Malaria_Detection.ipynb

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        "<a href=\"https://colab.research.google.com/github/Pratik-554/Deep-learning-with-TensorFlow2/blob/main/Malaria_Detection.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "import tensorflow as tf### models\n",
        "import numpy as np### math computations\n",
        "import matplotlib.pyplot as plt### plots\n",
        "import tensorflow_datasets as tfds\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.layers import Conv2D, MaxPool2D, Dense, Flatten, InputLayer, BatchNormalization, Input\n",
        "from tensorflow.keras.losses import BinaryCrossentropy\n",
        "from tensorflow.keras.metrics import BinaryAccuracy, FalsePositives, FalseNegatives, TruePositives, TrueNegatives, Precision, Recall, AUC\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from google.colab import drive"
      ]
    },
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          "text": [
            "Downloading and preparing dataset 337.08 MiB (download: 337.08 MiB, generated: Unknown size, total: 337.08 MiB) to /root/tensorflow_datasets/malaria/1.0.0...\n"
          ]
        },
        {
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          "data": {
            "text/plain": [
              "Dl Completed...: 0 url [00:00, ? url/s]"
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          "data": {
            "text/plain": [
              "Extraction completed...: 0 file [00:00, ? file/s]"
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          "data": {
            "text/plain": [
              "Generating splits...:   0%|          | 0/1 [00:00<?, ? splits/s]"
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          "data": {
            "text/plain": [
              "Generating train examples...:   0%|          | 0/27558 [00:00<?, ? examples/s]"
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          "data": {
            "text/plain": [
              "Shuffling /root/tensorflow_datasets/malaria/1.0.0.incompleteQO9K5A/malaria-train.tfrecord*...:   0%|          …"
            ],
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          "text": [
            "Dataset malaria downloaded and prepared to /root/tensorflow_datasets/malaria/1.0.0. Subsequent calls will reuse this data.\n"
          ]
        }
      ],
      "source": [
        "dataset, dataset_info = tfds.load('malaria', with_info = True, as_supervised=True, shuffle_files=True, split=['train'])"
      ]
    },
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          ]
        }
      ],
      "source": [
        "for data in dataset[0].take(4):\n",
        "  print(data)"
      ]
    },
    {
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        "id": "-c3JCLjqWL4j"
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      "source": [
        "def splits(dataset, TRAIN_RATIO, VAL_RATIO, TEST_RATIO):\n",
        "  DATASET_SIZE = len(dataset)\n",
        "\n",
        "  train_dataset = dataset.take(int(TRAIN_RATIO*DATASET_SIZE))\n",
        "\n",
        "  val_test_dataset = dataset.skip(int(TRAIN_RATIO*DATASET_SIZE))\n",
        "  val_dataset = val_test_dataset.take(int(VAL_RATIO*DATASET_SIZE))\n",
        "\n",
        "  test_dataset = val_test_dataset.skip(int(VAL_RATIO*DATASET_SIZE))\n",
        "  return train_dataset, val_dataset, test_dataset"
      ]
    },
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            "        [0, 0, 0]],\n",
            "\n",
            "       ...,\n",
            "\n",
            "       [[0, 0, 0],\n",
            "        [0, 0, 0],\n",
            "        [0, 0, 0],\n",
            "        ...,\n",
            "        [0, 0, 0],\n",
            "        [0, 0, 0],\n",
            "        [0, 0, 0]],\n",
            "\n",
            "       [[0, 0, 0],\n",
            "        [0, 0, 0],\n",
            "        [0, 0, 0],\n",
            "        ...,\n",
            "        [0, 0, 0],\n",
            "        [0, 0, 0],\n",
            "        [0, 0, 0]],\n",
            "\n",
            "       [[0, 0, 0],\n",
            "        [0, 0, 0],\n",
            "        [0, 0, 0],\n",
            "        ...,\n",
            "        [0, 0, 0],\n",
            "        [0, 0, 0],\n",
            "        [0, 0, 0]]], dtype=uint8), 0)]\n"
          ]
        }
      ],
      "source": [
        "TRAIN_RATIO = 0.8\n",
        "VAL_RATIO = 0.1\n",
        "TEST_RATIO = 0.1\n",
        "\n",
        "#dataset = tf.data.Dataset.range(10)\n",
        "train_dataset, val_dataset, test_dataset = splits(dataset[0], TRAIN_RATIO, VAL_RATIO, TEST_RATIO)\n",
