dengan setiap gambar dan Anda baru saja melatih classifier yang menentukan kelas gambar mengingat set fitur yang diekstraksi."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7x6z_yG8y5Xh",
        "colab_type": "code",
        "outputId": "e29c24c9-6761-47c7-b06b-f7f8c13c02f4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "base_model.trainable = False\n",
        "base_model.summary()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"mobilenet_1.00_224\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_1 (InputLayer)         [(None, 224, 224, 3)]     0         \n",
            "_________________________________________________________________\n",
            "conv1_pad (ZeroPadding2D)    (None, 225, 225, 3)       0         \n",
            "_________________________________________________________________\n",
            "conv1 (Conv2D)               (None, 112, 112, 32)      864       \n",
            "_________________________________________________________________\n",
            "conv1_bn (BatchNormalization (None, 112, 112, 32)      128       \n",
            "_________________________________________________________________\n",
            "conv1_relu (ReLU)            (None, 112, 112, 32)      0         \n",
            "_________________________________________________________________\n",
            "conv_dw_1 (DepthwiseConv2D)  (None, 112, 112, 32)      288       \n",
            "_________________________________________________________________\n",
            "conv_dw_1_bn (BatchNormaliza (None, 112, 112, 32)      128       \n",
            "_________________________________________________________________\n",
            "conv_dw_1_relu (ReLU)        (None, 112, 112, 32)      0         \n",
            "_________________________________________________________________\n",
            "conv_pw_1 (Conv2D)           (None, 112, 112, 64)      2048      \n",
            "_________________________________________________________________\n",
            "conv_pw_1_bn (BatchNormaliza (None, 112, 112, 64)      256       \n",
            "_________________________________________________________________\n",
            "conv_pw_1_relu (ReLU)        (None, 112, 112, 64)      0         \n",
            "_________________________________________________________________\n",
            "conv_pad_2 (ZeroPadding2D)   (None, 113, 113, 64)      0         \n",
            "_________________________________________________________________\n",
            "conv_dw_2 (DepthwiseConv2D)  (None, 56, 56, 64)        576       \n",
            "_________________________________________________________________\n",
            "conv_dw_2_bn (BatchNormaliza (None, 56, 56, 64)        256       \n",
            "_________________________________________________________________\n",
            "conv_dw_2_relu (ReLU)        (None, 56, 56, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv_pw_2 (Conv2D)           (None, 56, 56, 128)       8192      \n",
            "_________________________________________________________________\n",
            "conv_pw_2_bn (BatchNormaliza (None, 56, 56, 128)       512       \n",
            "_________________________________________________________________\n",
            "conv_pw_2_relu (ReLU)        (None, 56, 56, 128)       0         \n",
            "_________________________________________________________________\n",
            "conv_dw_3 (DepthwiseConv2D)  (None, 56, 56, 128)       1152      \n",
            "_________________________________________________________________\n",
            "conv_dw_3_bn (BatchNormaliza (None, 56, 56, 128)       512       \n",
            "_________________________________________________________________\n",
            "conv_dw_3_relu (ReLU)        (None, 56, 56, 128)       0         \n",
            "_________________________________________________________________\n",
            "conv_pw_3 (Conv2D)           (None, 56, 56, 128)       16384     \n",
            "_________________________________________________________________\n",
            "conv_pw_3_bn (BatchNormaliza (None, 56, 56, 128)       512       \n",
            "_________________________________________________________________\n",
            "conv_pw_3_relu (ReLU)        (None, 56, 56, 128)       0         \n",
            "_________________________________________________________________\n",
            "conv_pad_4 (ZeroPadding2D)   (None, 57, 57, 128)       0         \n",
            "_________________________________________________________________\n",
            "conv_dw_4 (DepthwiseConv2D)  (None, 28, 28, 128)       1152      \n",
            "_________________________________________________________________\n",
            "conv_dw_4_bn (BatchNormaliza (None, 28, 28, 128)       512       \n",
            "_________________________________________________________________\n",
            "conv_dw_4_relu (ReLU)        (None, 28, 28, 128)       0         \n",
            "_________________________________________________________________\n",
            "conv_pw_4 (Conv2D)           (None, 28, 28, 256)       32768     \n",
            "_________________________________________________________________\n",
            "conv_pw_4_bn (BatchNormaliza (None, 28, 28, 256)       1024      \n",
            "_________________________________________________________________\n",
            "conv_pw_4_relu (ReLU)        (None, 28, 28, 256)       0         \n",
            "_________________________________________________________________\n",
            "conv_dw_5 (DepthwiseConv2D)  (None, 28, 28, 256)       2304      \n",
            "_________________________________________________________________\n",
            "conv_dw_5_bn (BatchNormaliza (None, 28, 28, 256)       1024      \n",
            "_________________________________________________________________\n",
            "conv_dw_5_relu (ReLU)        (None, 28, 28, 256)       0         \n",
            "_________________________________________________________________\n",
            "conv_pw_5 (Conv2D)           (None, 28, 28, 256)       65536     \n",
            "_________________________________________________________________\n",
            "conv_pw_5_bn (BatchNormaliza (None, 28, 28, 256)       1024      \n",
            "_________________________________________________________________\n",
            "conv_pw_5_relu (ReLU)        (None, 28, 28, 256)       0         \n",
            "_________________________________________________________________\n",
            "conv_pad_6 (ZeroPadding2D)   (None, 29, 29, 256)       0         \n",
            "_________________________________________________________________\n",
            "conv_dw_6 (DepthwiseConv2D)  (None, 14, 14, 256)       2304      \n",
            "_________________________________________________________________\n",
            "conv_dw_6_bn (BatchNormaliza (None, 14, 14, 256)       1024      \n",
            "_________________________________________________________________\n",
            "conv_dw_6_relu (ReLU)        (None, 14, 14, 256)       0         \n",
            "_________________________________________________________________\n",
            "conv_pw_6 (Conv2D)           (None, 14, 14, 512)       131072    \n",
            "_________________________________________________________________\n",
            "conv_pw_6_bn (BatchNormaliza (None, 14, 14, 512)       2048      \n",
            "_________________________________________________________________\n",
            "conv_pw_6_relu (ReLU)        (None, 14, 14, 512)       0         \n",
            "_________________________________________________________________\n",
            "conv_dw_7 (DepthwiseConv2D)  (None, 14, 14, 512)       4608      \n",
            "_________________________________________________________________\n",
            "conv_dw_7_bn (BatchNormaliza (None, 14, 14, 512)       2048      \n",
            "_________________________________________________________________\n",
            "conv_dw_7_relu (ReLU)        (None, 14, 14, 512)       0         \n",
            "_________________________________________________________________\n",
            "conv_pw_7 (Conv2D)           (None, 14, 14, 512)       262144    \n",
            "_________________________________________________________________\n",
            "conv_pw_7_bn (BatchNormaliza (None, 14, 14, 512)       2048      \n",
            "_________________________________________________________________\n",
            "conv_pw_7_relu (ReLU)        (None, 14, 14, 512)       0         \n",
            "_________________________________________________________________\n",
