EfficientNetB0(86%).ipynb

{
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  "metadata": {
    "colab": {
      "name": "EfficientNetB0.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "mount_file_id": "1pWiQNIt-LeyK1iJH79SBSAjD-NgoKxoT",
      "authorship_tag": "ABX9TyMjYOO/Q9u1zSh3JH26JJLE",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/nasiksami/FYP2-Codes/blob/main/EfficientNetB0(94%25).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dib_3OCGbhxw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ef853fa0-8aa6-4a19-cfb3-8c6a1b533eb8"
      },
      "source": [
        "!nvidia-smi"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Wed Nov 25 19:58:02 2020       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 455.38       Driver Version: 418.67       CUDA Version: 10.1     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   32C    P8     9W /  70W |      0MiB / 15079MiB |      0%      Default |\n",
            "|                               |                      |                 ERR! |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qhxdddtAsTH6",
        "outputId": "15f79413-5b88-48fc-c82c-afd716f411de"
      },
      "source": [
        "\n",
        "import psutil\n",
        "import humanize\n",
        "import os\n",
        "from IPython.display import display_html\n",
        "import numpy as np # linear algebra\n",
        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
        "import os\n",
        "print(os.listdir())\n",
        "dataDirectory= \"/content/drive/MyDrive/dataset_resized\" \n",
        "print(os.listdir(dataDirectory))"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['.config', 'drive', 'sample_data']\n",
            "['testing', 'training', 'models', 'SAVED_MODELS']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K1F0FqX9sgTl",
        "outputId": "3f289845-9257-4cbb-b123-6345365d1ab8"
      },
      "source": [
        "!rm -r ~/.keras\n",
        "!mkdir ~/.keras\n",
        "!mkdir ~/.keras/models\n",
        "# not enough space for both\n",
        "#!cp ../input/keras-pretrained-models/* ~/.keras/models/ \n",
        "#!cp ../input/vgg19/* ~/.keras/models\n",
        "!cp ../input/keras-pretrained-models/*notop* ~/.keras/models/\n",
        "!cp ../input/keras-pretrained-models/imagenet_class_index.json ~/.keras/models/\n",
        "#!cp ../input/keras-pretrained-models/resnet50* ~/.keras/models/"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "cp: cannot stat '../input/keras-pretrained-models/*notop*': No such file or directory\n",
            "cp: cannot stat '../input/keras-pretrained-models/imagenet_class_index.json': No such file or directory\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4Yvn1V_Wsnv1"
      },
      "source": [
        "import numpy as np\n",
        "import keras\n",
        "from keras import backend as K\n",
        "from keras.models import Sequential\n",
        "from keras.models import Model\n",
        "from keras.layers import Activation\n",
        "from keras.layers.core import Dense, Flatten\n",
        "from keras.optimizers import Adam\n",
        "from keras.metrics import categorical_crossentropy\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "from keras.layers.normalization import BatchNormalization\n",
        "from keras.layers.core import Dropout\n",
        "from keras.layers.convolutional import *\n",
        "from keras.callbacks import ModelCheckpoint\n",
        "from keras.applications.inception_v3 import InceptionV3\n",
        "from keras.applications.inception_v3 import preprocess_input\n",
        "from keras.applications.inception_v3 import decode_predictions\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import average_precision_score\n",
        "from sklearn.metrics import recall_score\n",
        "from sklearn.metrics import precision_score\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import classification_report\n",
        "from keras.preprocessing.image import load_img,img_to_array\n",
        "from keras.models import model_from_json\n",
        "import itertools\n",
        "import matplotlib.pyplot as plt\n",
        "import time\n",
        "import pandas as pd\n",
        "%matplotlib inline\n",
        "import skimage.io\n",
        "import os \n",
        "import glob\n",
        "import tqdm\n",
        "import tensorflow"
      ],
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_qeclVABspUi",
        "outputId": "d4f34301-ca06-4ab2-cead-f7bd0771e752"
      },
      "source": [
        "train_path = dataDirectory+'/training'\n",
        "test_path  = dataDirectory+'/testing'\n",
        "print(os.listdir(train_path))\n",
        "print(os.listdir(test_path))"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['ewaste', 'cardboard', 'glass', 'paper', 'metal', 'trash', 'plastic']\n",
            "['trash', 'metal', 'plastic', 'ewaste', 'cardboard', 'glass', 'paper']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eGL4RaSwsrh1"
      },
      "source": [
        "train_datagen = ImageDataGenerator(\n",
        "        rotation_range=40,\n",
        "        width_shift_range=0.2,\n",
        "        height_shift_range=0.2,\n",
        "        shear_range=0.2,\n",
        "        zoom_range=0.2,\n",
        "        fill_mode='nearest',\n",
        "        validation_split=0.2) # set validation split"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nX65JSj7s32b"
      },
      "source": [
        "selectedClasses = [ 'cardboard','ewaste', 'glass', 'metal','paper', 'plastic', 'trash'] "
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VkuGZskDtL9o",
        "outputId": "e4aa59ed-d304-4778-9f5f-47d868669474"
      },
      "source": [
        "batchSize=64\n",
        "\n",
        "train_generator = train_datagen.flow_from_directory(\n",
        "    train_path,\n",
        "    target_size=(224, 224),\n",
        "    batch_size=batchSize,\n",
        "    classes=selectedClasses,\n",
        "    subset='training') # set as training data\n",
        "\n",
        "validation_generator = train_datagen.flow_from_directory(\n",
        "    train_path, # same directory as training data\n",
        "    target_size=(224, 224),\n",
        "    batch_size=batchSize,\n",
        "    classes=selectedClasses,\n",
        "    subset='validation') # set as validation data\n",
        "\n",
        "test_generator = ImageDataGenerator().flow_from_directory(\n",
        "    test_path, \n",
        "    target_size=(224,224), \n",
        "    classes=selectedClasses,\n",
        "    shuffle= False,\n",
        "    batch_size = batchSize)# set as test data"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 1933 images belonging to 7 classes.\n",
            "Found 481 images belonging to 7 classes.\n",
            "Found 608 images belonging to 7 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n4MWPjmltRcr",
        "outputId": "eda01a15-c0c7-4850-df50-1ad72c813a90"
      },
      "source": [
        "print (\"In train_generator \")\n",
        "for cls in range(len (train_generator.class_indices)):\n",
        "    print(selectedClasses[cls],\":\\t\",list(train_generator.classes).count(cls))\n",
        "print (\"\") \n",
        "\n",
        "print (\"In validation_generator \")\n",
        "for cls in range(len (validation_generator.class_indices)):\n",
        "    print(selectedClasses[cls],\":\\t\",list(validation_generator.classes).count(cls))\n",
        "print (\"\") \n",
        "\n",
        "print (\"In test_generator \")\n",
        "for cls in range(len (test_generator.class_indices)):\n",
        "    print(selectedClasses[cls],\":\\t\",list(test_generator.classes).count(cls))"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "In train_generator \n",
            "cardboard :\t 258\n",
            "ewaste :\t 316\n",
            "glass :\t 320\n",
            "metal :\t 263\n",
            "paper :\t 380\n",
            "plastic :\t 308\n",
            "trash :\t 88\n",
            "\n",
            "In validation_generator \n",
            "cardboard :\t 64\n",
            "ewaste :\t 79\n",
            "glass :\t 80\n",
            "metal :\t 65\n",
            "paper :\t 95\n",
            "plastic :\t 77\n",
            "trash :\t 21\n",
            "\n",
            "In test_generator \n",
            "cardboard :\t 81\n",
            "ewaste :\t 100\n",
            "glass :\t 101\n",
            "metal :\t 82\n",
            "paper :\t 119\n",
            "plastic :\t 97\n",
            "trash :\t 28\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "56C9T9mRtWAa"
      },
      "source": [
        "#plots images with labels within jupyter notebook\n",
        "def plots(ims, figsize = (22,22), rows=4, interp=False, titles=None, maxNum = 9):\n",
        "    if type(ims[0] is np.ndarray):\n",
        "        ims = np.array(ims).astype(np.uint8)\n",
        "        if(ims.shape[-1] != 3):\n",
        "            ims = ims.transpose((0,2,3,1))\n",
        "           \n",
        "    f = plt.figure(figsize=figsize)\n",
        "    #cols = len(ims) //rows if len(ims) % 2 == 0 else len(ims)//rows + 1\n",
        "    cols = maxNum // rows if maxNum % 2 == 0 else maxNum//rows + 1\n",
        "    #for i in range(len(ims)):\n",
