X_Ray_TB_Segmentation_U-Net.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "X-Ray_TB_Segmentation_Classification.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "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/zabir-nabil/lesion-segmentation-melanoma-tl/blob/master/X_Ray_TB_Segmentation_U-Net.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "hOct7IzaJ9Aa",
        "colab_type": "code",
        "outputId": "f328b2d0-fbdc-4b03-8d27-000f2c97b1e5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "print('zabiralnazi@yahoo.com')"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "zabiralnazi@yahoo.com\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Ka2LZr2dvqFT",
        "colab_type": "code",
        "outputId": "2c7514cf-cda3-4f34-a28d-7e0efb376e8a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        }
      },
      "cell_type": "code",
      "source": [
        "# Load the Drive helper and mount\n",
        "from google.colab import drive\n",
        "\n",
        "# This will prompt for authorization.\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "WAfeBFz-6JGH",
        "colab_type": "code",
        "outputId": "d0f64f89-de98-4a04-dd66-0195506fb3bc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "% cd /content/drive/My Drive/data_xray_tb/"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/drive/My Drive/data_xray_tb\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "hK43rwJp6t-l",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "imgs = np.load('imgs.npy')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "VwwsIbz_7LAe",
        "colab_type": "code",
        "outputId": "a4ec6d68-28b4-4a54-a43f-26f552047b7a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "imgs.shape"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(138, 224, 224, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "metadata": {
        "id": "NL13vUCOGeQW",
        "colab_type": "code",
        "outputId": "31e4317a-54bf-4d22-9b01-61e056b78cd4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "type(imgs[0,0,0,0])"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.uint8"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "metadata": {
        "id": "xf-kR3Yv7NZY",
        "colab_type": "code",
        "outputId": "bb3119dd-25da-4a3b-db6b-3c982463bd51",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "masks = np.load('masks.npy')\n",
        "masks.shape"
      ],
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(138, 224, 224)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 113
        }
      ]
    },
    {
      "metadata": {
        "id": "ocU-rBPl7c5W",
        "colab_type": "code",
        "outputId": "9fa24498-d026-43f6-b222-8ebd0fdeda38",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "y = np.load('y.npy')\n",
        "y.shape"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(138, 2)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "metadata": {
        "id": "kmmxnYhT7izW",
        "colab_type": "code",
        "outputId": "82038488-7492-4963-cfcf-c5d1ca8e52c8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 201
        }
      },
      "cell_type": "code",
      "source": [
        "! pip install Augmentor "
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting Augmentor\n",
            "  Downloading https://files.pythonhosted.org/packages/4d/f4/b0eaa9d3b4120a5450ac92d4417907ca60fad5749c1f50ed95f720792350/Augmentor-0.2.3-py2.py3-none-any.whl\n",
            "Requirement already satisfied: numpy>=1.11.0 in /usr/local/lib/python3.6/dist-packages (from Augmentor) (1.14.6)\n",
            "Requirement already satisfied: tqdm>=4.9.0 in /usr/local/lib/python3.6/dist-packages (from Augmentor) (4.28.1)\n",
            "Requirement already satisfied: future>=0.16.0 in /usr/local/lib/python3.6/dist-packages (from Augmentor) (0.16.0)\n",
            "Requirement already satisfied: Pillow>=4.0.0 in /usr/local/lib/python3.6/dist-packages (from Augmentor) (4.0.0)\n",
            "Requirement already satisfied: olefile in /usr/local/lib/python3.6/dist-packages (from Pillow>=4.0.0->Augmentor) (0.46)\n",
            "Installing collected packages: Augmentor\n",
            "Successfully installed Augmentor-0.2.3\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "iQI5twL47878",
        "colab_type": "code",
        "outputId": "a4093245-e3fa-46b6-d153-aabec3d62b3e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        }
      },
      "cell_type": "code",
      "source": [
        "print(imgs.shape)\n",
        "print(masks.shape)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(138, 224, 224, 3)\n",
            "(138, 224, 224)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "-KdoNb84A5TJ",
        "colab_type": "code",
        "outputId": "4f478840-589c-40a2-bdba-a2ac76227dad",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "masks = np.reshape(masks, (138,224,224))\n",
        "print(masks.shape)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(138, 224, 224)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "8cWg4PfDQSI_",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# need to convert to 3 channels\n",
        "\n",
        "masks_3d = masks[:, :, :, None] * np.ones(3, dtype=int)[None, None, None, :]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "4-5bCHrQQ4mA",
        "colab_type": "code",
        "outputId": "a8ad6877-71de-45a4-ac70-d9f5bc786c5b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "masks_3d.shape"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(138, 224, 224, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "metadata": {
        "id": "IZuBnEhRARvk",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "z = []\n",
        "for i in range(138):\n",
        "  z.append([imgs[i,:,:,:], masks[i,:,:]])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ovkcXUpRAmy4",
        "colab_type": "code",
        "outputId": "e6c6d1fa-1c67-445f-a7f3-b92c185b5392",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        }
      },
      "cell_type": "code",
      "source": [
        "print(len(z))\n",
        "print(len(z[0]))\n",
        "print((z[0][1].shape))"
      ],
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "138\n",
            "2\n",
