notebooks/train_document_classifier.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "train_document_classifier.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "gpuClass": "standard",
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.image as mpimg\n",
        "from PIL import Image\n",
        "import numpy as np\n",
        "import glob\n",
        "import json\n",
        "from pathlib import Path\n",
        "import random"
      ],
      "metadata": {
        "id": "TXiQt772Cxoe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Creating the Dataset\n"
      ],
      "metadata": {
        "id": "400w_4vXCFUw"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VpHMhKhj9Qco",
        "outputId": "3a0f7533-d8b1-4aa6-dba1-8f286ce97ec4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading selfieimagedetectiondataset.zip to /content\n",
            "100% 2.81G/2.81G [00:22<00:00, 97.6MB/s]\n",
            "100% 2.81G/2.81G [00:22<00:00, 136MB/s] \n"
          ]
        }
      ],
      "source": [
        "#downloding selfies and invalid photos\n",
        "!mkdir ~/.kaggle\n",
        "!touch ~/.kaggle/kaggle.json\n",
        "\n",
        "api_token = {\"username\":\"yourkaggleusename\",\"key\":\"yourkaggleapikey\"}\n",
        "\n",
        "\n",
        "with open('/root/.kaggle/kaggle.json', 'w') as file:\n",
        "    json.dump(api_token, file)\n",
        "\n",
        "!chmod 600 ~/.kaggle/kaggle.json\n",
        "\n",
        "!kaggle datasets download -d jigrubhatt/selfieimagedetectiondataset"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#downloding documents photos\n",
        "!gdown --id 1Oi88TRcpdjZmJ79WDLb9qFlBNG8q2De6\n",
        "\n",
        "!unzip 'BID Dataset.zip'"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qJcYSV8N-vSw",
        "outputId": "69f850b6-342f-491c-a09b-544843e25f99"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
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            "  inflating: BID Dataset/RG_Verso/022229888_gt_ocr.txt  \n",
            "  inflating: BID Dataset/RG_Verso/022229888_gt_segmentation.jpg  \n",
            "  inflating: BID Dataset/RG_Verso/022229888_in.jpg  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#selecting only the pictures and moving it to a folder:\n",
        "\n",
        "files = glob.glob(\"BID Dataset/**/*_in.jpg\", recursive=True)\n",
        "\n",
        "!mkdir documents\n",
        "\n",
        "for i in files:\n",
        "  Path(i).rename(f\"documents/{i.split('/')[-1]}\")"
      ],
      "metadata": {
        "id": "p3nrcBzJ_FoY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#moving train selfies to a 'selfies' folder:\n",
        "\n",
        "!unzip /content/selfieimagedetectiondataset.zip\n",
        "\n",
        "files = glob.glob(\"Selfie-Image-Detection-Dataset/Training_data/Selfie/*.jpg\", recursive=True)\n",
        "\n",
        "!mkdir selfies\n",
        "\n",
        "for i in files:\n",
        "  Path(i).rename(f\"selfies/{i.split('/')[-1]}\")"
      ],
      "metadata": {
        "id": "4OWPby6i_YZh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5dfbfe33-b0c5-4161-ab5a-996b134a081d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#creating a folder and storing the test selfies\n",
        "\n",
        "files_test = glob.glob(\"/content/Selfie-Image-Detection-Dataset/Test_data/Selfie/*.jpg\", recursive=True)\n",
        "\n",
        "!mkdir selfies_test\n",
        "\n",
        "for i in files_test:\n",
        "  Path(i).rename(f\"selfies_test/{i.split('/')[-1]}\")\n",
        "\n",
        "files_test = glob.glob(\"/content/documents/*.jpg\", recursive=True)"
      ],
      "metadata": {
        "id": "c7dKaebI_c2B"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "documents_test = []\n",
        "\n",
        "while len(documents_test) < (len(files_test) * 0.2):\n",
        "  random_photo = random.choice(files_test)\n",
        "  if random_photo not in documents_test:\n",
        "    documents_test.append(random_photo)"
      ],
      "metadata": {
        "id": "X7FKnIFF_sqG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir documents_test"
