UNet_Implementation.ipynb

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  "metadata": {
    "colab": {
      "name": "UNet-Implementation.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "tOG84vhD_Fgr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import tensorflow as tf"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZmGU2WF0_Uu5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def Conv2D_Block(input_tensor , n_filters):\n",
        "  x = tf.keras.layers.Conv2D(filters = n_filters , kernel_size = (3 , 3) , kernel_initializer = 'he_normal' , padding = 'same')(input_tensor)\n",
        "  x = tf.keras.layers.BatchNormalization()(x)\n",
        "  x = tf.keras.layers.Activation('relu')(x)\n",
        "  \n",
        "  x = tf.keras.layers.Conv2D(filters = n_filters , kernel_size = (3 , 3) , kernel_initializer = 'he_normal' , padding = 'same')(x)\n",
        "  x = tf.keras.layers.BatchNormalization()(x)\n",
        "  x = tf.keras.layers.Activation('relu')(x)\n",
        "  \n",
        "  return x"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xt_4VuyFAzRI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def U_Net(img_tensor , n_filters = 16):\n",
        "  conv1 = Conv2D_Block(img_tensor , n_filters * 1)\n",
        "  pool1 = tf.keras.layers.MaxPooling2D((2 , 2))(conv1)\n",
        "  pool1 = tf.keras.layers.Dropout(0.05)(pool1)\n",
        "  \n",
        "  conv2 = Conv2D_Block(pool1 , n_filters * 2)\n",
        "  pool2 = tf.keras.layers.MaxPooling2D((2 , 2))(conv2)\n",
        "  pool2 = tf.keras.layers.Dropout(0.05)(pool2)\n",
        "  \n",
        "  conv3 = Conv2D_Block(pool2 , n_filters * 4)\n",
        "  pool3 = tf.keras.layers.MaxPooling2D((2 , 2))(conv3)\n",
        "  pool3 = tf.keras.layers.Dropout(0.05)(pool3)\n",
        "  \n",
        "  conv4 = Conv2D_Block(pool3 , n_filters * 8)\n",
        "  pool4 = tf.keras.layers.MaxPooling2D((2 , 2))(conv4)\n",
        "  pool4 = tf.keras.layers.Dropout(0.05)(pool4)\n",
        "  \n",
        "  conv5 = Conv2D_Block(pool4 , n_filters * 16)\n",
        "  \n",
        "  pool6 = tf.keras.layers.Conv2DTranspose(n_filters * 8 , (3 , 3) , (2, 2) , padding = 'same')(conv5)\n",
        "  pool6 = tf.keras.layers.concatenate([pool6 , conv4])\n",
        "  pool6 = tf.keras.layers.Dropout(0.05)(pool6)\n",
        "  conv6 = Conv2D_Block(pool6 , n_filters * 8)\n",
        "  \n",
        "  pool7 = tf.keras.layers.Conv2DTranspose(n_filters * 4 , (3 , 3) , (2 , 2) , padding = 'same')(conv6)\n",
        "  pool7 = tf.keras.layers.concatenate([pool7 , conv3])\n",
        "  pool7 = tf.keras.layers.Dropout(0.05)(pool7)\n",
        "  conv7 = Conv2D_Block(pool7 , n_filters * 4)\n",
        "  \n",
        "  pool8 = tf.keras.layers.Conv2DTranspose(n_filters * 2 , (3 , 3) , (2 , 2) , padding = 'same')(conv7)\n",
        "  pool8 = tf.keras.layers.concatenate([pool8 , conv2])\n",
        "  pool8 = tf.keras.layers.Dropout(0.05)(pool8)\n",
        "  conv8 = Conv2D_Block(pool8 , n_filters * 2)\n",
        "  \n",
        "  pool9 = tf.keras.layers.Conv2DTranspose(n_filters * 1 , (3 , 3) , (2 , 2) , padding = 'same')(conv8)\n",
        "  pool9 = tf.keras.layers.concatenate([pool9 , conv1])\n",
        "  pool9 = tf.keras.layers.Dropout(0.05)(pool9)\n",
        "  conv9 = Conv2D_Block(pool9 , n_filters * 1)\n",
        "  \n",
        "  output = tf.keras.layers.Conv2D(1 , (1 , 1) , activation = 'sigmoid')(conv9)\n",
        "  \n",
        "  u_net = tf.keras.Model(inputs = [img_tensor] , outputs = [output])\n",
        "  \n",
        "  return u_net"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zDjLdPS8Gvye",
        "colab_type": "code",
        "outputId": "ab0ad199-52d1-45bb-cdad-10138584905d",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
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",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 91
        }
      },
      "source": [
        "from google.colab import files\n",
        "files.upload()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-cfef3ac9-9cbe-43be-b5ed-c1cf41e39940\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-cfef3ac9-9cbe-43be-b5ed-c1cf41e39940\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving kaggle.json to kaggle.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'kaggle.json': b'{\"username\":\"ajaxlegrand\",\"key\":\"baf5b2d32fc89c872a17f185d4649596\"}'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q7fUscunLF8G",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!mkdir ~/.kaggle"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6Dx6UhY0LNgz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!pip install -q kaggle"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bh44yzZiLRzz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!cp kaggle.json ~/.kaggle/\n",
        "!chmod 600 ~/.kaggle/kaggle.json"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x2eQmPQ_LXFR",
        "colab_type": "code",
        "outputId": "8318da45-2103-4ac1-8d60-df8a80c2583f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        }
      },
      "source": [