            "(224, 224)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "r4V__hgbUocm",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# for testing\n",
        "# Augmentor takes a list of list of numpy.ndarray for image, masks\n",
        "images = [\n",
        "    [imgs[0,:,:,:], masks[0,:,:]],\n",
        "    [imgs[1,:,:,:], masks[1,:,:]],\n",
        "    [imgs[2,:,:,:], masks[2,:,:]]\n",
        "\n",
        "]\n",
        "# Augmentor takes a list of list for labels [Not numpy.ndarray]\n",
        "y_p = [[0, 1], [1, 0], [0, 1]]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "e_-8OPxViSPZ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "y = y.tolist() # must convert from numpy ndarray to list"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "QSSzxLc8HMKv",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# augmentation\n",
        "import Augmentor\n",
        "\n",
        "p = Augmentor.DataPipeline(z, y)\n",
        "\n",
        "p.rotate(probability=0.2, max_left_rotation=5, max_right_rotation=5)\n",
        "p.flip_left_right(probability=0.2)\n",
        "p.zoom_random(probability=0.1, percentage_area=0.8)\n",
        "p.flip_top_bottom(probability=0.3)\n",
        "p.gaussian_distortion(probability=0.05, grid_width=4, grid_height=4, magnitude=3, corner='bell', method='in', mex=0.5, mey=0.5, sdx=0.05, sdy=0.05)\n",
        "p.random_brightness(probability=0.05, min_factor=0.7, max_factor=1.3)\n",
        "p.random_color(probability=0.05, min_factor=0.6, max_factor=0.9)\n",
        "p.random_contrast(probability=0.05, min_factor=0.6, max_factor=0.9)\n",
        "p.random_distortion(probability=0.2, grid_width=4, grid_height=4, magnitude=2)\n",
        "\n",
        "\n",
        "\n",
        "augmented_images, labels = p.sample(3000)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "87z9NWmBT_E-",
        "colab_type": "code",
        "outputId": "117f5c29-b23e-4173-e994-597c8436b83f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "len(augmented_images)"
      ],
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 89
        }
      ]
    },
    {
      "metadata": {
        "id": "AzrtzcDpUQqe",
        "colab_type": "code",
        "outputId": "5b9d3a03-14d3-41b4-dcdd-7deadba1dd0e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 2087
        }
      },
      "cell_type": "code",
      "source": [
        "plt.imshow(augmented_images[500][0])\n",
        "plt.title('Sample augmented image')\n",
        "plt.show()\n",
        "\n",
        "plt.imshow(augmented_images[500][1])\n",
        "plt.title('Sample augmented mask')\n",
        "plt.show()\n",
        "\n",
        "plt.imshow(augmented_images[100][0])\n",
        "plt.title('Sample augmented image')\n",
        "plt.show()\n",
        "\n",
        "plt.imshow(augmented_images[100][1])\n",
        "plt.title('Sample augmented mask')\n",
        "plt.show()\n",
        "\n",
        "plt.imshow(augmented_images[2300][0])\n",
        "plt.title('Sample augmented image')\n",
        "plt.show()\n",
        "\n",
        "plt.imshow(augmented_images[2300][1])\n",
        "plt.title('Sample augmented mask')\n",
        "plt.show()"
      ],
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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rhhQMBi2lzXFh33meZzYxftb7c5xcOEHHS5XofO+g7eHv9XrXFZ/P9T9SFetBx0Q//OEP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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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0dDQcHBzg6emJgIAAlJaWmqRIIiKlMjpEL168iIEDBxpuFxQUID09HcC9g1E/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\nIpLgfwHqLJXmrgcCkwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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tRU1z+amUOmGkLNnHa7kBse2qEKnI1f1kIEoxNt1ASqUSMpmMGxvWFFAlpxtS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\nAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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0kCTqRrLUs3GkJerd1ClogwYNkiEPBzIm6kaSRBvHW5LomTNntNsWi6XG9J5A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\nAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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YapqnxYNnZmbcXOT9NU0ym816G3Kn03FKttFoYGRkxOM9KoeZMIkGojZv3uwl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\nJwysW7fOU7pqMNHLsUFfzjV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YYbvddtX1gR55Xy1DWqP8vSp9Wg/2fHcKs7O0ULOOx6JF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kd1grVOEU6elg/nNUzIrDy1naB0ew+b2pL70Gt7T0xMxOI4i8VijF/X8D+G+vE8aSl1wmpCbq7L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\nIAjRgICAgCYQhGhAQEBAE/j/AMvfHXJHB4/wAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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JCQnAH3srecqBAwfYv38/gHQslWKz2Xj77beJi4sDSt6r6pRQpU1UCCGcIEvh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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "eFBNJCcQpkMR",
        "colab_type": "code",
        "outputId": "38507bd9-94d4-4078-b85c-5912e6c3c2af",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "import keras.backend as K"
      ],
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "CbGOQBiopOip",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def dice_coef(y_true, y_pred):\n",
        "    y_true_f = (y_true).flatten()\n",
        "    y_pred_f = (y_pred).flatten()\n",
        "    intersection = np.sum(y_true_f * y_pred_f)\n",
        "    # +1 added to avoid 0/0 division\n",
        "    return (2.0 * intersection + 1.0) / (np.sum(y_true_f) + np.sum(y_pred_f) + 1.0)\n",
        "  \n",
        "def jacard_coef(y_true, y_pred):\n",
        "    y_true_f = (y_true).flatten()\n",
        "    y_pred_f = (y_pred).flatten()\n",
        "    intersection = np.sum(y_true_f * y_pred_f)\n",
        "    return (intersection + 1.0) / (np.sum(y_true_f) + np.sum(y_pred_f) - intersection + 1.0)\n",
        "  \n",
        "  \n",
        "def weighted_loss(y_true, y_pred):\n",
        "  loss1 = dice_coef(y_true, y_pred)\n",
        "  loss2 = jacard_coef(y_true, y_pred)\n",
        "  return -(loss1*0.4 + loss2*0.6)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "sWgOkNE373Xr",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# updated tensor definition\n",
        "def dice_coef(y_true, y_pred):\n",
        "    y_true_f = K.flatten(y_true)\n",
        "    y_pred_f = K.flatten(y_pred)\n",