        "    for i in range(maxNum):\n",
        "        sp = f.add_subplot(rows, cols, i+1)\n",
        "        sp.axis('Off')\n",
        "        if titles is not None:\n",
        "            sp.set_title(titles[i], fontsize=20)\n",
        "        plt.imshow(ims[i], interpolation = None if interp else 'none')   "
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p7vE_WOztZkL"
      },
      "source": [
        "train_generator.reset()\n",
        "imgs, labels = train_generator.next()\n",
        "\n",
        "#print(labels)\n",
        "\n",
        "labelNames=[]\n",
        "labelIndices=[np.where(r==1)[0][0] for r in labels]\n",
        "#print(labelIndices)\n",
        "\n",
        "for ind in labelIndices:\n",
        "    for labelName,labelIndex in train_generator.class_indices.items():\n",
        "        if labelIndex == ind:\n",
        "            #print (labelName)\n",
        "            labelNames.append(labelName)\n",
        "\n",
        "#labels"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "GuhD6ydOtbUc",
        "outputId": "69a43de6-54c6-4c9b-8a08-ca79e2026153"
      },
      "source": [
        "plots(imgs, rows=4, titles = labelNames, maxNum=8)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1584x1584 with 8 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fvEgObcZ_mB9",
        "outputId": "cb8a9efc-3770-4e33-f86e-ef0b3d0ef686"
      },
      "source": [
        "from tensorflow.keras.applications import EfficientNetB0\n",
        "model = EfficientNetB0(weights='imagenet')"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/keras-applications/efficientnetb0.h5\n",
            "21839872/21834768 [==============================] - 0s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S6IXSnDBtfqb",
        "outputId": "e62055ef-958d-4b58-eac9-ca769abbd6b0"
      },
      "source": [
        "# Create the base model from the pre-trained model MobileNet V2\n",
        "import tensorflow as tf\n",
        "base_model = tf.keras.applications.EfficientNetB0(input_shape=(224, 224, 3),include_top=False,weights='imagenet')\n",
        "\n"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/keras-applications/efficientnetb0_notop.h5\n",
            "16711680/16705208 [==============================] - 0s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zG0-w_FHtwkc"
      },
      "source": [
        "base_model.trainable = False"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aEoup3yHt5FN"
      },
      "source": [
        "x = Flatten()(base_model.output)"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3gz6254-uC6A"
      },
      "source": [
        "prediction = Dense(7, activation='softmax')(x)\n",
        "\n",
        "# create a model object\n",
        "model = Model(inputs=base_model.input, outputs=prediction)"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eRXMXJjOvvQ2",
        "outputId": "c9801d53-5d1e-4953-a89f-9f920cd23a14"
      },
      "source": [
        "# view the structure of the model\n",
        "model.summary()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"functional_1\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_2 (InputLayer)            [(None, 224, 224, 3) 0                                            \n",
            "__________________________________________________________________________________________________\n",
            "rescaling_1 (Rescaling)         (None, 224, 224, 3)  0           input_2[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "normalization_1 (Normalization) (None, 224, 224, 3)  7           rescaling_1[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "stem_conv_pad (ZeroPadding2D)   (None, 225, 225, 3)  0           normalization_1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "stem_conv (Conv2D)              (None, 112, 112, 32) 864         stem_conv_pad[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "stem_bn (BatchNormalization)    (None, 112, 112, 32) 128         stem_conv[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "stem_activation (Activation)    (None, 112, 112, 32) 0           stem_bn[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "block1a_dwconv (DepthwiseConv2D (None, 112, 112, 32) 288         stem_activation[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block1a_bn (BatchNormalization) (None, 112, 112, 32) 128         block1a_dwconv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block1a_activation (Activation) (None, 112, 112, 32) 0           block1a_bn[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "block1a_se_squeeze (GlobalAvera (None, 32)           0           block1a_activation[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block1a_se_reshape (Reshape)    (None, 1, 1, 32)     0           block1a_se_squeeze[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block1a_se_reduce (Conv2D)      (None, 1, 1, 8)      264         block1a_se_reshape[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block1a_se_expand (Conv2D)      (None, 1, 1, 32)     288         block1a_se_reduce[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block1a_se_excite (Multiply)    (None, 112, 112, 32) 0           block1a_activation[0][0]         \n",
            "                                                                 block1a_se_expand[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block1a_project_conv (Conv2D)   (None, 112, 112, 16) 512         block1a_se_excite[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block1a_project_bn (BatchNormal (None, 112, 112, 16) 64          block1a_project_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block2a_expand_conv (Conv2D)    (None, 112, 112, 96) 1536        block1a_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block2a_expand_bn (BatchNormali (None, 112, 112, 96) 384         block2a_expand_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block2a_expand_activation (Acti (None, 112, 112, 96) 0           block2a_expand_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block2a_dwconv_pad (ZeroPadding (None, 113, 113, 96) 0           block2a_expand_activation[0][0]  \n",
            "__________________________________________________________________________________________________\n",
            "block2a_dwconv (DepthwiseConv2D (None, 56, 56, 96)   864         block2a_dwconv_pad[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block2a_bn (BatchNormalization) (None, 56, 56, 96)   384         block2a_dwconv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block2a_activation (Activation) (None, 56, 56, 96)   0           block2a_bn[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "block2a_se_squeeze (GlobalAvera (None, 96)           0           block2a_activation[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block2a_se_reshape (Reshape)    (None, 1, 1, 96)     0           block2a_se_squeeze[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block2a_se_reduce (Conv2D)      (None, 1, 1, 4)      388         block2a_se_reshape[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block2a_se_expand (Conv2D)      (None, 1, 1, 96)     480         block2a_se_reduce[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block2a_se_excite (Multiply)    (None, 56, 56, 96)   0           block2a_activation[0][0]         \n",
            "                                                                 block2a_se_expand[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block2a_project_conv (Conv2D)   (None, 56, 56, 24)   2304        block2a_se_excite[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block2a_project_bn (BatchNormal (None, 56, 56, 24)   96          block2a_project_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block2b_expand_conv (Conv2D)    (None, 56, 56, 144)  3456        block2a_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block2b_expand_bn (BatchNormali (None, 56, 56, 144)  576         block2b_expand_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block2b_expand_activation (Acti (None, 56, 56, 144)  0           block2b_expand_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block2b_dwconv (DepthwiseConv2D (None, 56, 56, 144)  1296        block2b_expand_activation[0][0]  \n",
            "__________________________________________________________________________________________________\n",
            "block2b_bn (BatchNormalization) (None, 56, 56, 144)  576         block2b_dwconv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block2b_activation (Activation) (None, 56, 56, 144)  0           block2b_bn[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "block2b_se_squeeze (GlobalAvera (None, 144)          0           block2b_activation[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block2b_se_reshape (Reshape)    (None, 1, 1, 144)    0           block2b_se_squeeze[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block2b_se_reduce (Conv2D)      (None, 1, 1, 6)      870         block2b_se_reshape[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block2b_se_expand (Conv2D)      (None, 1, 1, 144)    1008        block2b_se_reduce[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block2b_se_excite (Multiply)    (None, 56, 56, 144)  0           block2b_activation[0][0]         \n",