        "!kaggle competitions download -c ultrasound-nerve-segmentation"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading train_masks.csv.zip to /content\n",
            "\r  0% 0.00/749k [00:00<?, ?B/s]\n",
            "100% 749k/749k [00:00<00:00, 51.0MB/s]\n",
            "Downloading sample_submission.csv.zip to /content\n",
            "  0% 0.00/6.40k [00:00<?, ?B/s]\n",
            "100% 6.40k/6.40k [00:00<00:00, 7.22MB/s]\n",
            "Downloading test.zip to /content\n",
            " 99% 1.03G/1.04G [00:24<00:00, 26.3MB/s]\n",
            "100% 1.04G/1.04G [00:25<00:00, 44.5MB/s]\n",
            "Downloading train.zip to /content\n",
            " 99% 1.06G/1.07G [00:17<00:00, 60.8MB/s]\n",
            "100% 1.07G/1.07G [00:17<00:00, 65.8MB/s]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "09DRxW-ILq1Q",
        "colab_type": "code",
        "outputId": "93f243f2-21e5-43cb-b384-72f8fc6db0d7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "!unzip sample_submission.csv.zip"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  sample_submission.csv.zip\n",
            "  inflating: sample_submission.csv   \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FDqqE7rroEjt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!unzip test.zip\n",
        "!unzip train.zip\n",
        "!unzip train_masks.csv.zip"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uFX3URdtprWE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YUFuzV2Ep8NV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "d = pd.read_csv('train_masks.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pg8asx1cqBj4",
        "colab_type": "code",
        "outputId": "b0d31c57-d148-45fa-df71-f4d8f3da2c53",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "d.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>subject</th>\n",
              "      <th>img</th>\n",
              "      <th>pixels</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>168153 9 168570 15 168984 22 169401 26 169818 ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>96346 7 96762 16 97179 27 97595 35 98012 40 98...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>121957 2 122365 26 122784 28 123202 31 123618 ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>110980 9 111393 21 111810 33 112228 43 112647 ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   subject  img                                             pixels\n",
              "0        1    1  168153 9 168570 15 168984 22 169401 26 169818 ...\n",
              "1        1    2  96346 7 96762 16 97179 27 97595 35 98012 40 98...\n",
              "2        1    3  121957 2 122365 26 122784 28 123202 31 123618 ...\n",
              "3        1    4  110980 9 111393 21 111810 33 112228 43 112647 ...\n",
              "4        1    5                                                NaN"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "84E7_XNfuOj0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os\n",
        "import re"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "huRJGpSzqFfn",
        "colab_type": "code",
        "outputId": "35b8d0b5-44cc-41bb-865c-d4f88b028e81",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "train_path = os.listdir('/content/train/')\n",
        "test_path = os.listdir('/content/test/')\n",
        "\n",
        "print(len(train_path) , len(test_path))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "11270 5508\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AGh4_ERhutYb",
        "colab_type": "code",
        "outputId": "713c77bd-efd8-49ea-cb3f-c37a730333b6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 353
        }
      },
      "source": [
        "reg = re.compile(\"[0-9]+\")\n",
        "\n",
        "temp1 = list(map(lambda x: reg.match(x).group(), train_path)) \n",
        "temp1 = list(map(int, temp1))\n",
        "\n",
        "temp2 = list(map(lambda x: reg.match(x.split(\"_\")[1]).group(), train_path))\n",
        "temp2 = list(map(int, temp2))\n",
        "\n",
        "train_path = [x for _,_,x in sorted(zip(temp1, temp2, train_path))]\n",
        "train_path[:20]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['1_1.tif',\n",
              " '1_1_mask.tif',\n",
              " '1_2.tif',\n",
              " '1_2_mask.tif',\n",
              " '1_3.tif',\n",
              " '1_3_mask.tif',\n",
              " '1_4.tif',\n",
              " '1_4_mask.tif',\n",
              " '1_5.tif',\n",
              " '1_5_mask.tif',\n",
              " '1_6.tif',\n",
              " '1_6_mask.tif',\n",
              " '1_7.tif',\n",
              " '1_7_mask.tif',\n",
              " '1_8.tif',\n",
              " '1_8_mask.tif',\n",
              " '1_9.tif',\n",
              " '1_9_mask.tif',\n",
              " '1_10.tif',\n",
              " '1_10_mask.tif']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gv8xNqhQvRLB",
        "colab_type": "code",
        "outputId": "f449f850-b883-4d88-cf8c-44a82e151782",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "len(train_path)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "11270"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_oys8ldjwXVa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_image = []\n",
