CNNSat.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M36SiROof_J8"
      },
      "source": [
        "# Install Dependencies"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Smmdl3gWUCDu"
      },
      "source": [
        "%tensorflow_version 2.x"
      ],
      "execution_count": 9,
      "outputs": []
    },
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      "metadata": {
        "colab": {
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      "source": [
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      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.7/dist-packages (3.13)\n",
            "Requirement already satisfied: h5py in /usr/local/lib/python3.7/dist-packages (3.1.0)\n",
            "Requirement already satisfied: cached-property in /usr/local/lib/python3.7/dist-packages (from h5py) (1.5.2)\n",
            "Requirement already satisfied: numpy>=1.14.5 in /usr/local/lib/python3.7/dist-packages (from h5py) (1.19.5)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NtSnDMfQXrpq"
      },
      "source": [
        "!pip install -q tensorflow-datasets tensorflow"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L0iEj3NXUWwx"
      },
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "import tensorflow_datasets as tfds\n",
        "from keras import layers\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u2hCL7zQgaij"
      },
      "source": [
        "# Load Data"
      ]
    },
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        "id": "1nHkxuN-Uq00",
        "outputId": "fe9d4f35-0ac2-4c8d-c56b-ce1002036612"
      },
      "source": [
        "(tf_train, tf_val, tf_test), info = tfds.load('eurosat', split=['train[:80%]', 'train[80%:90%]', 'train[90%:]'], shuffle_files=True, as_supervised=True, with_info=True)\n",
        "classes = info.features['label'].num_classes\n",
        "labels = info.features['label']\n",
        "features = ['Forest', 'HerbaceousVegetation', 'Highway', 'Industrial', 'Pasture', 'PermanentCrop', 'Residential', 'River', 'SeaLake', 'AnnualCrop']\n",
        "labelList = []\n",
        "imgList = []\n",
        "\n",
        "test_labels = []\n",
        "test_imgs = []\n",
        "for i, j in tf_train:\n",
        "  labelList.append(j)\n",
        "  imgList.append(i)\n",
        "\n",
        "for k,z in tf_test:\n",
        "  test_imgs.append(k)\n",
        "  test_labels.append(z)\n",
        "print(labelList[3].numpy())\n",
        "print(imgList[3])"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1mDownloading and preparing dataset eurosat/rgb/2.0.0 (download: 89.91 MiB, generated: Unknown size, total: 89.91 MiB) to /root/tensorflow_datasets/eurosat/rgb/2.0.0...\u001b[0m\n"
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            "text/plain": [
              "Dl Completed...: 0 url [00:00, ? url/s]"
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            "\n",
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          "name": "stdout",
          "text": [
            "Shuffling and writing examples to /root/tensorflow_datasets/eurosat/rgb/2.0.0.incomplete44WE31/eurosat-train.tfrecord\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a1129c2b4db8402882ad7b949dc56025",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/27000 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1mDataset eurosat downloaded and prepared to /root/tensorflow_datasets/eurosat/rgb/2.0.0. Subsequent calls will reuse this data.\u001b[0m\n",
            "7\n",
            "tf.Tensor(\n",
            "[[[ 56  85  91]\n",
            "  [ 56  85  89]\n",
            "  [ 60  88  92]\n",
            "  ...\n",
            "  [ 75  93  97]\n",
            "  [ 76  94  98]\n",
            "  [ 74  93  97]]\n",
            "\n",
            " [[ 56  85  91]\n",
            "  [ 56  85  89]\n",
            "  [ 59  87  91]\n",
            "  ...\n",
            "  [ 76  91  96]\n",
            "  [ 74  92  96]\n",
            "  [ 73  92  96]]\n",
            "\n",
            " [[ 47  76  80]\n",
            "  [ 47  76  80]\n",
            "  [ 50  77  84]\n",
            "  ...\n",
            "  [ 76  91  94]\n",
            "  [ 76  91  96]\n",
            "  [ 74  91  98]]\n",
            "\n",
            " ...\n",
            "\n",
            " [[ 57  88  90]\n",
            "  [ 57  87  89]\n",
            "  [ 60  90  90]\n",
            "  ...\n",
            "  [ 80  99 105]\n",
            "  [ 72  91  98]\n",
            "  [ 74  94 103]]\n",
            "\n",
            " [[ 65  95  95]\n",
            "  [ 65  95  95]\n",
            "  [ 66  94  95]\n",
            "  ...\n",
            "  [ 74  93  97]\n",
            "  [ 68  87  93]\n",
            "  [ 81 100 107]]\n",
            "\n",
            " [[ 66  96  96]\n",
            "  [ 66  96  96]\n",
            "  [ 73  99 100]\n",
            "  ...\n",
            "  [ 67  88  91]\n",
            "  [ 70  87  94]\n",
            "  [ 70  87  94]]], shape=(64, 64, 3), dtype=uint8)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G1hqTGOmZJzo",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "outputId": "a266e809-bb4f-4c78-fce6-6ff32f3553c8"
      },
      "source": [
        "x = plt.imshow(imgList[19])\n",
        "y = plt.title(features[labelList[19].numpy()-1])"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K7BgqeGYgjvR"