            "                                                                 block2b_se_expand[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block2b_project_conv (Conv2D)   (None, 56, 56, 24)   3456        block2b_se_excite[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block2b_project_bn (BatchNormal (None, 56, 56, 24)   96          block2b_project_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block2b_drop (Dropout)          (None, 56, 56, 24)   0           block2b_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block2b_add (Add)               (None, 56, 56, 24)   0           block2b_drop[0][0]               \n",
            "                                                                 block2a_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block3a_expand_conv (Conv2D)    (None, 56, 56, 144)  3456        block2b_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block3a_expand_bn (BatchNormali (None, 56, 56, 144)  576         block3a_expand_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block3a_expand_activation (Acti (None, 56, 56, 144)  0           block3a_expand_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block3a_dwconv_pad (ZeroPadding (None, 59, 59, 144)  0           block3a_expand_activation[0][0]  \n",
            "__________________________________________________________________________________________________\n",
            "block3a_dwconv (DepthwiseConv2D (None, 28, 28, 144)  3600        block3a_dwconv_pad[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block3a_bn (BatchNormalization) (None, 28, 28, 144)  576         block3a_dwconv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block3a_activation (Activation) (None, 28, 28, 144)  0           block3a_bn[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "block3a_se_squeeze (GlobalAvera (None, 144)          0           block3a_activation[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block3a_se_reshape (Reshape)    (None, 1, 1, 144)    0           block3a_se_squeeze[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block3a_se_reduce (Conv2D)      (None, 1, 1, 6)      870         block3a_se_reshape[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block3a_se_expand (Conv2D)      (None, 1, 1, 144)    1008        block3a_se_reduce[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block3a_se_excite (Multiply)    (None, 28, 28, 144)  0           block3a_activation[0][0]         \n",
            "                                                                 block3a_se_expand[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block3a_project_conv (Conv2D)   (None, 28, 28, 40)   5760        block3a_se_excite[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block3a_project_bn (BatchNormal (None, 28, 28, 40)   160         block3a_project_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block3b_expand_conv (Conv2D)    (None, 28, 28, 240)  9600        block3a_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block3b_expand_bn (BatchNormali (None, 28, 28, 240)  960         block3b_expand_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block3b_expand_activation (Acti (None, 28, 28, 240)  0           block3b_expand_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block3b_dwconv (DepthwiseConv2D (None, 28, 28, 240)  6000        block3b_expand_activation[0][0]  \n",
            "__________________________________________________________________________________________________\n",
            "block3b_bn (BatchNormalization) (None, 28, 28, 240)  960         block3b_dwconv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block3b_activation (Activation) (None, 28, 28, 240)  0           block3b_bn[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "block3b_se_squeeze (GlobalAvera (None, 240)          0           block3b_activation[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block3b_se_reshape (Reshape)    (None, 1, 1, 240)    0           block3b_se_squeeze[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block3b_se_reduce (Conv2D)      (None, 1, 1, 10)     2410        block3b_se_reshape[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block3b_se_expand (Conv2D)      (None, 1, 1, 240)    2640        block3b_se_reduce[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block3b_se_excite (Multiply)    (None, 28, 28, 240)  0           block3b_activation[0][0]         \n",
            "                                                                 block3b_se_expand[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block3b_project_conv (Conv2D)   (None, 28, 28, 40)   9600        block3b_se_excite[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block3b_project_bn (BatchNormal (None, 28, 28, 40)   160         block3b_project_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block3b_drop (Dropout)          (None, 28, 28, 40)   0           block3b_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block3b_add (Add)               (None, 28, 28, 40)   0           block3b_drop[0][0]               \n",
            "                                                                 block3a_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block4a_expand_conv (Conv2D)    (None, 28, 28, 240)  9600        block3b_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block4a_expand_bn (BatchNormali (None, 28, 28, 240)  960         block4a_expand_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block4a_expand_activation (Acti (None, 28, 28, 240)  0           block4a_expand_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block4a_dwconv_pad (ZeroPadding (None, 29, 29, 240)  0           block4a_expand_activation[0][0]  \n",
            "__________________________________________________________________________________________________\n",
            "block4a_dwconv (DepthwiseConv2D (None, 14, 14, 240)  2160        block4a_dwconv_pad[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block4a_bn (BatchNormalization) (None, 14, 14, 240)  960         block4a_dwconv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block4a_activation (Activation) (None, 14, 14, 240)  0           block4a_bn[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "block4a_se_squeeze (GlobalAvera (None, 240)          0           block4a_activation[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block4a_se_reshape (Reshape)    (None, 1, 1, 240)    0           block4a_se_squeeze[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block4a_se_reduce (Conv2D)      (None, 1, 1, 10)     2410        block4a_se_reshape[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block4a_se_expand (Conv2D)      (None, 1, 1, 240)    2640        block4a_se_reduce[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block4a_se_excite (Multiply)    (None, 14, 14, 240)  0           block4a_activation[0][0]         \n",
            "                                                                 block4a_se_expand[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block4a_project_conv (Conv2D)   (None, 14, 14, 80)   19200       block4a_se_excite[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block4a_project_bn (BatchNormal (None, 14, 14, 80)   320         block4a_project_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block4b_expand_conv (Conv2D)    (None, 14, 14, 480)  38400       block4a_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block4b_expand_bn (BatchNormali (None, 14, 14, 480)  1920        block4b_expand_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block4b_expand_activation (Acti (None, 14, 14, 480)  0           block4b_expand_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block4b_dwconv (DepthwiseConv2D (None, 14, 14, 480)  4320        block4b_expand_activation[0][0]  \n",
            "__________________________________________________________________________________________________\n",
            "block4b_bn (BatchNormalization) (None, 14, 14, 480)  1920        block4b_dwconv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block4b_activation (Activation) (None, 14, 14, 480)  0           block4b_bn[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "block4b_se_squeeze (GlobalAvera (None, 480)          0           block4b_activation[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block4b_se_reshape (Reshape)    (None, 1, 1, 480)    0           block4b_se_squeeze[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block4b_se_reduce (Conv2D)      (None, 1, 1, 20)     9620        block4b_se_reshape[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block4b_se_expand (Conv2D)      (None, 1, 1, 480)    10080       block4b_se_reduce[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block4b_se_excite (Multiply)    (None, 14, 14, 480)  0           block4b_activation[0][0]         \n",
            "                                                                 block4b_se_expand[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block4b_project_conv (Conv2D)   (None, 14, 14, 80)   38400       block4b_se_excite[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block4b_project_bn (BatchNormal (None, 14, 14, 80)   320         block4b_project_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block4b_drop (Dropout)          (None, 14, 14, 80)   0           block4b_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block4b_add (Add)               (None, 14, 14, 80)   0           block4b_drop[0][0]               \n",