        "train_mask = []\n",
        "\n",
        "for index , item in enumerate(train_path):\n",
        "  if index % 2 == 0:\n",
        "    train_image.append(item)\n",
        "  else:\n",
        "    train_mask.append(item)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vhQDAH0xxDCZ",
        "colab_type": "code",
        "outputId": "6e84c0f0-8ad1-48af-d00f-f67ff71dd8f1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "print(len(train_image))\n",
        "print(len(train_mask))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "5635\n",
            "5635\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "6VY9fpk73VaS",
        "colab": {}
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import random\n",
        "from PIL import Image\n",
        "import numpy as np\n",
        "import skimage\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h3x8vVcVxVEE",
        "colab_type": "code",
        "outputId": "ceeac88b-da0f-4fbb-d012-74d7b1318a31",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 885
        }
      },
      "source": [
        "fig , ax = plt.subplots(3 , 2 , figsize=[10 , 10])\n",
        "fig.suptitle('Vizualizing Nerve Data')\n",
        "ax[0 , 0].set_title('Train Data')\n",
        "ax[0 , 1].set_title('Mask')\n",
        "\n",
        "# print(ax.shape)\n",
        "for i in range(0 , 3):\n",
        "  ind = random.randint(0 , len(train_image))\n",
        "  img1 = np.array(Image.open(\"/content/train/\" + str(train_image[ind])))\n",
        "  ax[i , 0].imshow(img1 , cmap = 'gray')\n",
        "\n",
        "  img2 = np.array(Image.open(\"/content/train/\" + str(train_mask[ind])))\n",
        "  img2 = np.ma.masked_where(img2 == 0, img2)\n",
        "  ax[i , 1].imshow(img1 , cmap = 'gray' , interpolation = 'none')\n",
        "  ax[i , 1].imshow(img2 , cmap = 'jet', interpolation = 'none', alpha = 0.7)\n",
        "    \n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/matplotlib/image.py:395: UserWarning: Warning: converting a masked element to nan.\n",
            "  dv = (np.float64(self.norm.vmax) -\n",
            "/usr/local/lib/python3.6/dist-packages/matplotlib/image.py:396: UserWarning: Warning: converting a masked element to nan.\n",
            "  np.float64(self.norm.vmin))\n",
            "/usr/local/lib/python3.6/dist-packages/matplotlib/image.py:403: UserWarning: Warning: converting a masked element to nan.\n",
            "  a_min = np.float64(newmin)\n",
            "/usr/local/lib/python3.6/dist-packages/matplotlib/image.py:408: UserWarning: Warning: converting a masked element to nan.\n",
            "  a_max = np.float64(newmax)\n",
            "/usr/local/lib/python3.6/dist-packages/matplotlib/colors.py:918: UserWarning: Warning: converting a masked element to nan.\n",
            "  dtype = np.min_scalar_type(value)\n",
            "/usr/local/lib/python3.6/dist-packages/numpy/ma/core.py:713: UserWarning: Warning: converting a masked element to nan.\n",
            "  data = np.array(a, copy=False, subok=subok)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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A1SSj0ajExQeDAZrNJobDIZrNprgq19fX4TiOxJk7nQ48Ho+49yggOp0OqtWq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oVxq8ALmY+Vxq0iAMszDKDIwEU9Go20WCw0m83sxF2SwqSN9eiBBWYgiY+1MBfu4cs6YT5v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\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pyWSiWq2mwWCgWq2m6XSqdDpt3tr6+rpqtZo+/vhjra+vaz6fmwYB6r3b7do9JpNJVSoV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\nkCCfOgkGTpAgQYIECRLkUyf/Pwo6bvTxTsJtAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x720 with 6 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mIwXSupE6m5Q",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X = np.zeros((len(train_image), 128, 128, 1), dtype=np.float32)\n",
        "y = np.zeros((len(train_image), 128, 128, 1), dtype=np.float32)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U8ei2UHrx26e",
        "colab_type": "code",
        "outputId": "3d911900-8ead-4cc2-bd9a-047507d364f0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        }
      },
      "source": [
        "for n , img_name in enumerate(train_image):\n",
        "  img = tf.keras.preprocessing.image.load_img('/content/train/'+img_name , grayscale=True)\n",
        "  in_img = tf.keras.preprocessing.image.img_to_array(img)\n",
        "  in_img = skimage.transform.resize(in_img , (128 , 128 , 1) , mode = 'constant' , preserve_range = True)\n",
        "  X[n] = in_img / 255.0\n",
        "  \n",
        "for n , img_name in enumerate(train_mask):\n",
        "  img = tf.keras.preprocessing.image.load_img('/content/train/'+img_name , grayscale=True)\n",
        "  in_img = tf.keras.preprocessing.image.img_to_array(img)\n",
        "  in_img = skimage.transform.resize(in_img , (128 , 128 , 1) , mode = 'constant' , preserve_range = True)\n",
        "  y[n] = in_img / 255.0"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/keras_preprocessing/image/utils.py:104: UserWarning: grayscale is deprecated. Please use color_mode = \"grayscale\"\n",
