plant_seedlings_classification.ipynb

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      "name": "plant-seedlings-classification.ipynb",
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      "toc_visible": true,
      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "accelerator": "TPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/brycemcwilliams/research-papers/blob/master/plant_seedlings_classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WCxSmokWEKUJ",
        "colab_type": "text"
      },
      "source": [
        "# Project: Plant Seedlings Classicication\n",
        "\n",
        "### Data Description:\n",
        "\n",
        "- You are provided with a training set and a test set of images of plant seedlings at various stages of grown. \n",
        "- Each image has a filename that is its unique id. \n",
        "- The dataset comprises 12 plant species.\n",
        "- The goal of the competition is to create a classifier capable of determining a plant's species from a photo.\n",
        "\n",
        "### Dataset:\n",
        "- The project is from a dataset from Kaggle.\n",
        "- Link to the Kaggle project site:https://www.kaggle.com/c/plant-seedlings-classification/data\n",
        "- The dataset has to be downloaded from the above Kagglewebsite.\n",
        "\n",
        "### Context:\n",
        "\n",
        "- Can you differentiate a weed from a crop seedling?\n",
        "- The ability to do so effectively can mean better crop yields and better stewardship of the environment.\n",
        "- The Aarhus University Signal Processing group, in collaboration with University of Southern Denmark, has recently released a dataset containing images of unique plants belonging to 12 species at several growth stages.\n",
        "\n",
        "### Objective:\n",
        "- To implement the techniques learnt as a part of the course.\n",
        "\n",
        "### Learning Outcomes:\n",
        "- Pre-processing of image data.\n",
        "- Visualization of images.\n",
        "- Building CNN.\n",
        "- Evaluate the Model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lw8IuZwV-PAL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        },
        "outputId": "ddc99aee-22c7-42f4-ea3d-f4a9cab1c6cd"
      },
      "source": [
        "# Import necessary libraries.\n",
        "import sys\n",
        "import cv2\n",
        "import math\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "\n",
        "%tensorflow_version 2.x\n",
        "import tensorflow as tf\n",
        "print(tf.__version__)\n",
        "\n",
        "from glob import glob\n",
        "from matplotlib import pyplot as plt\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "\n",
        "from tensorflow.keras.layers import Activation\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPool2D, GlobalMaxPooling2D\n",
        "from tensorflow.keras.layers import BatchNormalization, Conv2D, MaxPooling2D\n",
        "from tensorflow.keras.optimizers import RMSprop, Adam\n",
        "from keras.utils.np_utils import to_categorical # convert to one-hot-encoding\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from google.colab.patches import cv2_imshow"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "2.0.0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H038VPqFQtCz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "79d36af0-8804-4104-8e8a-ae63b4cae267"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G4U1Z7p__vOd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Set the path to the dataset folder. (The dataset contains image folder: \"train\")\n",
        "train_path = \"/content/drive/My Drive/Colab Notebooks/data/plant-seedlings-classification.zip\""
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K_sXNBZHA-FY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "c7f908fe-a246-4f6f-ec8f-f4373f901622"
      },
      "source": [
        "# Make different folders for train and test data in the current directory of Google Colab notebook. (using mkdir)\n",
        "!mkdir temp_train\n",
        "!ls -la ./drive/My\\ Drive/Colab\\ Notebooks/data/"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "mkdir: cannot create directory ‘temp_train’: File exists\n",
            "total 2414433\n",
            "-rw------- 1 root root     684858 Jun  8 19:57 bank.csv\n",
            "-rw------- 1 root root        151 Jun 12 18:47 bank.gsheet\n",
            "-rw------- 1 root root     104187 Jun 25 18:30 brycemcwilliams.png\n",
            "-rw------- 1 root root      18893 May 31 16:52 car-mpg.csv\n",
            "-rw------- 1 root root  150828752 Sep 19  2019 creditcard.csv\n",
            "-rw------- 1 root root   18322267 Jun 21 16:56 dataset_week_1.csv\n",
            "-rw------- 1 root root 1810679877 Jun 29 16:51 plant-seedlings-classification.zip\n",
            "-rw------- 1 root root  491644096 May 29 17:41 SVHN_single_grey1.h5\n",
            "-rw------- 1 root root      88235 Jun 17 19:18 test_image.png\n",