      },
      "source": [
        "# Normalize Images (Give them pixel values)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l899bPWhcK02"
      },
      "source": [
        "def normalize_img(image, label):\n",
        "  #Turns the imgs from uint8 values to pixel float32 values\n",
        "  return tf.cast(image, tf.float32) / 255., label"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s-qBbKiEgvww"
      },
      "source": [
        "# Data Augementation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rCXAeG24BAcm"
      },
      "source": [
        "dA = keras.Sequential([\n",
        "    keras.layers.experimental.preprocessing.RandomFlip(\"horizontal_and_vertical\"), #Flips images\n",
        "    keras.layers.experimental.preprocessing.RandomRotation(0.2)]) #Rotates images"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SVcsdC05AGQR"
      },
      "source": [
        "daImg = imgList[0]"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S7-S7xy7np-X"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 575
        },
        "id": "L2n3WOrnZxqE",
        "outputId": "72eca098-9c45-4c75-e1f9-aedb7ee32575"
      },
      "source": [
        "plt.figure(figsize=(10, 10)) #Display data augmented images\n",
        "for i in range(9):\n",
        "  augmented_image = dA(daImg)\n",
        "  ax = plt.subplot(3, 3, i + 1)\n",
        "  plt.imshow(augmented_image[0])\n",
        "  plt.axis(\"off\")"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 9 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C3WSTZ3ag_eX"
      },
      "source": [
        "# Turn into prefetched datasets and implementation of image normalization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uL_14X1VlMc_"
      },
      "source": [
        "tf_train = tf_train.map(normalize_img, num_parallel_calls=tf.data.experimental.AUTOTUNE)\n",
        "tf_train = tf_train.cache()\n",
        "tf_train = tf_train.shuffle(info.splits['train'].num_examples)\n",
        "tf_train = tf_train.batch(64)\n",
        "tf_train = tf_train.prefetch(tf.data.experimental.AUTOTUNE)"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dNGmOk20Ccn5"
      },
      "source": [
        "tf_test = tf_test.map(normalize_img, num_parallel_calls=tf.data.experimental.AUTOTUNE)\n",
        "tf_test = tf_test.batch(64)\n",
        "tf_test = tf_test.cache()\n",
        "tf_test = tf_test.prefetch(tf.data.experimental.AUTOTUNE)"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ORGd2rdlTpJ2"
      },
      "source": [
        "callback = keras.callbacks.EarlyStopping(monitor='loss', patience=3) #Prevents overfitting"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eCStjbfGhX8Q"
      },
      "source": [
        "# Convolutional Neural Network Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uTt5H2EADBZ-"
      },
      "source": [
        "model = tf.keras.Sequential()\n",
        "model.add(layers.Conv2D(32,(4,4), input_shape=(64,64,3)))\n",
        "model.add(layers.LeakyReLU(alpha=0.3))\n",
        "model.add(layers.MaxPooling2D(2,2))\n",
        "model.add(layers.Conv2D(64, (4,4)))\n",
        "model.add(layers.LeakyReLU(alpha=0.3))\n",
        "model.add(layers.MaxPooling2D(2,2))\n",
        "model.add(layers.Conv2D(64, (4,4)))\n",
        "model.add(layers.LeakyReLU(alpha=0.3))"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zLJCjRH4DFSy"
      },
      "source": [
        "model.add(layers.Flatten())\n",
        "model.add(layers.Dense(32,input_dim=64))\n",
        "model.add(layers.LeakyReLU(alpha=0.3))\n",
        "model.add(layers.Dense(10, activation='softmax')) #10 classes"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4MDAJBx9FAHx",
        "outputId": "ec50b050-c23d-4ee6-9522-c1adb3c2dc30"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d (Conv2D)              (None, 61, 61, 32)        1568      \n",
            "_________________________________________________________________\n",
            "leaky_re_lu (LeakyReLU)      (None, 61, 61, 32)        0         \n",
            "_________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D) (None, 30, 30, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 27, 27, 64)        32832     \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_1 (LeakyReLU)    (None, 27, 27, 64)        0         \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 13, 13, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 10, 10, 64)        65600     \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_2 (LeakyReLU)    (None, 10, 10, 64)        0         \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 6400)              0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 32)                204832    \n",
            "_________________________________________________________________\n",
            "leaky_re_lu_3 (LeakyReLU)    (None, 32)                0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 10)                330       \n",
            "=================================================================\n",
            "Total params: 305,162\n",
            "Trainable params: 305,162\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "92PgLuFyiRow"
      },
      "source": [
        "# Compiles and Trains the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JPVx2r09FCTN",