            "                                                                 block4a_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block4c_expand_conv (Conv2D)    (None, 14, 14, 480)  38400       block4b_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block4c_expand_bn (BatchNormali (None, 14, 14, 480)  1920        block4c_expand_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block4c_expand_activation (Acti (None, 14, 14, 480)  0           block4c_expand_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block4c_dwconv (DepthwiseConv2D (None, 14, 14, 480)  4320        block4c_expand_activation[0][0]  \n",
            "__________________________________________________________________________________________________\n",
            "block4c_bn (BatchNormalization) (None, 14, 14, 480)  1920        block4c_dwconv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block4c_activation (Activation) (None, 14, 14, 480)  0           block4c_bn[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "block4c_se_squeeze (GlobalAvera (None, 480)          0           block4c_activation[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block4c_se_reshape (Reshape)    (None, 1, 1, 480)    0           block4c_se_squeeze[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block4c_se_reduce (Conv2D)      (None, 1, 1, 20)     9620        block4c_se_reshape[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block4c_se_expand (Conv2D)      (None, 1, 1, 480)    10080       block4c_se_reduce[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block4c_se_excite (Multiply)    (None, 14, 14, 480)  0           block4c_activation[0][0]         \n",
            "                                                                 block4c_se_expand[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block4c_project_conv (Conv2D)   (None, 14, 14, 80)   38400       block4c_se_excite[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block4c_project_bn (BatchNormal (None, 14, 14, 80)   320         block4c_project_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block4c_drop (Dropout)          (None, 14, 14, 80)   0           block4c_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block4c_add (Add)               (None, 14, 14, 80)   0           block4c_drop[0][0]               \n",
            "                                                                 block4b_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block5a_expand_conv (Conv2D)    (None, 14, 14, 480)  38400       block4c_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block5a_expand_bn (BatchNormali (None, 14, 14, 480)  1920        block5a_expand_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block5a_expand_activation (Acti (None, 14, 14, 480)  0           block5a_expand_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block5a_dwconv (DepthwiseConv2D (None, 14, 14, 480)  12000       block5a_expand_activation[0][0]  \n",
            "__________________________________________________________________________________________________\n",
            "block5a_bn (BatchNormalization) (None, 14, 14, 480)  1920        block5a_dwconv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block5a_activation (Activation) (None, 14, 14, 480)  0           block5a_bn[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "block5a_se_squeeze (GlobalAvera (None, 480)          0           block5a_activation[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block5a_se_reshape (Reshape)    (None, 1, 1, 480)    0           block5a_se_squeeze[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block5a_se_reduce (Conv2D)      (None, 1, 1, 20)     9620        block5a_se_reshape[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block5a_se_expand (Conv2D)      (None, 1, 1, 480)    10080       block5a_se_reduce[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block5a_se_excite (Multiply)    (None, 14, 14, 480)  0           block5a_activation[0][0]         \n",
            "                                                                 block5a_se_expand[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block5a_project_conv (Conv2D)   (None, 14, 14, 112)  53760       block5a_se_excite[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block5a_project_bn (BatchNormal (None, 14, 14, 112)  448         block5a_project_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block5b_expand_conv (Conv2D)    (None, 14, 14, 672)  75264       block5a_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block5b_expand_bn (BatchNormali (None, 14, 14, 672)  2688        block5b_expand_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block5b_expand_activation (Acti (None, 14, 14, 672)  0           block5b_expand_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block5b_dwconv (DepthwiseConv2D (None, 14, 14, 672)  16800       block5b_expand_activation[0][0]  \n",
            "__________________________________________________________________________________________________\n",
            "block5b_bn (BatchNormalization) (None, 14, 14, 672)  2688        block5b_dwconv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block5b_activation (Activation) (None, 14, 14, 672)  0           block5b_bn[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "block5b_se_squeeze (GlobalAvera (None, 672)          0           block5b_activation[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block5b_se_reshape (Reshape)    (None, 1, 1, 672)    0           block5b_se_squeeze[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block5b_se_reduce (Conv2D)      (None, 1, 1, 28)     18844       block5b_se_reshape[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block5b_se_expand (Conv2D)      (None, 1, 1, 672)    19488       block5b_se_reduce[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block5b_se_excite (Multiply)    (None, 14, 14, 672)  0           block5b_activation[0][0]         \n",
            "                                                                 block5b_se_expand[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block5b_project_conv (Conv2D)   (None, 14, 14, 112)  75264       block5b_se_excite[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block5b_project_bn (BatchNormal (None, 14, 14, 112)  448         block5b_project_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block5b_drop (Dropout)          (None, 14, 14, 112)  0           block5b_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block5b_add (Add)               (None, 14, 14, 112)  0           block5b_drop[0][0]               \n",
            "                                                                 block5a_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block5c_expand_conv (Conv2D)    (None, 14, 14, 672)  75264       block5b_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block5c_expand_bn (BatchNormali (None, 14, 14, 672)  2688        block5c_expand_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block5c_expand_activation (Acti (None, 14, 14, 672)  0           block5c_expand_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block5c_dwconv (DepthwiseConv2D (None, 14, 14, 672)  16800       block5c_expand_activation[0][0]  \n",
            "__________________________________________________________________________________________________\n",
            "block5c_bn (BatchNormalization) (None, 14, 14, 672)  2688        block5c_dwconv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block5c_activation (Activation) (None, 14, 14, 672)  0           block5c_bn[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "block5c_se_squeeze (GlobalAvera (None, 672)          0           block5c_activation[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block5c_se_reshape (Reshape)    (None, 1, 1, 672)    0           block5c_se_squeeze[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block5c_se_reduce (Conv2D)      (None, 1, 1, 28)     18844       block5c_se_reshape[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block5c_se_expand (Conv2D)      (None, 1, 1, 672)    19488       block5c_se_reduce[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block5c_se_excite (Multiply)    (None, 14, 14, 672)  0           block5c_activation[0][0]         \n",
            "                                                                 block5c_se_expand[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block5c_project_conv (Conv2D)   (None, 14, 14, 112)  75264       block5c_se_excite[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block5c_project_bn (BatchNormal (None, 14, 14, 112)  448         block5c_project_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block5c_drop (Dropout)          (None, 14, 14, 112)  0           block5c_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block5c_add (Add)               (None, 14, 14, 112)  0           block5c_drop[0][0]               \n",
            "                                                                 block5b_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block6a_expand_conv (Conv2D)    (None, 14, 14, 672)  75264       block5c_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block6a_expand_bn (BatchNormali (None, 14, 14, 672)  2688        block6a_expand_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block6a_expand_activation (Acti (None, 14, 14, 672)  0           block6a_expand_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block6a_dwconv_pad (ZeroPadding (None, 17, 17, 672)  0           block6a_expand_activation[0][0]  \n",
