ANN.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tMj-_UzAs0b1"
      },
      "outputs": [],
      "source": [
        "from numpy import genfromtxt\n",
        "import numpy as np\n",
        "import random as rand\n",
        "def predict(x,theta):\n",
        "  return x.dot(theta)\n",
        "\n",
        "def gradient(o,y,x):\n",
        "  return 2*x*(o-y)\n",
        "\n",
        "def update_weight(theta,alpha,dtheta):\n",
        "  return theta - alpha*dtheta\n",
        "\n",
        "data = genfromtxt('advertising.csv',delimiter=',')\n",
        "X = data[1:,:3]\n",
        "Y = data[1:,3]\n",
        "\n",
        "#init weight\n",
        "alpha = 0.000001\n",
        "\n",
        "theta = np.array([rand.uniform(-1,1) for _ in range(4)])\n",
        "print(theta)\n",
        "\n",
        "epoch_max = 1\n",
        "\n",
        "for epoch in range(epoch_max):\n",
        "  for i in range(len(X)):\n",
        "    x = list(X[i])[:]\n",
        "    x.append(1)\n",
        "    x = np.array(x)\n",
        "    y = Y[i]\n",
        "\n",
        "    o = predict(x,theta)  \n",
        "    loss = (o-y)**2\n",
        "    print(loss)\n",
        "\n",
        "    dtheta = gradient(o,y,x)\n",
        "\n",
        "    theta = update_weight(theta,alpha,dtheta)\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gR7MTtFFLjMy",
        "outputId": "24d9efda-4209-4b8b-d2f2-e33daa4c5f14"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "tf.Tensor(\n",
            "[[8. 8.]\n",
            " [8. 8.]], shape=(2, 2), dtype=float32)\n"
          ]
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "\n",
        "x = tf.ones((2,2))\n",
        "\n",
        "with tf.GradientTape() as t:\n",
        "  t.watch(x)\n",
        "  y = tf.reduce_sum(x)\n",
        "  z = tf.multiply(y,y)\n",
        "\n",
        "dz_dx = t.gradient(z,x)\n",
        "print(dz_dx)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "elfZs1eruiVg",
        "outputId": "b62d714f-4706-4b01-ee6b-636b8767fca4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Ver: 2.11.0\n",
            "Keras Ver: 2.11.0\n"
          ]
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "\n",
        "print(\"Ver:\",tf.__version__)\n",
        "print(\"Keras Ver:\",tf.keras.__version__)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ab6D1H0lv3Lt",
        "outputId": "ef3ea2de-e10e-4e31-d002-999cb6dce580"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From <ipython-input-2-98ee14e75079>:2: is_gpu_available (from tensorflow.python.framework.test_util) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.config.list_physical_devices('GPU')` instead.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "GPU available: True\n"
          ]
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "print(\"GPU available:\",tf.test.is_gpu_available())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i2KoB7DL3aO2",
        "outputId": "53dd2220-9444-47ad-f6ac-b64819083e59"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "tf.Tensor([16  9  4  1], shape=(4,), dtype=int32)\n"
          ]
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "\n",
        "x = [1,2,3,4]\n",
        "y = 5\n",
        "\n",
        "sd = tf.math.squared_difference(x,y)\n",
        "print(sd)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LVhCgGUb7bk6",
        "outputId": "85f8871d-c4ec-462b-d176-30ac24326231"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "tf.Tensor([-8.], shape=(1,), dtype=float32)\n"
          ]
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "\n",
        "x = tf.Variable([1.0])\n",
        "\n",
        "with tf.GradientTape() as t:\n",
        "  t.watch(x)\n",
        "  y = x - 5.0\n",
        "  l = tf.multiply(y,y)\n",
        "\n",
        "dl_dx = t.gradient(l,x)\n",
        "print(dl_dx)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XgDYSTVFvEoq",
        "outputId": "12eb5d86-b4b1-4af3-c05b-8124d5d75d24"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[['5.1', '3.5', '1.4', '0.2'], ['4.9', '3.0', '1.4', '0.2'], ['4.7', '3.2', '1.3', '0.2'], ['4.6', '3.1', '1.5', '0.2'], ['5.0', '3.6', '1.4', '0.2'], ['5.4', '3.9', '1.7', '0.4'], ['4.6', '3.4', '1.4', '0.3'], ['5.0', '3.4', '1.5', '0.2'], ['4.4', '2.9', '1.4', '0.2'], ['4.9', '3.1', '1.5', '0.1'], ['5.4', '3.7', '1.5', '0.2'], ['4.8', '3.4', '1.6', '0.2'], ['4.8', '3.0', '1.4', '0.1'], ['4.3', '3.0', '1.1', '0.1'], ['5.8', '4.0', '1.2', '0.2'], ['5.7', '4.4', '1.5', '0.4'], ['5.4', '3.9', '1.3', '0.4'], ['5.1', '3.5', '1.4', '0.3'], ['5.7', '3.8', '1.7', '0.3'], ['5.1', '3.8', '1.5', '0.3'], ['5.4', '3.4', '1.7', '0.2'], ['5.1', '3.7', '1.5', '0.4'], ['4.6', '3.6', '1.0', '0.2'], ['5.1', '3.3', '1.7', '0.5'], ['4.8', '3.4', '1.9', '0.2'], ['5.0', '3.0', '1.6', '0.2'], ['5.0', '3.4', '1.6', '0.4'], ['5.2', '3.5', '1.5', '0.2'], ['5.2', '3.4', '1.4', '0.2'], ['4.7', '3.2', '1.6', '0.2'], ['4.8', '3.1', '1.6', '0.2'], ['5.4', '3.4', '1.5', '0.4'], ['5.2', '4.1', '1.5', '0.1'], ['5.5', '4.2', '1.4', '0.2'], ['4.9', '3.1', '1.5', '0.1'], ['5.0', '3.2', '1.2', '0.2'], ['5.5', '3.5', '1.3', '0.2'], ['4.9', '3.1', '1.5', '0.1'], ['4.4', '3.0', '1.3', '0.2'], ['5.1', '3.4', '1.5', '0.2'], ['5.0', '3.5', '1.3', '0.3'], ['4.5', '2.3', '1.3', '0.3'], ['4.4', '3.2', '1.3', '0.2'], ['5.0', '3.5', '1.6', '0.6'], ['5.1', '3.8', '1.9', '0.4'], ['4.8', '3.0', '1.4', '0.3'], ['5.1', '3.8', '1.6', '0.2'], ['4.6', '3.2', '1.4', '0.2'], ['5.3', '3.7', '1.5', '0.2'], ['5.0', '3.3', '1.4', '0.2'], ['7.0', '3.2', '4.7', '1.4'], ['6.4', '3.2', '4.5', '1.5'], ['6.9', '3.1', '4.9', '1.5'], ['5.5', '2.3', '4.0', '1.3'], ['6.5', '2.8', '4.6', '1.5'], ['5.7', '2.8', '4.5', '1.3'