            "conv_dw_8 (DepthwiseConv2D)  (None, 14, 14, 512)       4608      \n",
            "_________________________________________________________________\n",
            "conv_dw_8_bn (BatchNormaliza (None, 14, 14, 512)       2048      \n",
            "_________________________________________________________________\n",
            "conv_dw_8_relu (ReLU)        (None, 14, 14, 512)       0         \n",
            "_________________________________________________________________\n",
            "conv_pw_8 (Conv2D)           (None, 14, 14, 512)       262144    \n",
            "_________________________________________________________________\n",
            "conv_pw_8_bn (BatchNormaliza (None, 14, 14, 512)       2048      \n",
            "_________________________________________________________________\n",
            "conv_pw_8_relu (ReLU)        (None, 14, 14, 512)       0         \n",
            "_________________________________________________________________\n",
            "conv_dw_9 (DepthwiseConv2D)  (None, 14, 14, 512)       4608      \n",
            "_________________________________________________________________\n",
            "conv_dw_9_bn (BatchNormaliza (None, 14, 14, 512)       2048      \n",
            "_________________________________________________________________\n",
            "conv_dw_9_relu (ReLU)        (None, 14, 14, 512)       0         \n",
            "_________________________________________________________________\n",
            "conv_pw_9 (Conv2D)           (None, 14, 14, 512)       262144    \n",
            "_________________________________________________________________\n",
            "conv_pw_9_bn (BatchNormaliza (None, 14, 14, 512)       2048      \n",
            "_________________________________________________________________\n",
            "conv_pw_9_relu (ReLU)        (None, 14, 14, 512)       0         \n",
            "_________________________________________________________________\n",
            "conv_dw_10 (DepthwiseConv2D) (None, 14, 14, 512)       4608      \n",
            "_________________________________________________________________\n",
            "conv_dw_10_bn (BatchNormaliz (None, 14, 14, 512)       2048      \n",
            "_________________________________________________________________\n",
            "conv_dw_10_relu (ReLU)       (None, 14, 14, 512)       0         \n",
            "_________________________________________________________________\n",
            "conv_pw_10 (Conv2D)          (None, 14, 14, 512)       262144    \n",
            "_________________________________________________________________\n",
            "conv_pw_10_bn (BatchNormaliz (None, 14, 14, 512)       2048      \n",
            "_________________________________________________________________\n",
            "conv_pw_10_relu (ReLU)       (None, 14, 14, 512)       0         \n",
            "_________________________________________________________________\n",
            "conv_dw_11 (DepthwiseConv2D) (None, 14, 14, 512)       4608      \n",
            "_________________________________________________________________\n",
            "conv_dw_11_bn (BatchNormaliz (None, 14, 14, 512)       2048      \n",
            "_________________________________________________________________\n",
            "conv_dw_11_relu (ReLU)       (None, 14, 14, 512)       0         \n",
            "_________________________________________________________________\n",
            "conv_pw_11 (Conv2D)          (None, 14, 14, 512)       262144    \n",
            "_________________________________________________________________\n",
            "conv_pw_11_bn (BatchNormaliz (None, 14, 14, 512)       2048      \n",
            "_________________________________________________________________\n",
            "conv_pw_11_relu (ReLU)       (None, 14, 14, 512)       0         \n",
            "_________________________________________________________________\n",
            "conv_pad_12 (ZeroPadding2D)  (None, 15, 15, 512)       0         \n",
            "_________________________________________________________________\n",
            "conv_dw_12 (DepthwiseConv2D) (None, 7, 7, 512)         4608      \n",
            "_________________________________________________________________\n",
            "conv_dw_12_bn (BatchNormaliz (None, 7, 7, 512)         2048      \n",
            "_________________________________________________________________\n",
            "conv_dw_12_relu (ReLU)       (None, 7, 7, 512)         0         \n",
            "_________________________________________________________________\n",
            "conv_pw_12 (Conv2D)          (None, 7, 7, 1024)        524288    \n",
            "_________________________________________________________________\n",
            "conv_pw_12_bn (BatchNormaliz (None, 7, 7, 1024)        4096      \n",
            "_________________________________________________________________\n",
            "conv_pw_12_relu (ReLU)       (None, 7, 7, 1024)        0         \n",
            "_________________________________________________________________\n",
            "conv_dw_13 (DepthwiseConv2D) (None, 7, 7, 1024)        9216      \n",
            "_________________________________________________________________\n",
            "conv_dw_13_bn (BatchNormaliz (None, 7, 7, 1024)        4096      \n",
            "_________________________________________________________________\n",
            "conv_dw_13_relu (ReLU)       (None, 7, 7, 1024)        0         \n",
            "_________________________________________________________________\n",
            "conv_pw_13 (Conv2D)          (None, 7, 7, 1024)        1048576   \n",
            "_________________________________________________________________\n",
            "conv_pw_13_bn (BatchNormaliz (None, 7, 7, 1024)        4096      \n",
            "_________________________________________________________________\n",
            "conv_pw_13_relu (ReLU)       (None, 7, 7, 1024)        0         \n",
            "=================================================================\n",
            "Total params: 3,228,864\n",
            "Trainable params: 0\n",
            "Non-trainable params: 3,228,864\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4MPDz6JD_L2B",
        "colab_type": "text"
      },
      "source": [
        "# Mengelompokkan Klasifikasi Tiga Jenis Beras dengan *Deep learning* (*Convolutional Neural Network*)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mWIloTpemjLI",
        "colab_type": "text"
      },
      "source": [
        "Sesudah mengimpor dataset, kita akan menggunakan CNN, salah satu arsitektur Deep Learning yang dapat menghandle gambar. Dengan menggunakan Deep CNN, kita dibebaskan dari pendefinisian fitur yang cocok dan hanya memberikan gambar-gambar ke jaringan. CNN akan menentukan fitur yang paling cocok untuk pengklasifikasian."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ii2LX74sE2Cp",
        "colab_type": "code",
        "outputId": "91f5d1ee-4890-4dd9-a7da-8249e2c83787",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "import keras\n",
        "from keras import backend as K\n",
        "from keras.models import Sequential\n",
        "from keras import layers\n",
        "from keras.utils.np_utils import to_categorical\n",
        "\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "49atXcXNGdvk",
        "colab_type": "text"
      },
      "source": [
        "## Mempersiapkan Data Masukkan Pengujian"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P_FAIt4T1rNS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_test2 = to_categorical(y_test)\n",
        "X_test3, y_test3 = (X_test, y_test2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A5kWA1jHA3XI",
        "colab_type": "text"
      },
      "source": [
        "## Pembuatan Model (Menambah Model)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vDhGQRAvA5Wn",
        "colab_type": "code",
        "outputId": "068b5cca-1ab5-410b-8d94-9013b8f0c0a6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        }
      },
      "source": [
        "model = tf.keras.Sequential([\n",
        "    base_model,    \n",
        "    tf.keras.layers.Conv2D(32, 3, activation='relu'),\n",
        "    #tf.keras.layers.Dropout(0.2),\n",
        "    tf.keras.layers.GlobalAveragePooling2D(),\n",
        "    tf.keras.layers.Dense(3, activation='softmax')\n",
        "])\n",
        "\n",
        "model.compile(\"adam\",loss=\"categorical_crossentropy\",metrics=[\"acc\"])\n",
        "model.summary()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "mobilenet_1.00_224 (Model)   (None, 7, 7, 1024)        3228864   \n",