      ],
      "metadata": {
        "id": "qCSGA6Np_uYk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i in documents_test:\n",
        "  Path(i).rename(f\"documents_test/{i.split('/')[-1]}\")"
      ],
      "metadata": {
        "id": "5nDSWxDg_v4o"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir invalid"
      ],
      "metadata": {
        "id": "Zu3mpQapAnPi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "invalid_files = glob.glob(\"Selfie-Image-Detection-Dataset/Training_data/NonSelfie/*.jpg\", recursive=True)"
      ],
      "metadata": {
        "id": "WHy-nCQRAnaX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i in invalid_files:\n",
        "  Path(i).rename(f\"invalid/{i.split('/')[-1]}\")"
      ],
      "metadata": {
        "id": "eRiOnLZoAnhh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir invalid_test"
      ],
      "metadata": {
        "id": "Q1bnGAx7B4QW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "invalid_test = glob.glob(\"Selfie-Image-Detection-Dataset/Test_data/NonSelfie/*.jpg\", recursive=True)"
      ],
      "metadata": {
        "id": "tb-erpqRB6zf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i in invalid_test:\n",
        "  Path(i).rename(f\"invalid_test/{i.split('/')[-1]}\")"
      ],
      "metadata": {
        "id": "NqKP2z1jCBIt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!rm -rf 'BID Dataset' 'BID Dataset.zip' Selfie-Image-Detection-Dataset selfieimagedetectiondataset.zip"
      ],
      "metadata": {
        "id": "3Ut7XdYm_xvp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.image as mpimg\n",
        "from PIL import Image\n",
        "import numpy as np\n",
        "import glob\n",
        "import json\n",
        "from pathlib import Path\n",
        "import random"
      ],
      "metadata": {
        "id": "WbrjmferljtS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#storing in memory the images location\n",
        "\n",
        "files_train = glob.glob(\"selfies/*.jpg\", recursive=True)\n",
        "docs_train = glob.glob(\"documents/*.jpg\", recursive=True)\n",
        "invalid_train = glob.glob(\"invalid/*.jpg\", recursive=True)\n",
        "\n",
        "#checking the ammount of samples\n",
        "len(files_train) + len(docs_train) + len(invalid_train)"
      ],
      "metadata": {
        "id": "MSW4s29wCRJS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d0cfc382-f976-4245-d585-6bd09d74b840"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "85935"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Preparing images to the model"
      ],
      "metadata": {
        "id": "QB-1A17tFODd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#size of the resized image\n",
        "SIZE = 64\n",
        "\n",
        "#reading images\n",
        "raw_img = tf.io.read_file(f\"{files_train[0]}\")\n",
        "img = tf.io.decode_image(raw_img, dtype=tf.float32, channels=3)\n",
        "\n",
        "#converting it to grayscale\n",
        "img_gray = tf.image.rgb_to_grayscale(img)\n",
        "\n",
        "#resizing to the SIZE value\n",
        "image_resized = tf.image.resize(img_gray, (SIZE, SIZE))\n",
        "train_images = np.asarray(image_resized).reshape(1, SIZE, SIZE, 1)\n",
        "\n",
        "#doing the process to all images and append them to a list\n",
        "train_images_list = []\n",
        "train_images_list.append(train_images)\n",
        "\n",
        "j = 1\n",
        "for  i in (files_train[1:] + docs_train + invalid_train):\n",
        "  raw_img = tf.io.read_file(f\"{i}\")\n",
        "  img = tf.io.decode_image(raw_img, dtype=tf.float32, channels=3)\n",
        "  img_gray = tf.image.rgb_to_grayscale(img)\n",
        "  image_resized = tf.image.resize(img_gray, (SIZE, SIZE))\n",
        "  train_sample = np.asarray(image_resized).reshape(1, SIZE, SIZE, 1)\n",
        "  train_images_list.append(train_sample)\n",
        "\n",
        "  j += 1\n",
        "  if (j % 1000) == 0:\n",
        "     \n",
        "    print(j)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "toUJZiwbDSih",
