Image_Classification_Caltech101_dataset.ipynb

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      "name": "Image Classification Task",
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        "<a href=\"https://colab.research.google.com/github/shishir-joshi/notebooks/blob/master/Image_Classification_Caltech101_dataset.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
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        "id": "IM3qqx6AmRSg",
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      "source": [
        "##Training a CNN on the Caltech101 dataset\n",
        "\n",
        "[http://www.vision.caltech.edu/Image_Datasets/Caltech101/]\n",
        "\n",
        "There are 101 categories with 300x200 px per image"
      ]
    },
    {
      "cell_type": "code",
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        "id": "e_rHxBZAoN_3",
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        "outputId": "c04a711f-675f-498f-db0a-3698bf583b4f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 866
        }
      },
      "source": [
        "!pip install tensorflow-gpu==2.0.0-alpha0\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "import matplotlib.pyplot as plt\n",
        "import tensorflow.keras as keras\n",
        "import tarfile\n",
        "import random\n",
        "from pathlib import Path\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Embedding,Dense,Flatten,Conv2D,MaxPool2D,BatchNormalization\n",
        "from tensorflow.keras.optimizers import Adamax,Nadam,Adam,Adagrad,RMSprop\n",
        "from tensorflow.keras.losses import mean_squared_logarithmic_error,categorical_crossentropy\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "from sklearn.model_selection import train_test_split\n",
        "from tensorflow.keras.callbacks import ModelCheckpoint\n",
        "from random import shuffle\n",
        "#tf.enable_eager_execution()"
      ],
      "execution_count": 1,
      "outputs": [
        {
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          "text": [
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/13/82/f16063b4eed210dc2ab057930ac1da4fbe1e91b7b051a6c8370b401e6ae7/tf_estimator_nightly-1.14.0.dev2019030115-py2.py3-none-any.whl (411kB)\n",
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            "Installing collected packages: tb-nightly, tf-estimator-nightly, tensorflow-gpu\n",
            "Successfully installed tb-nightly-1.14.0a20190301 tensorflow-gpu-2.0.0a0 tf-estimator-nightly-1.14.0.dev2019030115\n"
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          "text": [
            "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:523: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:524: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:526: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:527: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:532: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R12HNNjQolB4",
        "colab_type": "text"
      },
      "source": [
        "Structure of the code:\n",
        "    1. Download the dataset\n",
        "    2. Define parameters: batch_size,epochs,num_classes\n",
        "    3. Make Util Methods: \n",
        "            Pre-process, Get Labels from prediction, \n",
        "            shuffle, Make labels as categorical\n",
        "    4. Define Dataset Preparation Methods:  \n",
        "            return (x_tr,y_tr),(x_te,y_te) shuffled, batched tuples as numpy/tf.data\n",
        "    6. %Make Learning rate scheduler\n",
        "    7. Define Model:\n",
        "            i. Transfer Learning on MobileNetv2 \n",
        "            ii. My Model\n",
        "    8. Train and Evaluate\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z-lz8pQdohq4",
        "colab_type": "code",
        "outputId": "2a456193-dcb4-484e-a18c-4fb8b70e1411",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        }
      },
      "source": [
        "!wget http://www.vision.caltech.edu/Image_Datasets/Caltech101/101_ObjectCategories.tar.gz\n",
        "\n",
        "to_extract = tarfile.open('101_ObjectCategories.tar.gz','r:gz')\n",
        "to_extract.extractall()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2019-12-05 13:29:08--  http://www.vision.caltech.edu/Image_Datasets/Caltech101/101_ObjectCategories.tar.gz\n",
            "Resolving www.vision.caltech.edu (www.vision.caltech.edu)... 34.208.54.77\n",
            "Connecting to www.vision.caltech.edu (www.vision.caltech.edu)|34.208.54.77|:80... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 131740031 (126M) [application/x-tar]\n",
            "Saving to: ‘101_ObjectCategories.tar.gz’\n",
            "\n",
            "101_ObjectCategorie 100%[===================>] 125.64M  6.52MB/s    in 22s     \n",
            "\n",
            "2019-12-05 13:29:31 (5.60 MB/s) - ‘101_ObjectCategories.tar.gz’ saved [131740031/131740031]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KgUvwjb9y_AV",
        "colab_type": "text"
      },
      "source": [
        "####Preparing tf.data dataset from the images present in the tar.gz"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1ifslmKtnLxV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Util functions\n",
        "\n",
        "\n",
        "def shuffle_list(*ls):\n",
        "  l = list(zip(*ls))\n",
        "  shuffle(l)\n",
        "  return zip(*l)\n",
        "\n",
        "def preprocess(path):\n",
        "  img_raw = tf.io.read_file(path)\n",
        "  img_decoded = tf.image.decode_jpeg(img_raw,channels=3)\n",