            "-rw------- 1 root root       4732 May 31 16:38 usedcars.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C6770cFdHDc9",
        "colab_type": "text"
      },
      "source": [
        "# Unziping train file:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L2IHlupb_s8Q",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Extract the files from dataset to temp_train and temp_test folders (as the dataset is a zip file.)\n",
        "from zipfile import ZipFile\n",
        "\n",
        "with ZipFile(train_path, 'r') as zip:\n",
        "  zip.extractall('./temp_train')"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tJKuVHXiRmMa",
        "colab_type": "text"
      },
      "source": [
        "# Here we are getting all our training images and labels into arrays so that we can use them:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AuVK04TO--Jr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "f3dc31f9-9168-4299-a3ad-3e1438bf1755"
      },
      "source": [
        "path = \"./temp_train/train/*/*.png\"                              # The path to all images in training set. (* means include all folders and files.)\n",
        "files = glob(path)\n",
        "\n",
        "trainImg = []                                              # Initialize empty list to store the image data as numbers.\n",
        "trainLabel = []                                            # Initialize empty list to store the labels of images\n",
        "j = 1\n",
        "num = len(files)\n",
        "\n",
        "# Obtain images and resizing, obtain labels\n",
        "for img in files:\n",
        "    '''\n",
        "    Append the image data to trainImg list.\n",
        "    Append the labels to trainLabel list.\n",
        "    '''\n",
        "    print(str(j) + \"/\" + str(num), end=\"\\r\")\n",
        "    trainImg.append(cv2.resize(cv2.imread(img), (128, 128)))  # Get image (with resizing to 128x128)\n",
        "    trainLabel.append(img.split('/')[-2])  # Get image label (folder name contains the class to which the image belong)\n",
        "    j += 1\n",
        "\n",
        "trainImg = np.asarray(trainImg)  # Train images set\n",
        "trainLabel = pd.DataFrame(trainLabel)  # Train labels set"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            ""
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2X1I66hDfg9j",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "50b8a90f-c91a-497d-ba31-a5eb4d11e2b6"
      },
      "source": [
        "print(trainImg.shape)\n",
        "print(trainLabel.shape)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(4750, 128, 128, 3)\n",
            "(4750, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SaF6mbssR4P9",
        "colab_type": "text"
      },
      "source": [
        "# We can see from the above commands that we have 4750 images of 128x128x3 and their corresponding class labels for classification. We will need some kind of mapping for the class labels to make our lives eaiser in the future."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NrHRoCYUx4KR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def classes_to_int(label):\n",
        "    if label == \"Black-grass\":  return 0\n",
        "    if label == \"Charlock\":  return 1\n",
        "    if label == \"Cleavers\":  return 2\n",
        "    if label == \"Common Chickweed\":  return 3\n",
        "    if label == \"Common wheat\":  return 4\n",
        "    if label == \"Fat Hen\":  return 5\n",
        "    if label == \"Loose Silky-bent\": return 6\n",
        "    if label == \"Maize\":  return 7\n",
        "    if label == \"Scentless Mayweed\": return 8\n",
        "    if label == \"Shepherds Purse\": return 9\n",
        "    if label == \"Small-flowered Cranesbill\": return 10\n",
        "    if label == \"Sugar beet\": return 11\n",
        "    print(\"Invalid Label\", label)\n",
        "    return 12"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YF4oq0Q7x636",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def int_to_classes(i):\n",
        "    if i == 0: return \"Black-grass\"\n",
        "    elif i == 1: return \"Charlock\"\n",
        "    elif i == 2: return \"Cleavers\"\n",
        "    elif i == 3: return \"Common Chickweed\"\n",
        "    elif i == 4: return \"Common wheat\"\n",
        "    elif i == 5: return \"Fat Hen\"\n",
        "    elif i == 6: return \"Loose Silky-bent\"\n",
        "    elif i == 7: return \"Maize\"\n",
        "    elif i == 8: return \"Scentless Mayweed\"\n",
        "    elif i == 9: return \"Shepherds Purse\"\n",
        "    elif i == 10: return \"Small-flowered Cranesbill\"\n",
        "    elif i == 11: return \"Sugar beet\"\n",
        "    print(\"Invalid class \", i)\n",
        "    return \"Invalid Class\""
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Le0a5QSBSUaf",
        "colab_type": "text"
      },
      "source": [