        "outputId": "1e0912c0-e0f4-47ce-a8e0-99081a554232"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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    },
    {
      "cell_type": "code",
      "source": [
        "#reshaping it to a array\n",
        "train_images = np.array(train_images_list).reshape((85935,  64, 64, 1))"
      ],
      "metadata": {
        "id": "uTRHbUNODuSY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#free memory space\n",
        "del train_images_list"
      ],
      "metadata": {
        "id": "h_E1n3VowUy0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#creating the label\n",
        "label_selfies = [0 for i in range(len(files_train))] \n",
        "label_docs = [1 for i in range(len(docs_train))] \n",
        "label_invalid = [2 for i in range(len(invalid_train))] \n",
        "\n",
        "train_labels = np.array(label_selfies + label_docs + label_invalid).reshape(-1, 1)\n",
        "\n",
        "print(train_labels.shape)"
      ],
      "metadata": {
        "id": "1aPbyYOHD9k0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a0ba17cb-3980-4402-e573-af91aaafcd13"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(85935, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tf.keras.backend.clear_session()"
      ],
      "metadata": {
        "id": "7M9iTJePYKoI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Training the model and testing it"
      ],
      "metadata": {
        "id": "Dtg6cwt6Fuzd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#setting a random seed\n",
        "tf.random.set_seed(42)"
      ],
      "metadata": {
        "id": "g2Q4i3uyEqQ0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = tf.keras.models.Sequential()\n",
        "model.add(tf.keras.layers.Conv2D(64, (3, 3), activation='relu', input_shape=(SIZE, SIZE, 1)))\n",
        "model.add(tf.keras.layers.MaxPooling2D((2, 2)))\n",
        "model.add(tf.keras.layers.RandomCrop(16, 16))\n",
        "model.add(tf.keras.layers.Conv2D(128, (3, 3), activation='relu'))\n",
        "model.add(tf.keras.layers.MaxPooling2D((2, 2)))\n",
        "model.add(tf.keras.layers.Dropout(.1))\n",
        "\n",
        "model.add(tf.keras.layers.Flatten())\n",
        "model.add(tf.keras.layers.Dense(256, activation='relu'))\n",
        "model.add(tf.keras.layers.Dense(3))\n",
        "\n",
        "\n",
        "model.compile(optimizer='adam',\n",
        "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), \n",
        "              metrics=['accuracy'])\n",
        "\n",
        "history = model.fit(train_images, \n",
        "                    train_labels,\n",
        "                    validation_split=0.2, \n",
        "                    callbacks=tf.keras.callbacks.EarlyStopping(patience=10), \n",
        "                    epochs=300, \n",
        "                    batch_size=64)\n",
        "\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "s7bU2nf7ErqO",
        "outputId": "b79f862c-767f-40d8-aa49-4492f43b7551"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/300\n",
            "1075/1075 [==============================] - 16s 14ms/step - loss: 0.3494 - accuracy: 0.8649 - val_loss: 1.3735 - val_accuracy: 0.4132\n",
            "Epoch 2/300\n",
            "1075/1075 [==============================] - 15s 14ms/step - loss: 0.2258 - accuracy: 0.9140 - val_loss: 1.0140 - val_accuracy: 0.5880\n",
            "Epoch 3/300\n",
            "1075/1075 [==============================] - 14s 13ms/step - loss: 0.2052 - accuracy: 0.9231 - val_loss: 0.6866 - val_accuracy: 0.7392\n",
            "Epoch 4/300\n",
            "1075/1075 [==============================] - 14s 13ms/step - loss: 0.1878 - accuracy: 0.9288 - val_loss: 1.4296 - val_accuracy: 0.4596\n",
            "Epoch 5/300\n",
            "1075/1075 [==============================] - 14s 13ms/step - loss: 0.1765 - accuracy: 0.9338 - val_loss: 0.7572 - val_accuracy: 0.7072\n",
            "Epoch 6/300\n",
            "1075/1075 [==============================] - 14s 13ms/step - loss: 0.1691 - accuracy: 0.9367 - val_loss: 0.8440 - val_accuracy: 0.6672\n",
            "Epoch 7/300\n",