        "  img_decoded = tf.image.resize(img_decoded,[128,128])\n",
        "  img_decoded /= 255\n",
        "  return img_decoded\n",
        "\n",
        "def preprocess2(path):\n",
        "  img_raw = tf.io.read_file(path)\n",
        "  img_decoded = tf.image.decode_jpeg(img_raw,channels=3)\n",
        "#   img_decoded = tf.image.resize(img_decoded,[128,128])\n",
        "#   img_decoded /= 255\n",
        "  return img_decoded\n",
        "\n",
        "def get_label_from_prediction(top_pred):\n",
        "  return list(class_to_index.keys())[list(class_to_index.values()).index(top_pred)]\n",
        "\n",
        "# Parameters\n",
        "BATCH_SIZE=100\n",
        "categories_used = 101\n",
        "# Takes image folder path and returns train and test datasets\n",
        "def make_dataset(folder_path, train_split=0.8, categories_used = 101, batch_size=100):\n",
        "  paths = Path(folder_path)\n",
        "  class_paths = [x for x in paths.iterdir() if x.is_dir()]\n",
        "  classes = [x.stem for x in class_paths[:categories_used]]\n",
        "  class_to_index = dict((v,k) for k,v in enumerate(classes[:categories_used]))\n",
        "  file_paths = [list(x.glob('*.jpg')) for x in class_paths[:categories_used]]\n",
        "  print(\"Number of file paths:{0}\".format(len(file_paths)))\n",
        "  \n",
        "  # \"Unrolling\" the file_paths \n",
        "  label_paths = list()\n",
        "  for path in file_paths:\n",
        "    for pt in path:\n",
        "      label_paths.append(pt)\n",
        "  print(len(label_paths))\n",
        "  labels = [class_to_index[l.parent.name] for l in label_paths]\n",
        "  \n",
        "  # shuffling \n",
        "  shuff_paths, shuff_labels = shuffle_list(label_paths,labels)\n",
        "  shuff_paths = [str(p) for p in shuff_paths]\n",
        "  shuff_labels = [p for p in shuff_labels]\n",
        "  \n",
        "  # making labels categorical\n",
        "  shuff_labels = to_categorical(shuff_labels,num_classes=categories_used)\n",
        "  \n",
        "  # train test split\n",
        "  X_train, X_test, y_train, y_test = train_test_split(shuff_paths,\n",
        "                                                      shuff_labels,\n",
        "                                                      test_size=1-train_split)\n",
        "  \n",
        "  # make the datasets\n",
        "  path_ds_train = tf.data.Dataset.from_tensor_slices(X_train)\n",
        "  path_ds_test = tf.data.Dataset.from_tensor_slices(X_test)\n",
        "  image_ds_train = path_ds_train.map(preprocess,num_parallel_calls=4)\n",
        "  image_ds_test = path_ds_test.map(preprocess,num_parallel_calls=4)\n",
        "  label_ds_train = tf.data.Dataset.from_tensor_slices(tf.cast(y_train, tf.int64))\n",
        "  label_ds_test = tf.data.Dataset.from_tensor_slices(tf.cast(y_test, tf.int64))\n",
        "  train_ds = tf.data.Dataset.zip((image_ds_train,label_ds_train))\n",
        "  test_ds = tf.data.Dataset.zip((image_ds_test,label_ds_test))\n",
        "  \n",
        "  # Batch\n",
        "  train_ds = train_ds.batch(batch_size)\n",
        "  test_ds = test_ds.batch(batch_size)\n",
        "\n",
        "  # Prefetch\n",
        "  train_ds = train_ds.prefetch(buffer_size = 1000)\n",
        "  test_ds = test_ds.prefetch(buffer_size = 1000)\n",
        "  return (train_ds,test_ds,shuff_paths,class_to_index,X_test,y_test)\n",
        "    \n",
        "# datast = make_dataset('101_ObjectCategories/') \n",
        "\n",
        "\n",
        "# Takes image file path and feeds it to model\n",
        "def input_pipe(image_path, model=None, plot=False):\n",
        "  image = preprocess(image_path)\n",
        "  if(plot==False):\n",
        "    return np.reshape(image,(-1,128,128,3))\n",
        "  else:\n",
        "    return image\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
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        "id": "uG3Q3YXWM0Xv",
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      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "source": [
        "plt.imshow(input_pipe('101_ObjectCategories/airplanes/image_0002.jpg',plot=True))"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f2d83bc2400>"
            ]
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          "metadata": {
            "tags": []
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          "execution_count": 4
        },
        {
          "output_type": "display_data",
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a69QuQUaGRrVWq1W693I1YN++LouyHJN2Yei0PA/XVzDWpKYg6UhBl8A4Wxci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\n9OixxdEvAj16bHH8f68I4FhJh15kAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YfRVA_TVc625",
        "colab_type": "code",
        "outputId": "84da9dd2-c2be-4087-ed47-5e1e765f5f8b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "# DATASET\n",
        "train_ds,test_ds,shuff_paths,class_to_index,X_test,y_test = make_dataset('101_ObjectCategories/')"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of file paths:101\n",
            "9091\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m4BOMFaKHSRl",
        "colab_type": "code",
        "outputId": "2a6c873e-88b5-4914-afca-866e7d4e0b83",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        }
      },
      "source": [
        "n=1\n",
        "for record,_ in train_ds.take(4):\n",
        "  label = record[0]\n",
        "  image = record[1]\n",
        "  plt.subplot(2,2,++n)\n",
        "  plt.imshow(image.numpy()[:, :, :].astype(np.float32))\n",
        "  plt.grid(False)\n",