        "# Now that we have our class mappings we need to define our independant variables or X and our dependant variable or Y"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mtHETOLAv3VR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "bc4a2a30-5732-4b52-86b1-06b0d1d937f4"
      },
      "source": [
        "X = np.array(trainImg, dtype=\"float\") / 255.0\n",
        "pd.DataFrame(X[0][0]).head()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
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      "execution_count": 8,
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      },
      "source": [
        "# We need to define our global variables since we know that there are certain constraints we can enforce from understanding the training data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j1SXs51ex-si",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "RANDOM_STATE = 42\n",
        "NUM_CLASSES = 12\n",
        "WIDTH = 128\n",
        "HEIGHT = 128\n",
        "DEPTH = 3\n",
        "INPUT_SHAPE = (WIDTH, HEIGHT, DEPTH)\n",
        "EPOCHS = 15\n",
        "LEARNING_RATE = 0.001\n",
        "BATCH_SIZE = 32\n",
        "DECAY = LEARNING_RATE / EPOCHS\n",
        "MIN_DELTA = 0.001\n",
        "PATIENCE = 10"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M3OvBC53VvUZ",
        "colab_type": "text"
      },
      "source": [
        "# CNN\n",
        "Here we build our Convolutional Neural Network:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2uvkrpyJyL_E",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def create_model():\n",
        "    model = Sequential()\n",
        "\n",
        "    model.add(Conv2D(32, (3,3), padding=\"same\", input_shape=INPUT_SHAPE))\n",
        "    #model.add(BatchNormalization()) \n",
        "    model.add(Activation(\"relu\"))\n",
        "    model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))\n",
        "    model.add(Dropout(0.25))\n",
        "\n",
        "    model.add(Conv2D(64, (3,3), padding=\"same\"))\n",
        "    #model.add(BatchNormalization())\n",
        "    model.add(Activation(\"relu\"))\n",
        "    model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))\n",
        "    model.add(Dropout(0.25))\n",
        "\n",
        "    model.add(Conv2D(128, (3,3), padding=\"same\"))\n",
        "    #model.add(BatchNormalization())\n",
        "    model.add(Activation(\"relu\"))\n",
        "    model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))\n",
        "    model.add(Dropout(0.25))\n",
        "\n",
        "    model.add(Flatten())\n",
        "\n",
        "    model.add(Dense(units=500))\n",
        "    model.add(Activation(\"relu\"))\n",
        "    model.add(Dropout(0.5))\n",
        "    model.add(Dense(units=12))\n",
        "    model.add(Activation(\"softmax\"))\n",
        "\n",
        "    optimizer = Adam(lr=LEARNING_RATE, decay=DECAY)\n",
        "\n",
        "    model.compile(loss=\"categorical_crossentropy\", optimizer=optimizer, metrics=[\"accuracy\"])\n",
        "    model.summary()\n",
        "\n",
        "    return model"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "41_v0BIQje2h",
        "colab_type": "text"
      },
      "source": [
        "## Split our data into training and validation set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zi_c4QbexJ6v",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "(x_train, x_validation, y_train, y_validation) = train_test_split(X, Y, test_size=0.25, random_state=RANDOM_STATE)"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m442NUqmC8xI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "30992690-3275-4898-d009-3dc93009b53f"
      },
      "source": [
        "print(x_train.shape)\n",
        "print(x_validation.shape)\n",
        "print(y_train.shape)\n",
        "print(y_validation.shape)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(3562, 128, 128, 3)\n",
            "(1188, 128, 128, 3)\n",
            "(3562, 12)\n",
            "(1188, 12)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZHEFfXm-iUtC",
        "colab_type": "text"
      },
      "source": [
        "## Create an image data generator to handle input data normalization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nt5F2NrciLzs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "imageDataGenerator = ImageDataGenerator(rotation_range=30,\n",
        "                                        width_shift_range=0.1,\n",
        "                                        height_shift_range=0.1,\n",
        "                                        shear_range=0.2,\n",
        "                                        zoom_range=0.2,\n",
        "                                        horizontal_flip=True,\n",
        "                                        fill_mode=\"nearest\")\n"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZYooOHLXipQe",
        "colab_type": "text"
      },
      "source": [
        "## Build the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zsPFvRy_iwag",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 765
        },
        "outputId": "fdbad7f8-e9df-4fd8-c69e-b80bff40af5f"