            "_________________________________________________________________\n",
            "conv2d (Conv2D)              (None, 5, 5, 32)          294944    \n",
            "_________________________________________________________________\n",
            "global_average_pooling2d (Gl (None, 32)                0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 3)                 99        \n",
            "=================================================================\n",
            "Total params: 3,523,907\n",
            "Trainable params: 295,043\n",
            "Non-trainable params: 3,228,864\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9YUZJT7wDpHF",
        "colab_type": "text"
      },
      "source": [
        "## Pelatihan"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6ONTwfhBDrC6",
        "colab_type": "code",
        "outputId": "9e277e38-491f-4147-98ae-ebb9cdab8b1f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "#from keras.callbacks import EarlyStopping, ModelCheckpoint\n",
        "\n",
        "#Menyimpan file model bobot yang terbaik selama pelatihan (dalam format keras \".h5\")\n",
        "#ckpt = ModelCheckpoint(\"Klasifikasi Beras Tumpukan.h5\", monitor='val_loss', verbose=1, save_best_only=True, save_weights_only=False, mode='auto', period=1)\n",
        "history = model.fit_generator(train_generator, \n",
        "                    epochs=100, \n",
        "                    validation_data=val_generator)\n",
        "#history = model.fit(x = X_train3, y = y_train3, batch_size=120, epochs= 100,validation_data=(X_valid3,y_valid3),callbacks = [ckpt])"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From <ipython-input-13-1e3be6d532c9>:3: Model.fit_generator (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use Model.fit, which supports generators.\n",
            "WARNING:tensorflow:sample_weight modes were coerced from\n",
            "  ...\n",
            "    to  \n",
            "  ['...']\n",
            "WARNING:tensorflow:sample_weight modes were coerced from\n",
            "  ...\n",
            "    to  \n",
            "  ['...']\n",
            "Train for 2 steps, validate for 1 steps\n",
            "Epoch 1/100\n",
            "2/2 [==============================] - 9s 5s/step - loss: 7.5156 - acc: 0.3389 - val_loss: 2.2572 - val_acc: 0.6667\n",
            "Epoch 2/100\n",
            "2/2 [==============================] - 2s 794ms/step - loss: 3.5886 - acc: 0.5000 - val_loss: 0.4303 - val_acc: 0.7333\n",
            "Epoch 3/100\n",
            "2/2 [==============================] - 2s 768ms/step - loss: 0.3346 - acc: 0.8389 - val_loss: 0.8771 - val_acc: 0.5556\n",
            "Epoch 4/100\n",
            "2/2 [==============================] - 2s 764ms/step - loss: 0.4577 - acc: 0.7944 - val_loss: 1.3553 - val_acc: 0.5111\n",
            "Epoch 5/100\n",
            "2/2 [==============================] - 2s 754ms/step - loss: 0.4169 - acc: 0.8333 - val_loss: 1.3943 - val_acc: 0.3778\n",
            "Epoch 6/100\n",
            "2/2 [==============================] - 2s 756ms/step - loss: 0.1932 - acc: 0.9389 - val_loss: 1.0839 - val_acc: 0.4889\n",
            "Epoch 7/100\n",
            "2/2 [==============================] - 2s 766ms/step - loss: 0.1282 - acc: 0.9611 - val_loss: 0.7980 - val_acc: 0.6444\n",
            "Epoch 8/100\n",
            "2/2 [==============================] - 2s 815ms/step - loss: 0.0664 - acc: 0.9944 - val_loss: 0.6463 - val_acc: 0.6889\n",
            "Epoch 9/100\n",
            "2/2 [==============================] - 2s 751ms/step - loss: 0.0448 - acc: 0.9944 - val_loss: 0.5572 - val_acc: 0.6889\n",
            "Epoch 10/100\n",
            "2/2 [==============================] - 2s 770ms/step - loss: 0.0369 - acc: 0.9944 - val_loss: 0.4980 - val_acc: 0.7333\n",
            "Epoch 11/100\n",
            "2/2 [==============================] - 2s 782ms/step - loss: 0.0386 - acc: 0.9778 - val_loss: 0.4699 - val_acc: 0.7333\n",
            "Epoch 12/100\n",
            "2/2 [==============================] - 2s 788ms/step - loss: 0.0353 - acc: 0.9778 - val_loss: 0.4861 - val_acc: 0.7333\n",
            "Epoch 13/100\n",
            "2/2 [==============================] - 2s 776ms/step - loss: 0.0248 - acc: 0.9944 - val_loss: 0.5414 - val_acc: 0.7333\n",
            "Epoch 14/100\n",
            "2/2 [==============================] - 2s 793ms/step - loss: 0.0138 - acc: 1.0000 - val_loss: 0.6205 - val_acc: 0.7333\n",
            "Epoch 15/100\n",
            "2/2 [==============================] - 2s 765ms/step - loss: 0.0093 - acc: 1.0000 - val_loss: 0.7124 - val_acc: 0.6889\n",
            "Epoch 16/100\n",
            "2/2 [==============================] - 2s 763ms/step - loss: 0.0049 - acc: 1.0000 - val_loss: 0.7976 - val_acc: 0.6889\n",
            "Epoch 17/100\n",
            "2/2 [==============================] - 2s 763ms/step - loss: 0.0031 - acc: 1.0000 - val_loss: 0.8704 - val_acc: 0.6889\n",
            "Epoch 18/100\n",
            "2/2 [==============================] - 2s 755ms/step - loss: 0.0023 - acc: 1.0000 - val_loss: 0.9287 - val_acc: 0.6889\n",
            "Epoch 19/100\n",
            "2/2 [==============================] - 2s 780ms/step - loss: 0.0018 - acc: 1.0000 - val_loss: 0.9751 - val_acc: 0.6889\n",
            "Epoch 20/100\n",
            "2/2 [==============================] - 2s 780ms/step - loss: 0.0016 - acc: 1.0000 - val_loss: 1.0075 - val_acc: 0.6889\n",
            "Epoch 21/100\n",
            "2/2 [==============================] - 2s 760ms/step - loss: 0.0015 - acc: 1.0000 - val_loss: 1.0244 - val_acc: 0.6889\n",
            "Epoch 22/100\n",
            "2/2 [==============================] - 2s 783ms/step - loss: 0.0013 - acc: 1.0000 - val_loss: 1.0344 - val_acc: 0.6889\n",
            "Epoch 23/100\n",
            "2/2 [==============================] - 2s 770ms/step - loss: 0.0012 - acc: 1.0000 - val_loss: 1.0331 - val_acc: 0.6889\n",
            "Epoch 24/100\n",
            "2/2 [==============================] - 2s 778ms/step - loss: 0.0010 - acc: 1.0000 - val_loss: 1.0281 - val_acc: 0.6889\n",
            "Epoch 25/100\n",
            "2/2 [==============================] - 1s 750ms/step - loss: 9.2905e-04 - acc: 1.0000 - val_loss: 1.0166 - val_acc: 0.6889\n",
            "Epoch 26/100\n",
            "2/2 [==============================] - 2s 769ms/step - loss: 7.6470e-04 - acc: 1.0000 - val_loss: 1.0044 - val_acc: 0.6889\n",
            "Epoch 27/100\n",
            "2/2 [==============================] - 2s 787ms/step - loss: 7.1114e-04 - acc: 1.0000 - val_loss: 0.9891 - val_acc: 0.6889\n",
            "Epoch 28/100\n",
            "2/2 [==============================] - 2s 754ms/step - loss: 6.2939e-04 - acc: 1.0000 - val_loss: 0.9735 - val_acc: 0.6889\n",
            "Epoch 29/100\n",
            "2/2 [==============================] - 2s 777ms/step - loss: 5.4451e-04 - acc: 1.0000 - val_loss: 0.9594 - val_acc: 0.6889\n",
            "Epoch 30/100\n",
            "2/2 [==============================] - 2s 794ms/step - loss: 5.0174e-04 - acc: 1.0000 - val_loss: 0.9460 - val_acc: 0.6889\n",
            "Epoch 31/100\n",
            "2/2 [==============================] - 2s 793ms/step - loss: 4.6865e-04 - acc: 1.0000 - val_loss: 0.9335 - val_acc: 0.6889\n",
            "Epoch 32/100\n",
            "2/2 [==============================] - 1s 746ms/step - loss: 4.5319e-04 - acc: 1.0000 - val_loss: 0.9216 - val_acc: 0.6889\n",
            "Epoch 33/100\n",
            "2/2 [==============================] - 2s 759ms/step - loss: 4.3084e-04 - acc: 1.0000 - val_loss: 0.9111 - val_acc: 0.6889\n",
            "Epoch 34/100\n",
            "2/2 [==============================] - 2s 777ms/step - loss: 4.0777e-04 - acc: 1.0000 - val_loss: 0.9023 - val_acc: 0.6889\n",
            "Epoch 35/100\n",
            "2/2 [==============================] - 2s 788ms/step - loss: 3.9626e-04 - acc: 1.0000 - val_loss: 0.8949 - val_acc: 0.6889\n",
            "Epoch 36/100\n",
            "2/2 [==============================] - 2s 788ms/step - loss: 3.8716e-04 - acc: 1.0000 - val_loss: 0.8887 - val_acc: 0.6889\n",
            "Epoch 37/100\n",
            "2/2 [==============================] - 2s 763ms/step - loss: 3.8277e-04 - acc: 1.0000 - val_loss: 0.8833 - val_acc: 0.6889\n",
            "Epoch 38/100\n",
            "2/2 [==============================] - 1s 750ms/step - loss: 3.7590e-04 - acc: 1.0000 - val_loss: 0.8791 - val_acc: 0.6889\n",
            "Epoch 39/100\n",
