MapBiomas.ipynb

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    "colab": {
      "name": "MapBiomas.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "accelerator": "GPU"
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  "cells": [
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      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4H4G9duRZ_oA",
        "outputId": "fa2560f5-1b77-4ce4-c0e0-14299c27d77e"
      },
      "source": [
        "#in case of running this in colab \n",
        "\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "94CV2iW0pHqU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7458b0a7-5e1e-4ac7-d164-4c306e6f3dd2"
      },
      "source": [
        "#not included by default\n",
        "!pip install rasterio \n",
        "!pip install geopandas\n",
        "!pip install pycrs"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting rasterio\n",
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            "Requirement already satisfied: pyparsing>=2.1.6 in /usr/local/lib/python3.7/dist-packages (from snuggs>=1.4.1->rasterio) (2.4.7)\n",
            "Installing collected packages: click, cligj, affine, snuggs, click-plugins, rasterio\n",
            "  Found existing installation: click 8.0.0\n",
            "    Uninstalling click-8.0.0:\n",
            "      Successfully uninstalled click-8.0.0\n",
            "Successfully installed affine-2.3.0 click-7.1.2 click-plugins-1.1.1 cligj-0.7.1 rasterio-1.2.3 snuggs-1.4.7\n",
            "Collecting geopandas\n",
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            "Collecting fiona>=1.8\n",
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            "\u001b[?25hCollecting pyproj>=2.2.0\n",
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            "Requirement already satisfied: attrs>=17 in /usr/local/lib/python3.7/dist-packages (from fiona>=1.8->geopandas) (21.2.0)\n",
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            "Requirement already satisfied: click-plugins>=1.0 in /usr/local/lib/python3.7/dist-packages (from fiona>=1.8->geopandas) (1.1.1)\n",
            "Installing collected packages: munch, fiona, pyproj, geopandas\n",
            "Successfully installed fiona-1.8.19 geopandas-0.9.0 munch-2.5.0 pyproj-3.0.1\n",
            "Collecting pycrs\n",
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            "Building wheels for collected packages: pycrs\n",
            "  Building wheel for pycrs (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pycrs: filename=PyCRS-1.0.2-cp37-none-any.whl size=32683 sha256=ab85ffa8cc8b85bfcd4e978224858266867a571d371369eeade1cb4d3cffbb1e\n",
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            "Successfully built pycrs\n",
            "Installing collected packages: pycrs\n",
            "Successfully installed pycrs-1.0.2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HaE53bJJYqpV"
      },
      "source": [
        "#imports \n",
        "\n",
        "import rasterio \n",
        "from rasterio.plot import show\n",
        "from rasterio.plot import show_hist\n",
        "from rasterio.mask import mask\n",
        "from shapely.geometry import box\n",
        "import geopandas as gpd\n",
        "from fiona.crs import from_epsg\n",
        "import pycrs\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from matplotlib import image as im\n",
        "from matplotlib import pyplot as plt\n",
        "from affine import Affine\n",
        "from keras import layers\n",
        "from keras import Input\n",
        "import keras\n",
        "import tensorflow as tf\n",
        "from tensorflow.data import Dataset\n",
        "from tensorflow.keras.metrics import MeanIoU"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8SRucfADpQ4W"
      },
      "source": [
        "#MapBiomas label \n",
        "label_path = \"/content/drive/MyDrive/Honors/MapBiomas/COLECAO_5_DOWNLOADS_COLECOES_ANUAL_AMAZONIA_AMAZONIA-2019.tif\"\n",
        "\n",
        "#a portion of the original satellite image\n",
        "image_path = \"/content/drive/MyDrive/Honors/MapBiomas/MAPBIOMAS-EXPORT/input.tif\""
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xexLYLS2xBe9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "351fce27-c352-4318-d65e-2f7abe2dc333"
      },
      "source": [
        "#check the basic info - note how much the label is bigger than the satellite image\n",
        "with rasterio.open(image_path) as image:\n",
        "    print(image.profile)\n",
        "\n",
        "with rasterio.open(label_path) as label:\n",
        "    print(label.profile)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'driver': 'GTiff', 'dtype': 'int32', 'nodata': None, 'width': 3242, 'height': 3328, 'count': 102, 'crs': CRS.from_epsg(4326), 'transform': Affine(0.00026949458523585647, 0.0, -55.80990315107875,\n",
            "       0.0, -0.00026949458523585647, -6.617709035051692), 'blockxsize': 256, 'blockysize': 256, 'tiled': True, 'compress': 'lzw', 'interleave': 'pixel'}\n",