], ['6.3', '3.3', '4.7', '1.6'], ['4.9', '2.4', '3.3', '1.0'], ['6.6', '2.9', '4.6', '1.3'], ['5.2', '2.7', '3.9', '1.4'], ['5.0', '2.0', '3.5', '1.0'], ['5.9', '3.0', '4.2', '1.5'], ['6.0', '2.2', '4.0', '1.0'], ['6.1', '2.9', '4.7', '1.4'], ['5.6', '2.9', '3.6', '1.3'], ['6.7', '3.1', '4.4', '1.4'], ['5.6', '3.0', '4.5', '1.5'], ['5.8', '2.7', '4.1', '1.0'], ['6.2', '2.2', '4.5', '1.5'], ['5.6', '2.5', '3.9', '1.1'], ['5.9', '3.2', '4.8', '1.8'], ['6.1', '2.8', '4.0', '1.3'], ['6.3', '2.5', '4.9', '1.5'], ['6.1', '2.8', '4.7', '1.2'], ['6.4', '2.9', '4.3', '1.3'], ['6.6', '3.0', '4.4', '1.4'], ['6.8', '2.8', '4.8', '1.4'], ['6.7', '3.0', '5.0', '1.7'], ['6.0', '2.9', '4.5', '1.5'], ['5.7', '2.6', '3.5', '1.0'], ['5.5', '2.4', '3.8', '1.1'], ['5.5', '2.4', '3.7', '1.0'], ['5.8', '2.7', '3.9', '1.2'], ['6.0', '2.7', '5.1', '1.6'], ['5.4', '3.0', '4.5', '1.5'], ['6.0', '3.4', '4.5', '1.6'], ['6.7', '3.1', '4.7', '1.5'], ['6.3', '2.3', '4.4', '1.3'], ['5.6', '3.0', '4.1', '1.3'], ['5.5', '2.5', '4.0', '1.3'], ['5.5', '2.6', '4.4', '1.2'], ['6.1', '3.0', '4.6', '1.4'], ['5.8', '2.6', '4.0', '1.2'], ['5.0', '2.3', '3.3', '1.0'], ['5.6', '2.7', '4.2', '1.3'], ['5.7', '3.0', '4.2', '1.2'], ['5.7', '2.9', '4.2', '1.3'], ['6.2', '2.9', '4.3', '1.3'], ['5.1', '2.5', '3.0', '1.1'], ['5.7', '2.8', '4.1', '1.3'], ['6.3', '3.3', '6.0', '2.5'], ['5.8', '2.7', '5.1', '1.9'], ['7.1', '3.0', '5.9', '2.1'], ['6.3', '2.9', '5.6', '1.8'], ['6.5', '3.0', '5.8', '2.2'], ['7.6', '3.0', '6.6', '2.1'], ['4.9', '2.5', '4.5', '1.7'], ['7.3', '2.9', '6.3', '1.8'], ['6.7', '2.5', '5.8', '1.8'], ['7.2', '3.6', '6.1', '2.5'], ['6.5', '3.2', '5.1', '2.0'], ['6.4', '2.7', '5.3', '1.9'], ['6.8', '3.0', '5.5', '2.1'], ['5.7', '2.5', '5.0', '2.0'], ['5.8', '2.8', '5.1', '2.4'], ['6.4', '3.2', '5.3', '2.3'], ['6.5', '3.0', '5.5', '1.8'], ['7.7', '3.8', '6.7', '2.2'], ['7.7', '2.6', '6.9', '2.3'], ['6.0', '2.2', '5.0', '1.5'], ['6.9', '3.2', '5.7', '2.3'], ['5.6', '2.8', '4.9', '2.0'], ['7.7', '2.8', '6.7', '2.0'], ['6.3', '2.7', '4.9', '1.8'], ['6.7', '3.3', '5.7', '2.1'], ['7.2', '3.2', '6.0', '1.8'], ['6.2', '2.8', '4.8', '1.8'], ['6.1', '3.0', '4.9', '1.8'], ['6.4', '2.8', '5.6', '2.1'], ['7.2', '3.0', '5.8', '1.6'], ['7.4', '2.8', '6.1', '1.9'], ['7.9', '3.8', '6.4', '2.0'], ['6.4', '2.8', '5.6', '2.2'], ['6.3', '2.8', '5.1', '1.5'], ['6.1', '2.6', '5.6', '1.4'], ['7.7', '3.0', '6.1', '2.3'], ['6.3', '3.4', '5.6', '2.4'], ['6.4', '3.1', '5.5', '1.8'], ['6.0', '3.0', '4.8', '1.8'], ['6.9', '3.1', '5.4', '2.1'], ['6.7', '3.1', '5.6', '2.4'], ['6.9', '3.1', '5.1', '2.3'], ['5.8', '2.7', '5.1', '1.9'], ['6.8', '3.2', '5.9', '2.3'], ['6.7', '3.3', '5.7', '2.5'], ['6.7', '3.0', '5.2', '2.3'], ['6.3', '2.5', '5.0', '1.9'], ['6.5', '3.0', '5.2', '2.0'], ['6.2', '3.4', '5.4', '2.3'], ['5.9', '3.0', '5.1', '1.8']]\n",
            "['Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica']\n"
          ]
        }
      ],
      "source": [
        "X = []\n",
        "Y = []\n",
        "with open(\"iris.data\") as f:\n",
        "  for val in f.readlines():\n",
        "    l = val.strip()\n",
        "    spl = l.split(\",\")\n",
        "    if len(spl)>1:\n",
        "      X.append(spl[:-1])\n",
        "      Y.append(spl[-1])\n",
        "print(X)\n",
        "print(Y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LQ-dwpogE4lz",
        "outputId": "5e9a861a-5794-4b57-8526-59251c3bbeff"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "--2023-04-20 00:40:54--  https://storage.googleapis.com/mledu-datasets/cats_and_dogs_filtered.zip\n",
            "Resolving storage.googleapis.com (storage.googleapis.com)... 172.253.62.128, 172.253.122.128, 172.253.63.128, ...\n",
            "Connecting to storage.googleapis.com (storage.googleapis.com)|172.253.62.128|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 68606236 (65M) [application/zip]\n",
            "Saving to: ‘/tmp/cats_and_dogs_filtered.zip’\n",
            "\n",
            "/tmp/cats_and_dogs_ 100%[===================>]  65.43M   183MB/s    in 0.4s    \n",
            "\n",
            "2023-04-20 00:40:55 (183 MB/s) - ‘/tmp/cats_and_dogs_filtered.zip’ saved [68606236/68606236]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "!wget --no-check-certificate \\\n",
        "    https://storage.googleapis.com/mledu-datasets/cats_and_dogs_filtered.zip \\\n",
        "    -O /tmp/cats_and_dogs_filtered.zip"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tfJIZL3zFQN8"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import zipfile\n",
        "\n",
        "local_zip = '/tmp/cats_and_dogs_filtered.zip'\n",
        "zip_ref = zipfile.ZipFile(local_zip, 'r')\n",
        "zip_ref.extractall('/tmp')\n",
        "zip_ref.close()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4oQQF08hGPlx",
        "outputId": "f5981511-d7cd-481a-8d11-63b97741c2e1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2.12.0\n",
            "2.12.0\n"
          ]
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import os\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "\n",
        "from tensorflow.keras.preprocessing import image_dataset_from_directory\n",
        "\n",
        "print(tf.__version__)\n",
        "print(keras.__version__)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ft0DClndF1ZI",
        "outputId": "9af4f1bd-82ab-490f-8c9e-92d4d8c9e70a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Found 2000 files belonging to 2 classes.\n",
            "Found 1000 files belonging to 2 classes.\n",
            "['cats', 'dogs']\n",
            "['cats', 'dogs']\n"
          ]
        }
      ],
      "source": [
        "# training data\n",