            "  warnings.warn('grayscale is deprecated. Please use '\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xtaavdNc4iSD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train , X_valid , y_train , y_valid = train_test_split(X , y , test_size = 0.1 , random_state = 42)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P6bKNYDv8PxF",
        "colab_type": "code",
        "outputId": "53c556e0-1b76-409a-c4aa-ba58c472930e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 138
        }
      },
      "source": [
        "img_tensor = tf.keras.layers.Input((128 , 128 , 1) , name = 'img')\n",
        "model = U_Net(img_tensor)\n",
        "model.compile(optimizer = tf.keras.optimizers.Adam(),\n",
        "             loss = 'binary_crossentropy',\n",
        "             metrics = ['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "W0614 17:19:48.266108 140477369186176 deprecation.py:506] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
            "W0614 17:19:49.240010 140477369186176 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/nn_impl.py:180: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ewoQSgb9-Owo",
        "colab_type": "code",
        "outputId": "e7d76ce5-ee4d-42a5-9437-9be4dc6ccb98",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 2772
        }
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "img (InputLayer)                [(None, 128, 128, 1) 0                                            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_2 (Conv2D)               (None, 128, 128, 16) 160         img[0][0]                        \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_2 (BatchNor (None, 128, 128, 16) 64          conv2d_2[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_2 (Activation)       (None, 128, 128, 16) 0           batch_normalization_2[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_3 (Conv2D)               (None, 128, 128, 16) 2320        activation_2[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_3 (BatchNor (None, 128, 128, 16) 64          conv2d_3[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_3 (Activation)       (None, 128, 128, 16) 0           batch_normalization_3[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2D)  (None, 64, 64, 16)   0           activation_3[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "dropout (Dropout)               (None, 64, 64, 16)   0           max_pooling2d_1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_4 (Conv2D)               (None, 64, 64, 32)   4640        dropout[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_4 (BatchNor (None, 64, 64, 32)   128         conv2d_4[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_4 (Activation)       (None, 64, 64, 32)   0           batch_normalization_4[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_5 (Conv2D)               (None, 64, 64, 32)   9248        activation_4[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_5 (BatchNor (None, 64, 64, 32)   128         conv2d_5[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_5 (Activation)       (None, 64, 64, 32)   0           batch_normalization_5[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2D)  (None, 32, 32, 32)   0           activation_5[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "dropout_1 (Dropout)             (None, 32, 32, 32)   0           max_pooling2d_2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_6 (Conv2D)               (None, 32, 32, 64)   18496       dropout_1[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_6 (BatchNor (None, 32, 32, 64)   256         conv2d_6[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_6 (Activation)       (None, 32, 32, 64)   0           batch_normalization_6[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_7 (Conv2D)               (None, 32, 32, 64)   36928       activation_6[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_7 (BatchNor (None, 32, 32, 64)   256         conv2d_7[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_7 (Activation)       (None, 32, 32, 64)   0           batch_normalization_7[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2D)  (None, 16, 16, 64)   0           activation_7[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "dropout_2 (Dropout)             (None, 16, 16, 64)   0           max_pooling2d_3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_8 (Conv2D)               (None, 16, 16, 128)  73856       dropout_2[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_8 (BatchNor (None, 16, 16, 128)  512         conv2d_8[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_8 (Activation)       (None, 16, 16, 128)  0           batch_normalization_8[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_9 (Conv2D)               (None, 16, 16, 128)  147584      activation_8[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_9 (BatchNor (None, 16, 16, 128)  512         conv2d_9[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_9 (Activation)       (None, 16, 16, 