            "{'driver': 'GTiff', 'dtype': 'uint8', 'nodata': None, 'width': 3243, 'height': 3328, 'count': 1, 'crs': CRS.from_wkt('GEOGCS[\"unknown\",DATUM[\"unknown\",SPHEROID[\"WGS 84\",6378137,298.257223563]],PRIMEM[\"unknown\",0],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST]]'), 'transform': Affine(0.00026949458523585647, 0.0, -55.80990315107876,\n",
            "       0.0, -0.00026949458523585647, -6.617709035051692), 'tiled': False, 'interleave': 'band'}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n9n6-jLuQQwb",
        "outputId": "9d59a400-810f-4cd7-d422-2c0bf8832c81"
      },
      "source": [
        "#to clip the label to the same size and area as the image, we need to get to its transformation\n",
        "image.transform"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Affine(0.00026949458523585647, 0.0, -55.80990315107875,\n",
              "       0.0, -0.00026949458523585647, -6.617709035051692)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lyXuFWuxEaNP",
        "outputId": "d2a6947f-3fd3-400c-efec-85e8f0e40d9f"
      },
      "source": [
        "#to get the coordinates of the upper left and lower right of the image\n",
        "print(image.transform * (0,0))\n",
        "print(image.transform * (image.width, image.height))"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(-55.80990315107875, -6.617709035051692)\n",
            "(-54.936201705744104, -7.514587014716622)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4C4QbUXdaUo5"
      },
      "source": [
        "For interpretation of these keys visit: https://rasterio.readthedocs.io/en/latest/quickstart.html"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xEEQEg1ggpoL"
      },
      "source": [
        "# Clipping the label map to fit the sizes of the small satellite image"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kpKPE4sIWrgf"
      },
      "source": [
        "#these are now the coordinates to which we fit the label\n",
        "minx, miny = -55.80990315107875, -6.617709035051692\n",
        "maxx, maxy = -54.936201705744104, -7.514587014716622\n",
        "bbox = box(minx, miny, maxx, maxy)"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rf1iJQWyW8x5",
        "outputId": "ece216cc-e89c-424a-dfe6-f21d1ae5440c"
      },
      "source": [
        "#insert it into a GeoDataFrame\n",
        "geo = gpd.GeoDataFrame({'geometry': bbox}, index=[0], crs=from_epsg(4326))\n",
        "geo = geo.to_crs(crs=label.crs.data)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/pyproj/crs/crs.py:53: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
            "  return _prepare_from_string(\" \".join(pjargs))\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NUdSHELrXK4K"
      },
      "source": [
        "def getFeatures(gdf):\n",
        "    \"\"\"Function to parse features from GeoDataFrame in such a manner that rasterio wants them\"\"\"\n",
        "    import json\n",
        "    return [json.loads(gdf.to_json())['features'][0]['geometry']]"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RLOO1VDhXPEv",
        "outputId": "692e59df-9c3c-475e-b7fc-4449cc5923c7"
      },
      "source": [
        "#get the coordinates\n",
        "coords = getFeatures(geo)\n",
        "print(coords)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[{'type': 'Polygon', 'coordinates': [[[-54.936201705744104, -6.617709035051692], [-54.936201705744104, -7.514587014716622], [-55.80990315107875, -7.514587014716622], [-55.80990315107875, -6.617709035051692], [-54.936201705744104, -6.617709035051692]]]}]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iyCNCWuhXi1G"
      },
      "source": [
        "#clip the raster with the polygon of proper coordinates\n",
        "label = rasterio.open(label_path)\n",
        "out_label, out_transform = mask(label, shapes=coords, crop=True)"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IXCJ5QrVYm3F",
        "outputId": "17356db3-e9a0-4c27-86f6-44c3340a02ce"
      },
      "source": [
        "#copy the metadata that needs to be modified after the clipping\n",
        "out_meta = label.meta.copy()\n",
        "print(out_meta)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'driver': 'GTiff', 'dtype': 'uint8', 'nodata': None, 'width': 3243, 'height': 3328, 'count': 1, 'crs': CRS.from_wkt('GEOGCS[\"unknown\",DATUM[\"unknown\",SPHEROID[\"WGS 84\",6378137,298.257223563]],PRIMEM[\"unknown\",0],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST]]'), 'transform': Affine(0.00026949458523585647, 0.0, -55.80990315107876,\n",
            "       0.0, -0.00026949458523585647, -6.617709035051692)}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BaMe3GmnY37d"
      },
      "source": [
        "#finally update the metadata\n",
        "out_meta.update({\"driver\": \"GTiff\",\n",
        "                  \"height\": out_label.shape[1],\n",
        "                  \"width\": out_label.shape[2],\n",
        "                  \"transform\": out_transform,\n",
        "                  \"crs\": pycrs.parse.from_epsg_code(4326).to_proj4()} #the epsg code is the same as from the original meta\n",