            "2/2 [==============================] - 1s 741ms/step - loss: 3.7004e-04 - acc: 1.0000 - val_loss: 0.8760 - val_acc: 0.6889\n",
            "Epoch 40/100\n",
            "2/2 [==============================] - 2s 773ms/step - loss: 3.6326e-04 - acc: 1.0000 - val_loss: 0.8740 - val_acc: 0.6889\n",
            "Epoch 41/100\n",
            "2/2 [==============================] - 2s 783ms/step - loss: 3.5872e-04 - acc: 1.0000 - val_loss: 0.8729 - val_acc: 0.6889\n",
            "Epoch 42/100\n",
            "2/2 [==============================] - 2s 755ms/step - loss: 3.5549e-04 - acc: 1.0000 - val_loss: 0.8722 - val_acc: 0.6889\n",
            "Epoch 43/100\n",
            "2/2 [==============================] - 2s 785ms/step - loss: 3.4996e-04 - acc: 1.0000 - val_loss: 0.8723 - val_acc: 0.6889\n",
            "Epoch 44/100\n",
            "2/2 [==============================] - 2s 785ms/step - loss: 3.4574e-04 - acc: 1.0000 - val_loss: 0.8730 - val_acc: 0.6889\n",
            "Epoch 45/100\n",
            "2/2 [==============================] - 2s 778ms/step - loss: 3.4157e-04 - acc: 1.0000 - val_loss: 0.8740 - val_acc: 0.6889\n",
            "Epoch 46/100\n",
            "2/2 [==============================] - 2s 764ms/step - loss: 3.3823e-04 - acc: 1.0000 - val_loss: 0.8751 - val_acc: 0.6889\n",
            "Epoch 47/100\n",
            "2/2 [==============================] - 2s 765ms/step - loss: 3.3405e-04 - acc: 1.0000 - val_loss: 0.8765 - val_acc: 0.6889\n",
            "Epoch 48/100\n",
            "2/2 [==============================] - 2s 775ms/step - loss: 3.2927e-04 - acc: 1.0000 - val_loss: 0.8784 - val_acc: 0.6889\n",
            "Epoch 49/100\n",
            "2/2 [==============================] - 2s 759ms/step - loss: 3.2598e-04 - acc: 1.0000 - val_loss: 0.8802 - val_acc: 0.6889\n",
            "Epoch 50/100\n",
            "2/2 [==============================] - 2s 776ms/step - loss: 3.2142e-04 - acc: 1.0000 - val_loss: 0.8823 - val_acc: 0.6889\n",
            "Epoch 51/100\n",
            "2/2 [==============================] - 2s 776ms/step - loss: 3.1768e-04 - acc: 1.0000 - val_loss: 0.8844 - val_acc: 0.6889\n",
            "Epoch 52/100\n",
            "2/2 [==============================] - 2s 781ms/step - loss: 3.1402e-04 - acc: 1.0000 - val_loss: 0.8865 - val_acc: 0.6889\n",
            "Epoch 53/100\n",
            "2/2 [==============================] - 2s 776ms/step - loss: 3.1103e-04 - acc: 1.0000 - val_loss: 0.8883 - val_acc: 0.6889\n",
            "Epoch 54/100\n",
            "2/2 [==============================] - 2s 783ms/step - loss: 3.0684e-04 - acc: 1.0000 - val_loss: 0.8903 - val_acc: 0.6889\n",
            "Epoch 55/100\n",
            "2/2 [==============================] - 2s 785ms/step - loss: 3.0343e-04 - acc: 1.0000 - val_loss: 0.8923 - val_acc: 0.6889\n",
            "Epoch 56/100\n",
            "2/2 [==============================] - 2s 758ms/step - loss: 3.0070e-04 - acc: 1.0000 - val_loss: 0.8939 - val_acc: 0.6889\n",
            "Epoch 57/100\n",
            "2/2 [==============================] - 2s 768ms/step - loss: 2.9674e-04 - acc: 1.0000 - val_loss: 0.8956 - val_acc: 0.6889\n",
            "Epoch 58/100\n",
            "2/2 [==============================] - 2s 762ms/step - loss: 2.9413e-04 - acc: 1.0000 - val_loss: 0.8971 - val_acc: 0.6889\n",
            "Epoch 59/100\n",
            "2/2 [==============================] - 2s 757ms/step - loss: 2.9087e-04 - acc: 1.0000 - val_loss: 0.8984 - val_acc: 0.6889\n",
            "Epoch 60/100\n",
            "2/2 [==============================] - 2s 786ms/step - loss: 2.8705e-04 - acc: 1.0000 - val_loss: 0.9000 - val_acc: 0.6889\n",
            "Epoch 61/100\n",
            "2/2 [==============================] - 1s 743ms/step - loss: 2.8462e-04 - acc: 1.0000 - val_loss: 0.9011 - val_acc: 0.6889\n",
            "Epoch 62/100\n",
            "2/2 [==============================] - 2s 774ms/step - loss: 2.8147e-04 - acc: 1.0000 - val_loss: 0.9023 - val_acc: 0.6889\n",
            "Epoch 63/100\n",
            "2/2 [==============================] - 1s 744ms/step - loss: 2.7835e-04 - acc: 1.0000 - val_loss: 0.9033 - val_acc: 0.6889\n",
            "Epoch 64/100\n",
            "2/2 [==============================] - 2s 777ms/step - loss: 2.7464e-04 - acc: 1.0000 - val_loss: 0.9045 - val_acc: 0.6889\n",
            "Epoch 65/100\n",
            "2/2 [==============================] - 2s 778ms/step - loss: 2.7171e-04 - acc: 1.0000 - val_loss: 0.9057 - val_acc: 0.6889\n",
            "Epoch 66/100\n",
            "2/2 [==============================] - 2s 777ms/step - loss: 2.6946e-04 - acc: 1.0000 - val_loss: 0.9065 - val_acc: 0.6889\n",
            "Epoch 67/100\n",
            "2/2 [==============================] - 2s 756ms/step - loss: 2.6642e-04 - acc: 1.0000 - val_loss: 0.9073 - val_acc: 0.6889\n",
            "Epoch 68/100\n",
            "2/2 [==============================] - 2s 779ms/step - loss: 2.6270e-04 - acc: 1.0000 - val_loss: 0.9084 - val_acc: 0.6889\n",
            "Epoch 69/100\n",
            "2/2 [==============================] - 2s 790ms/step - loss: 2.5981e-04 - acc: 1.0000 - val_loss: 0.9093 - val_acc: 0.6889\n",
            "Epoch 70/100\n",
            "2/2 [==============================] - 2s 764ms/step - loss: 2.5759e-04 - acc: 1.0000 - val_loss: 0.9100 - val_acc: 0.6889\n",
            "Epoch 71/100\n",
            "2/2 [==============================] - 1s 740ms/step - loss: 2.5449e-04 - acc: 1.0000 - val_loss: 0.9107 - val_acc: 0.6889\n",
            "Epoch 72/100\n",
            "2/2 [==============================] - 2s 786ms/step - loss: 2.5058e-04 - acc: 1.0000 - val_loss: 0.9116 - val_acc: 0.6889\n",
            "Epoch 73/100\n",
            "2/2 [==============================] - 2s 781ms/step - loss: 2.4752e-04 - acc: 1.0000 - val_loss: 0.9125 - val_acc: 0.6889\n",
            "Epoch 74/100\n",
            "2/2 [==============================] - 2s 767ms/step - loss: 2.4524e-04 - acc: 1.0000 - val_loss: 0.9131 - val_acc: 0.6889\n",
            "Epoch 75/100\n",
            "2/2 [==============================] - 2s 777ms/step - loss: 2.4117e-04 - acc: 1.0000 - val_loss: 0.9140 - val_acc: 0.6889\n",
            "Epoch 76/100\n",
            "2/2 [==============================] - 2s 764ms/step - loss: 2.3798e-04 - acc: 1.0000 - val_loss: 0.9149 - val_acc: 0.6889\n",
            "Epoch 77/100\n",
            "2/2 [==============================] - 2s 774ms/step - loss: 2.3476e-04 - acc: 1.0000 - val_loss: 0.9158 - val_acc: 0.6889\n",
            "Epoch 78/100\n",
            "2/2 [==============================] - 2s 768ms/step - loss: 2.3155e-04 - acc: 1.0000 - val_loss: 0.9167 - val_acc: 0.6889\n",
            "Epoch 79/100\n",
            "2/2 [==============================] - 2s 753ms/step - loss: 2.2911e-04 - acc: 1.0000 - val_loss: 0.9175 - val_acc: 0.6889\n",
            "Epoch 80/100\n",
            "2/2 [==============================] - 2s 752ms/step - loss: 2.2579e-04 - acc: 1.0000 - val_loss: 0.9183 - val_acc: 0.6889\n",
            "Epoch 81/100\n",
            "2/2 [==============================] - 2s 768ms/step - loss: 2.2166e-04 - acc: 1.0000 - val_loss: 0.9194 - val_acc: 0.6889\n",
            "Epoch 82/100\n",
            "2/2 [==============================] - 2s 782ms/step - loss: 2.1848e-04 - acc: 1.0000 - val_loss: 0.9205 - val_acc: 0.6889\n",
            "Epoch 83/100\n",
            "2/2 [==============================] - 2s 754ms/step - loss: 2.1609e-04 - acc: 1.0000 - val_loss: 0.9215 - val_acc: 0.6889\n",
            "Epoch 84/100\n",
            "2/2 [==============================] - 1s 745ms/step - loss: 2.1277e-04 - acc: 1.0000 - val_loss: 0.9226 - val_acc: 0.6889\n",
            "Epoch 85/100\n",
            "2/2 [==============================] - 2s 764ms/step - loss: 2.0953e-04 - acc: 1.0000 - val_loss: 0.9238 - val_acc: 0.6889\n",
            "Epoch 86/100\n",
            "2/2 [==============================] - 2s 774ms/step - loss: 2.0641e-04 - acc: 1.0000 - val_loss: 0.9250 - val_acc: 0.6889\n",
            "Epoch 87/100\n",
            "2/2 [==============================] - 2s 768ms/step - loss: 2.0270e-04 - acc: 1.0000 - val_loss: 0.9265 - val_acc: 0.6889\n",
            "Epoch 88/100\n",
            "2/2 [==============================] - 2s 777ms/step - loss: 1.9977e-04 - acc: 1.0000 - val_loss: 0.9280 - val_acc: 0.6889\n",
            "Epoch 89/100\n",
            "2/2 [==============================] - 1s 746ms/step - loss: 1.9748e-04 - acc: 1.0000 - val_loss: 0.9294 - val_acc: 0.6889\n",
            "Epoch 90/100\n",
            "2/2 [==============================] - 2s 763ms/step - loss: 1.9453e-04 - acc: 1.0000 - val_loss: 0.9309 - val_acc: 0.6889\n",