        "    intersection = K.sum(y_true_f * y_pred_f)\n",
        "    # +1 added to avoid 0/0 division\n",
        "    return (2.0 * intersection + 1.0) / (K.sum(y_true_f) + K.sum(y_pred_f) + 1.0)\n",
        "  \n",
        "def jacard_coef(y_true, y_pred):\n",
        "    y_true_f = K.flatten(y_true)\n",
        "    y_pred_f = K.flatten(y_pred)\n",
        "    intersection = K.sum(y_true_f * y_pred_f)\n",
        "    return (intersection + 1.0) / (K.sum(y_true_f) + K.sum(y_pred_f) - intersection + 1.0)\n",
        "  \n",
        "  \n",
        "def weighted_loss(y_true, y_pred):\n",
        "  loss1 = dice_coef(y_true, y_pred)\n",
        "  loss2 = jacard_coef(y_true, y_pred)\n",
        "  return -(loss1*0.4 + loss2*0.6)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "40v-0KJ28Y20",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from keras.models import Model\n",
        "from keras.layers import Input, Conv2D, MaxPooling2D, UpSampling2D, Dense, Flatten\n",
        "from keras.layers.normalization import BatchNormalization\n",
        "from keras.layers.core import SpatialDropout2D, Activation\n",
        "from keras import backend as K\n",
        "from keras.layers.merge import concatenate\n",
        "from keras.utils.data_utils import get_file\n",
        "\n",
        "# Number of image channels (for example 3 in case of RGB, or 1 for grayscale images)\n",
        "INPUT_CHANNELS = 3\n",
        "# Number of output masks (1 in case you predict only one type of objects)\n",
        "OUTPUT_MASK_CHANNELS = 1\n",
        "# Pretrained weights\n",
        "\n",
        "\n",
        "def jacard_coef_loss(y_true, y_pred):\n",
        "    return -jacard_coef(y_true, y_pred)\n",
        "\n",
        "\n",
        "def dice_coef_loss(y_true, y_pred):\n",
        "    return -dice_coef(y_true, y_pred)\n",
        "\n",
        "\n",
        "def double_conv_layer(x, size, dropout=0.10, batch_norm=True):\n",
        "    if K.image_dim_ordering() == 'th':\n",
        "        axis = 1\n",
        "    else:\n",
        "        axis = 3\n",
        "    conv = Conv2D(size, (3, 3), padding='same')(x)\n",
        "    if batch_norm is True:\n",
        "        conv = BatchNormalization(axis=axis)(conv)\n",
        "    conv = Activation('relu')(conv)\n",
        "    conv = Conv2D(size, (3, 3), padding='same')(conv)\n",
        "    if batch_norm is True:\n",
        "        conv = BatchNormalization(axis=axis)(conv)\n",
        "    conv = Activation('relu')(conv)\n",
        "    if dropout > 0:\n",
        "        conv = SpatialDropout2D(dropout)(conv)\n",
        "    return conv\n",
        "\n",
        "\n",
        "def mini_UNET(dropout_val=0.40, weights=None): # No dropout by default\n",
        "    if K.image_dim_ordering() == 'th':\n",
        "        inputs = Input((INPUT_CHANNELS, 224, 224))\n",
        "        axis = 1\n",
        "    else:\n",
        "        inputs = Input((224, 224, INPUT_CHANNELS))\n",
        "        axis = 3\n",
        "    filters = 32\n",
        "\n",
        "    conv_224 = double_conv_layer(inputs, filters)\n",
        "    pool_112 = MaxPooling2D(pool_size=(2, 2))(conv_224)\n",
        "\n",
        "    conv_112 = double_conv_layer(pool_112, 2*filters)\n",
        "    pool_56 = MaxPooling2D(pool_size=(2, 2))(conv_112)\n",
        "\n",
        "    conv_56 = double_conv_layer(pool_56, 4*filters)\n",
        "    pool_28 = MaxPooling2D(pool_size=(2, 2))(conv_56)\n",
        "\n",
        "    conv_28 = double_conv_layer(pool_28, 8*filters)\n",
        "    pool_14 = MaxPooling2D(pool_size=(2, 2))(conv_28)\n",
        "\n",
        "    conv_14 = double_conv_layer(pool_14, 16*filters)\n",
        "    pool_7 = MaxPooling2D(pool_size=(2, 2))(conv_14)\n",
        "\n",
        "    conv_7 = double_conv_layer(pool_7, 32*filters)\n",
        "\n",
        "    up_14 = concatenate([UpSampling2D(size=(2, 2))(conv_7), conv_14], axis=axis)\n",
        "    up_conv_14 = double_conv_layer(up_14, 16*filters)\n",
        "\n",
        "    up_28 = concatenate([UpSampling2D(size=(2, 2))(up_conv_14), conv_28], axis=axis)\n",
        "    up_conv_28 = double_conv_layer(up_28, 8*filters)\n",
        "\n",
        "    up_56 = concatenate([UpSampling2D(size=(2, 2))(up_conv_28), conv_56], axis=axis)\n",
        "    up_conv_56 = double_conv_layer(up_56, 4*filters)\n",
        "\n",
        "    up_112 = concatenate([UpSampling2D(size=(2, 2))(up_conv_56), conv_112], axis=axis)\n",
        "    up_conv_112 = double_conv_layer(up_112, 2*filters)\n",
        "\n",
        "    up_224 = concatenate([UpSampling2D(size=(2, 2))(up_conv_112), conv_224], axis=axis)\n",
        "    up_conv_224 = double_conv_layer(up_224, filters, dropout_val)\n",
        "\n",
        "    conv_final = Conv2D(OUTPUT_MASK_CHANNELS, (1, 1))(up_conv_224)\n",
        "    conv_final = Activation('sigmoid', name='final_seg')(conv_final)\n",
        "    \n",
        "    \n",
        "    # classification\n",
        "    #flat = Flatten()(conv_7)\n",
        "    #dense1 = Dense(512, activation='relu')(flat)\n",
        "    #dense2 = Dense(2)(dense1)\n",
        "    #class_layer = Activation('softmax', name='final_class')(dense2)\n",
        "    model = Model(inputs, conv_final, name=\"mini_UNET\")\n",
        "\n",
        "\n",
        "    return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "1kA_c5kc8bM-",
        "colab_type": "code",
        "outputId": "0abfe154-a5b5-4fb2-c48d-f1c761cece88",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 3693
        }
      },
      "cell_type": "code",
      "source": [
        "model = mini_UNET()\n",
        "model.summary()"
      ],