        "  plt.xticks([])\n",
        "  plt.yticks([])\n",
        "  plt.xlabel(shuff_paths[n])"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:5: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
            "  \"\"\"\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ZJohBGglSK+T2s/4n8vp3wb9Dnr9YppXE5968wqUMlPrIy92seVgv8LBEdnahY66lwFaE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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2CWaPE7IrGS9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pXgi-PAuGbrc",
        "colab_type": "text"
      },
      "source": [
        "###_Transfer Learning MobileNetv2_"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TDnklsViHErf",
        "colab_type": "code",
        "outputId": "ec05e480-71bf-460a-8a99-82ca887ad43d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        }
      },
      "source": [
        "IMGSIZE = (128,128,3)\n",
        "mob_net_model = tf.keras.applications.MobileNetV2(input_shape=IMGSIZE,\n",
        "                                               include_top=False,\n",
        "                                               weights='imagenet')"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://github.com/JonathanCMitchell/mobilenet_v2_keras/releases/download/v1.1/mobilenet_v2_weights_tf_dim_ordering_tf_kernels_1.0_128_no_top.h5\n",
            "9412608/9406464 [==============================] - 1s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o-y2RGAEGjCe",
        "colab_type": "code",
        "outputId": "20c24f89-7b81-44ad-ddc9-2074a423d8b3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "mob_net_model.input.shape"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TensorShape([None, 128, 128, 3])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LLt3EDPoH1fO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "mob_net_model.trainable = False\n",
        "global_average_layer = tf.keras.layers.GlobalAveragePooling2D()\n",
        "dense_layer = tf.keras.layers.Dense(1000, activation='sigmoid')\n",
        "soft_layer = tf.keras.layers.Dense(101, activation='softmax')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NZNMpDTyMDln",
        "colab_type": "code",
        "outputId": "e4dc0248-76fe-4c5a-90b0-3f3fcbdf0408",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 277
        }
      },
      "source": [
        "TL_model = tf.keras.Sequential([\n",
        "              mob_net_model,\n",
        "              global_average_layer,\n",
        "              dense_layer,\n",
        "              soft_layer\n",
        "        ])\n",
        "\n",
        "# Configure a model for categorical classification.\n",
        "TL_model.compile(optimizer=tf.keras.optimizers.Adam(0.1),\n",
        "              loss=tf.keras.losses.CategoricalCrossentropy(),\n",
        "              metrics=[tf.keras.metrics.CategoricalAccuracy()])\n",
        "\n",
        "TL_model.summary()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "mobilenetv2_1.00_128 (Model) (None, 4, 4, 1280)        2257984   \n",
            "_________________________________________________________________\n",
            "global_average_pooling2d (Gl (None, 1280)              0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 1000)              1281000   \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 101)               101101    \n",
            "=================================================================\n",
            "Total params: 3,640,085\n",
            "Trainable params: 1,382,101\n",
            "Non-trainable params: 2,257,984\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "1Fn3Tz4tTSYY",
        "colab": {}
      },
      "source": [
        "# define the checkpoints\n",
        "filepath=\"weights-improvement-{epoch:02d}-{loss:.4f}.hdf5\"\n",
        "checkpoint = ModelCheckpoint(filepath, monitor='loss', verbose=1, save_best_only=False, mode='min')\n",
        "callbacks_list = [checkpoint]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "P8j5cesBTSYc",
        "colab": {}
      },
      "source": [
        "TL_model.fit(train_ds,\n",
        "          validation_data = test_ds,\n",
        "          epochs=200,\n",
        "          #steps_per_epoch=100,\n",
        "          callbacks = callbacks_list)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dwVhWsLuLEE4",
        "colab_type": "text"
      },
      "source": [
        "###_My Model_\n",
        "\n",
        "        v0.1:\n",
        "          CONV 50\n",
        "          MAXPOOL\n",
        "          CONV 20\n",
        "          MAXPOOL\n",
        "          CONV 10\n",
        "          MAXPOOL\n",
        "          FC 100 \n",
        "          FC 50\n",
        "          FC num_classes\n",
        "          "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xOof6yJNL2Ht",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "my_model = keras.Sequential([\n",
        "    keras.layers.Conv2D(50,kernel_size=3,strides=1,activation='relu'),\n",
        "    keras.layers.MaxPool2D(pool_size=2,strides=2,padding=\"same\"),\n",
        "    keras.layers.BatchNormalization(),\n",
        "    keras.layers.Conv2D(20,kernel_size=3,activation='relu'),\n",
        "    keras.layers.MaxPool2D(pool_size=2,strides=2,padding=\"same\"),\n",
        "    keras.layers.BatchNormalization(),\n",
        "    keras.layers.Conv2D(10,kernel_size=3,activation='relu'),\n",
        "    keras.layers.MaxPool2D(pool_size=2,strides=2,padding=\"same\"),\n",
        "    keras.layers.BatchNormalization(),\n",