        "train_dataset = image_dataset_from_directory('/tmp/cats_and_dogs_filtered/train',\n",
        "                                             shuffle=True,\n",
        "                                             batch_size=512,\n",
        "                                             image_size=(160, 160))\n",
        "\n",
        "# validation data\n",
        "validation_dataset = image_dataset_from_directory('/tmp/cats_and_dogs_filtered/validation',\n",
        "                                                  shuffle=True,\n",
        "                                                  batch_size=512,\n",
        "                                                  image_size=(160, 160))\n",
        "\n",
        "print(train_dataset.class_names)\n",
        "print(validation_dataset.class_names)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "s6eFSL9aIJGa"
      },
      "outputs": [],
      "source": [
        "class_names = train_dataset.class_names\n",
        "\n",
        "plt.figure(figsize=(15, 15))\n",
        "for images, labels in train_dataset.take(1):\n",
        "    # images (512,160,160,3)\n",
        "    for i in range(16):\n",
        "        ax = plt.subplot(4, 4, i+1)\n",
        "        plt.imshow(images[i].numpy().astype(\"uint8\"))\n",
        "        plt.title(class_names[labels[i]])\n",
        "        plt.axis(\"off\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5LMSS4pDIv4F",
        "outputId": "1d20b480-34f2-43c0-bc6b-fef3903991b3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d (Conv2D)             (None, 160, 160, 6)       168       \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2D  (None, 80, 80, 6)        0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 76, 76, 16)        2416      \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPooling  (None, 38, 38, 16)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 23104)             0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 84)                1940820   \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 10)                850       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 1,944,254\n",
            "Trainable params: 1,944,254\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "# LeNet-like\n",
        "import tensorflow as tf\n",
        "\n",
        "# model architecture\n",
        "model = tf.keras.Sequential()\n",
        "# input shape (28,28,1)\n",
        "model.add(tf.keras.Input(shape=(160, 160, 3)))\n",
        "\n",
        "# convolution 1\n",
        "model.add(tf.keras.layers.Conv2D(6, (3,3), padding='same', activation='relu')) \n",
        "# max pooling 1\n",
        "model.add(tf.keras.layers.MaxPooling2D(pool_size=2))\n",
        "\n",
        "# convolution 2\n",
        "model.add(tf.keras.layers.Conv2D(filters=16, kernel_size=5, activation='relu'))\n",
        "# max pooling 2\n",
        "model.add(tf.keras.layers.MaxPooling2D(pool_size=2))\n",
        "\n",
        "# Flatten\n",
        "model.add(tf.keras.layers.Flatten())\n",
        "\n",
        "# fully connected\n",
        "# model.add(tf.keras.layers.Dense(120, activation='relu'))\n",
        "model.add(tf.keras.layers.Dense(84, activation='relu'))\n",
        "model.add(tf.keras.layers.Dense(10, activation='softmax'))\n",
        "\n",
        "# model summary\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 764
        },
        "id": "6_C-BZRPJC15",
        "outputId": "79678082-af39-41bd-a062-b5371440c442"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "4/4 [==============================] - 19s 1s/step - loss: 34.8507 - accuracy: 0.3995 - val_loss: 60.9451 - val_accuracy: 0.5000\n",
            "Epoch 2/100\n",
            "4/4 [==============================] - 4s 582ms/step - loss: 50.0538 - accuracy: 0.5000 - val_loss: 10.0578 - val_accuracy: 0.5090\n",
            "Epoch 3/100\n",
            "4/4 [==============================] - 4s 485ms/step - loss: 20.1615 - accuracy: 0.5035 - val_loss: 17.3612 - val_accuracy: 0.5060\n",
            "Epoch 4/100\n",
            "4/4 [==============================] - 3s 495ms/step - loss: 8.7884 - accuracy: 0.5455 - val_loss: 7.5029 - val_accuracy: 0.5050\n",
            "Epoch 5/100\n",
            "4/4 [==============================] - 4s 728ms/step - loss: 8.0732 - accuracy: 0.5020 - val_loss: 5.1795 - val_accuracy: 0.5130\n",
            "Epoch 6/100\n",
            "4/4 [==============================] - 4s 538ms/step - loss: 2.5931 - accuracy: 0.5915 - val_loss: 3.8465 - val_accuracy: 0.5410\n",
            "Epoch 7/100\n",
            "4/4 [==============================] - 3s 495ms/step - loss: 3.6839 - accuracy: 0.5480 - val_loss: 3.0467 - val_accuracy: 0.5530\n",
            "Epoch 8/100\n",
            "4/4 [==============================] - 4s 575ms/step - loss: 1.6368 - accuracy: 0.6390 - val_loss: 1.5726 - val_accuracy: 0.5630\n",
            "Epoch 9/100\n",
            "4/4 [==============================] - 3s 498ms/step - loss: 1.5319 - accuracy: 0.6040 - val_loss: 1.8701 - val_accuracy: 0.5360\n",
            "Epoch 10/100\n",
            "4/4 [==============================] - 3s 517ms/step - loss: 0.9474 - accuracy: 0.6685 - val_loss: 1.2573 - val_accuracy: 0.5990\n",
            "Epoch 11/100\n",
            "4/4 [==============================] - ETA: 0s - loss: 0.9456 - accuracy: 0.6825"
          ]
        },
        {