        "                          )"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6NoI7eVtbHya"
      },
      "source": [
        "#save the clipped file \n",
        "with rasterio.open(label_path, \"w\", **out_meta) as dest:\n",
        "  dest.write(out_label)"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nyTvx68Gba9G",
        "outputId": "8d78ddbd-c294-4b9f-b4cd-c0b0bb9e680e"
      },
      "source": [
        "with rasterio.open(label_path) as label:\n",
        "    print(label.profile)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'driver': 'GTiff', 'dtype': 'uint8', 'nodata': None, 'width': 3243, 'height': 3328, 'count': 1, 'crs': CRS.from_wkt('GEOGCS[\"unknown\",DATUM[\"unknown\",SPHEROID[\"WGS 84\",6378137,298.257223563]],PRIMEM[\"unknown\",0],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST]]'), 'transform': Affine(0.00026949458523585647, 0.0, -55.80990315107876,\n",
            "       0.0, -0.00026949458523585647, -6.617709035051692), 'tiled': False, 'interleave': 'band'}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WXu354pxj3Eq",
        "outputId": "de2c800c-fbe0-44ae-d0e8-a72b10309b8c"
      },
      "source": [
        "#check if the coordinates are the same as with the image\n",
        "print(label.transform * (0, 0))\n",
        "print(label.transform * (label.width, label.height))"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(-55.80990315107876, -6.617709035051692)\n",
            "(-54.93593221115887, -7.514587014716622)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e3ksmPlaPfM_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 557
        },
        "outputId": "33b17bff-4529-4cfe-b723-6becf6f56810"
      },
      "source": [
        "#visualize part of the map\n",
        "\n",
        "window = rasterio.windows.Window(0, 0, 1000, 1000)\n",
        "\n",
        "with rasterio.open(label_path) as src:\n",
        "    subset = src.read(1, window=window)\n",
        "\n",
        "plt.figure(figsize=(10,12))\n",
        "plt.imshow(subset)\n",
        "plt.colorbar(shrink=0.5)\n",
        "plt.title(f'Band 4 Subset\\n{window}')\n",
        "plt.xlabel('Column #')\n",
        "plt.ylabel('Row #')"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Row #')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x864 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u_A0Vmu5bOO_"
      },
      "source": [
        "# Data generator"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pgum5Hl6w_iO"
      },
      "source": [
        "def data_gen(src, lab, for_train: bool):\n",
        "    '''\n",
        "    generate labels to feed to the U-net\n",
        "    filter the image using its several bands\n",
        "    change the value for_train to False to use it for test data\n",
        "    batch size could be added aswell, this version is used for debug for now\n",
        "    '''\n",
        "    \n",
        "    #list of the selected bands\n",
        "    bands = [1,2,3,4,5] # for example\n",
        "\n",
        "    split_thresh = 0.8\n",
        "    width = 12 # 158 = width/256\n",
        "    height = 13 # 221 = height/256\n",
        "\n",
        "    train_width = int(width*split_thresh)\n",
        "    \n",
        "    if for_train:\n",
        "        for train_i in range(train_width):\n",
        "            for train_j in range(height):\n",
        "                train_window = rasterio.windows.Window(int(train_i*256), int(train_j*256), 256, 256)\n",
        "                img_crop = np.zeros((256, 256, len(bands)))\n",
        "                # filter the bands\n",
        "                for i, band in enumerate(bands):\n",
        "                    img_crop[:,:,i] = src.read(band, window=train_window)\n",
        "                label_crop = lab.read(1, window=train_window).reshape((256, 256, 1))\n",
        "                \n",
        "                yield (img_crop, label_crop)\n",
        "                \n",
        "    else:\n",
        "        for test_i in range(train_width, width): # the rest 20% part of the image\n",
        "            for test_j in range(height):\n",
        "                test_window = rasterio.windows.Window(int(test_i*256), int(test_j*256), 256, 256)\n",
        "                img_crop = np.zeros((256, 256, len(bands)))\n",
        "                for i, band in enumerate(bands):\n",
        "                    img_crop[:,:,i] = src.read(band, window=test_window)\n",
        "                label_crop = lab.read(1, window=train_window).reshape((256, 256, 1))\n",
        "                \n",
        "                yield (img_crop, label_crop)"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KzAMwcDn6qzw"
      },
      "source": [
        "def make_batches(size, src, lab, for_train):\n",
        "  \"\"\"\n",
        "  converts the python generator into a tf.data.Dataset object\n",
        "  and builds batches for of data\n",
        "  \"\"\"\n",
        "  gen = lambda: (row for row in data_gen(src, lab, for_train)) #to make the generator a callable for the line bellow\n",