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_2 (InputLayer)            (None, 224, 224, 3)  0                                            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_24 (Conv2D)              (None, 224, 224, 32) 896         input_2[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_23 (BatchNo (None, 224, 224, 32) 128         conv2d_24[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_23 (Activation)      (None, 224, 224, 32) 0           batch_normalization_23[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_25 (Conv2D)              (None, 224, 224, 32) 9248        activation_23[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_24 (BatchNo (None, 224, 224, 32) 128         conv2d_25[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_24 (Activation)      (None, 224, 224, 32) 0           batch_normalization_24[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "spatial_dropout2d_12 (SpatialDr (None, 224, 224, 32) 0           activation_24[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_6 (MaxPooling2D)  (None, 112, 112, 32) 0           spatial_dropout2d_12[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_26 (Conv2D)              (None, 112, 112, 64) 18496       max_pooling2d_6[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_25 (BatchNo (None, 112, 112, 64) 256         conv2d_26[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_25 (Activation)      (None, 112, 112, 64) 0           batch_normalization_25[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_27 (Conv2D)              (None, 112, 112, 64) 36928       activation_25[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_26 (BatchNo (None, 112, 112, 64) 256         conv2d_27[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_26 (Activation)      (None, 112, 112, 64) 0           batch_normalization_26[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "spatial_dropout2d_13 (SpatialDr (None, 112, 112, 64) 0           activation_26[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_7 (MaxPooling2D)  (None, 56, 56, 64)   0           spatial_dropout2d_13[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_28 (Conv2D)              (None, 56, 56, 128)  73856       max_pooling2d_7[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_27 (BatchNo (None, 56, 56, 128)  512         conv2d_28[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_27 (Activation)      (None, 56, 56, 128)  0           batch_normalization_27[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_29 (Conv2D)              (None, 56, 56, 128)  147584      activation_27[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_28 (BatchNo (None, 56, 56, 128)  512         conv2d_29[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_28 (Activation)      (None, 56, 56, 128)  0           batch_normalization_28[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "spatial_dropout2d_14 (SpatialDr (None, 56, 56, 128)  0           activation_28[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_8 (MaxPooling2D)  (None, 28, 28, 128)  0           spatial_dropout2d_14[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_30 (Conv2D)              (None, 28, 28, 256)  295168      max_pooling2d_8[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_29 (BatchNo (None, 28, 28, 256)  1024        conv2d_30[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_29 (Activation)      (None, 28, 28, 256)  0           batch_normalization_29[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_31 (Conv2D)              (None, 28, 28, 256)  590080      activation_29[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_30 (BatchNo (None, 28, 28, 256)  1024        conv2d_31[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_30 (Activation)      (None, 28, 28, 256)  0           batch_normalization_30[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "spatial_dropout2d_15 (SpatialDr (None, 28, 28, 256)  0           activation_30[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_9 (MaxPooling2D)  (None, 14, 14, 256)  0           spatial_dropout2d_15[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_32 (Conv2D)              (None, 14, 14, 512)  1180160     max_pooling2d_9[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_31 (BatchNo (None, 14, 14, 512)  2048        conv2d_32[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_31 (Activation)      (None, 14, 14, 512)  0           batch_normalization_31[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_33 (Conv2D)              (None, 14, 14, 512)  2359808     activation_31[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_32 (BatchNo (None, 14, 14, 512)  2048        conv2d_33[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_32 (Activation)      (None, 14, 14, 512)  0           batch_normalization_32[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "spatial_dropout2d_16 (SpatialDr (None, 14, 14, 512)  0           activation_32[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_10 (MaxPooling2D) (None, 7, 7, 512)    0           spatial_dropout2d_16[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_34 (Conv2D)              (None, 7, 7, 1024)   4719616     max_pooling2d_10[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_33 (BatchNo (None, 7, 7, 1024)   4096        conv2d_34[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_33 (Activation)      (None, 7, 7, 1024)   0           batch_normalization_33[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_35 (Conv2D)              (None, 7, 7, 1024)   9438208     activation_33[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_34 (BatchNo (None, 7, 7, 1024)   4096        conv2d_35[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_34 (Activation)      (None, 7, 7, 1024)   0           batch_normalization_34[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "spatial_dropout2d_17 (SpatialDr (None, 7, 7, 1024)   0           activation_34[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "up_sampling2d_6 (UpSampling2D)  (None, 14, 14, 1024) 0           spatial_dropout2d_17[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_6 (Concatenate)     (None, 14, 14, 1536) 0           up_sampling2d_6[0][0]            \n",
            "                                                                 spatial_dropout2d_16[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_36 (Conv2D)              (None, 14, 14, 512)  7078400     concatenate_6[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_35 (BatchNo (None, 14, 14, 512)  2048        conv2d_36[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_35 (Activation)      (None, 14, 14, 512)  0           batch_normalization_35[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_37 (Conv2D)              (None, 14, 14, 512)  2359808     activation_35[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_36 (BatchNo (None, 14, 14, 512)  2048        conv2d_37[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_36 (Activation)      (None, 14, 14, 512)  0           batch_normalization_36[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "spatial_dropout2d_18 (SpatialDr (None, 14, 14, 512)  0           activation_36[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "up_sampling2d_7 (UpSampling2D)  (None, 28, 28, 512)  0           spatial_dropout2d_18[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_7 (Concatenate)     (None, 28, 28, 768)  0           up_sampling2d_7[0][0]            \n",