            "__________________________________________________________________________________________________\n",
            "block6a_dwconv (DepthwiseConv2D (None, 7, 7, 672)    16800       block6a_dwconv_pad[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6a_bn (BatchNormalization) (None, 7, 7, 672)    2688        block6a_dwconv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block6a_activation (Activation) (None, 7, 7, 672)    0           block6a_bn[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "block6a_se_squeeze (GlobalAvera (None, 672)          0           block6a_activation[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6a_se_reshape (Reshape)    (None, 1, 1, 672)    0           block6a_se_squeeze[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6a_se_reduce (Conv2D)      (None, 1, 1, 28)     18844       block6a_se_reshape[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6a_se_expand (Conv2D)      (None, 1, 1, 672)    19488       block6a_se_reduce[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block6a_se_excite (Multiply)    (None, 7, 7, 672)    0           block6a_activation[0][0]         \n",
            "                                                                 block6a_se_expand[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block6a_project_conv (Conv2D)   (None, 7, 7, 192)    129024      block6a_se_excite[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block6a_project_bn (BatchNormal (None, 7, 7, 192)    768         block6a_project_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block6b_expand_conv (Conv2D)    (None, 7, 7, 1152)   221184      block6a_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6b_expand_bn (BatchNormali (None, 7, 7, 1152)   4608        block6b_expand_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block6b_expand_activation (Acti (None, 7, 7, 1152)   0           block6b_expand_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block6b_dwconv (DepthwiseConv2D (None, 7, 7, 1152)   28800       block6b_expand_activation[0][0]  \n",
            "__________________________________________________________________________________________________\n",
            "block6b_bn (BatchNormalization) (None, 7, 7, 1152)   4608        block6b_dwconv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block6b_activation (Activation) (None, 7, 7, 1152)   0           block6b_bn[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "block6b_se_squeeze (GlobalAvera (None, 1152)         0           block6b_activation[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6b_se_reshape (Reshape)    (None, 1, 1, 1152)   0           block6b_se_squeeze[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6b_se_reduce (Conv2D)      (None, 1, 1, 48)     55344       block6b_se_reshape[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6b_se_expand (Conv2D)      (None, 1, 1, 1152)   56448       block6b_se_reduce[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block6b_se_excite (Multiply)    (None, 7, 7, 1152)   0           block6b_activation[0][0]         \n",
            "                                                                 block6b_se_expand[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block6b_project_conv (Conv2D)   (None, 7, 7, 192)    221184      block6b_se_excite[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block6b_project_bn (BatchNormal (None, 7, 7, 192)    768         block6b_project_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block6b_drop (Dropout)          (None, 7, 7, 192)    0           block6b_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6b_add (Add)               (None, 7, 7, 192)    0           block6b_drop[0][0]               \n",
            "                                                                 block6a_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6c_expand_conv (Conv2D)    (None, 7, 7, 1152)   221184      block6b_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block6c_expand_bn (BatchNormali (None, 7, 7, 1152)   4608        block6c_expand_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block6c_expand_activation (Acti (None, 7, 7, 1152)   0           block6c_expand_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block6c_dwconv (DepthwiseConv2D (None, 7, 7, 1152)   28800       block6c_expand_activation[0][0]  \n",
            "__________________________________________________________________________________________________\n",
            "block6c_bn (BatchNormalization) (None, 7, 7, 1152)   4608        block6c_dwconv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block6c_activation (Activation) (None, 7, 7, 1152)   0           block6c_bn[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "block6c_se_squeeze (GlobalAvera (None, 1152)         0           block6c_activation[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6c_se_reshape (Reshape)    (None, 1, 1, 1152)   0           block6c_se_squeeze[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6c_se_reduce (Conv2D)      (None, 1, 1, 48)     55344       block6c_se_reshape[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6c_se_expand (Conv2D)      (None, 1, 1, 1152)   56448       block6c_se_reduce[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block6c_se_excite (Multiply)    (None, 7, 7, 1152)   0           block6c_activation[0][0]         \n",
            "                                                                 block6c_se_expand[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block6c_project_conv (Conv2D)   (None, 7, 7, 192)    221184      block6c_se_excite[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block6c_project_bn (BatchNormal (None, 7, 7, 192)    768         block6c_project_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block6c_drop (Dropout)          (None, 7, 7, 192)    0           block6c_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6c_add (Add)               (None, 7, 7, 192)    0           block6c_drop[0][0]               \n",
            "                                                                 block6b_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block6d_expand_conv (Conv2D)    (None, 7, 7, 1152)   221184      block6c_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block6d_expand_bn (BatchNormali (None, 7, 7, 1152)   4608        block6d_expand_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block6d_expand_activation (Acti (None, 7, 7, 1152)   0           block6d_expand_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block6d_dwconv (DepthwiseConv2D (None, 7, 7, 1152)   28800       block6d_expand_activation[0][0]  \n",
            "__________________________________________________________________________________________________\n",
            "block6d_bn (BatchNormalization) (None, 7, 7, 1152)   4608        block6d_dwconv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block6d_activation (Activation) (None, 7, 7, 1152)   0           block6d_bn[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "block6d_se_squeeze (GlobalAvera (None, 1152)         0           block6d_activation[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6d_se_reshape (Reshape)    (None, 1, 1, 1152)   0           block6d_se_squeeze[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6d_se_reduce (Conv2D)      (None, 1, 1, 48)     55344       block6d_se_reshape[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6d_se_expand (Conv2D)      (None, 1, 1, 1152)   56448       block6d_se_reduce[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block6d_se_excite (Multiply)    (None, 7, 7, 1152)   0           block6d_activation[0][0]         \n",
            "                                                                 block6d_se_expand[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block6d_project_conv (Conv2D)   (None, 7, 7, 192)    221184      block6d_se_excite[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block6d_project_bn (BatchNormal (None, 7, 7, 192)    768         block6d_project_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block6d_drop (Dropout)          (None, 7, 7, 192)    0           block6d_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6d_add (Add)               (None, 7, 7, 192)    0           block6d_drop[0][0]               \n",
            "                                                                 block6c_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block7a_expand_conv (Conv2D)    (None, 7, 7, 1152)   221184      block6d_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block7a_expand_bn (BatchNormali (None, 7, 7, 1152)   4608        block7a_expand_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block7a_expand_activation (Acti (None, 7, 7, 1152)   0           block7a_expand_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block7a_dwconv (DepthwiseConv2D (None, 7, 7, 1152)   10368       block7a_expand_activation[0][0]  \n",
            "__________________________________________________________________________________________________\n",