      },
      "source": [
        "model = create_model()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d (Conv2D)              (None, 128, 128, 32)      896       \n",
            "_________________________________________________________________\n",
            "activation (Activation)      (None, 128, 128, 32)      0         \n",
            "_________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D) (None, 64, 64, 32)        0         \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 64, 64, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 64, 64, 64)        18496     \n",
            "_________________________________________________________________\n",
            "activation_1 (Activation)    (None, 64, 64, 64)        0         \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 32, 32, 64)        0         \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 32, 32, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 32, 32, 128)       73856     \n",
            "_________________________________________________________________\n",
            "activation_2 (Activation)    (None, 32, 32, 128)       0         \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 16, 16, 128)       0         \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 16, 16, 128)       0         \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 32768)             0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 500)               16384500  \n",
            "_________________________________________________________________\n",
            "activation_3 (Activation)    (None, 500)               0         \n",
            "_________________________________________________________________\n",
            "dropout_3 (Dropout)          (None, 500)               0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 12)                6012      \n",
            "_________________________________________________________________\n",
            "activation_4 (Activation)    (None, 12)                0         \n",
            "=================================================================\n",
            "Total params: 16,483,760\n",
            "Trainable params: 16,483,760\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LD2ngJcJj7SY",
        "colab_type": "text"
      },
      "source": [
        "## Implement early stopping"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CCtp5UGoi5Mh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Adding Early stopping callback to the fit function is going to stop the training,\n",
        "# if the val_loss is not going to change even '0.001' for more than 10 continous epochs\n",
        "\n",
        "early_stopping = EarlyStopping(monitor='val_loss', min_delta=MIN_DELTA, patience=PATIENCE)"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ez3kIOrWkfXQ",
        "colab_type": "text"
      },
      "source": [
        "## Add a model checkpoint to save on"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZFWygZW_yGW7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "1d24673f-2592-47aa-fc80-643c4d800d2b"
      },
      "source": [
        "# Adding Model Checkpoint callback to the fit function is going to save the weights whenever val_loss achieves a new low value. \n",
        "# Hence saving the best weights occurred during training\n",
        "\n",
        "model_checkpoint =  ModelCheckpoint('seedling_cnn_checkpoint_{epoch:02d}_loss_{val_loss:.4f}.h5',\n",
        "                                    monitor='val_loss',\n",
        "                                    verbose=1,\n",
        "                                    save_best_only=True,\n",
        "                                    save_weights_only=True,\n",
        "                                    mode='auto',\n",
        "                                    period=1)\n"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:`period` argument is deprecated. Please use `save_freq` to specify the frequency in number of samples seen.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y-jeeBdak_XT",
        "colab_type": "text"
      },
      "source": [
        "## Capture the history of the models training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gmPWNVV5kuJL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "35860b83-b7ef-4208-b3ad-f11567e540f7"
      },
      "source": [
        "history = model.fit(imageDataGenerator.flow(x_train,\n",
        "                                          y_train,\n",
        "                                          batch_size=BATCH_SIZE),\n",
        "              validation_data=(x_validation, y_validation),\n",
        "              steps_per_epoch=len(x_train) // BATCH_SIZE,\n",
        "              epochs=EPOCHS,\n",
        "              verbose=1,\n",
        "              callbacks=[early_stopping, model_checkpoint])"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train for 111 steps, validate on 1188 samples\n",