            "Epoch 91/100\n",
            "2/2 [==============================] - 1s 742ms/step - loss: 1.9164e-04 - acc: 1.0000 - val_loss: 0.9324 - val_acc: 0.6889\n",
            "Epoch 92/100\n",
            "2/2 [==============================] - 2s 781ms/step - loss: 1.8886e-04 - acc: 1.0000 - val_loss: 0.9340 - val_acc: 0.6889\n",
            "Epoch 93/100\n",
            "2/2 [==============================] - 2s 793ms/step - loss: 1.8559e-04 - acc: 1.0000 - val_loss: 0.9358 - val_acc: 0.6889\n",
            "Epoch 94/100\n",
            "2/2 [==============================] - 2s 756ms/step - loss: 1.8354e-04 - acc: 1.0000 - val_loss: 0.9375 - val_acc: 0.6889\n",
            "Epoch 95/100\n",
            "2/2 [==============================] - 1s 749ms/step - loss: 1.8087e-04 - acc: 1.0000 - val_loss: 0.9393 - val_acc: 0.6889\n",
            "Epoch 96/100\n",
            "2/2 [==============================] - 2s 782ms/step - loss: 1.7771e-04 - acc: 1.0000 - val_loss: 0.9411 - val_acc: 0.6889\n",
            "Epoch 97/100\n",
            "2/2 [==============================] - 2s 788ms/step - loss: 1.7527e-04 - acc: 1.0000 - val_loss: 0.9430 - val_acc: 0.6889\n",
            "Epoch 98/100\n",
            "2/2 [==============================] - 2s 762ms/step - loss: 1.7344e-04 - acc: 1.0000 - val_loss: 0.9448 - val_acc: 0.6889\n",
            "Epoch 99/100\n",
            "2/2 [==============================] - 2s 781ms/step - loss: 1.7046e-04 - acc: 1.0000 - val_loss: 0.9467 - val_acc: 0.6889\n",
            "Epoch 100/100\n",
            "2/2 [==============================] - 2s 775ms/step - loss: 1.6873e-04 - acc: 1.0000 - val_loss: 0.9485 - val_acc: 0.6889\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qu4RfPQXJJWS",
        "colab_type": "text"
      },
      "source": [
        "## Menggambarkan Hasil Pelatihan"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cr4T5jKSHiT-",
        "colab_type": "code",
        "outputId": "a09aa2ee-7b6f-4351-e4c3-99c3fd18be26",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 843
        }
      },
      "source": [
        "plt.plot(history.history[\"acc\"],label=\"Akurasi Pelatihan\")\n",
        "plt.plot(history.history[\"val_acc\"],label=\"Validasi Akurasi\")\n",
        "plt.legend()\n",
        "plt.show()\n",
        "\n",
        "plt.plot(history.history[\"loss\"],label=\"Kesalahan Pelatihan\")\n",
        "plt.plot(history.history[\"val_loss\"],label=\"Validasi Kesalahan\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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M1KHc7pDDcP7ct0NLmUxSdl6xjhSUqNzu0NYs5/NqKF4FAKDhCCN1qLr6anxkuM5vGy1J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\nAgAArPoB4dVr9POrbDYAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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5wULNphAiIiK5KC9Y2G+bzmBBREQkOeUFC3tTSCH7WBAREUlNecGiqMai0MYa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\nLIiIiMhtGCyIiIjIbf4fFDfxGX5VlGYAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C2KdKqZFxgvA",
        "colab_type": "code",
        "outputId": "555b07ed-1094-4bda-a8a9-a66216c9f69a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print('Number of trainable variables = {}'.format(len(model.trainable_variables)))"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of trainable variables = 4\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QLmgPgJMwN5m",
        "colab_type": "text"
      },
      "source": [
        "## Menggunakan Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QfzV_FsuvyIF",
        "colab_type": "code",
        "outputId": "caf67c0a-1016-465c-e306-cf0e50d54feb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(train_generator)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<keras_preprocessing.image.directory_iterator.DirectoryIterator object at 0x7fc2ad662ba8>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7RgBHC1fwacN",
        "colab_type": "code",
        "outputId": "9ec4ab93-0438-4cd5-d1ff-fbd0ae682789",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "#Prediksi Label Validasi dengan Pelatihan\n",
        "n = 44\n",
        "input_image = image_batch[n][np.newaxis,...]\n",
        "print(\"Labelnya adalah: \", label_batch[n])\n",
        "\n",
        "predictions = model.predict(input_image)\n",
        "print(\"Prediksinya adalah\",predictions[0])"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Labelnya adalah:  [1. 0. 0.]\n",
            "Prediksinya adalah [0.99083775 0.00218648 0.00697582]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_NlIkTYKnMej",
        "colab_type": "text"
      },
      "source": [
        "Hasil tersebut merupakan hasil prediksi dari training dari 3 jenis data pelatihan yaitu jenis beras Cilamaya, IR 64, dan Ketan. Menggunakan inputan gambar dengan piksel 224 x 224, 1 filter dengan fungsi aktivasi relu, dan 1 layer output dengan fungsi aktivasi softmax"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J-8AG_vUJi6o",
        "colab_type": "text"
      },
      "source": [
        "## Evaluasi"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BsSWGwr6J6yr",
        "colab_type": "code",
        "outputId": "1cc829b1-3080-4130-ee92-8f854c14b9f2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "#Memeriksa matriks model\n",
        "print(model.metrics_names)\n",
        "#Evaluasi data test\n",
        "print(model.evaluate(x= X_test3, y = y_test3)) "
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['loss', 'acc']\n",
            "45/45 [==============================] - 1s 16ms/sample - loss: 1.5410 - acc: 0.5556\n",
            "[1.5410099534822317, 0.5555556]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FuIQXxs7J9YF",
        "colab_type": "code",
        "outputId": "3f18db6a-cbe4-4285-be95-bfd179085638",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "#Menampilkan matriks yang benar dan matriks hasil prediksi\n",
        "\n",
        "#Label yang benar\n",
        "y_true = np.argmax(y_test2,axis=1)\n",
        "\n",
        "#Label prediksi\n",
        "Y_pred = model.predict(X_test)\n",
        "y_pred = np.argmax(Y_pred, axis=1)\n",
        "\n",
        "print(y_true)\n",
        "print(y_pred)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2\n",
            " 2 2 2 2 2 2 2 2]\n",
            "[0 0 0 0 0 0 0 0 0 0 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
            " 2 2 2 2 2 2 2 2]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A7EbbjTEJmPs",
        "colab_type": "text"
      },
      "source": [
        "## Prediksi Gambar Individual"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jtXbaxUoJwIs",
        "colab_type": "code",
        "outputId": "e4837969-8337-49d5-eba2-ff15df67b662",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 502
        }
      },
      "source": [
        "n = 44 #Jangan melampaui (nilai dari gambar test - 1)\n",
        "\n",
        "plt.imshow(X_test[n])\n",
        "plt.show()\n",
        "\n",
        "true_label = np.argmax(y_test2,axis=1)[n]\n",
        "print(\"Label yang benar adalah:\",true_label,\":\",labels[true_label])\n",
        "prediction = model.predict(X_test[n][np.newaxis,...])[0]\n",
        "print(\"Nilai yang diprediksi adalah:\",prediction)\n",
        "predicted_label = np.argmax(prediction)\n",
        "print(\"Label yang diprediksi adalah:\",predicted_label,\":\",labels[predicted_label])\n",
        "\n",
        "if true_label == predicted_label:\n",
        "    print(\"Prediksi benar\")\n",
        "else:\n",
        "    print(\"Prediksi salah\")"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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cs14vUlWmPH6ec0xQSOA3+q0n4jp7FGg8zxgDU0v/5NT7KVB/70j6xKvJvkECUZCdrEhS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\n8+1WclUvUt5XgClV3EI4bq3a62kb55zuPKccnTk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rkfBBu/VOTE3h8OGjAICN9TA2H/vR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            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Label yang benar adalah: 2 : Ketan\n",
            "Nilai yang diprediksi adalah: [6.3587740e-09 3.0451247e-05 9.9996948e-01]\n",
            "Label yang diprediksi adalah: 2 : Ketan\n",
            "Prediksi benar\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r7ORE_1reP6J",
        "colab_type": "text"
      },
      "source": [
        "## *Confusion Matrix*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GD-gesx5eWrP",
        "colab_type": "code",
        "outputId": "0d607cd8-ea26-4d05-8cbe-bfbde8610586",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 592