        "print(list(train_dataset.take(1).as_numpy_iterator()),\n",
        "      list(val_dataset.take(1).as_numpy_iterator()), list(test_dataset.take(1).as_numpy_iterator()))\n",
        "# The take(1) method is used to take the first element from each dataset, and as_numpy_iterator() is used to convert the TensorFlow Dataset into a numpy iterator, which is then converted to a list for printing."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ykcDmwMMshFm",
        "outputId": "0752f553-1f9f-4308-b527-250ec4b4c12c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<_PrefetchDataset element_spec=(TensorSpec(shape=(None, None, 3), dtype=tf.uint8, name=None), TensorSpec(shape=(), dtype=tf.int64, name=None))>]"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cAHLXTeLdije"
      },
      "source": [
        "## Dataset Visualization"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "id": "_if9wbZ0snCE",
        "outputId": "76037168-6fc6-447b-b69e-100e0b3cb257"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 16 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "for i, (image, label) in enumerate(train_dataset.take(16)): # enumerate function is used to get the index of the current element in addition to the element itself\n",
        "  ax = plt.subplot(4, 4, i + 1)  # creating a subplot in a 4x4 grid (for a total of 16 subplots) at the position specified by i + 1\n",
        "  plt.imshow(image)  # this loop will iterate over the first 16 elements (image-label pairs) in train_dataset and display current image in the current subplot\n",
        "  plt.title(dataset_info.features['label'].int2str(label))  # int2str method looks up the string that corresponds to the given integer in the mapping.\n",
        "  plt.axis('off')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "g1pL-ptDiLnY",
        "outputId": "b9dbd649-745f-449e-bf03-0cb7d9a3e562"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'parasitized'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "# To know about particular label\n",
        "dataset_info.features['label'].int2str(0)  # label '0'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "imeKRRWAfUmU",
        "outputId": "477a5cb0-1073-445d-cb72-8353929a11b0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tfds.core.DatasetInfo(\n",
              "    name='malaria',\n",
              "    full_name='malaria/1.0.0',\n",
              "    description=\"\"\"\n",
              "    The Malaria dataset contains a total of 27,558 cell images with equal instances\n",
              "    of parasitized and uninfected cells from the thin blood smear slide images of\n",
              "    segmented cells.\n",
              "    \"\"\",\n",
              "    homepage='https://lhncbc.nlm.nih.gov/publication/pub9932',\n",
              "    data_dir=PosixGPath('/tmp/tmpen9md1mntfds'),\n",
              "    file_format=tfrecord,\n",
              "    download_size=337.08 MiB,\n",
              "    dataset_size=317.62 MiB,\n",
              "    features=FeaturesDict({\n",
              "        'image': Image(shape=(None, None, 3), dtype=uint8),\n",
              "        'label': ClassLabel(shape=(), dtype=int64, num_classes=2),\n",
              "    }),\n",
              "    supervised_keys=('image', 'label'),\n",
              "    disable_shuffling=False,\n",
              "    splits={\n",
              "        'train': <SplitInfo num_examples=27558, num_shards=4>,\n",
              "    },\n",
              "    citation=\"\"\"@article{rajaraman2018pre,\n",
              "      title={Pre-trained convolutional neural networks as feature extractors toward\n",
              "      improved malaria parasite detection in thin blood smear images},\n",
              "      author={Rajaraman, Sivaramakrishnan and Antani, Sameer K and Poostchi, Mahdieh\n",
              "      and Silamut, Kamolrat and Hossain, Md A and Maude, Richard J and Jaeger,\n",
              "      Stefan and Thoma, George R},\n",
              "      journal={PeerJ},\n",
              "      volume={6},\n",
              "      pages={e4568},\n",
              "      year={2018},\n",
              "      publisher={PeerJ Inc.}\n",
              "    }\"\"\",\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "dataset_info"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RHjOkf76izl4"
      },
      "source": [
        "## Data Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gVRpQB3VizIo"
      },
      "outputs": [],
      "source": [
        "IM_SIZE = 224\n",
        "def resize_rescale(image, label):\n",