        "  dataset = Dataset.from_generator(gen, output_types=(tf.float32, tf.float32))\n",
        "  dataset = dataset.shuffle(156) #shuffle as many as there are samples\n",
        "  dataset = dataset.repeat(2)    #repeat some samples at most twice\n",
        "  dataset = dataset.batch(size)  \n",
        "  return(dataset)\n"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B5RA_hQYHEQ9"
      },
      "source": [
        "def build_model(input_layer, start_neurons):\n",
        "    \"\"\"\n",
        "    makes a U-net with 4 down- and up-sampling layers with the functional API\n",
        "    \"\"\"\n",
        "    conv1 = layers.Conv2D(start_neurons * 1, (3, 3), activation=\"relu\", padding=\"same\")(input_layer)\n",
        "    conv1 = layers.Conv2D(start_neurons * 1, (3, 3), activation=\"relu\", padding=\"same\")(conv1)\n",
        "    pool1 = layers.MaxPooling2D((2, 2))(conv1)\n",
        "    pool1 = layers.Dropout(0.5)(pool1)\n",
        "\n",
        "    conv2 = layers.Conv2D(start_neurons * 2, (3, 3), activation=\"relu\", padding=\"same\")(pool1)\n",
        "    conv2 = layers.Conv2D(start_neurons * 2, (3, 3), activation=\"relu\", padding=\"same\")(conv2)\n",
        "    pool2 = layers.MaxPooling2D((2, 2))(conv2)\n",
        "    pool2 = layers.Dropout(0.5)(pool2)\n",
        "\n",
        "    conv3 = layers.Conv2D(start_neurons * 4, (3, 3), activation=\"relu\", padding=\"same\")(pool2)\n",
        "    conv3 = layers.Conv2D(start_neurons * 4, (3, 3), activation=\"relu\", padding=\"same\")(conv3)\n",
        "    pool3 = layers.MaxPooling2D((2, 2))(conv3)\n",
        "    pool3 = layers.Dropout(0.5)(pool3)\n",
        "\n",
        "    conv4 = layers.Conv2D(start_neurons * 8, (3, 3), activation=\"relu\", padding=\"same\")(pool3)\n",
        "    conv4 = layers.Conv2D(start_neurons * 8, (3, 3), activation=\"relu\", padding=\"same\")(conv4)\n",
        "    pool4 = layers.MaxPooling2D((2, 2))(conv4)\n",
        "    pool4 = layers.Dropout(0.5)(pool4)\n",
        "\n",
        "    convm = layers.Conv2D(start_neurons * 16, (3, 3), activation=\"relu\", padding=\"same\")(pool4)\n",
        "    convm = layers.Conv2D(start_neurons * 16, (3, 3), activation=\"relu\", padding=\"same\")(convm)\n",
        "    \n",
        "    deconv4 = layers.Conv2DTranspose(start_neurons * 8, (3, 3), strides=(2, 2), padding=\"same\")(convm)\n",
        "    uconv4 = layers.concatenate([deconv4, conv4])\n",
        "    uconv4 = layers.Dropout(0.5)(uconv4)\n",
        "    uconv4 = layers.Conv2D(start_neurons * 8, (3, 3), activation=\"relu\", padding=\"same\")(uconv4)\n",
        "    uconv4 = layers.Conv2D(start_neurons * 8, (3, 3), activation=\"relu\", padding=\"same\")(uconv4)\n",
        "\n",
        "    deconv3 = layers.Conv2DTranspose(start_neurons * 4, (3, 3), strides=(2, 2), padding=\"same\")(uconv4)\n",
        "    uconv3 = layers.concatenate([deconv3, conv3])\n",
        "    uconv3 = layers.Dropout(0.5)(uconv3)\n",
        "    uconv3 = layers.Conv2D(start_neurons * 4, (3, 3), activation=\"relu\", padding=\"same\")(uconv3)\n",
        "    uconv3 = layers.Conv2D(start_neurons * 4, (3, 3), activation=\"relu\", padding=\"same\")(uconv3)\n",
        "\n",
        "    deconv2 = layers.Conv2DTranspose(start_neurons * 2, (3, 3), strides=(2, 2), padding=\"same\")(uconv3)\n",
        "    uconv2 = layers.concatenate([deconv2, conv2])\n",
        "    uconv2 = layers.Dropout(0.5)(uconv2)\n",
        "    uconv2 = layers.Conv2D(start_neurons * 2, (3, 3), activation=\"relu\", padding=\"same\")(uconv2)\n",
        "    uconv2 = layers.Conv2D(start_neurons * 2, (3, 3), activation=\"relu\", padding=\"same\")(uconv2)\n",
        "\n",
        "    deconv1 = layers.Conv2DTranspose(start_neurons * 1, (3, 3), strides=(2, 2), padding=\"same\")(uconv2)\n",
        "    uconv1 = layers.concatenate([deconv1, conv1])\n",
        "    uconv1 = layers.Dropout(0.5)(uconv1)\n",
        "    uconv1 = layers.Conv2D(start_neurons * 1, (3, 3), activation=\"relu\", padding=\"same\")(uconv1)\n",
        "    uconv1 = layers.Conv2D(start_neurons * 1, (3, 3), activation=\"relu\", padding=\"same\")(uconv1)\n",
        "\n",
        "    output_layer = layers.Conv2D(1, (1,1), padding=\"same\", activation=\"sigmoid\")(uconv1)\n",
        "    \n",
        "    return output_layer\n",
        "\n",
        "input_layer = Input(shape=(256,256,1))\n",
        "output_layer = build_model(input_layer, 64)"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vOkM58FPIPFk"
      },
      "source": [
        "model = keras.Model(inputs=input_layer, outputs=output_layer)\n",
        "\n",
        "#the best and most used metric for image segmentation we use Intersection over Union\n",
        "model.compile(\n",
        "    optimizer=keras.optimizers.Adam(learning_rate = 0.001),\n",
        "    loss='categorical_crossentropy', \n",
        "    metrics=MeanIoU(num_classes=42))"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ySIyG_KSHQxZ"
      },
      "source": [
        "label = rasterio.open(label_path)\n",