        "outputId": "8213547b-5e39-4b95-cef6-16cde7dae977"
      },
      "source": [
        "model.compile(optimizer='adam', loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True), metrics=['accuracy'])\n",
        "model.fit(tf_train, epochs=15, batch_size=64, callbacks=[callback], verbose=2)"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/15\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras/backend.py:4907: UserWarning: \"`sparse_categorical_crossentropy` received `from_logits=True`, but the `output` argument was produced by a sigmoid or softmax activation and thus does not represent logits. Was this intended?\"\n",
            "  '\"`sparse_categorical_crossentropy` received `from_logits=True`, but '\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "338/338 - 37s - loss: 1.1936 - accuracy: 0.5469\n",
            "Epoch 2/15\n",
            "338/338 - 5s - loss: 0.7905 - accuracy: 0.7113\n",
            "Epoch 3/15\n",
            "338/338 - 5s - loss: 0.6971 - accuracy: 0.7507\n",
            "Epoch 4/15\n",
            "338/338 - 5s - loss: 0.6045 - accuracy: 0.7838\n",
            "Epoch 5/15\n",
            "338/338 - 5s - loss: 0.5373 - accuracy: 0.8081\n",
            "Epoch 6/15\n",
            "338/338 - 5s - loss: 0.5006 - accuracy: 0.8231\n",
            "Epoch 7/15\n",
            "338/338 - 5s - loss: 0.4666 - accuracy: 0.8365\n",
            "Epoch 8/15\n",
            "338/338 - 5s - loss: 0.4172 - accuracy: 0.8544\n",
            "Epoch 9/15\n",
            "338/338 - 5s - loss: 0.3855 - accuracy: 0.8661\n",
            "Epoch 10/15\n",
            "338/338 - 5s - loss: 0.3583 - accuracy: 0.8738\n",
            "Epoch 11/15\n",
            "338/338 - 5s - loss: 0.3358 - accuracy: 0.8822\n",
            "Epoch 12/15\n",
            "338/338 - 5s - loss: 0.3061 - accuracy: 0.8911\n",
            "Epoch 13/15\n",
            "338/338 - 5s - loss: 0.2652 - accuracy: 0.9070\n",
            "Epoch 14/15\n",
            "338/338 - 5s - loss: 0.2437 - accuracy: 0.9123\n",
            "Epoch 15/15\n",
            "338/338 - 5s - loss: 0.2219 - accuracy: 0.9205\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f2f070861d0>"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zXUL6a6NiaZY"
      },
      "source": [
        "# Model Evaluation on testing data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "49Q7Pz5pFsaJ",
        "outputId": "d2444204-4ab8-4cdb-fb8a-6e3dc342ca54"
      },
      "source": [
        "model.evaluate(tf_test, batch_size=64, verbose=1)"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 1/43 [..............................] - ETA: 12s - loss: 1.1804 - accuracy: 0.7500"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras/backend.py:4907: UserWarning: \"`sparse_categorical_crossentropy` received `from_logits=True`, but the `output` argument was produced by a sigmoid or softmax activation and thus does not represent logits. Was this intended?\"\n",
            "  '\"`sparse_categorical_crossentropy` received `from_logits=True`, but '\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "43/43 [==============================] - 1s 23ms/step - loss: 0.7112 - accuracy: 0.7970\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.7111700177192688, 0.7970370650291443]"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4ZG22UUI_qv0"
      },
      "source": [
        "model.save('euro.h5') #Saves model, still configuring it"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "urUeb6xuIvQj"
      },
      "source": [
        "euroModel = keras.models.load_model('euro.h5') #loads model"
      ],
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rJsgy4UBitJb"
      },
      "source": [
        "Classification function"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tfasp2xJ2k9b"
      },
      "source": [
        "def classify():\n",
        "  if feature_predict == \"Pasture\" or feature_predict == \"AnnualCrop\" or feature_predict == \"PermanentCrop\" or feature_predict == \"HerbaceousVegetation\":\n",
        "    print(\"Safe\")\n",
        "  else:\n",
        "    print(\"Unsafe\")"
      ],
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "URrtKzfDiwTO"
      },
      "source": [
        "# Prediction"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 370
        },
        "id": "cgyuuTdgVaXE",
        "outputId": "eed943fe-1a4e-4751-9fc3-56b7d954a937"
      },
      "source": [
        "target = 91\n",
        "prediction = model.predict(tf_test)\n",
        "print(prediction[target])\n",
        "#y = np.amax(prediction[target])*10\n",
        "#y = int(y)\n",
        "#print(y)\n",
        "feature_predict = features[np.argmax(prediction[target])-1]\n",
        "#print(prediction[target][y])\n",
        "print(\"Predicted:\", feature_predict)\n",
        "print(\"Expected:\", features[test_labels[target].numpy()-1])\n",
        "plt.imshow(test_imgs[target])\n",
        "classify()"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1.3968963e-03 1.3144372e-10 7.8641679e-06 2.0983236e-07 8.3688712e-14\n",
            " 1.7999422e-07 2.2762929e-09 7.7888031e-16 6.2114414e-06 9.9858862e-01]\n",
            "9\n",
            "Predicted: SeaLake\n",
            "Expected: SeaLake\n",
            "Unsafe\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}