        "  return tf.image.resize(image, (IM_SIZE, IM_SIZE))/255.0, label # returns the original label along with the resized and rescaled image"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jtyy8GJTpk5a"
      },
      "outputs": [],
      "source": [
        "train_dataset = train_dataset.map(resize_rescale) # The 'map' method applies a given function (here 'resize_rescale) to each element in the dataset.\n",
        "val_dataset = val_dataset.map(resize_rescale)\n",
        "test_dataset = test_dataset.map(resize_rescale)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IST5Ywagpsw5",
        "outputId": "5a14363f-16b8-4f43-8657-8b44d0de87ae"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tf.Tensor(\n",
            "[[[0. 0. 0.]\n",
            "  [0. 0. 0.]\n",
            "  [0. 0. 0.]\n",
            "  ...\n",
            "  [0. 0. 0.]\n",
            "  [0. 0. 0.]\n",
            "  [0. 0. 0.]]\n",
            "\n",
            " [[0. 0. 0.]\n",
            "  [0. 0. 0.]\n",
            "  [0. 0. 0.]\n",
            "  ...\n",
            "  [0. 0. 0.]\n",
            "  [0. 0. 0.]\n",
            "  [0. 0. 0.]]\n",
            "\n",
            " [[0. 0. 0.]\n",
            "  [0. 0. 0.]\n",
            "  [0. 0. 0.]\n",
            "  ...\n",
            "  [0. 0. 0.]\n",
            "  [0. 0. 0.]\n",
            "  [0. 0. 0.]]\n",
            "\n",
            " ...\n",
            "\n",
            " [[0. 0. 0.]\n",
            "  [0. 0. 0.]\n",
            "  [0. 0. 0.]\n",
            "  ...\n",
            "  [0. 0. 0.]\n",
            "  [0. 0. 0.]\n",
            "  [0. 0. 0.]]\n",
            "\n",
            " [[0. 0. 0.]\n",
            "  [0. 0. 0.]\n",
            "  [0. 0. 0.]\n",
            "  ...\n",
            "  [0. 0. 0.]\n",
            "  [0. 0. 0.]\n",
            "  [0. 0. 0.]]\n",
            "\n",
            " [[0. 0. 0.]\n",
            "  [0. 0. 0.]\n",
            "  [0. 0. 0.]\n",
            "  ...\n",
            "  [0. 0. 0.]\n",
            "  [0. 0. 0.]\n",
            "  [0. 0. 0.]]], shape=(224, 224, 3), dtype=float32) tf.Tensor(1, shape=(), dtype=int64)\n"
          ]
        }
      ],
      "source": [
        "for image,label in train_dataset.take(1):  # starts a loop that will iterate over the elements in the dataset created by take(1). Since this dataset only has one element, the loop will only run once\n",
        "  print(image, label)  # prints the image and label of the current element."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iflHeUIuse6I"
      },
      "outputs": [],
      "source": [
        "BATCH_SIZE = 32\n",
        "train_dataset = train_dataset.shuffle(buffer_size = 8, reshuffle_each_iteration = True).batch(BATCH_SIZE).prefetch(tf.data.AUTOTUNE)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OtoyAWvJVBSf"
      },
      "outputs": [],
      "source": [
        "val_dataset = val_dataset.shuffle(buffer_size = 8, reshuffle_each_iteration = True).batch(BATCH_SIZE).prefetch(tf.data.AUTOTUNE)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ewT9deDlWQvs",
        "outputId": "e27c54f5-2ff9-467d-fe52-7fa8e94863e1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<_PrefetchDataset element_spec=(TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name=None), TensorSpec(shape=(None,), dtype=tf.int64, name=None))>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "val_dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "op7IDjiHWSer",
        "outputId": "d50e17ee-4449-4ae2-9da8-a05249fde430"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<_PrefetchDataset element_spec=(TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name=None), TensorSpec(shape=(None,), dtype=tf.int64, name=None))>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "train_dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UDbi5PryWFug"
      },
      "source": [
        "## Model Creation and Training"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XsZ2ewCgL1ts"
      },
      "source": [
        "## Sequential API"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IKi3Hf30tH5I",
        "outputId": "7dcc660e-1245-4cd6-d11d-c22bbd366e92"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d (Conv2D)             (None, 222, 222, 6)       168       \n",
            "                                                                 \n",
            " batch_normalization (Batch  (None, 222, 222, 6)       24        \n",
            " Normalization)                                                  \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2  (None, 111, 111, 6)       0         \n",