        "    keras.layers.Flatten(),\n",
        "    keras.layers.Dense(100,activation='tanh'),\n",
        "    keras.layers.Dense(25,activation='tanh'),\n",
        "    keras.layers.Dense(10,activation='tanh'),    \n",
        "    keras.layers.Dense(categories_used,activation='sigmoid')\n",
        "])\n",
        "\n",
        "# Configure a model for categorical classification.\n",
        "my_model.compile(optimizer=tf.keras.optimizers.RMSprop(),\n",
        "              loss=tf.keras.losses.CategoricalCrossentropy(),\n",
        "              metrics=[tf.keras.metrics.CategoricalAccuracy()])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7Az9Cm4PPFgq",
        "colab_type": "code",
        "outputId": "34938f0d-d5c6-4141-e06d-41adb6bdd3c6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 602
        }
      },
      "source": [
        "my_model.summary()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d (Conv2D)              multiple                  1400      \n",
            "_________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D) multiple                  0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_v2 (Batc multiple                  200       \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            multiple                  9020      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 multiple                  0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_v2_1 (Ba multiple                  80        \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            multiple                  1810      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 multiple                  0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_v2_2 (Ba multiple                  40        \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            multiple                  0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              multiple                  225100    \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              multiple                  2525      \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              multiple                  260       \n",
            "_________________________________________________________________\n",
            "dense_5 (Dense)              multiple                  1111      \n",
            "=================================================================\n",
            "Total params: 241,546\n",
            "Trainable params: 241,386\n",
            "Non-trainable params: 160\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XhikBEO-R_sh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# define the checkpointA\n",
        "filepath=\"weights-improvement-{epoch:02d}-{loss:.4f}.hdf5\"\n",
        "checkpoint = ModelCheckpoint(filepath, monitor='loss', verbose=1, save_best_only=True, mode='min')\n",
        "callbacks_list = [checkpoint]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xuMAUqU0ZYod",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "8e39d8ee-209d-459a-99e0-1c14f8614147"
      },
      "source": [
        "my_model.fit(train_ds,\n",
        "          validation_data = test_ds,\n",
        "          epochs=50,\n",
        "          #steps_per_epoch=10000,\n",
        "\n",
        "          callbacks = callbacks_list)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Expected a shuffled dataset but input dataset `x` is not shuffled. Please invoke `shuffle()` on input dataset.\n",
            "Epoch 1/50\n",
            "72/73 [============================>.] - ETA: 0s - loss: 4.4656 - categorical_accuracy: 0.1432\n",
            "Epoch 00001: loss improved from inf to 4.46495, saving model to weights-improvement-01-4.4650.hdf5\n",
            "73/73 [==============================] - 18s 240ms/step - loss: 4.4647 - categorical_accuracy: 0.1451 - val_loss: 4.5296 - val_categorical_accuracy: 0.0995\n",
            "Epoch 2/50\n",
            "72/73 [============================>.] - ETA: 0s - loss: 4.3384 - categorical_accuracy: 0.3193\n",
            "Epoch 00002: loss improved from 4.46495 to 4.33779, saving model to weights-improvement-02-4.3378.hdf5\n",
            "73/73 [==============================] - 10s 142ms/step - loss: 4.3376 - categorical_accuracy: 0.3193 - val_loss: 4.4432 - val_categorical_accuracy: 0.0962\n",
            "Epoch 3/50\n",
            "72/73 [============================>.] - ETA: 0s - loss: 4.2335 - categorical_accuracy: 0.3304\n",
            "Epoch 00003: loss improved from 4.33779 to 4.23285, saving model to weights-improvement-03-4.2329.hdf5\n",
            "73/73 [==============================] - 10s 141ms/step - loss: 4.2326 - categorical_accuracy: 0.3304 - val_loss: 4.3854 - val_categorical_accuracy: 0.0995\n",
            "Epoch 4/50\n",
            "72/73 [============================>.] - ETA: 0s - loss: 4.1331 - categorical_accuracy: 0.3288\n",
            "Epoch 00004: loss improved from 4.23285 to 4.13251, saving model to weights-improvement-04-4.1325.hdf5\n",
            "73/73 [==============================] - 10s 140ms/step - loss: 4.1323 - categorical_accuracy: 0.3287 - val_loss: 4.3703 - val_categorical_accuracy: 0.0962\n",
            "Epoch 5/50\n",
            "72/73 [============================>.] - ETA: 0s - loss: 4.0278 - categorical_accuracy: 0.3104\n",
            "Epoch 00005: loss improved from 4.13251 to 4.02709, saving model to weights-improvement-05-4.0271.hdf5\n",
            "73/73 [==============================] - 10s 142ms/step - loss: 4.0268 - categorical_accuracy: 0.3103 - val_loss: 4.2275 - val_categorical_accuracy: 0.1391\n",