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-7-42d9d1c6e4ca>\u001b[0m in \u001b[0;36m<cell line: 3>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m model.compile(optimizer='adam',metrics=['accuracy'],\n\u001b[1;32m      2\u001b[0m               loss='sparse_categorical_crossentropy') \n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_dataset\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalidation_dataset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/keras/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     63\u001b[0m         \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     64\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 65\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     66\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     67\u001b[0m             \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   1727\u001b[0m                             \u001b[0msteps_per_execution\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_steps_per_execution\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1728\u001b[0m                         )\n\u001b[0;32m-> 1729\u001b[0;31m                     val_logs = self.evaluate(\n\u001b[0m\u001b[1;32m   1730\u001b[0m                         \u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mval_x\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1731\u001b[0m                         \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mval_y\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/keras/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     63\u001b[0m         \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     64\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 65\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     66\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     67\u001b[0m             \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, x, y, batch_size, verbose, sample_weight, steps, callbacks, max_queue_size, workers, use_multiprocessing, return_dict, **kwargs)\u001b[0m\n\u001b[1;32m   2070\u001b[0m                         ):\n\u001b[1;32m   2071\u001b[0m                             \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_test_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2072\u001b[0;31m                             \u001b[0mtmp_logs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtest_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2073\u001b[0m                             \u001b[0;32mif\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshould_sync\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2074\u001b[0m                                 \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masync_wait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/tensorflow/python/util/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    148\u001b[0m     \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    151\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    892\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    893\u001b[0m       \u001b[0;32mwith\u001b[0m \u001b[0mOptionalXlaContext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jit_compile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 894\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    895\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    896\u001b[0m       \u001b[0mnew_tracing_count\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexperimental_get_tracing_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    931\u001b[0m       \u001b[0;31m# In this case we have not created variables on the first call. So we can\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    932\u001b[0m       \u001b[0;31m# run the first trace but we should fail if variables are created.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 933\u001b[0;31m       \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_variable_creation_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    934\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_created_variables\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mALLOW_DYNAMIC_VARIABLE_CREATION\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    935\u001b[0m         raise ValueError(\"Creating variables on a non-first call to a function\"\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/tensorflow/python/eager/polymorphic_function/tracing_compiler.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    141\u001b[0m       (concrete_function,\n\u001b[1;32m    142\u001b[0m        filtered_flat_args) = self._maybe_define_function(args, kwargs)\n\u001b[0;32m--> 143\u001b[0;31m     return concrete_function._call_flat(\n\u001b[0m\u001b[1;32m    144\u001b[0m         filtered_flat_args, captured_inputs=concrete_function.captured_inputs)  # pylint: disable=protected-access\n\u001b[1;32m    145\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/tensorflow/python/eager/polymorphic_function/monomorphic_function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1755\u001b[0m         and executing_eagerly):\n\u001b[1;32m   1756\u001b[0m       \u001b[0;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1757\u001b[0;31m       return self._build_call_outputs(self._inference_function.call(\n\u001b[0m\u001b[1;32m   1758\u001b[0m           ctx, args, cancellation_manager=cancellation_manager))\n\u001b[1;32m   1759\u001b[0m     forward_backward = self._select_forward_and_backward_functions(\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/tensorflow/python/eager/polymorphic_function/monomorphic_function.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m    379\u001b[0m       \u001b[0;32mwith\u001b[0m \u001b[0m_InterpolateFunctionError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    380\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcancellation_manager\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 381\u001b[0;31m           outputs = execute.execute(\n\u001b[0m\u001b[1;32m    382\u001b[0m               \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msignature\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    383\u001b[0m               \u001b[0mnum_outputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_num_outputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     50\u001b[0m   \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     51\u001b[0m     \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 52\u001b[0;31m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0m\u001b[1;32m     53\u001b[0m                                         inputs, attrs, num_outputs)\n\u001b[1;32m     54\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ],