            " D)                                                              \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 109, 109, 16)      880       \n",
            "                                                                 \n",
            " batch_normalization_1 (Bat  (None, 109, 109, 16)      64        \n",
            " chNormalization)                                                \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPoolin  (None, 54, 54, 16)        0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 46656)             0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 100)               4665700   \n",
            "                                                                 \n",
            " batch_normalization_2 (Bat  (None, 100)               400       \n",
            " chNormalization)                                                \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 10)                1010      \n",
            "                                                                 \n",
            " batch_normalization_3 (Bat  (None, 10)                40        \n",
            " chNormalization)                                                \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 1)                 11        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 4668297 (17.81 MB)\n",
            "Trainable params: 4668033 (17.81 MB)\n",
            "Non-trainable params: 264 (1.03 KB)\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "\n",
        "lenet_model = tf.keras.Sequential([\n",
        "                            InputLayer(input_shape = (IM_SIZE, IM_SIZE, 3)),\n",
        "\n",
        "\n",
        "                            Conv2D(filters=6, kernel_size=3, strides=(1), padding='valid', activation = 'relu'),\n",
        "                            BatchNormalization(),\n",
        "                            MaxPool2D (pool_size = 2, strides = 2),\n",
        "\n",
        "                            Conv2D(filters=16, kernel_size=3, strides=(1), padding='valid', activation = 'relu'),\n",
        "                            BatchNormalization(),\n",
        "                            MaxPool2D (pool_size = 2, strides = 2),\n",
        "\n",
        "                            Flatten(),\n",
        "\n",
        "                            Dense(100, activation = \"relu\"),\n",
        "                            BatchNormalization(),\n",
        "\n",
        "                            Dense(10, activation = \"relu\"),\n",
        "                            BatchNormalization(),\n",
        "\n",
        "                            Dense(1, activation = \"sigmoid\"),\n",
        "])\n",
        "lenet_model.summary()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NNrbG86SL7Zq"
      },
      "source": [
        "## Functional API"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uhEiHkAvQx_z",
        "outputId": "d4f5a099-7c13-4e31-a500-3bd2770baceb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"Feature_Extractor\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " Input Image (InputLayer)    [(None, 224, 224, 3)]     0         \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 222, 222, 6)       168       \n",
            "                                                                 \n",
            " batch_normalization_4 (Bat  (None, 222, 222, 6)       24        \n",
            " chNormalization)                                                \n",
            "                                                                 \n",
            " max_pooling2d_2 (MaxPoolin  (None, 111, 111, 6)       0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_3 (Conv2D)           (None, 109, 109, 16)      880       \n",
            "                                                                 \n",
            " batch_normalization_5 (Bat  (None, 109, 109, 16)      64        \n",
            " chNormalization)                                                \n",
            "                                                                 \n",
            " max_pooling2d_3 (MaxPoolin  (None, 54, 54, 16)        0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 1136 (4.44 KB)\n",
            "Trainable params: 1092 (4.27 KB)\n",
            "Non-trainable params: 44 (176.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "func_input = Input(shape = (IM_SIZE, IM_SIZE, 3), name = \"Input Image\")\n",
        "\n",
        "\n",
        "x = Conv2D(filters=6, kernel_size=3, strides=(1), padding='valid', activation = 'relu')(func_input)\n",
        "x = BatchNormalization()(x)\n",
        "x = MaxPool2D (pool_size = 2, strides = 2)(x)\n",