            "Epoch 1/15\n",
            "110/111 [============================>.] - ETA: 1s - loss: 2.4822 - accuracy: 0.1432\n",
            "Epoch 00001: val_loss improved from inf to 2.42157, saving model to seedling_cnn_checkpoint_01_loss_2.4216.h5\n",
            "111/111 [==============================] - 168s 2s/step - loss: 2.4829 - accuracy: 0.1425 - val_loss: 2.4216 - val_accuracy: 0.1448\n",
            "Epoch 2/15\n",
            "110/111 [============================>.] - ETA: 1s - loss: 2.2690 - accuracy: 0.2161\n",
            "Epoch 00002: val_loss improved from 2.42157 to 1.85682, saving model to seedling_cnn_checkpoint_02_loss_1.8568.h5\n",
            "111/111 [==============================] - 166s 1s/step - loss: 2.2662 - accuracy: 0.2178 - val_loss: 1.8568 - val_accuracy: 0.4082\n",
            "Epoch 3/15\n",
            "110/111 [============================>.] - ETA: 1s - loss: 1.6835 - accuracy: 0.4214\n",
            "Epoch 00003: val_loss improved from 1.85682 to 1.36690, saving model to seedling_cnn_checkpoint_03_loss_1.3669.h5\n",
            "111/111 [==============================] - 176s 2s/step - loss: 1.6817 - accuracy: 0.4218 - val_loss: 1.3669 - val_accuracy: 0.5455\n",
            "Epoch 4/15\n",
            "110/111 [============================>.] - ETA: 1s - loss: 1.3684 - accuracy: 0.5289\n",
            "Epoch 00004: val_loss improved from 1.36690 to 1.07568, saving model to seedling_cnn_checkpoint_04_loss_1.0757.h5\n",
            "111/111 [==============================] - 168s 2s/step - loss: 1.3712 - accuracy: 0.5278 - val_loss: 1.0757 - val_accuracy: 0.6347\n",
            "Epoch 5/15\n",
            "110/111 [============================>.] - ETA: 1s - loss: 1.2450 - accuracy: 0.5669\n",
            "Epoch 00005: val_loss improved from 1.07568 to 1.00514, saving model to seedling_cnn_checkpoint_05_loss_1.0051.h5\n",
            "111/111 [==============================] - 166s 1s/step - loss: 1.2424 - accuracy: 0.5674 - val_loss: 1.0051 - val_accuracy: 0.6970\n",
            "Epoch 6/15\n",
            "110/111 [============================>.] - ETA: 1s - loss: 1.1295 - accuracy: 0.6221\n",
            "Epoch 00006: val_loss improved from 1.00514 to 0.96477, saving model to seedling_cnn_checkpoint_06_loss_0.9648.h5\n",
            "111/111 [==============================] - 165s 1s/step - loss: 1.1288 - accuracy: 0.6227 - val_loss: 0.9648 - val_accuracy: 0.6810\n",
            "Epoch 7/15\n",
            "110/111 [============================>.] - ETA: 1s - loss: 1.0801 - accuracy: 0.6464\n",
            "Epoch 00007: val_loss improved from 0.96477 to 0.84512, saving model to seedling_cnn_checkpoint_07_loss_0.8451.h5\n",
            "111/111 [==============================] - 166s 1s/step - loss: 1.0778 - accuracy: 0.6465 - val_loss: 0.8451 - val_accuracy: 0.7239\n",
            "Epoch 8/15\n",
            "110/111 [============================>.] - ETA: 1s - loss: 1.0114 - accuracy: 0.6607\n",
            "Epoch 00008: val_loss improved from 0.84512 to 0.79739, saving model to seedling_cnn_checkpoint_08_loss_0.7974.h5\n",
            "111/111 [==============================] - 165s 1s/step - loss: 1.0105 - accuracy: 0.6606 - val_loss: 0.7974 - val_accuracy: 0.7332\n",
            "Epoch 9/15\n",
            "110/111 [============================>.] - ETA: 1s - loss: 0.9518 - accuracy: 0.6772\n",
            "Epoch 00009: val_loss did not improve from 0.79739\n",
            "111/111 [==============================] - 169s 2s/step - loss: 0.9544 - accuracy: 0.6773 - val_loss: 0.7991 - val_accuracy: 0.7374\n",
            "Epoch 10/15\n",
            "110/111 [============================>.] - ETA: 1s - loss: 0.9289 - accuracy: 0.6858\n",
            "Epoch 00010: val_loss improved from 0.79739 to 0.75346, saving model to seedling_cnn_checkpoint_10_loss_0.7535.h5\n",
            "111/111 [==============================] - 165s 1s/step - loss: 0.9279 - accuracy: 0.6864 - val_loss: 0.7535 - val_accuracy: 0.7542\n",
            "Epoch 11/15\n",
            "110/111 [============================>.] - ETA: 1s - loss: 0.8906 - accuracy: 0.7015\n",
            "Epoch 00011: val_loss improved from 0.75346 to 0.71355, saving model to seedling_cnn_checkpoint_11_loss_0.7136.h5\n",
            "111/111 [==============================] - 165s 1s/step - loss: 0.8893 - accuracy: 0.7008 - val_loss: 0.7136 - val_accuracy: 0.7744\n",
            "Epoch 12/15\n",
            "110/111 [============================>.] - ETA: 1s - loss: 0.8453 - accuracy: 0.7153\n",
            "Epoch 00012: val_loss improved from 0.71355 to 0.63316, saving model to seedling_cnn_checkpoint_12_loss_0.6332.h5\n",
            "111/111 [==============================] - 165s 1s/step - loss: 0.8428 - accuracy: 0.7161 - val_loss: 0.6332 - val_accuracy: 0.7837\n",
            "Epoch 13/15\n",
            "110/111 [============================>.] - ETA: 1s - loss: 0.8487 - accuracy: 0.7095\n",
            "Epoch 00013: val_loss improved from 0.63316 to 0.62461, saving model to seedling_cnn_checkpoint_13_loss_0.6246.h5\n",