        }
      },
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.utils.multiclass import unique_labels\n",
        "\n",
        "def plot_confusion_matrix(y_true, y_pred, classes,\n",
        "                          normalize=False,\n",
        "                          title=None,\n",
        "                          cmap=plt.cm.Blues):\n",
        "    \"\"\"\n",
        "    This function prints and plots the confusion matrix.\n",
        "    Normalization can be applied by setting `normalize=True`.\n",
        "    \"\"\"\n",
        "    if not title:\n",
        "        if normalize:\n",
        "            title = 'Normalized confusion matrix'\n",
        "        else:\n",
        "            title = 'Confusion matrix, without normalization'\n",
        "\n",
        "    # Compute confusion matrix\n",
        "    cm = confusion_matrix(y_true, y_pred)\n",
        "    # Only use the labels that appear in the data\n",
        "    #classes = classes[unique_labels(y_true, y_pred)]\n",
        "    if normalize:\n",
        "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
        "        print(\"Normalized confusion matrix\")\n",
        "    else:\n",
        "        print('Confusion matrix, without normalization')\n",
        "\n",
        "    print(cm)\n",
        "\n",
        "    fig, ax = plt.subplots(figsize=(5,5))\n",
        "    im = ax.imshow(cm, interpolation='nearest', cmap=cmap)\n",
        "    #ax.figure.colorbar(im, ax=ax)\n",
        "    # We want to show all ticks...\n",
        "    ax.set(xticks=np.arange(cm.shape[1]),\n",
        "           yticks=np.arange(cm.shape[0]),\n",
        "           # ... and label them with the respective list entries\n",
        "           xticklabels=classes, yticklabels=classes,\n",
        "           title=title,\n",
        "           ylabel='Label Benar',\n",
        "           xlabel='Label Prediksi')\n",
        "\n",
        "    # Rotate the tick labels and set their alignment.\n",
        "    plt.setp(ax.get_xticklabels(), rotation=45, ha=\"right\",\n",
        "             rotation_mode=\"anchor\")\n",
        "\n",
        "    # Loop over data dimensions and create text annotations.\n",
        "    fmt = '.2f' if normalize else 'd'\n",
        "    thresh = cm.max() / 2.\n",
        "    for i in range(cm.shape[0]):\n",
        "        for j in range(cm.shape[1]):\n",
        "            ax.text(j, i, format(cm[i, j], fmt),\n",
        "                    ha=\"center\", va=\"center\",\n",
        "                    color=\"white\" if cm[i, j] > thresh else \"black\")\n",
        "    fig.tight_layout()\n",
        "    return ax\n",
        "\n",
        "\n",
        "np.set_printoptions(precision=2)\n",
        "\n",
        "plot_confusion_matrix(y_true, y_pred, classes=labels, normalize=True,\n",
        "                      title='Normalized confusion matrix')"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Normalized confusion matrix\n",
            "[[0.67 0.   0.33]\n",
            " [0.   0.   1.  ]\n",
            " [0.   0.   1.  ]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc268657278>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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FXV1dqa5i/t+Vl5jRrgruv07AP68T0KN2WWioSXHk34ucZrarjDcJaVh79hkA\nYN/NV+hWywzjWthg17UwmJfUhHv98th1LUxhuRIAbX8wxdG7EcgQPl0riWnUmHEYPMANtWrVRu06\ndbFqxXIkJyWhn1t/AMBA934wK1sWc+d7AwCGjxiNls2aYPmypWjdui1279qBG9evYfXa9QAy3wOG\njxqDhb/Mg41NRVhaWmH2rBkoY2aGDh07Fdl2fq/a9RmM1TPHooKtPWyq1cCR7RuQ+u4dXDpmnoJb\nOX0UShqXQe9RUwAA+39fCWs7B5iWs8D7tDTcPH8a547sxeApmfs/5V0y9m38DbWbtIRhaRPEx77F\nn7v88DYyHPVbKO8dKkAxCNzU1FT5RRIxMTFYtWoVEhMT0b59e8THxyM0NBQ7duxAnTp1cOTIEYXR\n67t37zBhwgR07doVVlZWePnyJa5evYouXbp8dT3ly5eHmpoaVq5ciaFDh+LevXuYO3euQh8LCwtI\nJBL4+/ujTZs20NTUVDhETR+cevAGBlolMNjJEqW01fAoMhFjd97F2+TMC6lM9TQgfBSYkQmpGL3z\nLsY0q4BtA2vjTUIqdl4Lw9bLiofz61gaooy+Bg7z6uQi1617D0S9eYM5s2ciIjwc9g7VcdD/OEz+\nvejpxYtQ+TUZAFC/QQP4bd2O2V7T4TV9KmwqVsSuvQdgV62avM94z4lITkrCiGFDEBsbiwYNG+GQ\n/3FoaGiIvn3fu4auHREf8xY71y5BbPQbWFa2w7TV2+SHlKPCX0Hy0f5NSUnGxl+mIDoyHGrqGihr\nWQEj561AQ9eOADIPN4eFPMGZw0OQEPsWuvqGqGDngDmb9sG8QuUi2UaxSARBKLLxgbu7OzZv3ix/\nrquriypVqmDSpEny0Jw4cSI2bdqE1NRUtG3bFvXq1cOsWbMQGxuLtLQ0uLm54cKFC4iIiEDp0qXx\n448/YvHixdDQ0ICfnx/GjBmj8AUVs2bNwoEDB3Dr1i2FOmJjY+W36/zvf//D1KlT8fr1a9SsWRNT\npkxBhw4dcPPmTVSvXh0AMHfuXKxZswYRERHo168f/Pz84OzsjOrVq2P58uXyZXfq1AkGBgbw8/MD\nkPnpff/+/ejUKW+f1OPj46Gvr49aXkegqqH9dX9oKvbOeDYp6hKokPnfe1XUJVAhSU5MgJtTFcTF\nxUFPL+dbHoEiDlz6Mgbu94GBq/wYuMorr4Fb5BdNERERfQ8YuERERCJg4BIREYmAgUtERCQCBi4R\nEZEIGLhEREQiYOASERGJgIFLREQkAgYuERGRCBi4REREImDgEhERiYCBS0REJAIGLhERkQgYuERE\nRCJg4BIREYmAgUtERCQCBo+5oFoAACAASURBVC4REZEIGLhEREQiYOASERGJgIFLREQkAgYuERGR\nCBi4REREImDgEhERiYCBS0REJAIGLhERkQgYuERERCJg4BIREYmAgUtERCQCBi4REZEIGLhEREQi\nYOASERGJgIFLREQkAgYuERGRCBi4REREImDgEhERiYCBS0REJAIGLhERkQgYuERERCJg4BIREYmA\ngUtERCQCBi4REZEIGLhEREQiYOASERGJgIFLREQkAgYuERGRCBi4REREImDgEhERiYCBS0REJAIG\nLhERkQgYuERERCJg4BIREYmAgUtERCQCBi4REZEIVIu6AMqbo6MaQU9Pr6jLIKKv1Lf/L0VdAhUS\nISMtT/04wiUiIhIBA5eIiEgEDFwiIiIRMHCJiIhEwMAlIiISAQOXiIhIBAxcIiIiETBwiYiIRMDA\nJSIiEgEDl4iISAQMXCIiIhEwcImIiETAwCUiIhIBA5eIiEgEDFwiIiIRMHCJiIhEwMAlIiISAQOX\niIhIBAxcIiIiEeQrcDMyMnDu3DnExsYWVj1ERERKKV+Bq6KigpYtWyImJqaw6iEiIlJK+T6kXK1a\nNTx9+rQwaiEiIlJa+Q7cefPmwdPTE/7+/nj9+jXi4+MVHkRERJSdan5naNOmDQCgQ4cOkEgk8nZB\nECCRSJCRkVFw1RERESmJfAduYGBgYdRBRESk1PIduE2aNCmMOoiIiJRavgM3S3JyMkJDQ5GWlqbQ\nbm9v/5+LIiIiUjb5Dtw3b96gf//+OHbsWI7TeQ6XiIgou3xfpTxmzBjExsYiKCgImpqaOH78ODZv\n3oyKFSvi0KFDhVEjERHRNy/fI9yAgAAcPHgQtWvXhlQqhYWFBVq0aAE9PT14e3ujbdu2hVEnERHR\nNy3fI9ykpCQYGxsDAAwNDfHmzRsAwA8//IAbN24UbHVERERKIt+BW7lyZQQHBwMAHBwc4OPjg7Cw\nMKxbtw5lypQp8AKJiIiUQb4PKY8ePRqvX78GAHh5eaFVq1b4448/oKamBj8/v4Kuj4iISCnkO3D7\n9Okj/3etWrXw/PlzPHjwAOXLl0fp0qULtDgiIiJl8dX34WbR0tJCzZo1C6IWIiIipZXvwM3IyICf\nnx9Onz6NyMhIyGQyhekBAQEFVhwREZGy+KpzuH5+fmjbti2qVaum8AMGRERElLN8B+6OHTuwa9cu\n+a8GERER0Zfl+7YgNTU12NjYFEYtRERESivfgTt+/Hj89ttvEAShMOohIiJSSvk+pHz+/HkEBgbi\n2LFjsLOzQ4kSJRSm79u3r8CKIyIiUhb5DlwDAwN07ty5MGohIiJSWvkOXF9f38Kog4iISKnl+xwu\nAKSnp+PUqVPw8fFBQkICAODVq1dITEws0OKIiIiURb5HuM+fP0erVq0QGhqK1NRUtGjRArq6uli4\ncCFSU1Oxbt26wqiTiIjom5bvEe7o0aNRu3ZtxMTEQFNTU97euXNnnD59ukCLIyIiUhb5HuH+9ddf\nuHjxItTU1BTaLS0tERYWVmCFERERKZN8j3BlMhkyMjKytb98+RK6uroFUhQREZGyyXfgtmzZEsuX\nL5c/l0gkSExMhJeXF7/ukYiIKBf5PqS8dOlSuLq6wtbWFikpKejVqxcePXqE0qVL43//+19h1EhE\nRPTNy3fglitXDrdv38bOnTtx+/ZtJCYmYuDAgejdu7fCRVRERET0wVf9AL2qqip69+6N3r17F3Q9\nRERESinfgRsdHY1SpUoBAF68eIENGzbg3bt3aN++PRo3blzgBRIRESmDPF80dffuXVhaWsLY2BhV\nqlTBrVu3UKdOHSxbtgzr169H06ZNceDAgcKslYiI6JuV58CdOHEifvjhB5w7dw7Ozs5o164d2rZt\ni7i4OMTExODnn3/GggULCrNW+katW7MalW0sYaCjAacGjrh65cpn++/dsxsO1arAQEcDtav/gOPH\njipMFwQBc2bNhJV5GRjqaqKNa3M8fvSoMDeBvoD7WHk1rFkBe5b/jKcn5uPdzVVo72z/xXmcalXE\nxe2TEBu0DPcOeqFPe8dsfX7u3hgPjsxGzOVlOLfFE7XtLAqj/GIlz4F79epVzJ8/Hw0bNsSSJUvw\n6tUreHh4QCqVQiqVYuTIkXjw4EFh1krfoN27dmLShHGYNt0Ll67cgL29Azq0dUVkZGSO/S9dvAi3\nPj3h1n8gLl+9ifYdO6F7l074+949eZ+lSxZhzaoVWLF6Hc5dCIK2tjbat3VFSkqKWJtFH+E+Vm7a\nmuq4+zAMY7x35qm/hVkp7F85FOeuPYTjTwuwansg1s7sheb1q8r7dG1ZEwvHd8Z8n2Oo32sh7jwM\nw6E1w2FkqFNYm1EsSIQ8/pK8VCpFeHg4jI2NAQC6urq4ffs2rK2tAQAREREwMzPL8Usxijt3d3fE\nxsbiwIEDcHd3x+bNmwFkXhxWrlw5dOvWDXPmzIGGhobCfEeOHMGcOXNw584daGhooEmTJjkeVo+O\njoaDgwPCwsIQExMDAwODPNcWHx8PfX19RETHQU9P779taBFwauCIWrXrYPmKVQAyvzjFxsocw4aP\nxISJk7P179OrB5KTkrDvoL+8rXHDenBwqI6Va9ZBEARYlzfDqLHjMXacJwAgLi4OFmVNsP53P3Tv\n8ZM4G0Zy3Md5Y1hnRFGX8J+9u7kK3ceux+Ezd3LtM29UR7RyskPtbr/I27Ys6A99HU10HLEGAHBu\niyeu//0cYxfuBpD5fQ6Pj8/F2h1nscT3ZOFuRCEQMtKQencD4uI+/z6dry++kEgkn32uLFq1aoXX\nr1/j6dOnWLZsGXx8fODl5aXQZ+/evejbty/69++P27dv48KFC+jVq1eOyxs4cCDs7b98GEbZpKWl\n4eaN62jarLm8TSqVomnT5rhy+VKO8wRdvgSXps0V2lq0dEXQv/1Dnj1DeHg4mn7UR19fH3XqOsr7\nkHi4j+lTjg5WCAwKVmg7efE+HO2tAAAlVFVQo6o5Aj7qIwgCAoKCUfffPsoqX1cpu7u7Q11dHQCQ\nkpKCoUOHQltbGwCQmppa8NUVEXV1dZiamgIAzM3N0bx5c5w8eRILFy4EkPnzhKNHj8bixYsxcOBA\n+Xy2trbZlrV27VrExsZi5syZOHbsmDgbUExERUUhIyMDxsYmCu3GJiYIDs759ENEeDiMTT7pb2yC\niIhwAEB4eLh8GZ8uM6sPiYf7mD5lUkoPEW8TFNoi38ZDX1cTGuolYKinBVVVFUR+2ic6HpUtFfe5\nsslz4Lq5uSk879OnT7Y+/fr1++8VFTP37t3DxYsXYWHx4YT+jRs3EBYWBqlUiho1aiA8PBzVq1fH\n4sWLUa1aNXm/f/75B3PmzEFQUBCePn2ap/WlpqYqfHiJj48vuI0hIqIik+fA9fX1Lcw6ihV/f3/o\n6OggPT0dqampkEqlWLVqlXx6VnjOmjULv/76KywtLbF06VI4Ozvj4cOHKFmyJFJTU9GzZ08sXrwY\n5cuXz3Pgent7Y/bs2YWyXWIrXbo0VFRUEBkZodAeGREhP4LwKRNTU0RGfNI/MgImJpn9s+aLjIhA\nmTJlFJZp71C9IMunPOA+pk9FRMfDpKTiD9kYl9RDXMI7pKS+R1RMItLTM2D8aZ9SegiPVu4BRr5/\nvOB74OLiglu3biEoKAhubm7o378/unTpIp8uk8kAANOmTUOXLl1Qq1Yt+Pr6QiKRYPfuzIsApkyZ\ngqpVq+Z4JOBzpkyZgri4OPnjxYsXBbdhIlNTU0ONmrUQGPDhd5JlMhkCA0+jbr36Oc7jWK8+zgQq\n/q7y6VMn4fhvf0srK5iamiLwoz7x8fG4eiVI3ofEw31Mnwq6/QzOdSsrtDWrVwVBd54BAN6nZ+Dm\n/RdwcfzQRyKRwKVuJVz5t4+yYuDmQFtbGzY2NnBwcMCmTZsQFBSE33//XT4961P3x+ds1dXVYW1t\njdDQUABAQEAAdu/eDVVVVaiqqqJZs2YAMkcEn16A9TF1dXXo6ekpPL5lo8aMg+/vG7Bty2Y8uH8f\no4YPQ3JSEvq59QcADHTvhxnTpsj7Dx8xGif+PI7ly5Yi+MEDzJszCzeuX8NQj8wrPCUSCYaPGoOF\nv8yD/+FDuHf3Lgb274cyZmbo0LFTkWzj9477WLlpa6rBvlJZ2FcqCwCwLFsK9pXKwtzUEAAwZ2QH\nbJzbV95/w57zsCpXCvNHd0QlSxMM6eaELi1qYOUfgfI+K7YFoH/nBujd3hGVrUywYmoPaGmqY8vB\ny+JunMi+6ruUvydSqRRTp07FuHHj0KtXL2hqaqJWrVpQV1dHcHAwGjVqBAB4//49QkJC5Od69+7d\ni3fv3smXc/XqVQwYMAB//fUXKlSoUCTbUhS6de+BqDdvMGf2TESEh8PeoToO+h+Hyb8XxLx4EQqp\n9MPnvvoNGsBv63bM9poOr+lTYVOxInbtPQC7j86Nj/eciOSkJIwYNgSxsbFo0LARDvkfz3bbFomD\n+1i51bS1wImNo+XPF3lmHu3beugyhnhtg2lpPZiblpRPf/4qGp1HrsMizx8xvJczwiJiMWzOdpy6\ndF/eZ8+JGyhtqIOZw9rCpJQu7gSHoePw1dkupFI2eb4PV5l9eh9u1r+zpKenw9LSEmPGjIGnZ+Z9\ngWPGjMGePXuwadMmWFhYYPHixTh8+DAePHgAQ0PDbOs4c+YMXFxcvrv7cIkokzLch0s5K5T7cL9X\nqqqqGDFiBBYtWoSkpCQAwOLFi/HTTz+hb9++qFOnDp4/f46AgIAcw5aIiChPI9xDhw7leYEdOnT4\nTwWRIo5wiZQDR7jKK68j3Dydw+3UKW8XKkgkkm/yqx2JiIgKW54CN+s2GCIiIvo6/+kcLn+5g4iI\nKG/yHbgZGRmYO3cuypYtCx0dHfk3KM2YMUPhXlUiIiL6IN+BO3/+fPj5+WHRokVQU1OTt1erVg0b\nN24s0OKIiIiURb4Dd8uWLVi/fj169+4NFRUVebuDgwN/gJ6IiCgX+Q7csLAw2NjYZGuXyWR4//59\ngRRFRESkbPIduLa2tvjrr7+yte/Zswc1atQokKKIiIiUTb6/S3nmzJlwc3NDWFgYZDIZ9u3bh+Dg\nYGzZsgX+/v6FUSMREdE3L98j3I4dO+Lw4cM4deoUtLW1MXPmTNy/fx+HDx9GixYtCqNGIiKib95X\n/VqQk5MTTp48WdC1EBERKa2v/nm+a9eu4f79zJ9bsrW1Ra1atQqsKCIiImWT78B9+fIlevbsiQsX\nLsh/Zi42NhYNGjTAjh07UK5cuQIvkoiI6FuX73O4gwYNwvv373H//n28ffsWb9++xf379yGTyTBo\n0KDCqJGIiOibl+8R7tmzZ3Hx4kVUrlxZ3la5cmWsXLkSTk5OBVocERGRssj3CNfc3DzHL7jIyMiA\nmZlZgRRFRESkbPIduIsXL8bIkSNx7do1edu1a9cwevRoLFmypECLIyIiUhZ5OqRsaGgIiUQif56U\nlARHR0eoqmbOnp6eDlVVVQwYMCDPP1ZPRET0PclT4C5fvryw6yAiIlJqeQpcNze3wq6DiIhIqX31\nF18AQEpKCtLS0hTa9PT0/lNBREREyijfF00lJSVhxIgRMDY2hra2NgwNDRUeRERElF2+A3fixIkI\nCAjA2rVroa6ujo0bN2L27NkwMzPDli1bCqNGIiKib16+DykfPnwYW7ZsgbOzM/r37w8nJyfY2NjA\nwsICf/zxB3r37l0YdRIREX3T8j3Cffv2LaytrQFknq99+/YtAKBRo0Y4d+5cwVZHRESkJPIduNbW\n1nj27BkAoEqVKti1axeAzJFv1o8ZEBERkaJ8B27//v1x+/ZtAMDkyZOxevVqaGhoYOzYsZgwYUKB\nF0hERKQM8n0Od+zYsfJ/N2/eHA8ePMD169dhY2MDe3v7Ai2OiIhIWeR7hPspCwsL/PjjjyhZsiSG\nDBlSEDUREREpnf8cuFmio6Px+++/F9TiiIiIlEqBBS4RERHljoFLREQkAgYuERGRCPJ8lfKPP/74\n2emxsbH/uRgiIiJllefA1dfX/+L0fv36/eeCiIiIlFGeA9fX17cw6yAiIlJqPIdLREQkAgYuERGR\nCBi4REREImDgEhERiYCBS0REJAIGLhERkQgYuERERCJg4BIREYmAgUtERCQCBi4REZEIGLhEREQi\nYOASERGJgIFLREQkAgYuERGRCBi4REREImDgEhERiYCBS0REJAIGLhERkQgYuERERCJg4BIREYmA\ngUtERCQCBi4REZEIGLhEREQiYOASERGJgIFLREQkAgYuERGRCBi4REREImDgEhERiYCBS0REJAIG\nLhERkQgYuERERCJg4BIREYmAgUtERCQCBi4REZEIGLhEREQiYOASERGJgIFLREQkAgYuERGRCBi4\nREREImDgEhERiYCBS0REJAIGLhERkQgYuERERCJg4BIREYmAgUtERCQCBi4REZEIGLhEREQiYOAS\nERGJgIFLREQkAgYuERGRCBi4REREImDgEhERiYCBS0REJAIGLhERkQgYuERERCJg4BIREYmAgUtE\nRCQCBi4REZEIGLhEREQiYOASERGJgIFLREQkAgYuERGRCBi4REREImDgEhERiYCBS0REJAIGLhER\nkQgYuERERCJg4BIREYmAgUtERCQCBi4REZEIGLhEREQiYOASERGJgIFLhW7dmtWobGMJAx0NODVw\nxNUrVz7bf++e3XCoVgUGOhqoXf0HHD92VGG6IAiYM2smrMzLwFBXE21cm+Pxo0eFuQn0BdzHyqth\nzQrYs/xnPD0xH+9urkJ7Z/svzuNUqyIubp+E2KBluHfQC33aO2br83P3xnhwZDZiLi/DuS2eqG1n\nURjlFysMXCpUu3ftxKQJ4zBtuhcuXbkBe3sHdGjrisjIyBz7X7p4EW59esKt/0BcvnoT7Tt2Qvcu\nnfD3vXvyPkuXLMKaVSuwYvU6nLsQBG1tbbRv64qUlBSxNos+wn2s3LQ11XH3YRjGeO/MU38Ls1LY\nv3Iozl17CMefFmDV9kCsndkLzetXlffp2rImFo7vjPk+x1C/10LceRiGQ2uGw8hQp7A2o1iQCIIg\nFHURhcnd3R2xsbE4cOCAvG3Pnj3o06cP5s+fj/Hjx392/jNnzsDFxQUxMTEwMDAo7HKziY+Ph76+\nPiKi46Cnpyf6+v8rpwaOqFW7DpavWAUAkMlksLEyx7DhIzFh4uRs/fv06oHkpCTsO+gvb2vcsB4c\nHKpj5Zp1EAQB1uXNMGrseIwd5wkAiIuLg0VZE6z/3Q/de/wkzoaRHPdx3hjWGVHUJfxn726uQvex\n63H4zJ1c+8wb1RGtnOxQu9sv8rYtC/pDX0cTHUesAQCc2+KJ638/x9iFuwEAEokEj4/PxdodZ7HE\n92ThbkQhEDLSkHp3A+LiPv8+/d2NcDdu3IjevXtj7dq1Xwxb+m/S0tJw88Z1NG3WXN4mlUrRtGlz\nXLl8Kcd5gi5fgkvT5gptLVq6Iujf/iHPniE8PBxNP+qjr6+POnUd5X1IPNzH9ClHBysEBgUrtJ28\neB+O9lYAgBKqKqhR1RwBH/URBAEBQcGo+28fZfVdBe6iRYswcuRI7NixA/379weQ+Wnc29sbVlZW\n0NTUhIODA/bs2QMACAkJgYuLCwDA0NAQEokE7u7uAIDjx4+jUaNGMDAwQKlSpdCuXTs8efJEvq6Q\nkBBIJBLs27cPLi4u0NLSgoODAy5d+n7eMKKiopCRkQFjYxOFdmMTE4SHh+c4T0R4OIxNPulvbIKI\niMz+WfNl62PyoQ+Jh/uYPmVSSg8RbxMU2iLfxkNfVxMa6iVQ2lAHqqoqiPy0T3Q8TEt9e0fx8uO7\nCdxJkyZh7ty58Pf3R+fOneXt3t7e2LJlC9atW4e///4bY8eORZ8+fXD27FmYm5tj7969AIDg4GC8\nfv0av/32GwAgKSkJ48aNw7Vr13D69GlIpVJ07twZMplMYb3Tpk2Dp6cnbt26hUqVKqFnz55IT0/P\ntc7U1FTEx8crPIiI6NunWtQFiOHYsWM4ePAgTp8+jaZNm8rbU1NT8csvv+DUqVOoX78+AMDa2hrn\nz5+Hj48PmjRpgpIlSwIAjI2NFc7hdunSRWEdmzZtgpGREf755x9Uq1ZN3u7p6Ym2bdsCAGbPng07\nOzs8fvwYVapUybFWb29vzJ49u2A2vIiVLl0aKioqiIyMUGiPjIiAqalpjvOYmJoiMuKT/pERMDHJ\n7J81X2REBMqUKaOwTHuH6gVZPuUB9zF9KiI6HiYldRXajEvqIS7hHVJS3yMqJhHp6Rkw/rRPKT2E\nRyv3AOO7GOHa29vD0tISXl5eSExMlLc/fvwYycnJaNGiBXR0dOSPLVu2KBwezsmjR4/Qs2dPWFtb\nQ09PD5aWlgCA0NDQbOvOkvXmkdvVmwAwZcoUxMXFyR8vXrzI7+YWG2pqaqhRsxYCA07L22QyGQID\nT6Nuvfo5zuNYrz7OBJ5WaDt96iQc/+1vaWUFU1NTBH7UJz4+HlevBMn7kHi4j+lTQbefwbluZYW2\nZvWqIOjOMwDA+/QM3Lz/Ai6OH/pIJBK41K2EK//2UVbfxQi3bNmy2LNnD1xcXNCqVSscO3YMurq6\n8vA9cuQIypYtqzCPurr6Z5fZvn17WFhYYMOGDTAzM4NMJkO1atWQlpam0K9EiRLyf0skEgDIdtj5\n0/V+ad3fklFjxmHwADfUqlUbtevUxaoVy5GclIR+bpnn0Ae694NZ2bKYO98bADB8xGi0bNYEy5ct\nRevWbbF71w7cuH4Nq9euB5D5Nxw+agwW/jIPNjYVYWlphdmzZqCMmRk6dOxUZNv5PeM+Vm7ammqo\nYG4kf25ZthTsK5VFTHwyXoTHYM7IDjAz1segGVsBABv2nMfQnxpj/uiO2HzwMpzrVEKXFjXQedQ6\n+TJWbAvAhjl9cf2fUFy7F4IRvVygpamOLQcvi759YvouAhcALCwscPbsWXnoHj9+HLa2tlBXV0do\naCiaNGmS43xqamoAgIyMDHlbdHQ0goODsWHDBjg5OQEAzp8/X/gb8Q3q1r0Hot68wZzZMxERHg57\nh+o46H8cJv9eEPPiRSik0g8HWuo3aAC/rdsx22s6vKZPhU3Fiti19wDsPjpMP95zIpKTkjBi2BDE\nxsaiQcNGOOR/HBoaGqJvH3EfK7uathY4sXG0/Pkiz8zTaVsPXcYQr20wLa0Hc9OS8unPX0Wj88h1\nWOT5I4b3ckZYRCyGzdmOU5fuy/vsOXEDpQ11MHNYW5iU0sWd4DB0HL4624VUyua7uw/35cuXcHFx\ngZGREY4fP45FixZh3bp1WLp0KRo1aoS4uDhcuHABenp6cHNzQ1hYGMzNzeHr64s2bdpAU1MTWlpa\nMDY2RuvWreHl5YXQ0FBMnjwZV69exf79+9GpUyeEhITAysoKN2/eRPXqmeedYmNjYWhoiMDAQDg7\nO+ep/m/9PlwiyqQM9+FSzngfbi7KlSuHM2fOICoqCq6urpgwYQJmzJgBb29vVK1aFa1atcKRI0dg\nZZV5P1jZsmUxe/ZsTJ48GSYmJhgxYgSkUil27NiB69evo1q1ahg7diwWL15cxFtGRETFmdKPcL91\nHOESKQeOcJUXR7hERETFCAOXiIhIBAxcIiIiETBwiYiIRMDAJSIiEgEDl4iISAQMXCIiIhEwcImI\niETAwCUiIhIBA5eIiEgEDFwiIiIRMHCJiIhEwMAlIiISAQOXiIhIBAxcIiIiETBwiYiIRMDAJSIi\nEgEDl4iISAQMXCIiIhEwcImIiETAwCUiIhIBA5eIiEgEDFwiIiIRMHCJiIhEwMAlIiISAQOXiIhI\nBAxcIiIiETBwiYiIRMDAJSIiEgEDl4iISAQMXCIiIhEwcImIiETAwCUiIhIBA5eIiEgEDFwiIiIR\nMHCJiIhEwMAlIiISAQOXiIhIBAxcIiIiETBwiYiIRMDAJSIiEgEDl4iISAQMXCIiIhEwcImIiETA\nwCUiIhIBA5eIiEgEDFwiIiIRMHCJiIhEwMAlIiISAQOXiIhIBAxcIiIiETBwiYiIRKBa1AXQ5wmC\nAABIiI8v4kqI6L8QMtKKugQqJFn7Nuv9OjcM3GIuISEBAGBjZV7ElRAR0eckJCRAX18/1+kS4UuR\nTEVKJpPh1atX0NXVhUQiKepyCl18fDzMzc3x4sUL6OnpFXU5VAi4j5Xf97aPBUFAQkICzMzMIJXm\nfqaWI9xiTiqVoly5ckVdhuj09PS+i/+o3zPuY+X3Pe3jz41ss/CiKSIiIhEwcImIiESgMmvWrFlF\nXQTRx1RUVODs7AxVVZ7xUFbcx8qP+zg7XjRFREQkAh5SJiIiEgEDl4iISAQMXCIiIhEwcImIiETA\nwCUiIhIBA5eKjQMHDiAqKqqoyyAiKhQMXCpyMpkMd+7cQd++fZGWxl9U+R7IZLKiLoFIdAxcKnJS\nqRT29vZ4+fIlzMzMcP/+fbx586aoy6ICdu/ePQwaNAgZGRmQSqUMXSWU09c68KsePuAXX1CxkZGR\ngaioKJQpUwY///wz5s6di9KlSxd1WfQfCYIAmUwGR0dH3LhxA23atMHhw4chkUiQkZEBFRWVoi6R\nCoAgCJBIJAgMDMSFCxeQnJyMcePG8f/wRzjCpWJDRUUFJiYm2L59OzZt2oRffvmFI10lIJFIoKKi\ngkaNGsHT0xOhoaFo2rQp0tLSGLZKRCKRwN/fH23atMHp06exdetW1KhRA2fPnuUo918MXCoy6enp\n8v+IWeduBUHATz/9hG3btmH58uXw9vZm6CoJbW1txMbGws/PD48ePULbtm0BABs3bsTt27eLuDr6\nWln/h5OSknD8+HGsWbMGp0+fxsOHD1G3bl1069YNgYGBDF0wcKkI3LhxA+np6VBVVYVEIsGJEycw\nZMgQDB48GH/++SeSk5PRrVs37Ny5E8uXL+dI9xuWdTgZAKpXr47379+jZs2aOHz4MJ48eQIjIyPM\nnTsXpqamfEP+RkkkChvLswAAGQFJREFUEly+fBn29va4e/cuKlasCKlUCk1NTezduxdOTk7o0aMH\nzpw5893vYwYuiergwYPo06cPfHx8AAAXLlxAmzZtoKKignPnzsHLywuLFi1CYmKiPHTXrFmDqVOn\n8pahb8i7d+8AZL4ZS6WZbzP2/2/vzuNruvM/jr9ONlksIWgkCG1CLElFQmKJtUErtjBjqbUd9bBU\n21AatJZKCLE0omQwQZoiaC2xVHSGCSbUNLaKECVCKCUh0eRmud/fHyZ3mtb8RJEr8Xk+Hh4Pued7\nzv2cx13e93zP95yvuzunTp1Cr9fj4eGBq6sr9+7do3bt2rz00ktomiYDqcqpxo0bU79+fRISEsjM\nzAT+OxJ969atdOnSha5du5KQkGDMMo1OAleUKV9fX9zc3NiwYQMrVqwgLi6OpUuXsmbNGn744Qfa\nt2/PN998Q2hoqCF016xZw9atWykqKjJ2+aIUzp49y5///GeOHTtmeKyoqAgLCwsKCwvR6XSMHTuW\nEydO8Ne//pWsrCy8vLzQ6/WGcBblS/Xq1fnqq6/o0KED77//PsnJyZiYmBiOaDdt2sSwYcOwt7c3\ncqXGJe9uUWYKCgqoUaMGK1euxMnJic2bN7Nnzx4aNGgAgJmZGXPnzqVdu3bEx8cTFhZGdnY2Q4cO\nJS0tjZdeesm4OyAe6cyZM7Rr1w4nJyesrKwMj5uamtKwYUOaNGmCm5sb27dvZ8+ePYwYMYK1a9dS\nUFBAenq6ESsXpVUcohkZGVy9epUbN24AD0J3+/bt2Nvb06dPH5KTk9E0zdB+3bp1NGrUyGh1Pw8k\ncMUz9esuwuKJqC0sLFi9ejVOTk6kpKRw8OBBQxsrKyuCg4Pp2LEjGzduJDw8HKUUNjY2ZV67eDw5\nOTmMGzeOoUOHEhERgZubG7dv3+aHH34wtLG3t8fa2ppdu3bx6quvAtCuXTuOHj2Kk5OTsUoXpVR8\n6c+OHTvo1q0bfn5+NG3alDVr1pCZmUm1atXYvXs3tWrVYsCAAZw5cwZN04xd9vNDCfGMpaSkqNDQ\nUKWUUps2bVKenp4qKytLZWVlqeHDhysvLy+1YsUKVVhYaFgnNzdXzZgxQ126dMlIVYvHdf36deXh\n4aFSU1OVUkr16tVLeXp6Kk3TlL+/v9q/f79SSqmffvrJmGWKJ7Rr1y5VpUoVtWTJEnXlyhU1Y8YM\nZW1trebNm6du376tlFIqKytLNW3aVHl6eiqdTmfkip8fErjimdLr9SoqKkppmqYGDx6sNE1Ta9eu\nNSzPzMxUgwcPVm3atPld6IryJT09XTVt2lSdOnVKDRo0SL3xxhsqPj5eHTx4UHXs2FF16NBBHT16\n1Nhliidw48YN1bNnTzV//nyllFJpaWnK2dlZtW7dWmmapubMmaNu3ryplFLq7t278oP5N0xnzZo1\ny9hH2aLi0jSNFi1acOXKFb744