        "\n",
        "x = Conv2D(filters=16, kernel_size=3, strides=(1), padding='valid', activation = 'relu')(x)\n",
        "x = BatchNormalization()(x)\n",
        "output = MaxPool2D (pool_size = 2, strides = 2)(x)\n",
        "\n",
        "\n",
        "feature_extractor_model = Model(func_input, output, name = \"Feature_Extractor\")\n",
        "feature_extractor_model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AkGK6Z_rMBIT",
        "outputId": "3ccf5d99-11e5-47a5-9e51-2038fa5a5cf2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"Lenet_Model\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " Input Image (InputLayer)    [(None, 224, 224, 3)]     0         \n",
            "                                                                 \n",
            " Feature_Extractor (Functio  (None, 54, 54, 16)        1136      \n",
            " nal)                                                            \n",
            "                                                                 \n",
            " flatten_1 (Flatten)         (None, 46656)             0         \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 100)               4665700   \n",
            "                                                                 \n",
            " batch_normalization_6 (Bat  (None, 100)               400       \n",
            " chNormalization)                                                \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 10)                1010      \n",
            "                                                                 \n",
            " batch_normalization_7 (Bat  (None, 10)                40        \n",
            " chNormalization)                                                \n",
            "                                                                 \n",
            " dense_5 (Dense)             (None, 1)                 11        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 4668297 (17.81 MB)\n",
            "Trainable params: 4668033 (17.81 MB)\n",
            "Non-trainable params: 264 (1.03 KB)\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "func_input = Input(shape = (IM_SIZE, IM_SIZE, 3), name = \"Input Image\")\n",
        "\n",
        "\n",
        "x = feature_extractor_model(func_input)\n",
        "\n",
        "x = Flatten()(x)\n",
        "\n",
        "x = Dense(100, activation = \"relu\")(x)\n",
        "x = BatchNormalization()(x)\n",
        "\n",
        "x = Dense(10, activation = \"relu\")(x)\n",
        "x = BatchNormalization()(x)\n",
        "\n",
        "func_output = Dense(1, activation = \"sigmoid\")(x)\n",
        "\n",
        "lenet_model_func = Model(func_input, func_output, name = \"Lenet_Model\")\n",
        "lenet_model_func.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KsenWll_JZCg",
        "outputId": "7c185c25-93c4-4dcc-9116-f2e8c0d02fe0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(), dtype=float32, numpy=4.9340706>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "y_true = [0, 1, 0, 0]\n",
        "y_pred = [0.6, 0.51, 0.94, 1]\n",
        "bce = tf.keras.losses.BinaryCrossentropy() # \"from_logits=True\" ensure that values  lies between 0 and 1 and by default it is false\n",
        "bce(y_true, y_pred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "C645p8kHLZNo"
      },
      "outputs": [],
      "source": [
        "# compile our model\n",
        "lenet_model.compile(optimizer = Adam(learning_rate = 0.1),\n",
        "              loss = BinaryCrossentropy(),\n",
        "              metrics = 'accuracy') # a model's accuracy is total no. of times model predicted an o/p correctly divided by total no. of predictions\n",
        "              #metrics = RootMeanSquaredError())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cYDtFkyBSX9j",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f90003cb-f186-4b16-f8fe-574dcf6c2e36"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "689/689 [==============================] - 858s 1s/step - loss: 0.2882 - accuracy: 0.9034 - val_loss: 2.2174 - val_accuracy: 0.4929\n",
            "Epoch 2/5\n",
            "689/689 [==============================] - 842s 1s/step - loss: 0.2594 - accuracy: 0.9130 - val_loss: 0.2553 - val_accuracy: 0.9114\n",
            "Epoch 3/5\n",
            "689/689 [==============================] - 953s 1s/step - loss: 0.2292 - accuracy: 0.9295 - val_loss: 1.6230 - val_accuracy: 0.9307\n",
            "Epoch 4/5\n",
            "689/689 [==============================] - 904s 1s/step - loss: 0.2120 - accuracy: 0.9375 - val_loss: 27.0124 - val_accuracy: 0.9140\n",