            "                                                                 spatial_dropout2d_15[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_38 (Conv2D)              (None, 28, 28, 256)  1769728     concatenate_7[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_37 (BatchNo (None, 28, 28, 256)  1024        conv2d_38[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_37 (Activation)      (None, 28, 28, 256)  0           batch_normalization_37[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_39 (Conv2D)              (None, 28, 28, 256)  590080      activation_37[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_38 (BatchNo (None, 28, 28, 256)  1024        conv2d_39[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_38 (Activation)      (None, 28, 28, 256)  0           batch_normalization_38[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "spatial_dropout2d_19 (SpatialDr (None, 28, 28, 256)  0           activation_38[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "up_sampling2d_8 (UpSampling2D)  (None, 56, 56, 256)  0           spatial_dropout2d_19[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_8 (Concatenate)     (None, 56, 56, 384)  0           up_sampling2d_8[0][0]            \n",
            "                                                                 spatial_dropout2d_14[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_40 (Conv2D)              (None, 56, 56, 128)  442496      concatenate_8[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_39 (BatchNo (None, 56, 56, 128)  512         conv2d_40[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_39 (Activation)      (None, 56, 56, 128)  0           batch_normalization_39[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_41 (Conv2D)              (None, 56, 56, 128)  147584      activation_39[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_40 (BatchNo (None, 56, 56, 128)  512         conv2d_41[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_40 (Activation)      (None, 56, 56, 128)  0           batch_normalization_40[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "spatial_dropout2d_20 (SpatialDr (None, 56, 56, 128)  0           activation_40[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "up_sampling2d_9 (UpSampling2D)  (None, 112, 112, 128 0           spatial_dropout2d_20[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_9 (Concatenate)     (None, 112, 112, 192 0           up_sampling2d_9[0][0]            \n",
            "                                                                 spatial_dropout2d_13[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_42 (Conv2D)              (None, 112, 112, 64) 110656      concatenate_9[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_41 (BatchNo (None, 112, 112, 64) 256         conv2d_42[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_41 (Activation)      (None, 112, 112, 64) 0           batch_normalization_41[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_43 (Conv2D)              (None, 112, 112, 64) 36928       activation_41[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_42 (BatchNo (None, 112, 112, 64) 256         conv2d_43[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_42 (Activation)      (None, 112, 112, 64) 0           batch_normalization_42[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "spatial_dropout2d_21 (SpatialDr (None, 112, 112, 64) 0           activation_42[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "up_sampling2d_10 (UpSampling2D) (None, 224, 224, 64) 0           spatial_dropout2d_21[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_10 (Concatenate)    (None, 224, 224, 96) 0           up_sampling2d_10[0][0]           \n",
            "                                                                 spatial_dropout2d_12[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_44 (Conv2D)              (None, 224, 224, 32) 27680       concatenate_10[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_43 (BatchNo (None, 224, 224, 32) 128         conv2d_44[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_43 (Activation)      (None, 224, 224, 32) 0           batch_normalization_43[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_45 (Conv2D)              (None, 224, 224, 32) 9248        activation_43[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_44 (BatchNo (None, 224, 224, 32) 128         conv2d_45[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_44 (Activation)      (None, 224, 224, 32) 0           batch_normalization_44[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "spatial_dropout2d_22 (SpatialDr (None, 224, 224, 32) 0           activation_44[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_46 (Conv2D)              (None, 224, 224, 1)  33          spatial_dropout2d_22[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "final_seg (Activation)          (None, 224, 224, 1)  0           conv2d_46[0][0]                  \n",
            "==================================================================================================\n",
            "Total params: 31,466,753\n",
            "Trainable params: 31,454,721\n",
            "Non-trainable params: 12,032\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "tPVsMpbR8ldU",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from keras.models import load_model\n",
        "from keras.optimizers import Adam\n",
        "import os\n",
        "from keras.losses import binary_crossentropy\n",
        "\n",
        "out_model_path = 'unet_224_final_model.h5'\n",
        "epochs = 50\n",
        "patience = 20\n",
        "batch_size = 64\n",
        "optim_type = 'Adam'\n",
        "learning_rate = 0.01\n",
        "\n",
        "optim = Adam(lr=learning_rate)\n",
        "loss={'final_seg': weighted_loss} # , 'final_class': 'binary_crossentropy'\n",