128)  0           batch_normalization_9[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_4 (MaxPooling2D)  (None, 8, 8, 128)    0           activation_9[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "dropout_3 (Dropout)             (None, 8, 8, 128)    0           max_pooling2d_4[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_10 (Conv2D)              (None, 8, 8, 256)    295168      dropout_3[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_10 (BatchNo (None, 8, 8, 256)    1024        conv2d_10[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_10 (Activation)      (None, 8, 8, 256)    0           batch_normalization_10[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_11 (Conv2D)              (None, 8, 8, 256)    590080      activation_10[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_11 (BatchNo (None, 8, 8, 256)    1024        conv2d_11[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_11 (Activation)      (None, 8, 8, 256)    0           batch_normalization_11[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_transpose (Conv2DTranspo (None, 16, 16, 128)  295040      activation_11[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "concatenate (Concatenate)       (None, 16, 16, 256)  0           conv2d_transpose[0][0]           \n",
            "                                                                 activation_9[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "dropout_4 (Dropout)             (None, 16, 16, 256)  0           concatenate[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_12 (Conv2D)              (None, 16, 16, 128)  295040      dropout_4[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_12 (BatchNo (None, 16, 16, 128)  512         conv2d_12[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_12 (Activation)      (None, 16, 16, 128)  0           batch_normalization_12[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_13 (Conv2D)              (None, 16, 16, 128)  147584      activation_12[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_13 (BatchNo (None, 16, 16, 128)  512         conv2d_13[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_13 (Activation)      (None, 16, 16, 128)  0           batch_normalization_13[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_transpose_1 (Conv2DTrans (None, 32, 32, 64)   73792       activation_13[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_1 (Concatenate)     (None, 32, 32, 128)  0           conv2d_transpose_1[0][0]         \n",
            "                                                                 activation_7[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "dropout_5 (Dropout)             (None, 32, 32, 128)  0           concatenate_1[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_14 (Conv2D)              (None, 32, 32, 64)   73792       dropout_5[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_14 (BatchNo (None, 32, 32, 64)   256         conv2d_14[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_14 (Activation)      (None, 32, 32, 64)   0           batch_normalization_14[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_15 (Conv2D)              (None, 32, 32, 64)   36928       activation_14[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_15 (BatchNo (None, 32, 32, 64)   256         conv2d_15[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_15 (Activation)      (None, 32, 32, 64)   0           batch_normalization_15[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_transpose_2 (Conv2DTrans (None, 64, 64, 32)   18464       activation_15[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_2 (Concatenate)     (None, 64, 64, 64)   0           conv2d_transpose_2[0][0]         \n",
            "                                                                 activation_5[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "dropout_6 (Dropout)             (None, 64, 64, 64)   0           concatenate_2[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_16 (Conv2D)              (None, 64, 64, 32)   18464       dropout_6[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_16 (BatchNo (None, 64, 64, 32)   128         conv2d_16[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_16 (Activation)      (None, 64, 64, 32)   0           batch_normalization_16[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_17 (Conv2D)              (None, 64, 64, 32)   9248        activation_16[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_17 (BatchNo (None, 64, 64, 32)   128         conv2d_17[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_17 (Activation)      (None, 64, 64, 32)   0           batch_normalization_17[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_transpose_3 (Conv2DTrans (None, 128, 128, 16) 4624        activation_17[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_3 (Concatenate)     (None, 128, 128, 32) 0           conv2d_transpose_3[0][0]         \n",