gsCAgIICQkBHpzTtbGxoWvXrhw5coQjR45w9+5dvL29pRuqHCoq\nKiIiIoKCggIKCwuZNGkSvr6+ODk50b17dyIiItDr9fj5+Rm7VPEYCgsLMTExITc3l8qVK3P//n0G\nDhzIL7/8gp+fH+3bt2fnzp1kZWWxfPlyADw8PLC1tcXW1tbI1T9f5ByuKBM1a9Zk0KBBbN++neLf\neObm5uTn52Nra0tERAS2trbs3r2be/fuGbdY8YdUr16dOXPmEB4eTlRUFNnZ2cCDIK5Tpw7+/v4k\nJycbuUpRWhcvXuT8+fOYmZmxdetWJkyYgKZpBAQEYG9vz5o1a3B0dGTBggXAg894jRo1WL58uVxR\n8D+YGbsA8WIIDQ1Fr9fTsWNHxo8fD8CsWbOwsLAAQKfTERsby+3bt+VXcTlw6dIlNmzYQG5uLo0b\nN2bo0KEAvP7663z00UeEhISwc+dOWrduTZUqVQC4efMm9evXN2bZopQKCgqYPn06W7duJSwsjA8+\n+IB169ZhampKrVq1gAfvASsrK8Prm5mZSWRkJD4+PlStWtWY5T+3JHDFU6f+M5Lx9OnTXL9+nezs\nbPr374+JiQmjRo0CYMKECSilCAoKIjQ0lL1797Jv3z4cHByMXL14lJMnT9KjRw/c3d05f/48BQUF\nXLx4kZkzZ1KzZk1Gjx4NwKeffkp6ejpOTk7odDp27drFoUOHjFy9KA1zc3PWrVtHcnIyU6ZMISQk\nhGHDhpWYbKJFixa89957vP/++9y6dYu9e/fy9ttvS9j+f4x9EllULHq9Ximl1FdffaWcnJxUkyZN\nVIMGDVTr1q3VlStXlFJK5efnqzVr1ihN05S7u7uqVq2aOn78uDHLFqV06tQpZW1trWbMmKGKiorU\nhQsXVMuWLVWLFi0Mg2WUUqqgoEDFx8erXr16KT8/PzVw4EB16tQpI1YuHte9e/dU8+bNlYuLi6pd\nu7Y6e/asUurBa1ssJCRE+fr6qp49e6qTJ08aq9RyQwJXPHX79+9Xtra2avXq1aqoqEglJiYqTdOU\nt7e3On/+vKHd6dOn1fr169Xly5eNWK0orYyMDFW3bl3VuXPnEo/36tVLVa1aVZ09e7bEl7FSSt2/\nf18ppVReXl6Z1SmenqysLJWdna26deumatWq9dDQVUpe39KS+XDFU5WTk8PMmTOpXr06M2bMID09\nHV9fXzp16sTJkydRShEbG/vC33GmvMnPz+fevXuMGjWK7Oxs3nrrLYYPH86CBQsICgrCy8uLqlWr\nYmFhgYODA+PGjaNGjRqGm1mo/5xmEM+v4tfo+PHjnD17lrt37+Lj40OrVq24c+cOb775JklJSfz9\n73+nadOmLFiwgNTUVCIiIgxjMcQjGDXuRYW0fft2dfr0aXXnzh3l5eWl3nnnHaXUgwvmNU1TjRs3\nNtwcQTz/0tPT1YABA9SJEydURkaGGjJkiPL19VX9+vVTNWvWVPv27VM///yzOnfunIqJiVHe3t7K\n0dFRubm5qdzcXGOXL0qh+Pr3LVu2qBo1aqg+ffooDw8P5enpqYKCgpRSD94HvXv3ViYmJqpnz57K\n3NxcJSUlGbPsckcCVzyR4nO2D7Nr1y7VunVrdeHCBaWUUvHx8ap3796qa9euErjlyMWLF5W3t7d6\n44031JkzZwyhW7VqVRUYGPjQdRISElRaWloZVyoeV3Z2tuH/p0+fVg4ODmrlypVKKaW+//57ZWlp\nqaZNm2Zoo9frVVhYmJo2bZpKTk4u83rLO7kOV/xh6j9dUIcOHWLu3LnExsZy4cIFw/LU1FRSU1MN\nkw4cOHAAR0dH9u7dyyuvvGKsssVjevnll4mOjqaoqIjAwEBu377NkiVL8Pf3JzExkXXr1hna5ufn\nA9C+fXu5BOg5d+7cOfr168e1a9eAB5/X+vXrM2bMGC5dukS/fv0YPnw4wcHBAJw+fRpN05g0aRJz\n5szB1dXVmOWXSxK44g/TNI1du3bh5+dHXFwc48ePZ+rUqezbtw+AYcOGUaVKFZo1a0bHjh0JDw9n\nzJgxhkkMRPnh4uLCsmXLDF+4t27dIiwsjAYNGhAZGUl0dDSAnMsrRxITE7l//z6Ojo7Ag89znTp1\nSE9Pp0OHDnTv3p3PP/8cgISEBGJjY8nIyAAwXBokHo8Erngi8fHxLFu2jMTERKKjoyksLCQ0NJTd\nu3dTvXp1EhISCAgIoGPHjhw9etQwQ4wof34duoGBgdy5c4ewsDBcXFyYN28eGzduNHaJ4jFcv36d\ngoICw/R5DRs2ZOfOnTg7O9O/f38iIyMNwRobG8uJEydk1q4nJIErHkvxhzM9PZ3MzExycnJwdnYG\noEePHgQGBmJjY0NYWBjffPMN9erVY+nSpcyaNYsmTZoYs3TxFPw2dDMzMwkJCaF9+/b4+PgYuzzx\nCHl5eYb/FxYWYmtri6ZpFBUV4e7uzqpVq1BKUa9ePa5cucLFixeZMmUKMTExzJ8/n2rVqhmx+vJP\nAlc8Fk3T2Lp1K23atMHd3Z1t27Zx9epVw/JOnToRGBiIra0t06ZNY//+/QCYmMhbraIoDl0zMzPe\neustsrOzWbFiBQ0aNDB2aeL/ce3aNYYPH058fDzw4B7XdnZ2JdoEBAQwd+5cpk+fTtu2benTpw9x\ncXF8++23NGvWzBhlVyhyMk2USvEAqRs3bvDBBx/w0UcfYWZmxs6dO/n444+pUqUKffr0AR6EbkFB\nAVFRUbi4uBi5cvEsuLi4sGjRImbMmIG1tbWc0ysHdDodV69eZcmSJdjZ2ZGfn2845178+lWtWpUp\nU6YwevRoDh8+jKOjIw4ODoaBj+LJyI0vRKl9++23HD161HDuDiApKYnw8HCOHTvGvHnz6N27t6F9\nbm4uVlZWxipXlIFff2mL519qaioTJkzAxsaGtLQ0lFI0b94cExMTNE0jPz/f0MXs6OjIwoULjV1y\nhSL9fKJU8vLy+Prrr5kxYwbHjx83PO7h4cG7775Lq1at+Pjjj9m8ebNhmYRtxSdhW744Ozvz2Wef\nkZubS0pKCmlpaVhbW5ORkUFGRgZ5eXlkZ2dz9+5dhg0bZuxyKxw5whX/U3E3cl5eHpaWlly6dImI\niAiWLFnCli1bCAgIMLRNSkpi7ty5ZGRkEB8fj42NjdzKT4jnVGpqKu+//z75+fksWrQINzc3Y5f0\nQjCdVTwbuBC/Uhy2e/fuZeXKlTg5OeHi4oKrqys6nc5w4XvxyOM6derg6urKqFGjsLOzk7AV4jlW\no0YNvLy82Lt3L/Hx8Tg5ORnuew1y7+tnRbqUxUNpmsZXX33FwIEDSwyKcXJyYurUqQwbNoyRI0ey\nbds2wzqvvvqqzGcrRDnRqFEjli1bhrm5OVOmTOHo0aOGZRK2z4YErnioEydOMHbsWJYuXUpISIhh\ndp9bt25Rt25dw4TUAQEBxMXFGblaIcQf4eLiwsKFC6lbty516tQxdjkVnlwWJB7qp59+wsXFhT/9\n6U/k5OSwZcsWYmJiuHHjBm3atGHRokVMnToVa2trufRHiHLM1dWVmJgYGQBXBmTQlDD49XmbuLg4\n+vbty0cffURcXBz169enQYMG1KlTh1WrVvG3v/3NcL2tubm5kSsXQojnnxzhiocOkPD39+fTTz8l\nMTGRrl27MmrUKJo3b05BQQGxsbHodDoACVshhCglCdwXXHHY/vOf/2T79u0UFhbSqFEjxo8fT1BQ\nEFlZWdja2hraz549m+zsbLnNmxBCPCbpUhZ8/fXXjBo1il69elFYWMiZM2fw9vZm9erVAOj1etat\nW8eRI0fYtm0b+/btw8PDw8hVCyFE+SJHuC+448ePExgYSGhoKGPGjOHcuXN06NCB6Oho7t69y+bN\nmzExMUGv13P79m0OHjxI06ZNjV22EEKUO3KE+4LQ6/WYmJiglEIpZZi9Jzo6mkOHDhEZGcmVK1fo\n1KkTnTp1ol27dkyYMIGhQ4eyatUqAO7fvy/zYQohxB8kgfsCKA7b8+fPs2zZMq5du0bbtm2ZPHky\nAN999x0tW7bE39+f2rVrs27dOn7++Wfatm1LamoqgwYN4ssvv5S7zwghxBOQG19UcMVhe/LkSdq3\nb8/Vq1epVKkS06ZNIzQ0FIBWrVpx9epV0tPTeeutt4AH89d6e3uzfv16goODAbn7jBBCPAk5h1uB\nFYftqVOnaNOmDR988AHBwcHo9Xpq1qzJjRs3DBMTWFpaotPp2LJlCy1atGDhwoWkpKSwePFiatWq\nZexdEUKIck+6lCu49PR0WrZsSefOnYmNjTU8PmjQIFJSUsjLy6NBgwYEBARw//59Fi5ciKmpKfn5\n+ezZs0dGIwshxFMiXcoVXFFREQ0bNkSn03H48GEA5s+fz86dO+nfvz+TJ0/m8uXLLF++HE9PT/bv\n309ERATfffedhK0QQjxFcoT7Arhw4QITJ07EwsKC2rVrs2PHDqKjo+nWrRsAaWlpNGzYkMjISEaP\nHm3kaoUQomKSI9wXgIuLC5999hm5ubnExMQwZcoUunXrhlKKgoICzMzMcHNzo3r16sCDu08JIYR4\nuiRwXxCNGjVixYoV+Pr68u2335KQkICmaZibmxMZGUl2djbe3t6AjEYWQohnQbqUXzDF3ctKKebN\nm0d8fDwzZ87kyJEjcs5WCCGeIQncF9CFCxcIDAzk2LFjZGZm8q9//QtPT09jlyWEEBWadCm/gFxc\nXAgLC8PHx4ekpCQJWyGEKANyhPsCk8njhRCi7EjgCiGEEGVAupSFEEKIMiCBK4QQQpQBCVwhhBCi\nDEjgCiGEEGVAAlcIIYQoAxK4QgghRBmQwBXiBbV27VpsbW2feDuaprFt27anUNGTu3z5MpqmceLE\nCQAOHDiApmlkZWUBT7bPv922EI9LAleIcmrkyJH07dvX2GU80siRI9E0DU3TsLCwwNnZmTlz5lBY\nWPjMn7tt27Zcv36datWqPfG26tWrx/Xr12nevPlTqEy8iMyMXYAQouLr0aMHUVFR6HQ6du/ezfjx\n4zE3NycoKOh3bYuKitA0DROTJz8esLCwwN7e/om3A2BqavrUtiVeTHKEK0QFtXjxYtzc3LCxsaFe\nvXqMGzeOnJyc37Xbtm0bLi4uWFpa0r17d9LT00ss3759Oy1btsTS0pKXX36Z2bNnP/bRaaVKlbC3\nt8fJyYmxY8fy2muvsWPHDuC/3bw7duygadOmVKpUiStXrgCwevVqmjRpgqWlJa6urnz++ecltnvs\n2DE8PDywtLTEy8uLpKSkEst/26X8W7du3cLLy4t+/fqh0+nIzMzkzTffpFatWlhZWeHi4kJUVBQg\nXcriyckRrhAVlImJCeHh4TRs2JAff/yRcePGMWXKlBKh9csvvxAcHMz69euxsLBg3LhxDBo0iMOH\nDwOQkJDA8OHDCQ8Px9fXl4sXL/LOO+8AMHPmzD9cm5WVFbdv3y5RR2hoKKtXr8bOzo7atWsTExPD\nJ598QkREBB4eHiQlJTF69GhsbGwYMWIEOTk5+Pv74+fnxxdffMGlS5d47733Sl1Deno6fn5++Pj4\nsGbNGkxNTZk0aRJnz55lz5491KxZk9TUVHJzc//wfgpRghJClEsjRoxQffr0KXX7zZs3Kzs7O8Pf\nUVFRClCJiYmGx5KTkxWgjh49qpRSqmvXriokJKTEdqKjo1WdOnUMfwPq66+/LlWder1excfHq0qV\nKqnJkyeXqOPEiRMl1nvllVfUl19+WeKxTz/9VLVp00YppVRkZKSys7NTubm5huUrVqxQgEpKSlJK\nKfWPf/xDASozM9PwXNWqVVPnzp1T9erVUxMnTlR6vd6wfq9evdSoUaMeuh+XLl0qsW0hHpcc4QpR\nQe3fv5958+Zx7tw57t27R2FhIXl5efzyyy9YW1sDYGZmRqtWrQzruLq6YmtrS3JyMq1bt+bkyZMc\nPnyY4OBgQ5uioqLfbedR4uLiqFy5MgUFBej1eoYMGcKsWbMMyy0sLHB3dzf8ff/+fS5evMjbb7/N\n6NGjDY8XFhYaBkAlJyfj7u6OpaWlYXmbNm0eWUtubi6+vr4MGTKEpUuXllg2duxY+vfvz/fff0+3\nbt3o27cvbdu2LdU+CvEoErhCVECXL1/G39+fsWPHEhwcTI0aNTh06BBvv/02+fn5pQ7KnJwcZs+e\nTUBAwO+W/TroHqVz586sWLECCwsLHBwcMDMr+dVjZWWFpmklnhdg1apVeHt7l2hrampa6ud9mEqV\nKvHaa68RFxfHhx9+iKOjo2HZ66+/TlpaGrt37yY+Pp6uXbsyfvx4wsLCnug5hQAJXCEqpH//+9/o\n9XoWLVpkGO0bGxv7u3aFhYUcP36c1q1bA5CSkkJWVhZNmjQBoGXLlqSkpODs7PxE9djY2DzWNl56\n6SUcHBz48ccfefPNNx/apkmTJkRHR5OXl2cI/8TExEdu28TEhOjoaIYMGULnzp05cOAADg4OhuW1\natVixIgRjBgxAl9fXz788EMJXPFUSOAKUY7dvXv3d6Nm7ezscHZ2pqCggGXLltGrVy8OHz7MypUr\nf7e+ubk57777LuHh4ZiZmTFhwgR8fHwMAfzJJ5/g7+9P/fr1GTBgACYmJpw8eZIzZ84wd+7cZ7pv\ns2fPZuLEiVSrVo0ePXqg0+k4fvw4mZmZBAYGMmTIEKZPn87o0aMJCgri8uXLpQ5GU1NTYmJiGDx4\nMF26dOHAgQPY29vzySef4OnpSbNmzdDpdMTFxRl+fAjxpOSyICHKsQMHDuDh4VHi3+zZs3n11VdZ\nvHgxoaGhNG/enJiYGObNm/e79a2trZk6dSpDhgyhXbt2VK5cmU2bNhmWd+/enbi4OPbt20erVq3w\n8fFhyZIlODk5PfN9+8tf/sLq1auJiorCzc2Njh07snbtWho2bAhA5cqV2blzJ6dPn8bDw4Pp06cT\nGhpa6u2bmZmxYcMGmjVrRpcuXbh58yYWFhYEBQXh7u5Ohw4dMDU1ZePGjc9qF8ULRlNKKWMXIYQQ\nQlR0coQrhBBClAEJXCGEEKIMSOAKIYQQZUACVwghhCgDErhCCCFEGZDAFUIIIcqABK4QQghRBiRw\nhRBCiDIggSuEEEKUAQlcIYQQogxI4AohhBBlQAJXCCGEKAP/BxEfJBlfebGgAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mwebZKdOiXvQ",
        "colab_type": "text"
      },
      "source": [
        "# Menyimpan dan Konversi ke \".tflite\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3lLh9E8riyYZ",
        "colab_type": "text"
      },
      "source": [
        "Menyimpan model menggunakan tf.saved_model.save dan kemudian mengonversi model tersimpan ke format yang kompatibel tf lite."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G6Qxay0ei0jm",
        "colab_type": "code",
        "outputId": "c86f8e76-5f8c-478b-c929-2b89f721eca3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        }
      },
      "source": [
        "saved_model_dir = 'save/model'\n",
        "tf.saved_model.save(model, saved_model_dir)\n",
        "\n",
        "converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_dir)\n",
        "tflite_model = converter.convert()\n",
        "\n",
        "with open('Klasifikasi_3_Varietas_Beras_MobileNet_nf.tflite', 'wb') as f:\n",
        "  f.write(tflite_model)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /tensorflow-2.1.0/python3.6/tensorflow_core/python/ops/resource_variable_ops.py:1786: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "If using Keras pass *_constraint arguments to layers.\n",
            "INFO:tensorflow:Assets written to: save/model/assets\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}