            "Epoch 6/50\n",
            "72/73 [============================>.] - ETA: 0s - loss: 3.9132 - categorical_accuracy: 0.2932\n",
            "Epoch 00006: loss improved from 4.02709 to 3.91236, saving model to weights-improvement-06-3.9124.hdf5\n",
            "73/73 [==============================] - 10s 141ms/step - loss: 3.9120 - categorical_accuracy: 0.2930 - val_loss: 4.0785 - val_categorical_accuracy: 0.2023\n",
            "Epoch 7/50\n",
            "72/73 [============================>.] - ETA: 0s - loss: 3.7930 - categorical_accuracy: 0.2641\n",
            "Epoch 00007: loss improved from 3.91236 to 3.79203, saving model to weights-improvement-07-3.7920.hdf5\n",
            "73/73 [==============================] - 10s 141ms/step - loss: 3.7916 - categorical_accuracy: 0.2641 - val_loss: 4.0288 - val_categorical_accuracy: 0.1820\n",
            "Epoch 8/50\n",
            "72/73 [============================>.] - ETA: 0s - loss: 3.6665 - categorical_accuracy: 0.2494\n",
            "Epoch 00008: loss improved from 3.79203 to 3.66542, saving model to weights-improvement-08-3.6654.hdf5\n",
            "73/73 [==============================] - 10s 142ms/step - loss: 3.6650 - categorical_accuracy: 0.2494 - val_loss: 3.8853 - val_categorical_accuracy: 0.2463\n",
            "Epoch 9/50\n",
            "72/73 [============================>.] - ETA: 0s - loss: 3.5388 - categorical_accuracy: 0.2567\n",
            "Epoch 00009: loss improved from 3.66542 to 3.53773, saving model to weights-improvement-09-3.5377.hdf5\n",
            "73/73 [==============================] - 10s 141ms/step - loss: 3.5373 - categorical_accuracy: 0.2567 - val_loss: 3.8000 - val_categorical_accuracy: 0.2507\n",
            "Epoch 10/50\n",
            "72/73 [============================>.] - ETA: 0s - loss: 3.4138 - categorical_accuracy: 0.2562\n",
            "Epoch 00010: loss improved from 3.53773 to 3.41273, saving model to weights-improvement-10-3.4127.hdf5\n",
            "73/73 [==============================] - 10s 140ms/step - loss: 3.4123 - categorical_accuracy: 0.2562 - val_loss: 3.7209 - val_categorical_accuracy: 0.2490\n",
            "Epoch 11/50\n",
            "72/73 [============================>.] - ETA: 0s - loss: 3.2917 - categorical_accuracy: 0.2563\n",
            "Epoch 00011: loss improved from 3.41273 to 3.29078, saving model to weights-improvement-11-3.2908.hdf5\n",
            "73/73 [==============================] - 10s 141ms/step - loss: 3.2904 - categorical_accuracy: 0.2564 - val_loss: 3.6976 - val_categorical_accuracy: 0.2358\n",
            "Epoch 12/50\n",
            "72/73 [============================>.] - ETA: 0s - loss: 3.1706 - categorical_accuracy: 0.2563\n",
            "Epoch 00012: loss improved from 3.29078 to 3.16976, saving model to weights-improvement-12-3.1698.hdf5\n",
            "73/73 [==============================] - 10s 141ms/step - loss: 3.1694 - categorical_accuracy: 0.2563 - val_loss: 3.5978 - val_categorical_accuracy: 0.2529\n",
            "Epoch 13/50\n",
            "72/73 [============================>.] - ETA: 0s - loss: 3.0573 - categorical_accuracy: 0.2631\n",
            "Epoch 00013: loss improved from 3.16976 to 3.05669, saving model to weights-improvement-13-3.0567.hdf5\n",
            "73/73 [==============================] - 10s 144ms/step - loss: 3.0565 - categorical_accuracy: 0.2631 - val_loss: 3.5611 - val_categorical_accuracy: 0.2512\n",
            "Epoch 14/50\n",
            "72/73 [============================>.] - ETA: 0s - loss: 2.9483 - categorical_accuracy: 0.2676\n",
            "Epoch 00014: loss improved from 3.05669 to 2.94748, saving model to weights-improvement-14-2.9475.hdf5\n",
            "73/73 [==============================] - 10s 142ms/step - loss: 2.9472 - categorical_accuracy: 0.2677 - val_loss: 3.5102 - val_categorical_accuracy: 0.2512\n",
            "Epoch 15/50\n",
            "72/73 [============================>.] - ETA: 0s - loss: 2.8446 - categorical_accuracy: 0.2729\n",
            "Epoch 00015: loss improved from 2.94748 to 2.84380, saving model to weights-improvement-15-2.8438.hdf5\n",
            "73/73 [==============================] - 10s 142ms/step - loss: 2.8435 - categorical_accuracy: 0.2731 - val_loss: 3.4754 - val_categorical_accuracy: 0.2446\n",
            "Epoch 16/50\n",
            "72/73 [============================>.] - ETA: 0s - loss: 2.7458 - categorical_accuracy: 0.2834\n",
            "Epoch 00016: loss improved from 2.84380 to 2.74512, saving model to weights-improvement-16-2.7451.hdf5\n",
            "73/73 [==============================] - 10s 143ms/step - loss: 2.7449 - categorical_accuracy: 0.2835 - val_loss: 3.4365 - val_categorical_accuracy: 0.2490\n",
            "Epoch 17/50\n",
            "72/73 [============================>.] - ETA: 0s - loss: 2.6580 - categorical_accuracy: 0.2910\n",
            "Epoch 00017: loss improved from 2.74512 to 2.65721, saving model to weights-improvement-17-2.6572.hdf5\n",
            "73/73 [==============================] - 10s 142ms/step - loss: 2.6569 - categorical_accuracy: 0.2912 - val_loss: 3.4092 - val_categorical_accuracy: 0.2584\n",
            "Epoch 18/50\n",
            "72/73 [============================>.] - ETA: 0s - loss: 2.5678 - categorical_accuracy: 0.3035\n",
            "Epoch 00018: loss improved from 2.65721 to 2.56698, saving model to weights-improvement-18-2.5670.hdf5\n",
            "73/73 [==============================] - 10s 141ms/step - loss: 2.5667 - categorical_accuracy: 0.3036 - val_loss: 3.3930 - val_categorical_accuracy: 0.2578\n",