        "model.compile(optimizer=optim, loss=loss, metrics=[dice_coef, 'acc'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "UoOy5uMv8RNc",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "y = np.array(y, dtype='float32') # again converted to numpy array in case of classification"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "cvcdZVmQnQGB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "outputId": "13d0473e-3431-40f5-a2cd-7146f6fea9f1"
      },
      "cell_type": "code",
      "source": [
        "plt.imshow(masks[5])\n",
        "plt.show()"
      ],
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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effVVSVJ3d7fWrVunM2fO6Omnn9avf/3rMJTsHnZ3crccNG6pA9e54f1wQw1uFfKH7Xt7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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "RM44ZuRqna-X",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "5c0724fd-2b77-4fb8-c3b2-9c9186e14413"
      },
      "cell_type": "code",
      "source": [
        "augmented_images[0][1].shape"
      ],
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(224, 224)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 109
        }
      ]
    },
    {
      "metadata": {
        "id": "PGHVYIHvoGgx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "3a5b3be2-442e-497f-ad97-75ac0727398b"
      },
      "cell_type": "code",
      "source": [
        "masks.shape"
      ],
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(138, 224, 224)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 114
        }
      ]
    },
    {
      "metadata": {
        "id": "KKEVlRgUmrxR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "outputId": "dd00d6ba-c3b7-49b6-9af7-e7c6cfd4b5d0"
      },
      "cell_type": "code",
      "source": [
        "imgs = np.array(imgs, dtype='float32')\n",
        "masks = np.array(masks, dtype='float32')\n",
        "print(imgs.shape)\n",
        "masks = np.reshape(masks, (138,224,224,1))\n",
        "print(masks.shape)"
      ],
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(138, 224, 224, 3)\n",
            "(138, 224, 224, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "WuHelJ8gog5p",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "3bc9a3d4-4839-427d-902b-25afffd21800"
      },
      "cell_type": "code",
      "source": [
        "len(augmented_images)"
      ],
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 116
        }
      ]
    },
    {
      "metadata": {
        "id": "rei7ttpDoR4K",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "a_imgs = [a[0] for a in augmented_images]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "V7R3Ev_1ontA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "b79478de-cc65-44da-811e-04493da628f5"
      },
      "cell_type": "code",
      "source": [
        "print(a_imgs[0].shape)"
      ],
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(224, 224, 3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Lw73OLFeorxh",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "a_masks = [a[1] for a in augmented_images]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "NJxeL0I4ovEB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "f5edb4c1-ac86-4ffa-cb6b-781d67b97e7e"
      },
      "cell_type": "code",
      "source": [
        "print(a_masks[0].shape)"
      ],
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(224, 224)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "UHrSB-pnozkS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "outputId": "14364644-8f27-4aa6-b8e4-3380c29f853a"
      },
      "cell_type": "code",
      "source": [
        "a_imgs = np.array(a_imgs, dtype='float32')\n",
        "a_masks = np.array(a_masks, dtype='float32')\n",
        "a_masks = np.reshape(a_masks, (3000,224,224,1))\n",
        "print(a_imgs.shape)\n",
        "print(a_masks.shape)"
      ],
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(3000, 224, 224, 3)\n",
            "(3000, 224, 224, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "iZtmwdDkpTNq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "4e297559-3efc-4d27-c0f4-7d31771beb2a"
      },
      "cell_type": "code",
      "source": [
        "a_y = np.array(labels, dtype='float32')\n",
        "a_y.shape"
      ],
      "execution_count": 125,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3000, 2)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 125
        }
      ]
    },
    {
      "metadata": {
        "id": "O78Ij6zdpLYI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "outputId": "1cad2f79-8a47-480d-a9fa-c518be87ac77"
      },
      "cell_type": "code",
      "source": [
        "f_imgs = np.concatenate((imgs,a_imgs), axis=0)\n",
        "f_masks = np.concatenate((masks,a_masks), axis=0)\n",
        "print(f_imgs.shape)\n",
        "print(f_masks.shape)"
      ],
      "execution_count": 126,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(3138, 224, 224, 3)\n",
            "(3138, 224, 224, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Mz3vIRfMp3RZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 126
        },
        "outputId": "3dc966f2-9950-4fd0-eaaa-2ed709467726"
      },
      "cell_type": "code",
      "source": [
        "print(np.max(f_imgs))\n",
        "print(np.min(f_imgs))\n",
        "print(np.mean(f_imgs))\n",
        "\n",
        "print(np.max(f_masks))\n",
        "print(np.min(f_masks))\n",
        "print(np.mean(f_masks))"
      ],
      "execution_count": 127,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "255.0\n",
            "0.0\n",
            "115.843\n",
            "255.0\n",
            "0.0\n",
            "64.600655\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "dnzdU8oiqFNZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "0befaaea-71a5-4a25-cea6-a0d0a1c5ae0f"
      },
      "cell_type": "code",
      "source": [
        "f_y = np.concatenate((y,a_y), axis=0)\n",
        "f_y.shape"
      ],
      "execution_count": 128,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3138, 2)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 128
        }
      ]
    },
    {
      "metadata": {
        "id": "RlbymR5vqLbp",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "f_imgs = f_imgs/255.\n",
        "f_masks = f_masks/255."