      "source": [
        "model.compile(optimizer='adam',metrics=['accuracy'],\n",
        "              loss='sparse_categorical_crossentropy') \n",
        "model.fit(train_dataset,epochs=100,validation_data=validation_dataset)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 186
        },
        "id": "A3ysAxlhIx9p",
        "outputId": "2df56109-8b1b-4ac7-80ab-a66e9b3e76a9"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1800x1500 with 6 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# data augmentation\n",
        "data_augmentation = tf.keras.Sequential([\n",
        "  tf.keras.layers.experimental.preprocessing.RandomFlip('horizontal'),\n",
        "  tf.keras.layers.experimental.preprocessing.RandomRotation(0.2)\n",
        "])\n",
        "\n",
        "# training\n",
        "for images, _ in train_dataset.take(1):\n",
        "    # show images\n",
        "    plt.figure(figsize=(18, 15))\n",
        "    for i in range(6):\n",
        "        # data augmentation\n",
        "        augmented_image = data_augmentation(images[0:1]/255.)\n",
        "    \n",
        "        ax = plt.subplot(1, 6, i+1)\n",
        "        plt.imshow(augmented_image[0])\n",
        "        plt.axis('off')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qUcss3MSOyf3"
      },
      "outputs": [],
      "source": [
        "# LeNet-like\n",
        "import tensorflow as tf\n",
        "\n",
        "# model architecture\n",
        "model = data_augmentation\n",
        "# input shape (28,28,1)\n",
        "model.add(tf.keras.Input(shape=(160, 160, 3)))\n",
        "\n",
        "# convolution 1\n",
        "model.add(tf.keras.layers.Conv2D(6, (3,3), padding='same', activation='relu')) \n",
        "# max pooling 1\n",
        "model.add(tf.keras.layers.MaxPooling2D(pool_size=2))\n",
        "\n",
        "# convolution 2\n",
        "model.add(tf.keras.layers.Conv2D(filters=16, kernel_size=5, activation='relu'))\n",
        "# max pooling 2\n",
        "model.add(tf.keras.layers.MaxPooling2D(pool_size=2))\n",
        "\n",
        "# Flatten\n",
        "model.add(tf.keras.layers.Flatten())\n",
        "\n",
        "# fully connected\n",
        "model.add(tf.keras.layers.Dense(120, activation='relu'))\n",
        "model.add(tf.keras.layers.Dense(84, activation='relu'))\n",
        "model.add(tf.keras.layers.Dense(10, activation='softmax'))\n",
        "\n",
        "# model summary\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BuJwo9TYPAZq"
      },
      "outputs": [],
      "source": [
        "model.compile(optimizer='adam',metrics=['accuracy'],\n",
        "              loss='sparse_categorical_crossentropy') \n",
        "model.fit(train_dataset,epochs=100,validation_data=validation_dataset)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uBUbM4RZHttn"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "\n",
        "# data augmentation\n",
        "data_augmentation = tf.keras.Sequential([\n",
        "  tf.keras.layers.experimental.preprocessing.RandomFlip('horizontal'),\n",
        "  tf.keras.layers.experimental.preprocessing.RandomRotation(0.2)\n",
        "])\n",
        "\n",
        "# get VGG16\n",
        "base_model = tf.keras.applications.VGG16(input_shape=(160,160,3),\n",
        "                                               include_top=False,\n",
        "                                               weights=\"imagenet\")\n",
        "\n",
        "#base_model.trainable = False\n",
        "for layer in base_model.layers[:-2]:\n",
        "  layer.trainable = False\n",
        "\n",
        "# construct a new network\n",
        "inputs = tf.keras.Input(shape=(160, 160, 3))\n",
        "x = data_augmentation(inputs)\n",
        "x = base_model(x)\n",
        "x = tf.keras.layers.GlobalMaxPool2D()(x)\n",
        "x = tf.keras.layers.Flatten()(x)\n",
        "\n",
        "x = tf.keras.layers.Dense(84, activation='relu')(x)\n",
        "x = tf.keras.layers.Dense(10, activation='softmax')(x)\n",
        "\n",
        "#x = tf.keras.layers.Dense(2)(x)\n",
        "model = tf.keras.Model(inputs, x)\n",
        "\n",
        "# summary\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "G3wxy4yOIUFO",
        "outputId": "f12fef98-fd62-404c-fbbb-06356e2508ec"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "4/4 [==============================] - 12s 2s/step - loss: 66.5595 - accuracy: 0.4400 - val_loss: 27.1068 - val_accuracy: 0.6570\n",
            "Epoch 2/100\n",
            "4/4 [==============================] - 10s 2s/step - loss: 29.0202 - accuracy: 0.6485 - val_loss: 23.3778 - val_accuracy: 0.7540\n",
            "Epoch 3/100\n",
            "4/4 [==============================] - 9s 2s/step - loss: 14.4558 - accuracy: 0.7815 - val_loss: 4.4782 - val_accuracy: 0.9330\n",
            "Epoch 4/100\n",
            "4/4 [==============================] - 9s 2s/step - loss: 9.1184 - accuracy: 0.8305 - val_loss: 10.6632 - val_accuracy: 0.8850\n",