            "Epoch 19/50\n",
            "72/73 [============================>.] - ETA: 0s - loss: 2.4874 - categorical_accuracy: 0.3125\n",
            "Epoch 00019: loss improved from 2.56698 to 2.48655, saving model to weights-improvement-19-2.4866.hdf5\n",
            "73/73 [==============================] - 10s 142ms/step - loss: 2.4862 - categorical_accuracy: 0.3127 - val_loss: 3.3648 - val_categorical_accuracy: 0.2600\n",
            "Epoch 20/50\n",
            "72/73 [============================>.] - ETA: 0s - loss: 2.4110 - categorical_accuracy: 0.3222\n",
            "Epoch 00020: loss improved from 2.48655 to 2.41025, saving model to weights-improvement-20-2.4103.hdf5\n",
            "73/73 [==============================] - 10s 143ms/step - loss: 2.4100 - categorical_accuracy: 0.3224 - val_loss: 3.3469 - val_categorical_accuracy: 0.2699\n",
            "Epoch 21/50\n",
            "72/73 [============================>.] - ETA: 0s - loss: 2.3415 - categorical_accuracy: 0.3278\n",
            "Epoch 00021: loss improved from 2.41025 to 2.34075, saving model to weights-improvement-21-2.3407.hdf5\n",
            "73/73 [==============================] - 10s 142ms/step - loss: 2.3405 - categorical_accuracy: 0.3281 - val_loss: 3.3469 - val_categorical_accuracy: 0.2611\n",
            "Epoch 22/50\n",
            "72/73 [============================>.] - ETA: 0s - loss: 2.2768 - categorical_accuracy: 0.3373\n",
            "Epoch 00022: loss improved from 2.34075 to 2.27603, saving model to weights-improvement-22-2.2760.hdf5\n",
            "73/73 [==============================] - 10s 141ms/step - loss: 2.2757 - categorical_accuracy: 0.3375 - val_loss: 3.3605 - val_categorical_accuracy: 0.2606\n",
            "Epoch 23/50\n",
            "72/73 [============================>.] - ETA: 0s - loss: 2.2102 - categorical_accuracy: 0.3506\n",
            "Epoch 00023: loss improved from 2.27603 to 2.20938, saving model to weights-improvement-23-2.2094.hdf5\n",
            "73/73 [==============================] - 10s 142ms/step - loss: 2.2091 - categorical_accuracy: 0.3508 - val_loss: 3.3338 - val_categorical_accuracy: 0.2661\n",
            "Epoch 24/50\n",
            "72/73 [============================>.] - ETA: 0s - loss: 2.1539 - categorical_accuracy: 0.3541\n",
            "Epoch 00024: loss improved from 2.20938 to 2.15344, saving model to weights-improvement-24-2.1534.hdf5\n",
            "73/73 [==============================] - 10s 141ms/step - loss: 2.1533 - categorical_accuracy: 0.3545 - val_loss: 3.3263 - val_categorical_accuracy: 0.2749\n",
            "Epoch 25/50\n",
            "72/73 [============================>.] - ETA: 0s - loss: 2.1009 - categorical_accuracy: 0.3864\n",
            "Epoch 00025: loss improved from 2.15344 to 2.10025, saving model to weights-improvement-25-2.1002.hdf5\n",
            "73/73 [==============================] - 10s 142ms/step - loss: 2.1000 - categorical_accuracy: 0.3866 - val_loss: 3.3480 - val_categorical_accuracy: 0.2787\n",
            "Epoch 26/50\n",
            "72/73 [============================>.] - ETA: 0s - loss: 2.0453 - categorical_accuracy: 0.4018\n",
            "Epoch 00026: loss improved from 2.10025 to 2.04459, saving model to weights-improvement-26-2.0446.hdf5\n",
            "73/73 [==============================] - 10s 142ms/step - loss: 2.0443 - categorical_accuracy: 0.4020 - val_loss: 3.2935 - val_categorical_accuracy: 0.2930\n",
            "Epoch 27/50\n",
            "72/73 [============================>.] - ETA: 0s - loss: 1.9964 - categorical_accuracy: 0.4146\n",
            "Epoch 00027: loss improved from 2.04459 to 1.99576, saving model to weights-improvement-27-1.9958.hdf5\n",
            "73/73 [==============================] - 10s 143ms/step - loss: 1.9955 - categorical_accuracy: 0.4149 - val_loss: 3.2943 - val_categorical_accuracy: 0.2980\n",
            "Epoch 28/50\n",
            "72/73 [============================>.] - ETA: 0s - loss: 1.9527 - categorical_accuracy: 0.4350\n",
            "Epoch 00028: loss improved from 1.99576 to 1.95194, saving model to weights-improvement-28-1.9519.hdf5\n",
            "73/73 [==============================] - 10s 143ms/step - loss: 1.9516 - categorical_accuracy: 0.4352 - val_loss: 3.3305 - val_categorical_accuracy: 0.3062\n",
            "Epoch 29/50\n",
            "72/73 [============================>.] - ETA: 0s - loss: 1.9086 - categorical_accuracy: 0.4497\n",
            "Epoch 00029: loss improved from 1.95194 to 1.90815, saving model to weights-improvement-29-1.9081.hdf5\n",
            "73/73 [==============================] - 10s 142ms/step - loss: 1.9080 - categorical_accuracy: 0.4499 - val_loss: 3.3416 - val_categorical_accuracy: 0.3062\n",
            "Epoch 30/50\n",
            "72/73 [============================>.] - ETA: 0s - loss: 1.8639 - categorical_accuracy: 0.4667\n",
            "Epoch 00030: loss improved from 1.90815 to 1.86341, saving model to weights-improvement-30-1.8634.hdf5\n",
            "73/73 [==============================] - 10s 142ms/step - loss: 1.8632 - categorical_accuracy: 0.4670 - val_loss: 3.3276 - val_categorical_accuracy: 0.3194\n",
            "Epoch 31/50\n",
            "72/73 [============================>.] - ETA: 0s - loss: 1.8259 - categorical_accuracy: 0.4889\n",
            "Epoch 00031: loss improved from 1.86341 to 1.82523, saving model to weights-improvement-31-1.8252.hdf5\n",
            "73/73 [==============================] - 10s 143ms/step - loss: 1.8250 - categorical_accuracy: 0.4891 - val_loss: 3.3073 - val_categorical_accuracy: 0.3161\n",