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "uRWaAY_BqSJh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 126
        },
        "outputId": "325f4f7c-0f3c-40ed-e3c9-7ee3cf6e0380"
      },
      "cell_type": "code",
      "source": [
        "print(np.max(f_imgs))\n",
        "print(np.min(f_imgs))\n",
        "print(np.mean(f_imgs))\n",
        "\n",
        "print(np.max(f_masks))\n",
        "print(np.min(f_masks))\n",
        "print(np.mean(f_masks))"
      ],
      "execution_count": 130,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1.0\n",
            "0.0\n",
            "0.4542872\n",
            "1.0\n",
            "0.0\n",
            "0.25333413\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "mOC70ncarFKS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "outputId": "f7d57c78-f3ce-4744-8cf6-d721765784fe"
      },
      "cell_type": "code",
      "source": [
        "from keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau\n",
        "\n",
        "callbacks = [\n",
        "    ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=5, min_lr=1e-9, epsilon=0.00001, verbose=1, mode='min'),\n",
        "    #EarlyStopping(monitor='val_loss', patience=patience, verbose=0),\n",
        "    ModelCheckpoint('unet_224_best_weight.h5', monitor='val_loss', save_best_only=True, verbose=1),\n",
        "]\n"
      ],
      "execution_count": 132,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/keras/callbacks.py:1065: UserWarning: `epsilon` argument is deprecated and will be removed, use `min_delta` instead.\n",
            "  warnings.warn('`epsilon` argument is deprecated and '\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "6zq5fLtsDFG9",
        "colab_type": "code",
        "outputId": "1920abd7-8382-4071-e67c-11db8d1fc1b5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1802
        }
      },
      "cell_type": "code",
      "source": [
        "history = model.fit(\n",
        "    x = f_imgs,\n",
        "    y = f_masks, #[masks,y],\n",
        "    batch_size = 8,\n",
        "    epochs=50,\n",
        "    validation_split = 0.2,\n",
        "    verbose=1,\n",
        "    callbacks=callbacks)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 2510 samples, validate on 628 samples\n",
            "Epoch 1/50\n",
            "2510/2510 [==============================] - 186s 74ms/step - loss: -0.7241 - dice_coef: 0.8000 - acc: 0.8415 - val_loss: -0.7592 - val_dice_coef: 0.8302 - val_acc: 0.8686\n",
            "\n",
            "Epoch 00001: val_loss improved from inf to -0.75924, saving model to unet_224_best_weight.h5\n",
            "Epoch 2/50\n",
            "2510/2510 [==============================] - 178s 71ms/step - loss: -0.8088 - dice_coef: 0.8681 - acc: 0.8787 - val_loss: -0.8009 - val_dice_coef: 0.8623 - val_acc: 0.8812\n",
            "\n",
            "Epoch 00002: val_loss improved from -0.75924 to -0.80089, saving model to unet_224_best_weight.h5\n",
            "Epoch 3/50\n",
            "2510/2510 [==============================] - 178s 71ms/step - loss: -0.8326 - dice_coef: 0.8857 - acc: 0.8868 - val_loss: -0.8184 - val_dice_coef: 0.8753 - val_acc: 0.8866\n",
            "\n",
            "Epoch 00003: val_loss improved from -0.80089 to -0.81842, saving model to unet_224_best_weight.h5\n",
            "Epoch 4/50\n",
            "2510/2510 [==============================] - 178s 71ms/step - loss: -0.8507 - dice_coef: 0.8992 - acc: 0.8930 - val_loss: -0.8365 - val_dice_coef: 0.8888 - val_acc: 0.8923\n",
            "\n",
            "Epoch 00004: val_loss improved from -0.81842 to -0.83654, saving model to unet_224_best_weight.h5\n",
            "Epoch 5/50\n",
            "2510/2510 [==============================] - 177s 71ms/step - loss: -0.8650 - dice_coef: 0.9093 - acc: 0.8973 - val_loss: -0.8495 - val_dice_coef: 0.8983 - val_acc: 0.8967\n",
            "\n",
            "Epoch 00005: val_loss improved from -0.83654 to -0.84950, saving model to unet_224_best_weight.h5\n",
            "Epoch 6/50\n",
            "2510/2510 [==============================] - 178s 71ms/step - loss: -0.8722 - dice_coef: 0.9144 - acc: 0.8998 - val_loss: -0.8730 - val_dice_coef: 0.9150 - val_acc: 0.9051\n",
            "\n",
            "Epoch 00006: val_loss improved from -0.84950 to -0.87297, saving model to unet_224_best_weight.h5\n",
            "Epoch 7/50\n",
            "2510/2510 [==============================] - 178s 71ms/step - loss: -0.8851 - dice_coef: 0.9236 - acc: 0.9040 - val_loss: -0.8889 - val_dice_coef: 0.9262 - val_acc: 0.9099\n",
            "\n",
            "Epoch 00007: val_loss improved from -0.87297 to -0.88893, saving model to unet_224_best_weight.h5\n",
            "Epoch 8/50\n",
            "2510/2510 [==============================] - 178s 71ms/step - loss: -0.8897 - dice_coef: 0.9268 - acc: 0.9054 - val_loss: -0.8811 - val_dice_coef: 0.9207 - val_acc: 0.9080\n",
            "\n",
            "Epoch 00008: val_loss did not improve from -0.88893\n",
            "Epoch 9/50\n",