            "                                                                 activation_3[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "dropout_7 (Dropout)             (None, 128, 128, 32) 0           concatenate_3[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_18 (Conv2D)              (None, 128, 128, 16) 4624        dropout_7[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_18 (BatchNo (None, 128, 128, 16) 64          conv2d_18[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_18 (Activation)      (None, 128, 128, 16) 0           batch_normalization_18[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_19 (Conv2D)              (None, 128, 128, 16) 2320        activation_18[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_19 (BatchNo (None, 128, 128, 16) 64          conv2d_19[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_19 (Activation)      (None, 128, 128, 16) 0           batch_normalization_19[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_20 (Conv2D)              (None, 128, 128, 1)  17          activation_19[0][0]              \n",
            "==================================================================================================\n",
            "Total params: 2,164,305\n",
            "Trainable params: 2,161,361\n",
            "Non-trainable params: 2,944\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hx0xnc4kANzc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "callbacks = [\n",
        "    tf.keras.callbacks.EarlyStopping(patience=10, verbose=1),\n",
        "    tf.keras.callbacks.ReduceLROnPlateau(factor=0.1, patience=5, min_lr=0.00001, verbose=1),\n",
        "    tf.keras.callbacks.ModelCheckpoint('model_nerve.h5', verbose=1, save_best_only=True, save_weights_only=True)\n",
        "]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jiBmsTy_Anok",
        "colab_type": "code",
        "outputId": "d350828c-a393-41ad-89f8-38123f726333",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1616
        }
      },
      "source": [
        "results = model.fit(X_train, y_train, batch_size=32, epochs=50, callbacks=callbacks,\\\n",
        "                    validation_data=(X_valid, y_valid))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 5071 samples, validate on 564 samples\n",
            "Epoch 1/50\n",
            "5056/5071 [============================>.] - ETA: 0s - loss: 0.1248 - acc: 0.9815\n",
            "Epoch 00001: val_loss improved from inf to 0.18365, saving model to model_nerve.h5\n",
            "5071/5071 [==============================] - 26s 5ms/sample - loss: 0.1246 - acc: 0.9815 - val_loss: 0.1836 - val_acc: 0.9363\n",
            "Epoch 2/50\n",
            "5056/5071 [============================>.] - ETA: 0s - loss: 0.0444 - acc: 0.9851\n",
            "Epoch 00002: val_loss improved from 0.18365 to 0.04330, saving model to model_nerve.h5\n",
            "5071/5071 [==============================] - 18s 3ms/sample - loss: 0.0444 - acc: 0.9851 - val_loss: 0.0433 - val_acc: 0.9846\n",
            "Epoch 3/50\n",
            "5056/5071 [============================>.] - ETA: 0s - loss: 0.0315 - acc: 0.9856\n",
            "Epoch 00003: val_loss did not improve from 0.04330\n",
            "5071/5071 [==============================] - 18s 4ms/sample - loss: 0.0315 - acc: 0.9856 - val_loss: 0.0491 - val_acc: 0.9759\n",
            "Epoch 4/50\n",
            "5056/5071 [============================>.] - ETA: 0s - loss: 0.0262 - acc: 0.9860\n",
            "Epoch 00004: val_loss improved from 0.04330 to 0.03901, saving model to model_nerve.h5\n",
            "5071/5071 [==============================] - 18s 4ms/sample - loss: 0.0262 - acc: 0.9860 - val_loss: 0.0390 - val_acc: 0.9786\n",
            "Epoch 5/50\n",
            "5056/5071 [============================>.] - ETA: 0s - loss: 0.0236 - acc: 0.9861\n",
            "Epoch 00005: val_loss improved from 0.03901 to 0.02622, saving model to model_nerve.h5\n",
            "5071/5071 [==============================] - 18s 4ms/sample - loss: 0.0236 - acc: 0.9862 - val_loss: 0.0262 - val_acc: 0.9848\n",
            "Epoch 6/50\n",
            "5056/5071 [============================>.] - ETA: 0s - loss: 0.0218 - acc: 0.9865\n",
            "Epoch 00006: val_loss did not improve from 0.02622\n",
            "5071/5071 [==============================] - 19s 4ms/sample - loss: 0.0218 - acc: 0.9865 - val_loss: 0.0326 - val_acc: 0.9818\n",
            "Epoch 7/50\n",
            "5056/5071 [============================>.] - ETA: 0s - loss: 0.0205 - acc: 0.9866\n",
            "Epoch 00007: val_loss did not improve from 0.02622\n",
            "5071/5071 [==============================] - 19s 4ms/sample - loss: 0.0205 - acc: 0.9866 - val_loss: 0.0264 - val_acc: 0.9853\n",
            "Epoch 8/50\n",
            "5056/5071 [============================>.] - ETA: 0s - loss: 0.0194 - acc: 0.9868\n",
            "Epoch 00008: val_loss did not improve from 0.02622\n",
            "5071/5071 [==============================] - 19s 4ms/sample - loss: 0.0194 - acc: 0.9868 - val_loss: 0.0263 - val_acc: 0.9843\n",
            "Epoch 9/50\n",
            "5056/5071 [============================>.] - ETA: 0s - loss: 0.0184 - acc: 0.9871\n",
            "Epoch 00009: val_loss improved from 0.02622 to 0.02596, saving model to model_nerve.h5\n",