            "Epoch 32/50\n",
            "72/73 [============================>.] - ETA: 0s - loss: 1.7805 - categorical_accuracy: 0.5080\n",
            "Epoch 00032: loss improved from 1.82523 to 1.77989, saving model to weights-improvement-32-1.7799.hdf5\n",
            "73/73 [==============================] - 10s 142ms/step - loss: 1.7796 - categorical_accuracy: 0.5083 - val_loss: 3.3683 - val_categorical_accuracy: 0.3194\n",
            "Epoch 33/50\n",
            "72/73 [============================>.] - ETA: 0s - loss: 1.7460 - categorical_accuracy: 0.5197\n",
            "Epoch 00033: loss improved from 1.77989 to 1.74568, saving model to weights-improvement-33-1.7457.hdf5\n",
            "73/73 [==============================] - 10s 143ms/step - loss: 1.7456 - categorical_accuracy: 0.5199 - val_loss: 3.3688 - val_categorical_accuracy: 0.3255\n",
            "Epoch 34/50\n",
            "72/73 [============================>.] - ETA: 0s - loss: 1.7055 - categorical_accuracy: 0.5291\n",
            "Epoch 00034: loss improved from 1.74568 to 1.70513, saving model to weights-improvement-34-1.7051.hdf5\n",
            "73/73 [==============================] - 10s 142ms/step - loss: 1.7050 - categorical_accuracy: 0.5294 - val_loss: 3.4080 - val_categorical_accuracy: 0.3216\n",
            "Epoch 35/50\n",
            "72/73 [============================>.] - ETA: 0s - loss: 1.6714 - categorical_accuracy: 0.5420\n",
            "Epoch 00035: loss improved from 1.70513 to 1.67084, saving model to weights-improvement-35-1.6708.hdf5\n",
            "73/73 [==============================] - 10s 144ms/step - loss: 1.6706 - categorical_accuracy: 0.5423 - val_loss: 3.3601 - val_categorical_accuracy: 0.3326\n",
            "Epoch 36/50\n",
            "72/73 [============================>.] - ETA: 0s - loss: 1.6372 - categorical_accuracy: 0.5528\n",
            "Epoch 00036: loss improved from 1.67084 to 1.63673, saving model to weights-improvement-36-1.6367.hdf5\n",
            "73/73 [==============================] - 10s 141ms/step - loss: 1.6365 - categorical_accuracy: 0.5531 - val_loss: 3.3170 - val_categorical_accuracy: 0.3370\n",
            "Epoch 37/50\n",
            "72/73 [============================>.] - ETA: 0s - loss: 1.6057 - categorical_accuracy: 0.5645\n",
            "Epoch 00037: loss improved from 1.63673 to 1.60520, saving model to weights-improvement-37-1.6052.hdf5\n",
            "73/73 [==============================] - 10s 142ms/step - loss: 1.6050 - categorical_accuracy: 0.5647 - val_loss: 3.4481 - val_categorical_accuracy: 0.3255\n",
            "Epoch 38/50\n",
            "72/73 [============================>.] - ETA: 0s - loss: 1.5729 - categorical_accuracy: 0.5684\n",
            "Epoch 00038: loss improved from 1.60520 to 1.57244, saving model to weights-improvement-38-1.5724.hdf5\n",
            "73/73 [==============================] - 10s 142ms/step - loss: 1.5723 - categorical_accuracy: 0.5687 - val_loss: 3.3852 - val_categorical_accuracy: 0.3337\n",
            "Epoch 39/50\n",
            "72/73 [============================>.] - ETA: 0s - loss: 1.5426 - categorical_accuracy: 0.5755\n",
            "Epoch 00039: loss improved from 1.57244 to 1.54215, saving model to weights-improvement-39-1.5421.hdf5\n",
            "73/73 [==============================] - 10s 143ms/step - loss: 1.5420 - categorical_accuracy: 0.5756 - val_loss: 3.3616 - val_categorical_accuracy: 0.3332\n",
            "Epoch 40/50\n",
            "72/73 [============================>.] - ETA: 0s - loss: 1.5093 - categorical_accuracy: 0.5847\n",
            "Epoch 00040: loss improved from 1.54215 to 1.50897, saving model to weights-improvement-40-1.5090.hdf5\n",
            "73/73 [==============================] - 10s 142ms/step - loss: 1.5088 - categorical_accuracy: 0.5849 - val_loss: 3.3823 - val_categorical_accuracy: 0.3392\n",
            "Epoch 41/50\n",
            "72/73 [============================>.] - ETA: 0s - loss: 1.4848 - categorical_accuracy: 0.5882\n",
            "Epoch 00041: loss improved from 1.50897 to 1.48441, saving model to weights-improvement-41-1.4844.hdf5\n",
            "73/73 [==============================] - 10s 142ms/step - loss: 1.4842 - categorical_accuracy: 0.5884 - val_loss: 3.4255 - val_categorical_accuracy: 0.3255\n",
            "Epoch 42/50\n",
            "72/73 [============================>.] - ETA: 0s - loss: 1.4560 - categorical_accuracy: 0.5971\n",
            "Epoch 00042: loss improved from 1.48441 to 1.45566, saving model to weights-improvement-42-1.4557.hdf5\n",
            "73/73 [==============================] - 10s 143ms/step - loss: 1.4555 - categorical_accuracy: 0.5972 - val_loss: 3.4293 - val_categorical_accuracy: 0.3293\n",
            "Epoch 43/50\n",
            "72/73 [============================>.] - ETA: 0s - loss: 1.4259 - categorical_accuracy: 0.6034\n",
            "Epoch 00043: loss improved from 1.45566 to 1.42574, saving model to weights-improvement-43-1.4257.hdf5\n",
            "73/73 [==============================] - 10s 142ms/step - loss: 1.4257 - categorical_accuracy: 0.6037 - val_loss: 3.3679 - val_categorical_accuracy: 0.3370\n",
            "Epoch 44/50\n",
            "72/73 [============================>.] - ETA: 0s - loss: 1.4041 - categorical_accuracy: 0.6071\n",
            "Epoch 00044: loss improved from 1.42574 to 1.40376, saving model to weights-improvement-44-1.4038.hdf5\n",
            "73/73 [==============================] - 10s 142ms/step - loss: 1.4036 - categorical_accuracy: 0.6074 - val_loss: 3.4381 - val_categorical_accuracy: 0.3326\n",