            "block7a_bn (BatchNormalization) (None, 7, 7, 1152)   4608        block7a_dwconv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block7a_activation (Activation) (None, 7, 7, 1152)   0           block7a_bn[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "block7a_se_squeeze (GlobalAvera (None, 1152)         0           block7a_activation[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block7a_se_reshape (Reshape)    (None, 1, 1, 1152)   0           block7a_se_squeeze[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block7a_se_reduce (Conv2D)      (None, 1, 1, 48)     55344       block7a_se_reshape[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block7a_se_expand (Conv2D)      (None, 1, 1, 1152)   56448       block7a_se_reduce[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block7a_se_excite (Multiply)    (None, 7, 7, 1152)   0           block7a_activation[0][0]         \n",
            "                                                                 block7a_se_expand[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block7a_project_conv (Conv2D)   (None, 7, 7, 320)    368640      block7a_se_excite[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block7a_project_bn (BatchNormal (None, 7, 7, 320)    1280        block7a_project_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "top_conv (Conv2D)               (None, 7, 7, 1280)   409600      block7a_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "top_bn (BatchNormalization)     (None, 7, 7, 1280)   5120        top_conv[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "top_activation (Activation)     (None, 7, 7, 1280)   0           top_bn[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "flatten (Flatten)               (None, 62720)        0           top_activation[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "dense (Dense)                   (None, 7)            439047      flatten[0][0]                    \n",
            "==================================================================================================\n",
            "Total params: 4,488,618\n",
            "Trainable params: 439,047\n",
            "Non-trainable params: 4,049,571\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2j0AOxboxriw"
      },
      "source": [
        "from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau, ModelCheckpoint"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xVR7_4zHwtIv"
      },
      "source": [
        "#Atutomatic rename with epoch number and val accuracy:\n",
        "#filepath=\"checkpoints/weights-improvement-epeoch-{epoch:02d}-val_acc-{val_acc:.2f}.hdf5\"\n",
        "\n",
        "\n",
        " \n",
        "modelName= \"EfficientNetB0\"\n",
        "#save the best weights over the same file with the model name\n",
        "\n",
        "#filepath=\"checkpoints/\"+modelName+\"_bestweights.hdf5\"\n",
        "filepath=modelName+\"_bestweights.hdf5\"\n",
        "earlystopping = EarlyStopping(monitor = 'val_accuracy', \n",
        "                              mode = 'max' , \n",
        "                              patience = 10,\n",
        "                              verbose = 1)\n",
        "checkpoint = ModelCheckpoint(filepath, monitor='val_acc', verbose=1, save_best_only=True, mode='max')\n",
        "callbacks_list = [earlystopping,checkpoint]"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CgzrL-emxFnt"
      },
      "source": [
        "model.compile(Adam(lr=0.001), loss='categorical_crossentropy', metrics=['accuracy'])"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cpKm9mVWxQPS",
        "outputId": "1f97cf23-54f0-4d5f-e09c-b4d6ad2f8fe7"
      },
      "source": [
        "stepsPerEpoch= (train_generator.samples+ (batchSize-1)) // batchSize\n",
        "print(\"stepsPerEpoch: \", stepsPerEpoch)\n",
        "\n",
        "validationSteps=(validation_generator.samples+ (batchSize-1)) // batchSize\n",
        "print(\"validationSteps: \", validationSteps)\n"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "stepsPerEpoch:  31\n",
            "validationSteps:  8\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D-XmCAEvxUb7",
        "outputId": "537f3321-aa61-4868-ecb0-8609a391dacc"
      },
      "source": [
        "train_generator.reset()\n",
        "validation_generator.reset()\n",
        "\n",
        "# Fit the model\n",
        "history = model.fit_generator(\n",
        "    train_generator, \n",
        "    validation_data = validation_generator,\n",
        "    epochs = 50,\n",
        "    steps_per_epoch = stepsPerEpoch,\n",
        "    validation_steps= validationSteps,\n",
        "    callbacks=callbacks_list,\n",
        "    verbose=1)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/50\n",
            "31/31 [==============================] - ETA: 0s - loss: 2.0773 - accuracy: 0.6570 WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n",
            "31/31 [==============================] - 843s 27s/step - loss: 2.0773 - accuracy: 0.6570 - val_loss: 2.1774 - val_accuracy: 0.7214\n",
            "Epoch 2/50\n",
            "31/31 [==============================] - ETA: 0s - loss: 1.1193 - accuracy: 0.8184WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n",
            "31/31 [==============================] - 43s 1s/step - loss: 1.1193 - accuracy: 0.8184 - val_loss: 1.7838 - val_accuracy: 0.7360\n",
            "Epoch 3/50\n",
            "31/31 [==============================] - ETA: 0s - loss: 0.9249 - accuracy: 0.8407WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n",
            "31/31 [==============================] - 44s 1s/step - loss: 0.9249 - accuracy: 0.8407 - val_loss: 1.8194 - val_accuracy: 0.7464\n",
            "Epoch 4/50\n",
            "31/31 [==============================] - ETA: 0s - loss: 0.9928 - accuracy: 0.8396WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n",
            "31/31 [==============================] - 45s 1s/step - loss: 0.9928 - accuracy: 0.8396 - val_loss: 2.0662 - val_accuracy: 0.7588\n",
            "Epoch 5/50\n",
            "31/31 [==============================] - ETA: 0s - loss: 0.6138 - accuracy: 0.8971WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n",
            "31/31 [==============================] - 44s 1s/step - loss: 0.6138 - accuracy: 0.8971 - val_loss: 2.0610 - val_accuracy: 0.7672\n",
            "Epoch 6/50\n",
            "31/31 [==============================] - ETA: 0s - loss: 0.7508 - accuracy: 0.8914WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n",
            "31/31 [==============================] - 44s 1s/step - loss: 0.7508 - accuracy: 0.8914 - val_loss: 2.2572 - val_accuracy: 0.7484\n",
            "Epoch 7/50\n",
            "31/31 [==============================] - ETA: 0s - loss: 0.7282 - accuracy: 0.8919WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n",
            "31/31 [==============================] - 44s 1s/step - loss: 0.7282 - accuracy: 0.8919 - val_loss: 2.2336 - val_accuracy: 0.7734\n",
            "Epoch 8/50\n",
            "31/31 [==============================] - ETA: 0s - loss: 0.6479 - accuracy: 0.9038WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n",
            "31/31 [==============================] - 44s 1s/step - loss: 0.6479 - accuracy: 0.9038 - val_loss: 2.1098 - val_accuracy: 0.7630\n",
            "Epoch 9/50\n",
            "31/31 [==============================] - ETA: 0s - loss: 0.6029 - accuracy: 0.9146WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n",
            "31/31 [==============================] - 43s 1s/step - loss: 0.6029 - accuracy: 0.9146 - val_loss: 2.3768 - val_accuracy: 0.7838\n",
            "Epoch 10/50\n",
            "31/31 [==============================] - ETA: 0s - loss: 0.6555 - accuracy: 0.9126WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n",
            "31/31 [==============================] - 43s 1s/step - loss: 0.6555 - accuracy: 0.9126 - val_loss: 1.9781 - val_accuracy: 0.7630\n",
            "Epoch 11/50\n",
            "31/31 [==============================] - ETA: 0s - loss: 0.4995 - accuracy: 0.9234WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n",
            "31/31 [==============================] - 43s 1s/step - loss: 0.4995 - accuracy: 0.9234 - val_loss: 2.2244 - val_accuracy: 0.7921\n",
            "Epoch 12/50\n",
            "31/31 [==============================] - ETA: 0s - loss: 0.4322 - accuracy: 0.9359WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n",
            "31/31 [==============================] - 43s 1s/step - loss: 0.4322 - accuracy: 0.9359 - val_loss: 3.0919 - val_accuracy: 0.7568\n",
            "Epoch 13/50\n",
            "31/31 [==============================] - ETA: 0s - loss: 0.7583 - accuracy: 0.9100WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n",
            "31/31 [==============================] - 43s 1s/step - loss: 0.7583 - accuracy: 0.9100 - val_loss: 2.3195 - val_accuracy: 0.7817\n",
            "Epoch 14/50\n",
            "31/31 [==============================] - ETA: 0s - loss: 0.4420 - accuracy: 0.9302WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n",
            "31/31 [==============================] - 44s 1s/step - loss: 0.4420 - accuracy: 0.9302 - val_loss: 2.6912 - val_accuracy: 0.7609\n",
            "Epoch 15/50\n",