            "Epoch 5/5\n",
            "689/689 [==============================] - 939s 1s/step - loss: 0.1925 - accuracy: 0.9417 - val_loss: 0.2515 - val_accuracy: 0.9332\n"
          ]
        }
      ],
      "source": [
        "history = lenet_model.fit(train_dataset, validation_data=val_dataset, epochs = 5, verbose = 1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Tpjlz6Skeeyp"
      },
      "outputs": [],
      "source": [
        "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_loss', 'val_loss'])\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BZuvcGugfUpy"
      },
      "outputs": [],
      "source": [
        "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_accuracy', 'val_accuracy'])\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6cQOiQ-DgKFr"
      },
      "source": [
        "## Model Evaluation and Testing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oglw4np8gzIy"
      },
      "outputs": [],
      "source": [
        "test_dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nd0fsqG0g49t"
      },
      "outputs": [],
      "source": [
        "train_dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RnqCuXZag2U5"
      },
      "outputs": [],
      "source": [
        "test_dataset = test_dataset.batch(1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gSc-dwQ6hIrv"
      },
      "outputs": [],
      "source": [
        "test_dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Z1CrQyKTgPOX"
      },
      "outputs": [],
      "source": [
        "lenet_model.evaluate(test_dataset)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "48wMjieXi7t5"
      },
      "outputs": [],
      "source": [
        "# let's check how model is predicting\n",
        "parasite_or_not(lenet_model.predict(test_dataset.take(1))[0][0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FTvczm_JjLMO"
      },
      "outputs": [],
      "source": [
        "def parasite_or_not(x):\n",
        "  if(x<0.5):\n",
        "    return str('P')\n",
        "  else:\n",
        "    return str('U')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EAUZxRj8j18X"
      },
      "outputs": [],
      "source": [
        "for i, (image, label) in enumerate(test_dataset.take(9)):\n",
        "\n",
        "  ax = plt.subplot(3, 3, i+1)\n",
        "  plt.imshow(image[0])\n",
        "  plt.title(str(parasite_or_not(label.numpy()[0])) + \":\" + str(parasite_or_not(lenet_model.predict(image)[0][0])))\n",
        "\n",
        "  plt.axis('off')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w1nD3uiV6qBS"
      },
      "source": [
        "## Loading and Saving\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bLyO4xI76tiP"
      },
      "outputs": [],
      "source": [
        "lenet_model.save(\"lenet\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KDN1OvN96wUW"
      },
      "outputs": [],
      "source": [
        "lenet_loaded_model = tf.keras.models.load_model(\"lenets\")\n",
        "lenet_loaded_model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XRIMblEo6zIA"
      },
      "outputs": [],
      "source": [
        "lenet_model.save(\"lenet.hdf5\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wC0HD2K-60ym"
      },
      "outputs": [],
      "source": [
        "lenet_loaded_model = tf.keras.models.load_model(\"lenet.hdf5\")\n",
        "lenet_loaded_model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qXT56aex62_u"
      },
      "outputs": [],
      "source": [
        "lenet_model.save_weights(\"weights/lenet_weights\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qAuICERy64lG"
      },
      "outputs": [],
      "source": [
        "lenet_weights_model = lenet_model.load_weights(\"weights/lenet_weights\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2on7Jln365bN"
      },
      "source": [
        "## Saving to and Loading to Google drive\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "M7cl_vcw7AqN"
      },
      "outputs": [],
      "source": [
        "drive.mount('/content/drive/')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fFJrDgua7CdA"
      },
      "outputs": [],
      "source": [
        "!cp -r /content/lenet/ /content/drive/MyDrive/lenet_colab/"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yylkfTJp7EcG"
      },
      "outputs": [],
      "source": [
        "!cp -r /content/drive/MyDrive/lenet_colab/ /content/lenet_colab/"
      ]
    }
  ],
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    "accelerator": "GPU",
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      "include_colab_link": true
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