            "111/111 [==============================] - 165s 1s/step - loss: 0.8463 - accuracy: 0.7102 - val_loss: 0.6246 - val_accuracy: 0.7820\n",
            "Epoch 14/15\n",
            "110/111 [============================>.] - ETA: 1s - loss: 0.8240 - accuracy: 0.7101\n",
            "Epoch 00014: val_loss did not improve from 0.62461\n",
            "111/111 [==============================] - 165s 1s/step - loss: 0.8236 - accuracy: 0.7110 - val_loss: 0.6332 - val_accuracy: 0.7803\n",
            "Epoch 15/15\n",
            "110/111 [============================>.] - ETA: 1s - loss: 0.7787 - accuracy: 0.7341\n",
            "Epoch 00015: val_loss improved from 0.62461 to 0.60831, saving model to seedling_cnn_checkpoint_15_loss_0.6083.h5\n",
            "111/111 [==============================] - 168s 2s/step - loss: 0.7756 - accuracy: 0.7348 - val_loss: 0.6083 - val_accuracy: 0.7870\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t9HXsSkylV_Z",
        "colab_type": "text"
      },
      "source": [
        "## Plot training history"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XFzHiKmMlQ72",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "36b25bbd-4c7b-4114-ec82-33405c39efad"
      },
      "source": [
        "plt.plot(history.history['loss'], label='train')\n",
        "plt.plot(history.history['val_loss'], label='test')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JI512LwbxTmd",
        "colab_type": "text"
      },
      "source": [
        "## Save our shiny new model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d9bFs9ZDxQAz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#model.save(\"plant-seedlings-model.h5\")"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kG1KHT0_xZcu",
        "colab_type": "text"
      },
      "source": [
        "## Evaluating our model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "URR7-VtgT33b",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        },
        "outputId": "cc214ba9-0512-45d5-b521-ad7e45cf4b7c"
      },
      "source": [
        "# Score trained model.\n",
        "scores = model.evaluate(x_validation, y_validation, verbose=1)\n",
        "print('Test loss:', scores[0])\n",
        "print('Test accuracy:', scores[1])"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\r1188/1 [=======================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================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========================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================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=================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================] - 13s 11ms/sample - loss: 0.4957 - accuracy: 0.7870\n",
            "Test loss: 0.6083108226859609\n",
            "Test accuracy: 0.787037\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dt5iOxyuatmI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "85fb8ea7-afda-444e-f783-181b12aa5593"
      },
      "source": [
        "y_predict = model.predict(x_validation)\n",
        "y_predict = (y_predict > 0.5) # Force a prediction\n",
        "y_predict"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[False, False, False, ..., False, False, False],\n",
              "       [False, False, False, ..., False, False, False],\n",
              "       [False, False, False, ..., False,  True, False],\n",
              "       ...,\n",
              "       [False, False,  True, ..., False, False, False],\n",
              "       [False, False, False, ..., False, False, False],\n",
              "       [False,  True, False, ..., False, False, False]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lgdtHQcXajXz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.metrics import classification_report, confusion_matrix"
      ],
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2WxD4dUaampI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "6a603e38-cca4-4be9-faf0-448c798aec3d"
      },
      "source": [
        "cm = confusion_matrix(pd.DataFrame(y_validation).values.argmax(axis=1), pd.DataFrame(y_predict).values.argmax(axis=1))\n",
        "print(cm)"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[ 32   0   0   0   2   1  25   0   0   0   0   1]\n",
            " [  2  91   6   0   0   0   0   0   0   0   0   0]\n",
            " [  7   4  65   1   0   0   0   0   1   0   0   0]\n",
            " [ 12   0   0 119   0   0   0   0   3   1   3   1]\n",
            " [ 15   0   0   0  32   1   1   0   0   0   0   0]\n",
            " [ 14   1   5   1   0  94   0   0   1   0   0   4]\n",
            " [ 41   0   0   1   0   0 128   0   1   0   1   0]\n",
            " [  7   3   0   1   0   0   0  37   2   0   0   2]\n",
            " [ 22   3   3   3   0   0   0   0  89   2   1   5]\n",
            " [ 25   1   0   0   0   1   0   2   8  27   1   1]\n",
            " [  8   2   2   0   0   0   0   1   1   0 116   7]\n",