            "Epoch 5/100\n",
            "4/4 [==============================] - 10s 2s/step - loss: 6.5173 - accuracy: 0.8675 - val_loss: 3.5644 - val_accuracy: 0.9480\n",
            "Epoch 6/100\n",
            "4/4 [==============================] - 9s 2s/step - loss: 5.5445 - accuracy: 0.8760 - val_loss: 5.5497 - val_accuracy: 0.9160\n",
            "Epoch 7/100\n",
            "4/4 [==============================] - 9s 2s/step - loss: 3.8430 - accuracy: 0.9020 - val_loss: 3.1173 - val_accuracy: 0.9450\n",
            "Epoch 8/100\n",
            "4/4 [==============================] - 10s 2s/step - loss: 3.2992 - accuracy: 0.9055 - val_loss: 3.4909 - val_accuracy: 0.9270\n",
            "Epoch 9/100\n",
            "4/4 [==============================] - 10s 2s/step - loss: 2.6870 - accuracy: 0.9110 - val_loss: 2.0325 - val_accuracy: 0.9520\n",
            "Epoch 10/100\n",
            "4/4 [==============================] - 11s 3s/step - loss: 2.1496 - accuracy: 0.9320 - val_loss: 2.8085 - val_accuracy: 0.9340\n",
            "Epoch 11/100\n",
            "4/4 [==============================] - 9s 2s/step - loss: 1.9341 - accuracy: 0.9185 - val_loss: 1.3793 - val_accuracy: 0.9520\n",
            "Epoch 12/100\n",
            "4/4 [==============================] - 9s 2s/step - loss: 1.6297 - accuracy: 0.9295 - val_loss: 1.9161 - val_accuracy: 0.9450\n",
            "Epoch 13/100\n",
            "4/4 [==============================] - 10s 2s/step - loss: 1.2994 - accuracy: 0.9305 - val_loss: 1.1480 - val_accuracy: 0.9520\n",
            "Epoch 14/100\n",
            "4/4 [==============================] - 9s 2s/step - loss: 1.0306 - accuracy: 0.9375 - val_loss: 1.4320 - val_accuracy: 0.9480\n",
            "Epoch 15/100\n",
            "4/4 [==============================] - 9s 2s/step - loss: 1.1447 - accuracy: 0.9365 - val_loss: 0.9606 - val_accuracy: 0.9530\n",
            "Epoch 16/100\n",
            "4/4 [==============================] - 9s 2s/step - loss: 1.0252 - accuracy: 0.9350 - val_loss: 1.1666 - val_accuracy: 0.9540\n",
            "Epoch 17/100\n",
            "4/4 [==============================] - 9s 2s/step - loss: 1.0580 - accuracy: 0.9310 - val_loss: 0.8372 - val_accuracy: 0.9630\n",
            "Epoch 18/100\n",
            "4/4 [==============================] - 10s 2s/step - loss: 0.6890 - accuracy: 0.9445 - val_loss: 0.7708 - val_accuracy: 0.9510\n",
            "Epoch 19/100\n",
            "4/4 [==============================] - 9s 2s/step - loss: 0.6570 - accuracy: 0.9545 - val_loss: 1.0598 - val_accuracy: 0.9530\n",
            "Epoch 20/100\n",
            "4/4 [==============================] - 9s 2s/step - loss: 0.7182 - accuracy: 0.9465 - val_loss: 0.6769 - val_accuracy: 0.9550\n",
            "Epoch 21/100\n",
            "4/4 [==============================] - 10s 2s/step - loss: 0.5579 - accuracy: 0.9545 - val_loss: 0.5952 - val_accuracy: 0.9610\n",
            "Epoch 22/100\n",
            "4/4 [==============================] - 9s 2s/step - loss: 0.4238 - accuracy: 0.9640 - val_loss: 0.7142 - val_accuracy: 0.9630\n",
            "Epoch 23/100\n",
            "4/4 [==============================] - 9s 2s/step - loss: 0.3604 - accuracy: 0.9600 - val_loss: 0.6117 - val_accuracy: 0.9630\n",
            "Epoch 24/100\n",
            "4/4 [==============================] - 9s 2s/step - loss: 0.3843 - accuracy: 0.9640 - val_loss: 1.1152 - val_accuracy: 0.9460\n",
            "Epoch 25/100\n",
            "4/4 [==============================] - 10s 2s/step - loss: 0.3081 - accuracy: 0.9640 - val_loss: 0.6062 - val_accuracy: 0.9630\n",
            "Epoch 26/100\n",
            "4/4 [==============================] - 9s 2s/step - loss: 0.3122 - accuracy: 0.9705 - val_loss: 0.7432 - val_accuracy: 0.9570\n",
            "Epoch 27/100\n",
            "4/4 [==============================] - 9s 2s/step - loss: 0.2093 - accuracy: 0.9720 - val_loss: 0.5696 - val_accuracy: 0.9650\n",
            "Epoch 28/100\n",
            "4/4 [==============================] - 10s 2s/step - loss: 0.2488 - accuracy: 0.9690 - val_loss: 0.5467 - val_accuracy: 0.9640\n",
            "Epoch 29/100\n",
            "4/4 [==============================] - 11s 3s/step - loss: 0.2484 - accuracy: 0.9715 - val_loss: 0.9467 - val_accuracy: 0.9500\n",
            "Epoch 30/100\n",
            "4/4 [==============================] - 9s 2s/step - loss: 0.2801 - accuracy: 0.9710 - val_loss: 0.5835 - val_accuracy: 0.9630\n",
            "Epoch 31/100\n",
            "4/4 [==============================] - 10s 2s/step - loss: 0.2148 - accuracy: 0.9720 - val_loss: 0.7289 - val_accuracy: 0.9540\n",
            "Epoch 32/100\n",
            "4/4 [==============================] - 9s 2s/step - loss: 0.2891 - accuracy: 0.9675 - val_loss: 0.5303 - val_accuracy: 0.9640\n",
            "Epoch 33/100\n",
            "4/4 [==============================] - 9s 2s/step - loss: 0.1374 - accuracy: 0.9805 - val_loss: 0.5565 - val_accuracy: 0.9650\n",
            "Epoch 34/100\n",
            "4/4 [==============================] - 10s 2s/step - loss: 0.1103 - accuracy: 0.9830 - val_loss: 0.6839 - val_accuracy: 0.9610\n",
            "Epoch 35/100\n",
            "4/4 [==============================] - 9s 2s/step - loss: 0.0922 - accuracy: 0.9855 - val_loss: 0.5405 - val_accuracy: 0.9670\n",
            "Epoch 36/100\n",
            "4/4 [==============================] - 9s 2s/step - loss: 0.1102 - accuracy: 0.9825 - val_loss: 0.5406 - val_accuracy: 0.9630\n",
            "Epoch 37/100\n",
            "4/4 [==============================] - 9s 2s/step - loss: 0.0786 - accuracy: 0.9845 - val_loss: 0.5315 - val_accuracy: 0.9680\n",