            "31/31 [==============================] - ETA: 0s - loss: 0.5403 - accuracy: 0.9260WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n",
            "31/31 [==============================] - 44s 1s/step - loss: 0.5403 - accuracy: 0.9260 - val_loss: 2.4194 - val_accuracy: 0.7796\n",
            "Epoch 16/50\n",
            "31/31 [==============================] - ETA: 0s - loss: 0.4101 - accuracy: 0.9307WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n",
            "31/31 [==============================] - 43s 1s/step - loss: 0.4101 - accuracy: 0.9307 - val_loss: 2.7007 - val_accuracy: 0.7817\n",
            "Epoch 17/50\n",
            "31/31 [==============================] - ETA: 0s - loss: 0.4446 - accuracy: 0.9410WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n",
            "31/31 [==============================] - 44s 1s/step - loss: 0.4446 - accuracy: 0.9410 - val_loss: 2.4410 - val_accuracy: 0.7734\n",
            "Epoch 18/50\n",
            "31/31 [==============================] - ETA: 0s - loss: 0.4258 - accuracy: 0.9431WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n",
            "31/31 [==============================] - 43s 1s/step - loss: 0.4258 - accuracy: 0.9431 - val_loss: 3.2699 - val_accuracy: 0.7755\n",
            "Epoch 19/50\n",
            "31/31 [==============================] - ETA: 0s - loss: 0.4734 - accuracy: 0.9384WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n",
            "31/31 [==============================] - 42s 1s/step - loss: 0.4734 - accuracy: 0.9384 - val_loss: 3.4017 - val_accuracy: 0.7547\n",
            "Epoch 20/50\n",
            "31/31 [==============================] - ETA: 0s - loss: 0.4284 - accuracy: 0.9395WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n",
            "31/31 [==============================] - 42s 1s/step - loss: 0.4284 - accuracy: 0.9395 - val_loss: 2.9447 - val_accuracy: 0.7817\n",
            "Epoch 21/50\n",
            "31/31 [==============================] - ETA: 0s - loss: 0.5214 - accuracy: 0.9410WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n",
            "31/31 [==============================] - 42s 1s/step - loss: 0.5214 - accuracy: 0.9410 - val_loss: 3.0315 - val_accuracy: 0.7817\n",
            "Epoch 00021: early stopping\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 590
        },
        "id": "gTKufmwmxX0i",
        "outputId": "6a05c2ef-f394-4152-af8c-1b3ea4559554"
      },
      "source": [
        "# list all data in history\n",
        "print(history.history.keys())\n",
        "# summarize history for accuracy\n",
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['val_accuracy'])\n",
        "plt.title('model accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'Validation'], loc='upper left')\n",
        "plt.show()\n",
        "# summarize history for loss\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'validation'], loc='upper left')\n",
        "plt.show()"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pHUK3IlX54I4",
        "outputId": "975a08f9-5b12-43df-fdc7-fd4bbae8c6a2"
      },
      "source": [
        "validation_generator.reset()\n",
        "score = model.evaluate_generator(validation_generator, (validation_generator.samples + (batchSize-1)) //batchSize)\n",
        "print(\"For validation data set; Loss: \",score[0],\" Accuracy: \", score[1])"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From <ipython-input-28-217da5d4d1b4>:2: Model.evaluate_generator (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use Model.evaluate, which supports generators.\n",
            "For validation data set; Loss:  3.015019178390503  Accuracy:  0.7671517729759216\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4ivz0Sul6C6s",
        "outputId": "956d4873-46dd-4ecc-e7a8-4cf353eaf431"
      },
      "source": [
        "test_generator.reset()\n",
        "score = model.evaluate_generator(test_generator, (test_generator.samples + (batchSize-1)) // batchSize)\n",
        "print(\"For test data set; Loss: \",score[0],\" Accuracy: \", score[1])"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "For test data set; Loss:  2.045562505722046  Accuracy:  0.8552631735801697\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yV3M0PRHIXzj",
        "outputId": "170dd88a-fcfa-4e47-ea3e-56acf14c6944"
      },
      "source": [
        "train_generator.reset()\n",
        "score = model.evaluate_generator(train_generator, (train_generator.samples + (batchSize-1)) // batchSize)\n",
        "print(\"For training data set; Loss: \",score[0],\" Accuracy: \", score[1])"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "For training data set; Loss:  0.46815869212150574  Accuracy:  0.9436109662055969\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eEWkYlFlKqNC",
        "outputId": "556ac83a-103e-4882-a252-932ba7d5ed86"
      },
      "source": [
        "test_generator.reset()\n",
        "testStep = (test_generator.samples + (batchSize-1)) // batchSize\n",
        "print(\"testStep: \", testStep)\n",
        "predictions = model.predict_generator(test_generator, steps = testStep ,  verbose = 1)\n",
        "len(predictions)"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "testStep:  10\n",
            "10/10 [==============================] - 6s 561ms/step\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "608"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "INKJ37neKtJZ",
        "outputId": "0d80bf39-fd12-4b74-f374-da9549800cee"
      },
      "source": [
        "len(predictions)"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "608"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x0JsrG1zKvHk",
        "outputId": "fc8b0fcc-2eab-4889-b85d-2c2240a06887"
      },
      "source": [
        "predicted_class_indices=np.argmax(predictions,axis=1)\n",
        "print(predicted_class_indices)\n",
        "len(predicted_class_indices)"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 6 0 0 0 4 0 0 0 0 0 0 0 6 0 0 0 0\n",
            " 0 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 5 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 4 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 2 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2\n",
            " 2 2 2 2 2 2 2 2 3 2 2 2 2 2 2 2 2 2 5 2 2 2 2 5 2 2 2 2 3 2 2 2 2 3 2 2 2\n",
            " 2 2 2 2 2 2 2 2 2 2 2 2 2 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
            " 2 2 2 2 2 2 2 2 3 2 2 2 2 2 2 2 2 2 2 2 2 2 6 3 3 3 3 3 3 3 6 3 2 3 3 3 2\n",
            " 2 3 3 3 3 3 3 3 5 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 6 3 3 3 3 3 3\n",
            " 3 3 6 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 6 3 3 3 3 3 3 2 3 3 0 4 4 4 4 4\n",
            " 4 4 4 4 4 5 4 0 5 4 4 5 0 4 4 4 4 4 4 4 4 4 4 4 4 6 6 4 0 4 4 4 4 4 6 6 4\n",
            " 4 4 4 4 4 4 5 4 4 4 4 4 4 1 4 4 4 4 4 4 4 0 5 4 4 4 4 4 4 4 4 4 4 4 0 4 4\n",
            " 4 4 4 4 4 3 4 6 4 4 4 4 4 4 6 4 4 4 4 0 0 4 1 1 4 4 0 4 4 6 4 4 4 4 4 6 4\n",
            " 4 4 5 5 5 5 4 5 2 2 5 2 6 2 5 5 5 5 5 2 5 5 5 2 5 5 5 5 5 3 5 5 2 5 5 5 5\n",
            " 5 5 5 1 2 5 5 5 5 5 5 1 2 5 5 6 5 5 5 5 5 2 5 5 5 2 5 5 5 5 5 5 5 5 3 5 5\n",
            " 5 2 2 3 5 6 4 6 5 2 5 6 5 5 5 5 2 5 3 4 5 5 5 5 5 6 6 1 6 6 6 2 6 6 6 6 6\n",
            " 6 6 6 6 6 6 5 6 6 3 6 6 6 6 6 6]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "608"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i0EneTA46Q4b",
        "outputId": "e5d187d2-c16c-4816-c352-7cc1eed9045d"
      },
      "source": [
        "labels = (test_generator.class_indices)\n",
        "print(labels)"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'cardboard': 0, 'ewaste': 1, 'glass': 2, 'metal': 3, 'paper': 4, 'plastic': 5, 'trash': 6}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G3MxQEq-6SVu",
        "outputId": "70baa8ae-1d2f-416b-b75e-30ee8bf7a30e"
      },
      "source": [
        "labels = dict((v,k) for k,v in labels.items())\n",
        "print(labels)"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{0: 'cardboard', 1: 'ewaste', 2: 'glass', 3: 'metal', 4: 'paper', 5: 'plastic', 6: 'trash'}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QmV6HzDz6TrJ",
        "outputId": "5794292c-c5c6-4052-82aa-12a6d73a77cd"
      },
      "source": [
        "predictedLables= [labels[k] for k in predicted_class_indices]\n",
        "print(predictedLables)\n",