        "image = rasterio.open(image_path)"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CedeQZKfcOhn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "8d9b70e8-d334-4f5b-8c67-fba2e2ce52ac"
      },
      "source": [
        "model.fit(make_batches(10, image, label, for_train = True), validation_data = make_batches(10, image, label, for_train=False), epochs=2)"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-33-8c36e7fe6196>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmake_batches\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfor_train\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidation_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmake_batches\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfor_train\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   1098\u001b[0m                 _r=1):\n\u001b[1;32m   1099\u001b[0m               \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1100\u001b[0;31m               \u001b[0mtmp_logs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1101\u001b[0m               \u001b[0;32mif\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshould_sync\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1102\u001b[0m                 \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masync_wait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    826\u001b[0m     \u001b[0mtracing_count\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexperimental_get_tracing_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    827\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mtrace\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTrace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_name\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mtm\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 828\u001b[0;31m       \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    829\u001b[0m       \u001b[0mcompiler\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"xla\"\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_experimental_compile\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m\"nonXla\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    830\u001b[0m       \u001b[0mnew_tracing_count\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexperimental_get_tracing_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    869\u001b[0m       \u001b[0;31m# This is the first call of __call__, so we have to initialize.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    870\u001b[0m       \u001b[0minitializers\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 871\u001b[0;31m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_initialize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0madd_initializers_to\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minitializers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    872\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    873\u001b[0m       \u001b[0;31m# At this point we know that the initialization is complete (or less\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m_initialize\u001b[0;34m(self, args, kwds, add_initializers_to)\u001b[0m\n\u001b[1;32m    724\u001b[0m     self._concrete_stateful_fn = (\n\u001b[1;32m    725\u001b[0m         self._stateful_fn._get_concrete_function_internal_garbage_collected(  # pylint: disable=protected-access\n\u001b[0;32m--> 726\u001b[0;31m             *args, **kwds))\n\u001b[0m\u001b[1;32m    727\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    728\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0minvalid_creator_scope\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0munused_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0munused_kwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_get_concrete_function_internal_garbage_collected\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2967\u001b[0m       \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2968\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2969\u001b[0;31m       \u001b[0mgraph_function\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_define_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2970\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2971\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_maybe_define_function\u001b[0;34m(self, args, kwargs)\u001b[0m\n\u001b[1;32m   3359\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3360\u001b[0m           \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_function_cache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmissed\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcall_context_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3361\u001b[0;31m           \u001b[0mgraph_function\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_create_graph_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3362\u001b[0m           \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_function_cache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprimary\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcache_key\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3363\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_create_graph_function\u001b[0;34m(self, args, kwargs, override_flat_arg_shapes)\u001b[0m\n\u001b[1;32m   