            "Epoch 38/100\n",
            "4/4 [==============================] - 10s 2s/step - loss: 0.1123 - accuracy: 0.9850 - val_loss: 0.6581 - val_accuracy: 0.9580\n",
            "Epoch 39/100\n",
            "4/4 [==============================] - 9s 2s/step - loss: 0.1413 - accuracy: 0.9815 - val_loss: 0.6885 - val_accuracy: 0.9550\n",
            "Epoch 40/100\n",
            "4/4 [==============================] - 9s 2s/step - loss: 0.1180 - accuracy: 0.9825 - val_loss: 0.7493 - val_accuracy: 0.9570\n",
            "Epoch 41/100\n",
            "4/4 [==============================] - 10s 2s/step - loss: 0.1316 - accuracy: 0.9820 - val_loss: 0.5593 - val_accuracy: 0.9630\n",
            "Epoch 42/100\n",
            "4/4 [==============================] - 11s 3s/step - loss: 0.1377 - accuracy: 0.9820 - val_loss: 0.6365 - val_accuracy: 0.9630\n",
            "Epoch 43/100\n",
            "4/4 [==============================] - 11s 3s/step - loss: 0.0662 - accuracy: 0.9920 - val_loss: 0.5668 - val_accuracy: 0.9650\n",
            "Epoch 44/100\n",
            "4/4 [==============================] - 9s 2s/step - loss: 0.0686 - accuracy: 0.9845 - val_loss: 0.6461 - val_accuracy: 0.9610\n",
            "Epoch 45/100\n",
            "4/4 [==============================] - 9s 2s/step - loss: 0.0621 - accuracy: 0.9900 - val_loss: 0.5677 - val_accuracy: 0.9610\n",
            "Epoch 46/100\n",
            "4/4 [==============================] - 10s 2s/step - loss: 0.0476 - accuracy: 0.9910 - val_loss: 0.5835 - val_accuracy: 0.9590\n",
            "Epoch 47/100\n",
            "4/4 [==============================] - 9s 2s/step - loss: 0.0549 - accuracy: 0.9905 - val_loss: 0.5501 - val_accuracy: 0.9640\n",
            "Epoch 48/100\n",
            "4/4 [==============================] - 9s 2s/step - loss: 0.0530 - accuracy: 0.9895 - val_loss: 0.5355 - val_accuracy: 0.9620\n",
            "Epoch 49/100\n",
            "4/4 [==============================] - 10s 2s/step - loss: 0.0263 - accuracy: 0.9910 - val_loss: 0.5289 - val_accuracy: 0.9650\n",
            "Epoch 50/100\n",
            "4/4 [==============================] - 9s 2s/step - loss: 0.0372 - accuracy: 0.9905 - val_loss: 0.5337 - val_accuracy: 0.9690\n",
            "Epoch 51/100\n",
            "4/4 [==============================] - 9s 2s/step - loss: 0.0556 - accuracy: 0.9930 - val_loss: 0.5653 - val_accuracy: 0.9630\n",
            "Epoch 52/100\n",
            "4/4 [==============================] - 10s 2s/step - loss: 0.0272 - accuracy: 0.9940 - val_loss: 0.5481 - val_accuracy: 0.9650\n",
            "Epoch 53/100\n",
            "4/4 [==============================] - 10s 2s/step - loss: 0.0275 - accuracy: 0.9905 - val_loss: 0.6016 - val_accuracy: 0.9600\n",
            "Epoch 54/100\n",
            "4/4 [==============================] - 9s 2s/step - loss: 0.0521 - accuracy: 0.9920 - val_loss: 0.5565 - val_accuracy: 0.9670\n",
            "Epoch 55/100\n",
            "4/4 [==============================] - 9s 2s/step - loss: 0.0420 - accuracy: 0.9920 - val_loss: 0.6131 - val_accuracy: 0.9580\n",
            "Epoch 56/100\n",
            "4/4 [==============================] - 10s 2s/step - loss: 0.0441 - accuracy: 0.9900 - val_loss: 0.5804 - val_accuracy: 0.9650\n",
            "Epoch 57/100\n",
            "4/4 [==============================] - 9s 2s/step - loss: 0.0740 - accuracy: 0.9840 - val_loss: 0.8274 - val_accuracy: 0.9540\n",
            "Epoch 58/100\n",
            "4/4 [==============================] - 9s 2s/step - loss: 0.0469 - accuracy: 0.9895 - val_loss: 0.5125 - val_accuracy: 0.9650\n",
            "Epoch 59/100\n",
            "4/4 [==============================] - 12s 3s/step - loss: 0.0475 - accuracy: 0.9915 - val_loss: 0.5463 - val_accuracy: 0.9630\n",
            "Epoch 60/100\n",
            "4/4 [==============================] - 9s 2s/step - loss: 0.0482 - accuracy: 0.9930 - val_loss: 0.5136 - val_accuracy: 0.9640\n",
            "Epoch 61/100\n",
            "4/4 [==============================] - 9s 2s/step - loss: 0.0203 - accuracy: 0.9960 - val_loss: 0.7065 - val_accuracy: 0.9580\n",
            "Epoch 62/100\n",
            "4/4 [==============================] - 9s 2s/step - loss: 0.0122 - accuracy: 0.9950 - val_loss: 0.5086 - val_accuracy: 0.9610\n",
            "Epoch 63/100\n",
            "4/4 [==============================] - 9s 2s/step - loss: 0.0158 - accuracy: 0.9965 - val_loss: 0.5302 - val_accuracy: 0.9630\n",
            "Epoch 64/100\n",
            "4/4 [==============================] - 10s 2s/step - loss: 0.0149 - accuracy: 0.9950 - val_loss: 0.6684 - val_accuracy: 0.9580\n",
            "Epoch 65/100\n",
            "4/4 [==============================] - 9s 2s/step - loss: 0.0190 - accuracy: 0.9945 - val_loss: 0.4959 - val_accuracy: 0.9610\n",
            "Epoch 66/100\n",
            "4/4 [==============================] - 9s 2s/step - loss: 0.0395 - accuracy: 0.9935 - val_loss: 0.6354 - val_accuracy: 0.9580\n",
            "Epoch 67/100\n",
            "4/4 [==============================] - 10s 2s/step - loss: 0.0274 - accuracy: 0.9930 - val_loss: 0.4584 - val_accuracy: 0.9620\n",
            "Epoch 68/100\n",
            "4/4 [==============================] - 9s 2s/step - loss: 0.0472 - accuracy: 0.9925 - val_loss: 0.5418 - val_accuracy: 0.9620\n",
            "Epoch 69/100\n",
            "4/4 [==============================] - 9s 2s/step - loss: 0.0153 - accuracy: 0.9960 - val_loss: 0.4507 - val_accuracy: 0.9660\n",
            "Epoch 70/100\n",
            "4/4 [==============================] - 10s 2s/step - loss: 0.0178 - accuracy: 0.9950 - val_loss: 0.4572 - val_accuracy: 0.9650\n",