            "1075/1075 [==============================] - 15s 14ms/step - loss: 0.1617 - accuracy: 0.9391 - val_loss: 0.9967 - val_accuracy: 0.6527\n",
            "Epoch 8/300\n",
            "1075/1075 [==============================] - 15s 14ms/step - loss: 0.1558 - accuracy: 0.9414 - val_loss: 0.3831 - val_accuracy: 0.8688\n",
            "Epoch 9/300\n",
            "1075/1075 [==============================] - 15s 14ms/step - loss: 0.1503 - accuracy: 0.9438 - val_loss: 0.6223 - val_accuracy: 0.7677\n",
            "Epoch 10/300\n",
            "1075/1075 [==============================] - 16s 14ms/step - loss: 0.1482 - accuracy: 0.9440 - val_loss: 0.8107 - val_accuracy: 0.7126\n",
            "Epoch 11/300\n",
            "1075/1075 [==============================] - 14s 13ms/step - loss: 0.1427 - accuracy: 0.9461 - val_loss: 0.4816 - val_accuracy: 0.8325\n",
            "Epoch 12/300\n",
            "1075/1075 [==============================] - 15s 14ms/step - loss: 0.1443 - accuracy: 0.9457 - val_loss: 0.4997 - val_accuracy: 0.8219\n",
            "Epoch 13/300\n",
            "1075/1075 [==============================] - 15s 14ms/step - loss: 0.1396 - accuracy: 0.9472 - val_loss: 0.4969 - val_accuracy: 0.8060\n",
            "Epoch 14/300\n",
            "1075/1075 [==============================] - 15s 14ms/step - loss: 0.1388 - accuracy: 0.9468 - val_loss: 0.3551 - val_accuracy: 0.8864\n",
            "Epoch 15/300\n",
            "1075/1075 [==============================] - 15s 14ms/step - loss: 0.1329 - accuracy: 0.9492 - val_loss: 0.6423 - val_accuracy: 0.7790\n",
            "Epoch 16/300\n",
            "1075/1075 [==============================] - 15s 14ms/step - loss: 0.1338 - accuracy: 0.9491 - val_loss: 1.1701 - val_accuracy: 0.6827\n",
            "Epoch 17/300\n",
            "1075/1075 [==============================] - 15s 14ms/step - loss: 0.1325 - accuracy: 0.9498 - val_loss: 0.3719 - val_accuracy: 0.8721\n",
            "Epoch 18/300\n",
            "1075/1075 [==============================] - 14s 13ms/step - loss: 0.1294 - accuracy: 0.9499 - val_loss: 0.6929 - val_accuracy: 0.7674\n",
            "Epoch 19/300\n",
            "1075/1075 [==============================] - 15s 14ms/step - loss: 0.1278 - accuracy: 0.9511 - val_loss: 0.5730 - val_accuracy: 0.8016\n",
            "Epoch 20/300\n",
            "1075/1075 [==============================] - 14s 13ms/step - loss: 0.1272 - accuracy: 0.9511 - val_loss: 0.6098 - val_accuracy: 0.7916\n",
            "Epoch 21/300\n",
            "1075/1075 [==============================] - 14s 13ms/step - loss: 0.1286 - accuracy: 0.9510 - val_loss: 0.6353 - val_accuracy: 0.7516\n",
            "Epoch 22/300\n",
            "1075/1075 [==============================] - 14s 13ms/step - loss: 0.1244 - accuracy: 0.9523 - val_loss: 0.3359 - val_accuracy: 0.8923\n",
            "Epoch 23/300\n",
            "1075/1075 [==============================] - 14s 13ms/step - loss: 0.1212 - accuracy: 0.9531 - val_loss: 0.4714 - val_accuracy: 0.8200\n",
            "Epoch 24/300\n",
            "1075/1075 [==============================] - 14s 13ms/step - loss: 0.1201 - accuracy: 0.9536 - val_loss: 0.2805 - val_accuracy: 0.9082\n",
            "Epoch 25/300\n",
            "1075/1075 [==============================] - 15s 14ms/step - loss: 0.1203 - accuracy: 0.9545 - val_loss: 0.1778 - val_accuracy: 0.9425\n",
            "Epoch 26/300\n",
            "1075/1075 [==============================] - 15s 14ms/step - loss: 0.1188 - accuracy: 0.9550 - val_loss: 0.4624 - val_accuracy: 0.8398\n",
            "Epoch 27/300\n",
            "1075/1075 [==============================] - 14s 13ms/step - loss: 0.1162 - accuracy: 0.9558 - val_loss: 0.4960 - val_accuracy: 0.8363\n",
            "Epoch 28/300\n",
            "1075/1075 [==============================] - 14s 13ms/step - loss: 0.1148 - accuracy: 0.9558 - val_loss: 0.4358 - val_accuracy: 0.8564\n",
            "Epoch 29/300\n",
            "1075/1075 [==============================] - 15s 14ms/step - loss: 0.1168 - accuracy: 0.9550 - val_loss: 0.2824 - val_accuracy: 0.9116\n",