            "5071/5071 [==============================] - 19s 4ms/sample - loss: 0.0184 - acc: 0.9871 - val_loss: 0.0260 - val_acc: 0.9853\n",
            "Epoch 10/50\n",
            "5056/5071 [============================>.] - ETA: 0s - loss: 0.0175 - acc: 0.9872\n",
            "Epoch 00010: val_loss did not improve from 0.02596\n",
            "5071/5071 [==============================] - 19s 4ms/sample - loss: 0.0175 - acc: 0.9872 - val_loss: 0.0279 - val_acc: 0.9836\n",
            "Epoch 11/50\n",
            "5056/5071 [============================>.] - ETA: 0s - loss: 0.0166 - acc: 0.9874\n",
            "Epoch 00011: val_loss did not improve from 0.02596\n",
            "5071/5071 [==============================] - 19s 4ms/sample - loss: 0.0166 - acc: 0.9874 - val_loss: 0.0284 - val_acc: 0.9836\n",
            "Epoch 12/50\n",
            "5056/5071 [============================>.] - ETA: 0s - loss: 0.0155 - acc: 0.9877\n",
            "Epoch 00012: val_loss improved from 0.02596 to 0.02527, saving model to model_nerve.h5\n",
            "5071/5071 [==============================] - 19s 4ms/sample - loss: 0.0155 - acc: 0.9877 - val_loss: 0.0253 - val_acc: 0.9847\n",
            "Epoch 13/50\n",
            "5056/5071 [============================>.] - ETA: 0s - loss: 0.0154 - acc: 0.9878\n",
            "Epoch 00013: val_loss did not improve from 0.02527\n",
            "5071/5071 [==============================] - 19s 4ms/sample - loss: 0.0154 - acc: 0.9878 - val_loss: 0.0284 - val_acc: 0.9835\n",
            "Epoch 14/50\n",
            "5056/5071 [============================>.] - ETA: 0s - loss: 0.0151 - acc: 0.9878\n",
            "Epoch 00014: val_loss did not improve from 0.02527\n",
            "5071/5071 [==============================] - 19s 4ms/sample - loss: 0.0151 - acc: 0.9878 - val_loss: 0.0273 - val_acc: 0.9842\n",
            "Epoch 15/50\n",
            "5056/5071 [============================>.] - ETA: 0s - loss: 0.0140 - acc: 0.9882\n",
            "Epoch 00015: val_loss did not improve from 0.02527\n",
            "5071/5071 [==============================] - 19s 4ms/sample - loss: 0.0140 - acc: 0.9882 - val_loss: 0.0292 - val_acc: 0.9844\n",
            "Epoch 16/50\n",
            "5056/5071 [============================>.] - ETA: 0s - loss: 0.0140 - acc: 0.9882\n",
            "Epoch 00016: val_loss did not improve from 0.02527\n",
            "5071/5071 [==============================] - 19s 4ms/sample - loss: 0.0140 - acc: 0.9882 - val_loss: 0.0284 - val_acc: 0.9848\n",
            "Epoch 17/50\n",
            "5056/5071 [============================>.] - ETA: 0s - loss: 0.0133 - acc: 0.9884\n",
            "Epoch 00017: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
            "\n",
            "Epoch 00017: val_loss did not improve from 0.02527\n",
            "5071/5071 [==============================] - 19s 4ms/sample - loss: 0.0133 - acc: 0.9884 - val_loss: 0.0291 - val_acc: 0.9843\n",
            "Epoch 18/50\n",
            "5056/5071 [============================>.] - ETA: 0s - loss: 0.0112 - acc: 0.9890\n",
            "Epoch 00018: val_loss did not improve from 0.02527\n",
            "5071/5071 [==============================] - 19s 4ms/sample - loss: 0.0112 - acc: 0.9890 - val_loss: 0.0323 - val_acc: 0.9848\n",
            "Epoch 19/50\n",
            "5056/5071 [============================>.] - ETA: 0s - loss: 0.0106 - acc: 0.9892\n",
            "Epoch 00019: val_loss did not improve from 0.02527\n",
            "5071/5071 [==============================] - 19s 4ms/sample - loss: 0.0106 - acc: 0.9892 - val_loss: 0.0326 - val_acc: 0.9848\n",
            "Epoch 20/50\n",
            "5056/5071 [============================>.] - ETA: 0s - loss: 0.0104 - acc: 0.9893\n",
            "Epoch 00020: val_loss did not improve from 0.02527\n",
            "5071/5071 [==============================] - 19s 4ms/sample - loss: 0.0104 - acc: 0.9893 - val_loss: 0.0352 - val_acc: 0.9848\n",
            "Epoch 21/50\n",
            "5056/5071 [============================>.] - ETA: 0s - loss: 0.0102 - acc: 0.9894\n",
            "Epoch 00021: val_loss did not improve from 0.02527\n",
            "5071/5071 [==============================] - 19s 4ms/sample - loss: 0.0102 - acc: 0.9894 - val_loss: 0.0352 - val_acc: 0.9847\n",
            "Epoch 22/50\n",
            "5056/5071 [============================>.] - ETA: 0s - loss: 0.0099 - acc: 0.9894\n",
            "Epoch 00022: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.\n",
            "\n",
            "Epoch 00022: val_loss did not improve from 0.02527\n",
            "5071/5071 [==============================] - 19s 4ms/sample - loss: 0.0099 - acc: 0.9894 - val_loss: 0.0355 - val_acc: 0.9846\n",
            "Epoch 00022: early stopping\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "07hYPI5PA5cH",
        "colab_type": "code",
        "outputId": "e781f007-fce6-4844-f933-a13b5bb640e6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        }
      },
      "source": [
        "plt.figure(figsize=(8, 8))\n",