            "Epoch 71/100\n",
            "4/4 [==============================] - 9s 2s/step - loss: 0.0175 - accuracy: 0.9955 - val_loss: 0.4540 - val_accuracy: 0.9660\n",
            "Epoch 72/100\n",
            "4/4 [==============================] - 9s 2s/step - loss: 0.0177 - accuracy: 0.9965 - val_loss: 0.4484 - val_accuracy: 0.9670\n",
            "Epoch 73/100\n",
            "4/4 [==============================] - 10s 2s/step - loss: 0.0159 - accuracy: 0.9945 - val_loss: 0.5266 - val_accuracy: 0.9600\n",
            "Epoch 74/100\n",
            "4/4 [==============================] - 9s 2s/step - loss: 0.0167 - accuracy: 0.9965 - val_loss: 0.4435 - val_accuracy: 0.9700\n",
            "Epoch 75/100\n",
            "4/4 [==============================] - 11s 3s/step - loss: 0.0242 - accuracy: 0.9960 - val_loss: 0.5192 - val_accuracy: 0.9630\n",
            "Epoch 76/100\n",
            "4/4 [==============================] - 11s 3s/step - loss: 0.0123 - accuracy: 0.9955 - val_loss: 0.4350 - val_accuracy: 0.9690\n",
            "Epoch 77/100\n",
            "4/4 [==============================] - 11s 3s/step - loss: 0.0297 - accuracy: 0.9950 - val_loss: 0.5211 - val_accuracy: 0.9610\n",
            "Epoch 78/100\n",
            "4/4 [==============================] - 9s 2s/step - loss: 0.0232 - accuracy: 0.9955 - val_loss: 0.4138 - val_accuracy: 0.9670\n",
            "Epoch 79/100\n",
            "4/4 [==============================] - 10s 2s/step - loss: 0.0095 - accuracy: 0.9965 - val_loss: 0.5054 - val_accuracy: 0.9640\n",
            "Epoch 80/100\n",
            "4/4 [==============================] - 10s 2s/step - loss: 0.0067 - accuracy: 0.9965 - val_loss: 0.4970 - val_accuracy: 0.9630\n",
            "Epoch 81/100\n",
            "4/4 [==============================] - 9s 2s/step - loss: 0.0084 - accuracy: 0.9980 - val_loss: 0.4419 - val_accuracy: 0.9650\n",
            "Epoch 82/100\n",
            "4/4 [==============================] - 9s 2s/step - loss: 0.0055 - accuracy: 0.9980 - val_loss: 0.4917 - val_accuracy: 0.9640\n",
            "Epoch 83/100\n",
            "4/4 [==============================] - 9s 2s/step - loss: 0.0034 - accuracy: 0.9985 - val_loss: 0.4806 - val_accuracy: 0.9640\n",
            "Epoch 84/100\n",
            "4/4 [==============================] - 11s 3s/step - loss: 0.0093 - accuracy: 0.9985 - val_loss: 0.4580 - val_accuracy: 0.9650\n",
            "Epoch 85/100\n",
            "4/4 [==============================] - 9s 2s/step - loss: 0.0179 - accuracy: 0.9955 - val_loss: 0.5047 - val_accuracy: 0.9620\n",
            "Epoch 86/100\n",
            "4/4 [==============================] - 9s 2s/step - loss: 0.0082 - accuracy: 0.9970 - val_loss: 0.5326 - val_accuracy: 0.9610\n",
            "Epoch 87/100\n",
            "4/4 [==============================] - 10s 2s/step - loss: 0.0069 - accuracy: 0.9975 - val_loss: 0.4661 - val_accuracy: 0.9630\n",
            "Epoch 88/100\n",
            "4/4 [==============================] - 9s 2s/step - loss: 0.0178 - accuracy: 0.9965 - val_loss: 0.6115 - val_accuracy: 0.9600\n",
            "Epoch 89/100\n",
            "4/4 [==============================] - 9s 2s/step - loss: 0.0096 - accuracy: 0.9980 - val_loss: 0.4560 - val_accuracy: 0.9620\n",
            "Epoch 90/100\n",
            "4/4 [==============================] - 10s 2s/step - loss: 0.0267 - accuracy: 0.9960 - val_loss: 0.4901 - val_accuracy: 0.9620\n",
            "Epoch 91/100\n",
            "4/4 [==============================] - 9s 2s/step - loss: 0.0237 - accuracy: 0.9955 - val_loss: 0.5081 - val_accuracy: 0.9610\n",
            "Epoch 92/100\n",
            "4/4 [==============================] - 9s 2s/step - loss: 0.0345 - accuracy: 0.9940 - val_loss: 0.4806 - val_accuracy: 0.9610\n",
            "Epoch 93/100\n",
            "4/4 [==============================] - 10s 2s/step - loss: 0.0172 - accuracy: 0.9950 - val_loss: 0.6154 - val_accuracy: 0.9570\n",
            "Epoch 94/100\n",
            "4/4 [==============================] - 9s 2s/step - loss: 0.0141 - accuracy: 0.9975 - val_loss: 0.4772 - val_accuracy: 0.9600\n",
            "Epoch 95/100\n",
            "4/4 [==============================] - 9s 2s/step - loss: 0.0209 - accuracy: 0.9970 - val_loss: 0.5457 - val_accuracy: 0.9590\n",
            "Epoch 96/100\n",
            "4/4 [==============================] - 10s 2s/step - loss: 0.0206 - accuracy: 0.9950 - val_loss: 0.4992 - val_accuracy: 0.9620\n",
            "Epoch 97/100\n",
            "4/4 [==============================] - 9s 2s/step - loss: 0.0525 - accuracy: 0.9905 - val_loss: 0.4933 - val_accuracy: 0.9630\n",
            "Epoch 98/100\n",
            "4/4 [==============================] - 9s 2s/step - loss: 0.0328 - accuracy: 0.9930 - val_loss: 0.5351 - val_accuracy: 0.9610\n",
            "Epoch 99/100\n",
            "4/4 [==============================] - 9s 2s/step - loss: 0.0231 - accuracy: 0.9950 - val_loss: 0.5623 - val_accuracy: 0.9610\n",
            "Epoch 100/100\n",
            "4/4 [==============================] - 10s 2s/step - loss: 0.0104 - accuracy: 0.9975 - val_loss: 0.5143 - val_accuracy: 0.9650\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f34e0b632e0>"
            ]
          },
          "execution_count": 44,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model.compile(optimizer='adam',metrics=['accuracy'],\n",
        "              loss='sparse_categorical_crossentropy') \n",
        "model.fit(train_dataset,epochs=100,validation_data=validation_dataset)"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": []
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}