        "len(predictedLables)"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['cardboard', 'cardboard', 'cardboard', 'ewaste', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'paper', 'cardboard', 'cardboard', 'cardboard', 'trash', 'cardboard', 'cardboard', 'cardboard', 'paper', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'trash', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'paper', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'plastic', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'metal', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'paper', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'glass', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'glass', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'metal', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'plastic', 'glass', 'glass', 'glass', 'glass', 'plastic', 'glass', 'glass', 'glass', 'glass', 'metal', 'glass', 'glass', 'glass', 'glass', 'metal', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'metal', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'metal', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'trash', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'trash', 'metal', 'glass', 'metal', 'metal', 'metal', 'glass', 'glass', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'plastic', 'metal', 'metal', 'glass', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'trash', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'trash', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'trash', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'glass', 'metal', 'metal', 'cardboard', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'plastic', 'paper', 'cardboard', 'plastic', 'paper', 'paper', 'plastic', 'cardboard', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'trash', 'trash', 'paper', 'cardboard', 'paper', 'paper', 'paper', 'paper', 'paper', 'trash', 'trash', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'plastic', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'ewaste', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'cardboard', 'plastic', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'cardboard', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'metal', 'paper', 'trash', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'trash', 'paper', 'paper', 'paper', 'paper', 'cardboard', 'cardboard', 'paper', 'ewaste', 'ewaste', 'paper', 'paper', 'cardboard', 'paper', 'paper', 'trash', 'paper', 'paper', 'paper', 'paper', 'paper', 'trash', 'paper', 'paper', 'paper', 'plastic', 'plastic', 'plastic', 'plastic', 'paper', 'plastic', 'glass', 'glass', 'plastic', 'glass', 'trash', 'glass', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'glass', 'plastic', 'plastic', 'plastic', 'glass', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'metal', 'plastic', 'plastic', 'glass', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'ewaste', 'glass', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'ewaste', 'glass', 'plastic', 'plastic', 'trash', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'glass', 'plastic', 'plastic', 'plastic', 'glass', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'metal', 'plastic', 'plastic', 'plastic', 'glass', 'glass', 'metal', 'plastic', 'trash', 'paper', 'trash', 'plastic', 'glass', 'plastic', 'trash', 'plastic', 'plastic', 'plastic', 'plastic', 'glass', 'plastic', 'metal', 'paper', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'trash', 'trash', 'ewaste', 'trash', 'trash', 'trash', 'glass', 'trash', 'trash', 'trash', 'trash', 'trash', 'trash', 'trash', 'trash', 'trash', 'trash', 'trash', 'plastic', 'trash', 'trash', 'metal', 'trash', 'trash', 'trash', 'trash', 'trash', 'trash']\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "608"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nvKr1HIt7PJr",
        "outputId": "13e2f80c-877f-4bb0-e360-35b03ec31ee7"
      },
      "source": [
        "actualLables= [labels[k] for k in test_generator.classes]\n",
        "print(actualLables)\n",
        "len(actualLables)"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'cardboard', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'ewaste', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'glass', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'metal', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'paper', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'plastic', 'trash', 'trash', 'trash', 'trash', 'trash', 'trash', 'trash', 'trash', 'trash', 'trash', 'trash', 'trash', 'trash', 'trash', 'trash', 'trash', 'trash', 'trash', 'trash', 'trash', 'trash', 'trash', 'trash', 'trash', 'trash', 'trash', 'trash', 'trash']\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "608"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PgELFlFW6U_3",
        "outputId": "d77ce74a-97c3-44b5-c3f1-df0e43a9e937"
      },
      "source": [
        "accuracy_score(actualLables, predictedLables)"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8552631578947368"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nb-1Fh5Y6aKf",
        "outputId": "c97a213a-9e6e-4666-b203-0d6064f1b793"
      },
      "source": [
        "matrix = confusion_matrix(actualLables, predictedLables)\n",
        "print(labels)\n",
        "matrix"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{0: 'cardboard', 1: 'ewaste', 2: 'glass', 3: 'metal', 4: 'paper', 5: 'plastic', 6: 'trash'}\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[72,  1,  0,  1,  4,  1,  2],\n",
              "       [ 0, 98,  2,  0,  0,  0,  0],\n",
              "       [ 0,  0, 93,  5,  0,  2,  1],\n",
              "       [ 0,  0,  5, 72,  0,  1,  4],\n",
              "       [ 9,  3,  0,  1, 93,  5,  8],\n",
              "       [ 0,  2, 15,  4,  3, 68,  5],\n",
              "       [ 0,  1,  1,  1,  0,  1, 24]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vjsK7ZgD6bkJ",
        "outputId": "8485ad15-8f31-4175-cf1a-75795cfe573a"
      },
      "source": [
        "print(classification_report(actualLables, predictedLables))"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "   cardboard       0.89      0.89      0.89        81\n",
            "      ewaste       0.93      0.98      0.96       100\n",
            "       glass       0.80      0.92      0.86       101\n",
            "       metal       0.86      0.88      0.87        82\n",
            "       paper       0.93      0.78      0.85       119\n",
            "     plastic       0.87      0.70      0.78        97\n",
            "       trash       0.55      0.86      0.67        28\n",
            "\n",
            "    accuracy                           0.86       608\n",
            "   macro avg       0.83      0.86      0.84       608\n",
            "weighted avg       0.87      0.86      0.86       608\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q8DN2sAD6dko",
        "outputId": "1b65427b-0767-426a-8259-23eded80b73a"
      },
      "source": [
        "recall_score( actualLables, predictedLables,average='weighted') "
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8552631578947368"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qAT3e2yS6f5m",
        "outputId": "d201bf92-f8aa-4078-c77c-99095b138b24"
      },
      "source": [
        "precision_score( actualLables, predictedLables,average='weighted') "
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8669408147811051"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "StP6ldVI6hRu"
      },
      "source": [
        "#Prepared code that is taken from SKLearn Website, Creates Confusion Matrix\n",
        "def plot_confusion_matrix(cm, classes,\n",
        "                          normalize=False,\n",
        "                          title='Confusion matrix',\n",
        "                          cmap=plt.cm.Blues):\n",
        "    \"\"\"\n",
        "    This function prints and plots the confusion matrix.\n",
        "    Normalization can be applied by setting `normalize=True`.\n",
        "    \"\"\"\n",
        "    if normalize:\n",
        "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
        "        print(\"Normalized confusion matrix\")\n",
        "    else:\n",
        "        print('Confusion matrix, without normalization')\n",
        "\n",
        "    print(cm)\n",
        "\n",
        "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
        "    plt.title(title)\n",
        "    plt.colorbar()\n",
        "    tick_marks = np.arange(len(classes))\n",
        "    plt.xticks(tick_marks, classes, rotation=45)\n",
        "    plt.yticks(tick_marks, classes)\n",
        "\n",
        "    fmt = '.2f' if normalize else 'd'\n",
        "    thresh = cm.max() / 2.\n",
        "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
        "        plt.text(j, i, format(cm[i, j], fmt),\n",
        "                 horizontalalignment=\"center\",\n",
        "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.ylabel('True label')\n",
        "    plt.xlabel('Predicted label')"
      ],
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 865
        },
        "id": "9CqSG5oJ6kTT",
        "outputId": "f362c3f5-ddad-4d11-86f0-19320776b583"
      },
      "source": [
        "plt.figure(figsize=(10,10))\n",
        "cm_plot_labels = selectedClasses\n",
        "plot_confusion_matrix(matrix,cm_plot_labels, normalize=False\n",
        "                      , title = 'Confusion Matrix')\n"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Confusion matrix, without normalization\n",
            "[[72  1  0  1  4  1  2]\n",
            " [ 0 98  2  0  0  0  0]\n",
            " [ 0  0 93  5  0  2  1]\n",
            " [ 0  0  5 72  0  1  4]\n",
            " [ 9  3  0  1 93  5  8]\n",
            " [ 0  2 15  4  3 68  5]\n",
            " [ 0  1  1  1  0  1 24]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dq77bD_i8AD9"
      },
      "source": [
        "from tensorflow.keras.models import load_model\n",
        "\n",
        "model.save('/content/drive/MyDrive/(94%)EfficientNetB0.h5')"
      ],
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y04ADu1kLJtd"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}