            " [ 10   1   3   1   0   0   0   1   0   0   0  71]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lEJslmRO8wVX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        },
        "outputId": "5d1ab009-b36c-4253-87e7-f6daab14a19c"
      },
      "source": [
        "df_cm = pd.DataFrame(cm, index = [i for i in \"0123456789?*\"],\n",
        "                     columns = [i for i in \"0123456789?*\"])\n",
        "plt.figure(figsize = (10,7))\n",
        "sns.heatmap(df_cm, annot=True, fmt='d')"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f30d14d9d68>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aCTybqfbD3wE",
        "colab_type": "text"
      },
      "source": [
        "## We can see that our model has a problem identifying black-grass"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7jdtC4mdBk99",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "outputId": "93539af2-e657-44bb-cfbd-8e37312df665"
      },
      "source": [
        "print(classification_report(pd.DataFrame(y_validation).values.argmax(axis=1), pd.DataFrame(y_predict).values.argmax(axis=1)))"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.16      0.52      0.25        61\n",
            "           1       0.86      0.92      0.89        99\n",
            "           2       0.77      0.83      0.80        78\n",
            "           3       0.94      0.86      0.89       139\n",
            "           4       0.94      0.65      0.77        49\n",
            "           5       0.97      0.78      0.87       120\n",
            "           6       0.83      0.74      0.79       172\n",
            "           7       0.90      0.71      0.80        52\n",
            "           8       0.84      0.70      0.76       128\n",
            "           9       0.90      0.41      0.56        66\n",
            "          10       0.95      0.85      0.90       137\n",
            "          11       0.77      0.82      0.79        87\n",
            "\n",
            "    accuracy                           0.76      1188\n",
            "   macro avg       0.82      0.73      0.76      1188\n",
            "weighted avg       0.84      0.76      0.79      1188\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T08131pRESZW",
        "colab_type": "text"
      },
      "source": [
        "## We can observe that the network did not learn to identify the features of Black-grass correctly, we need to investigate as to why this is occuring"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hQeeb7wcgDMa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "img_4095 = cv2.resize(trainImg[4095], (256,256), interpolation = cv2.INTER_AREA)\n",
        "img_4749 = cv2.resize(trainImg[4749], (256,256), interpolation = cv2.INTER_AREA)"
      ],
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "htBzMOIhgWjZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        },
        "outputId": "b11baabc-bb34-48ea-90c7-02c5e7427798"
      },
      "source": [
        "h_img = cv2.hconcat([img_4095, img_4749])\n",
        "\n",
        "cv2_imshow(h_img)\n",
        "print(\"Black-grass vs Loose Silky-bent\")"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=512x256 at 0x7F30CFAC7550>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Black-grass vs Loose Silky-bent\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qlo5XsEgGzG3",
        "colab_type": "text"
      },
      "source": [
        "## We can observe that the two classes have visually similar features and are hard to distingush even for humans. We should put more weighting on getting this class wrong in our next model iteration."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9XyopEfXG-eQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "img_4095_sobel = cv2.resize(cv2.Sobel(trainImg[4095], cv2.CV_64F, 1, 1, ksize=5), (256,256), interpolation = cv2.INTER_AREA)\n",
        "img_4749_sobel = cv2.resize(cv2.Sobel(trainImg[4749], cv2.CV_64F, 1, 1, ksize=5), (256,256), interpolation = cv2.INTER_AREA)"
      ],
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zgxUGInFJnm4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        },
        "outputId": "03fce1bc-044b-439c-f41c-b4c420dd21cf"
      },
      "source": [
        "h_img_sobel = cv2.hconcat([img_4095_sobel, img_4749_sobel])\n",
        "\n",
        "cv2_imshow(h_img_sobel)\n",
        "print(\"Black-grass Sobel vs Loose Silky-bent Sobel\")"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=512x256 at 0x7F30D98EE358>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Black-grass Sobel vs Loose Silky-bent Sobel\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u7u4oYlMKs-z",
        "colab_type": "text"
      },
      "source": [
        "## We can observe that the features now present to us by applying the sobel filter are showing us that they are actually very similar visually as well."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mOT2s7JgLAK4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 53,
      "outputs": []
    }
  ]
}