            "2510/2510 [==============================] - 177s 71ms/step - loss: -0.8934 - dice_coef: 0.9293 - acc: 0.9066 - val_loss: -0.8903 - val_dice_coef: 0.9270 - val_acc: 0.9105\n",
            "\n",
            "Epoch 00009: val_loss improved from -0.88893 to -0.89025, saving model to unet_224_best_weight.h5\n",
            "Epoch 10/50\n",
            "2510/2510 [==============================] - 178s 71ms/step - loss: -0.8994 - dice_coef: 0.9334 - acc: 0.9082 - val_loss: -0.8802 - val_dice_coef: 0.9200 - val_acc: 0.9074\n",
            "\n",
            "Epoch 00010: val_loss did not improve from -0.89025\n",
            "Epoch 11/50\n",
            "2510/2510 [==============================] - 178s 71ms/step - loss: -0.9025 - dice_coef: 0.9357 - acc: 0.9094 - val_loss: -0.8852 - val_dice_coef: 0.9236 - val_acc: 0.9093\n",
            "\n",
            "Epoch 00011: val_loss did not improve from -0.89025\n",
            "Epoch 12/50\n",
            "2510/2510 [==============================] - 178s 71ms/step - loss: -0.9039 - dice_coef: 0.9365 - acc: 0.9097 - val_loss: -0.9031 - val_dice_coef: 0.9360 - val_acc: 0.9144\n",
            "\n",
            "Epoch 00012: val_loss improved from -0.89025 to -0.90313, saving model to unet_224_best_weight.h5\n",
            "Epoch 13/50\n",
            "2510/2510 [==============================] - 178s 71ms/step - loss: -0.9042 - dice_coef: 0.9368 - acc: 0.9098 - val_loss: -0.8993 - val_dice_coef: 0.9335 - val_acc: 0.9132\n",
            "\n",
            "Epoch 00013: val_loss did not improve from -0.90313\n",
            "Epoch 14/50\n",
            "2510/2510 [==============================] - 177s 70ms/step - loss: -0.9080 - dice_coef: 0.9394 - acc: 0.9109 - val_loss: -0.9011 - val_dice_coef: 0.9347 - val_acc: 0.9138\n",
            "\n",
            "Epoch 00014: val_loss did not improve from -0.90313\n",
            "Epoch 15/50\n",
            "2510/2510 [==============================] - 177s 71ms/step - loss: -0.9114 - dice_coef: 0.9417 - acc: 0.9120 - val_loss: -0.9020 - val_dice_coef: 0.9353 - val_acc: 0.9143\n",
            "\n",
            "Epoch 00015: val_loss did not improve from -0.90313\n",
            "Epoch 16/50\n",
            "2510/2510 [==============================] - 177s 71ms/step - loss: -0.9098 - dice_coef: 0.9406 - acc: 0.9115 - val_loss: -0.8791 - val_dice_coef: 0.9187 - val_acc: 0.9067\n",
            "\n",
            "Epoch 00016: val_loss did not improve from -0.90313\n",
            "Epoch 17/50\n",
            "2510/2510 [==============================] - 177s 71ms/step - loss: -0.9137 - dice_coef: 0.9434 - acc: 0.9128 - val_loss: -0.8953 - val_dice_coef: 0.9305 - val_acc: 0.9122\n",
            "\n",
            "Epoch 00017: ReduceLROnPlateau reducing learning rate to 0.004999999888241291.\n",
            "\n",
            "Epoch 00017: val_loss did not improve from -0.90313\n",
            "Epoch 18/50\n",
            "2510/2510 [==============================] - 178s 71ms/step - loss: -0.9208 - dice_coef: 0.9482 - acc: 0.9150 - val_loss: -0.9098 - val_dice_coef: 0.9406 - val_acc: 0.9164\n",
            "\n",
            "Epoch 00018: val_loss improved from -0.90313 to -0.90976, saving model to unet_224_best_weight.h5\n",
            "Epoch 19/50\n",
            "2510/2510 [==============================] - 177s 71ms/step - loss: -0.9240 - dice_coef: 0.9503 - acc: 0.9159 - val_loss: -0.9150 - val_dice_coef: 0.9443 - val_acc: 0.9181\n",
            "\n",
            "Epoch 00019: val_loss improved from -0.90976 to -0.91504, saving model to unet_224_best_weight.h5\n",
            "Epoch 20/50\n",
            "2510/2510 [==============================] - 178s 71ms/step - loss: -0.9254 - dice_coef: 0.9513 - acc: 0.9163 - val_loss: -0.9179 - val_dice_coef: 0.9462 - val_acc: 0.9193\n",
            "\n",
            "Epoch 00020: val_loss improved from -0.91504 to -0.91789, saving model to unet_224_best_weight.h5\n",
            "Epoch 21/50\n",
            "2510/2510 [==============================] - 177s 71ms/step - loss: -0.9265 - dice_coef: 0.9521 - acc: 0.9167 - val_loss: -0.9187 - val_dice_coef: 0.9467 - val_acc: 0.9193\n",
            "\n",
            "Epoch 00021: val_loss improved from -0.91789 to -0.91870, saving model to unet_224_best_weight.h5\n",
            "Epoch 22/50\n",
            "2510/2510 [==============================] - 178s 71ms/step - loss: -0.9278 - dice_coef: 0.9529 - acc: 0.9171 - val_loss: -0.9178 - val_dice_coef: 0.9461 - val_acc: 0.9192\n",
            "\n",
            "Epoch 00022: val_loss did not improve from -0.91870\n",
            "Epoch 23/50\n",
            "2510/2510 [==============================] - 178s 71ms/step - loss: -0.9299 - dice_coef: 0.9543 - acc: 0.9178 - val_loss: -0.9160 - val_dice_coef: 0.9448 - val_acc: 0.9182\n",
            "\n",
            "Epoch 00023: val_loss did not improve from -0.91870\n",
            "Epoch 24/50\n",
            " 968/2510 [==========>...................] - ETA: 1:41 - loss: -0.9308 - dice_coef: 0.9550 - acc: 0.9220"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "WzD3ZSx4DbRz",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}