3204\u001b[0m             \u001b[0marg_names\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0marg_names\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3205\u001b[0m             \u001b[0moverride_flat_arg_shapes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moverride_flat_arg_shapes\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3206\u001b[0;31m             capture_by_value=self._capture_by_value),\n\u001b[0m\u001b[1;32m   3207\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_function_attributes\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3208\u001b[0m         \u001b[0mfunction_spec\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunction_spec\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/func_graph.py\u001b[0m in \u001b[0;36mfunc_graph_from_py_func\u001b[0;34m(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)\u001b[0m\n\u001b[1;32m    988\u001b[0m         \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moriginal_func\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_decorator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munwrap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpython_func\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    989\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 990\u001b[0;31m       \u001b[0mfunc_outputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpython_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mfunc_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfunc_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    991\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    992\u001b[0m       \u001b[0;31m# invariant: `func_outputs` contains only Tensors, CompositeTensors,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36mwrapped_fn\u001b[0;34m(*args, **kwds)\u001b[0m\n\u001b[1;32m    632\u001b[0m             \u001b[0mxla_context\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mExit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    633\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 634\u001b[0;31m           \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mweak_wrapped_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__wrapped__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    635\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    636\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/func_graph.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    975\u001b[0m           \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint:disable=broad-except\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    976\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"ag_error_metadata\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 977\u001b[0;31m               \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mag_error_metadata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_exception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    978\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    979\u001b[0m               \u001b[0;32mraise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: in user code:\n\n    /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:805 train_function  *\n        return step_function(self, iterator)\n    /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:795 step_function  **\n        outputs = model.distribute_strategy.run(run_step, args=(data,))\n    /usr/local/lib/python3.7/dist-packages/tensorflow/python/distribute/distribute_lib.py:1259 run\n        return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)\n    /usr/local/lib/python3.7/dist-packages/tensorflow/python/distribute/distribute_lib.py:2730 call_for_each_replica\n        return self._call_for_each_replica(fn, args, kwargs)\n    /usr/local/lib/python3.7/dist-packages/tensorflow/python/distribute/distribute_lib.py:3417 _call_for_each_replica\n        return fn(*args, **kwargs)\n    /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:788 run_step  **\n        outputs = model.train_step(data)\n    /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:758 train_step\n        self.compiled_metrics.update_state(y, y_pred, sample_weight)\n    /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/compile_utils.py:408 update_state\n        metric_obj.update_state(y_t, y_p, sample_weight=mask)\n    /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/utils/metrics_utils.py:90 decorated\n        update_op = update_state_fn(*args, **kwargs)\n    /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/metrics.py:177 update_state_fn\n        return ag_update_state(*args, **kwargs)\n    /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/metrics.py:2803 update_state  **\n        if y_true.shape.ndims > 1:\n\n    TypeError: '>' not supported between instances of 'NoneType' and 'int'\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3BkNOOxwqgcJ"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}