            "Epoch 45/50\n",
            "72/73 [============================>.] - ETA: 0s - loss: 1.3806 - categorical_accuracy: 0.6159\n",
            "Epoch 00045: loss improved from 1.40376 to 1.38024, saving model to weights-improvement-45-1.3802.hdf5\n",
            "73/73 [==============================] - 10s 142ms/step - loss: 1.3801 - categorical_accuracy: 0.6160 - val_loss: 3.4336 - val_categorical_accuracy: 0.3337\n",
            "Epoch 46/50\n",
            "72/73 [============================>.] - ETA: 0s - loss: 1.3526 - categorical_accuracy: 0.6175\n",
            "Epoch 00046: loss improved from 1.38024 to 1.35223, saving model to weights-improvement-46-1.3522.hdf5\n",
            "73/73 [==============================] - 10s 141ms/step - loss: 1.3521 - categorical_accuracy: 0.6178 - val_loss: 3.4387 - val_categorical_accuracy: 0.3315\n",
            "Epoch 47/50\n",
            "72/73 [============================>.] - ETA: 0s - loss: 1.3298 - categorical_accuracy: 0.6220\n",
            "Epoch 00047: loss improved from 1.35223 to 1.32942, saving model to weights-improvement-47-1.3294.hdf5\n",
            "73/73 [==============================] - 10s 138ms/step - loss: 1.3293 - categorical_accuracy: 0.6223 - val_loss: 3.4452 - val_categorical_accuracy: 0.3332\n",
            "Epoch 48/50\n",
            "72/73 [============================>.] - ETA: 0s - loss: 1.3092 - categorical_accuracy: 0.6276\n",
            "Epoch 00048: loss improved from 1.32942 to 1.30909, saving model to weights-improvement-48-1.3091.hdf5\n",
            "73/73 [==============================] - 10s 138ms/step - loss: 1.3090 - categorical_accuracy: 0.6279 - val_loss: 3.4247 - val_categorical_accuracy: 0.3353\n",
            "Epoch 49/50\n",
            "72/73 [============================>.] - ETA: 0s - loss: 1.2817 - categorical_accuracy: 0.6378\n",
            "Epoch 00049: loss improved from 1.30909 to 1.28140, saving model to weights-improvement-49-1.2814.hdf5\n",
            "73/73 [==============================] - 10s 138ms/step - loss: 1.2813 - categorical_accuracy: 0.6380 - val_loss: 3.5248 - val_categorical_accuracy: 0.3271\n",
            "Epoch 50/50\n",
            "72/73 [============================>.] - ETA: 0s - loss: 1.2631 - categorical_accuracy: 0.6379\n",
            "Epoch 00050: loss improved from 1.28140 to 1.26268, saving model to weights-improvement-50-1.2627.hdf5\n",
            "73/73 [==============================] - 10s 139ms/step - loss: 1.2625 - categorical_accuracy: 0.6382 - val_loss: 3.4469 - val_categorical_accuracy: 0.3381\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f2c9b55ce10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "evSozVIThJWU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XT23o0oiGYBP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GZiAZ2ILfKyo",
        "colab_type": "text"
      },
      "source": [
        "###_Evaluating Model Performance_"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FKq68i-4ksDS",
        "colab_type": "code",
        "outputId": "5bc35f08-f04f-4ec0-ef54-d0d15ddc1a39",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        }
      },
      "source": [
        "# load the network weights weights-improvement-37-0.0005.hdf5\n",
        "filename = !ls -Art | tail -n 1\n",
        "print(filename[0])\n",
        "my_model.load_weights(filename[0])\n",
        "my_model.compile(loss='categorical_crossentropy', optimizer='adam',metrics=['categorical_accuracy'])"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "weights-improvement-50-1.2627.hdf5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4SlwJNbfVaQX",
        "colab_type": "code",
        "outputId": "3a8a4c93-ea00-47d2-da93-8a27dccd8bb9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66
        }
      },
      "source": [
        "test_eval = my_model.evaluate(train_ds,callbacks=callbacks_list)\n",
        "print(\" Final Loss: \",test_eval[0])\n",
        "print(\" Final Accuracy: {}%\".format(test_eval[1]*100))"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "73/73 [==============================] - 7s 94ms/step - loss: 1.2831 - categorical_accuracy: 0.6397\n",
            " Final Loss:  1.2830573418369031\n",
            " Final Accuracy: 63.971394300460815%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PF6UhGz2Rp5C",
        "colab_type": "code",
        "outputId": "90c830e6-1ded-42ec-c995-16b9e072d868",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        }
      },
      "source": [
        "#An example image\n",
        "# im_path = X_test[random.randint(0,len(X_test))]\n",
        "im_path = '/content/101_ObjectCategories/airplanes/image_0002.jpg'\n",
        "plt.imshow(preprocess2(im_path))\n",
        "\n",
        "img=input_pipe(im_path)\n",
        "prediction = np.argmax(my_model.predict(img))\n",
        "\n",
        "\n",
        "\n",
        "print(\"Predicted Label: \",get_label_from_prediction(prediction))\n",
        "print(\"Actual Label: \",Path(im_path).parent.stem)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Predicted Label:  airplanes\n",
            "Actual Label:  airplanes\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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FbEyy/lk/kOMj7X+8b7t3ts10Y7tF1lRLURDCcXYavSAcSS97dFnjW7DzJUjB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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kMQN52sxlNEP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "guPZT0fkW-j7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}