        "plt.title(\"Learning curve\")\n",
        "plt.plot(results.history[\"loss\"], label=\"loss\")\n",
        "plt.plot(results.history[\"val_loss\"], label=\"val_loss\")\n",
        "plt.plot( np.argmin(results.history[\"val_loss\"]), np.min(results.history[\"val_loss\"]), marker=\"x\", color=\"r\", label=\"best model\")\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"log_loss\")\n",
        "plt.legend();"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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qj4CIiKQtBYE4iIwuuF+NgJnmGxARkbTWrCBgZllmFop3YTJV27wc8oM5dY8u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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uXgvQE67EC8G",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.load_weights('model_nerve.h5')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tKYzde67ETwA",
        "colab_type": "code",
        "outputId": "ccf45614-24e2-4c82-bb05-28492b277a14",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "model.evaluate(X_valid, y_valid, verbose=1)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "564/564 [==============================] - 1s 1ms/sample - loss: 0.0253 - acc: 0.9847\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.025270367688532417, 0.98466146]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J4xteevJJpTs",
        "colab_type": "code",
        "outputId": "69fadf79-8375-46c1-f8a8-b1d62a004c2f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        }
      },
      "source": [
        "plt.imshow(y_valid[0].squeeze())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fc3001d9ba8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 119
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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sjBhy1umXIukaoA+4dRjPXQosBZjApCJlmFkdDTsUJH0QuBBYkE9BD7ALmFWzW0e+7kUi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\nEg4FM0s4FMws4VAws8T/AbLrxVutB/nsAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dViXNvC9FNuw",
        "colab_type": "code",
        "outputId": "ecb0a374-502d-4b34-c440-3636cc537edf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 339
        }
      },
      "source": [
        "img_name = '1000.tif'\n",
        "img = tf.keras.preprocessing.image.load_img('/content/test/'+img_name , grayscale=True)\n",
        "in_img = tf.keras.preprocessing.image.img_to_array(img)\n",
        "in_img = skimage.transform.resize(in_img , (128 , 128 , 1) , mode = 'constant' , preserve_range = True)\n",
        "test = in_img / 255.0\n",
        "plt.imshow(test.squeeze())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/keras_preprocessing/image/utils.py:104: UserWarning: grayscale is deprecated. Please use color_mode = \"grayscale\"\n",
            "  warnings.warn('grayscale is deprecated. Please use '\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fc2bcf47cf8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 126
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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hk8Of9Z0SUZ85Gbk6UlwSnZaWm00qOvOatuBmxrUMUluRrADTI2yFSZlvrhB2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\nrkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PCNNLw6nGZ8i",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "prediction = model.predict(np.expand_dims(test , 0))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y5FSA0jnGgUu",
        "colab_type": "code",
        "outputId": "10e60893-4a7c-49f8-892f-05b35d025a7d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "prediction.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1, 128, 128, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 128
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5kVnmLFHG9mh",
        "colab_type": "code",
        "outputId": "8917393a-7a48-4a1b-e734-1d54c7de160c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "prediction.squeeze().shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(128, 128)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 129
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dOF7vs3eIaMi",
        "colab_type": "code",
        "outputId": "f61c74c2-35f9-4bd6-f23f-fd95e3c3acdb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 262
        }
      },
      "source": [
        "fig , ax = plt.subplots(1 , 2)\n",
        "fig.suptitle('Vizualizing Nerve Data')\n",
        "ax[0].set_title('Test Image')\n",
        "ax[1].set_title('Mask')\n",
        "img1 = test.squeeze()\n",
        "ax[0].imshow(img1 , cmap = 'gray')\n",
        "\n",
        "img2 = prediction.squeeze()\n",
        "img2 = np.ma.masked_where(img2 == 0, img2)\n",
        "ax[1].imshow(img1 , cmap = 'gray' , interpolation = 'none')\n",
        "ax[1].imshow(img2 , cmap = 'jet', interpolation = 'none', alpha = 0.7)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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xf0TKGMiKS6guCcRKEF3A55HgdLrFA6+ezYFzUELnRBgOp9ZnneTzcUalc7P8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\nbBqCg8ZVTAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zUCxFmyrJOpR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}