            "Epoch 30/300\n",
            "1075/1075 [==============================] - 14s 13ms/step - loss: 0.1140 - accuracy: 0.9558 - val_loss: 0.2570 - val_accuracy: 0.9179\n",
            "Epoch 31/300\n",
            "1075/1075 [==============================] - 14s 13ms/step - loss: 0.1143 - accuracy: 0.9571 - val_loss: 0.2815 - val_accuracy: 0.9126\n",
            "Epoch 32/300\n",
            "1075/1075 [==============================] - 14s 13ms/step - loss: 0.1135 - accuracy: 0.9562 - val_loss: 0.3067 - val_accuracy: 0.9107\n",
            "Epoch 33/300\n",
            "1075/1075 [==============================] - 14s 13ms/step - loss: 0.1127 - accuracy: 0.9571 - val_loss: 0.3702 - val_accuracy: 0.8770\n",
            "Epoch 34/300\n",
            "1075/1075 [==============================] - 14s 13ms/step - loss: 0.1107 - accuracy: 0.9576 - val_loss: 0.1423 - val_accuracy: 0.9543\n",
            "Epoch 35/300\n",
            "1075/1075 [==============================] - 15s 14ms/step - loss: 0.1091 - accuracy: 0.9583 - val_loss: 0.2639 - val_accuracy: 0.9061\n",
            "Epoch 36/300\n",
            "1075/1075 [==============================] - 15s 13ms/step - loss: 0.1087 - accuracy: 0.9590 - val_loss: 0.2802 - val_accuracy: 0.9077\n",
            "Epoch 37/300\n",
            "1075/1075 [==============================] - 14s 13ms/step - loss: 0.1086 - accuracy: 0.9588 - val_loss: 0.3780 - val_accuracy: 0.8779\n",
            "Epoch 38/300\n",
            "1075/1075 [==============================] - 15s 14ms/step - loss: 0.1064 - accuracy: 0.9595 - val_loss: 0.4978 - val_accuracy: 0.8404\n",
            "Epoch 39/300\n",
            "1075/1075 [==============================] - 15s 14ms/step - loss: 0.1068 - accuracy: 0.9594 - val_loss: 0.1969 - val_accuracy: 0.9479\n",
            "Epoch 40/300\n",
            "1075/1075 [==============================] - 14s 13ms/step - loss: 0.1079 - accuracy: 0.9582 - val_loss: 0.2671 - val_accuracy: 0.9183\n",
            "Epoch 41/300\n",
            "1075/1075 [==============================] - 14s 13ms/step - loss: 0.1065 - accuracy: 0.9597 - val_loss: 0.4183 - val_accuracy: 0.8627\n",
            "Epoch 42/300\n",
            "1075/1075 [==============================] - 14s 13ms/step - loss: 0.1055 - accuracy: 0.9600 - val_loss: 0.2981 - val_accuracy: 0.9075\n",
            "Epoch 43/300\n",
            "1075/1075 [==============================] - 14s 13ms/step - loss: 0.1039 - accuracy: 0.9604 - val_loss: 0.4365 - val_accuracy: 0.8477\n",
            "Epoch 44/300\n",
            "1075/1075 [==============================] - 14s 13ms/step - loss: 0.1072 - accuracy: 0.9588 - val_loss: 0.3741 - val_accuracy: 0.8808\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f64160554d0>]"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#testing by making a simple prediction\n",
        "model.predict(train_images[88].reshape(-1, SIZE, SIZE, 1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2_RtYkflExuD",
        "outputId": "4007f862-c651-4044-b860-f4ce29c68816"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 5.041043 , -8.575204 ,  2.2043731]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#saving the model to a folder\n",
        "model.save('model_v1')\n",
        "\n",
        "#zipping the folder so we can download it\n",
        "!zip -r model_1.zip model_v1"
      ],
      "metadata": {
        "id": "earc3sWMFJcZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8a8d1a1c-cb74-4bbe-be2b-ac600c703169"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "updating: model_v1/ (stored 0%)\n",
            "updating: model_v1/assets/ (stored 0%)\n",
            "updating: model_v1/variables/ (stored 0%)\n",
            "updating: model_v1/variables/variables.data-00000-of-00001 (deflated 22%)\n",
            "updating: model_v1/variables/variables.index (deflated 63%)\n",
            "updating: model_v1/keras_metadata.pb (deflated 90%)\n",
            "updating: model_v1/saved_model.pb (deflated 88%)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "m0AKMpTZnXrM"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}