Satellite_Image_Processing.ipynb

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        "# Satellite Image Processing with Deep Learning\n",
        "\n",
        "In this notebook you will:\n",
        "- Solve the EuroSAT-10 classification problem,\n",
        "- to that end use Convolutional Neural Networks,\n",
        "- decrease overfitting with dropout,\n",
        "- decrease overfitting even further with data augmentation, and\n",
        "- solve the problem with transfer learning.\n",
        "\n",
        "First two cells provides with imports and data downloads."
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      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
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        "assert tf.__version__.startswith(\"2.\"), \"You have TensorFlow version {}, 2.X is required, please upgrade.\".format(tf.__version__)\n",
        "\n",
        "import tensorflow_datasets as tfds\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from tensorflow.keras import models, layers, Input"
      ],
      "execution_count": null,
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        "## Load and split EuroSAT-dataset.\n",
        "\n",
        "We split the data into three subsets:\n",
        "- Train: For training the Neural Network.\n",
        "- Validate: To see how good the Neural Network is after each epoch.\n",
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            "95593fa3d1214b9182e270da851964b7",
            "4af77e84fe61472ca846b71bf75cd6a8",
            "2e99de023a1d4a848dacdf4c95d158f3",
            "fc95d8ba5bfe4c92b6fe33018aa867e5",
            "71a65103e729473ca1476b20c06a2265",
            "b329ffeb7df24600b5fc7dcde82c2580",
            "c47af314a6c64488a44659b709d49513",
            "230fba45dbc14f2abbdc2518e9584e0c",
            "58e27fc891084e18902e7a505f7d0c65",
            "fbd5a9abd8d240a0a8c3b25e081f9f74",
            "6718418e93e04fa7b41abff2d27c4da5",
            "f49c4fe3477b4be49aeea64c2b02f0aa",
            "50f33f91c7844fcb8d113bafd0958497",
            "c5c353ea974e44e0b9ae6f9aad3ed557"
          ]
        },
        "outputId": "999484ff-718f-4ac3-f991-0a31abd9245a"
      },
      "source": [
        "(dataset_train_original, dataset_validate_original, dataset_test_original), info = tfds.load(\n",
        "    name=\"eurosat/rgb\", \n",
        "    split=[\"train[:70%]\", \"train[70%:90%]\", \"train[90%:]\"],\n",
        "    with_info=True,\n",
        "    as_supervised=True\n",
        ")\n",
        "print(info)\n",
        "print(\"Train:   \", len(list(dataset_train_original)))\n",
        "print(\"Validate:\", len(list(dataset_validate_original)))\n",
        "print(\"Test:    \", len(list(dataset_test_original)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "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"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5444dd5192e2428a99470903ac9e5ac1",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Dl Completed...', max=1.0, style=Progre…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "23f964a3fdda4b5693b02e9b28f56dbd",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Dl Size...', max=1.0, style=ProgressSty…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "cd4c95fe93c8460a9101b5dad859884c",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Extraction completed...', max=1.0, styl…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a974107d5f4d4963965f1b351705855a",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\rShuffling and writing examples to /root/tensorflow_datasets/eurosat/rgb/2.0.0.incompleteKCBTTD/eurosat-train.tfrecord\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c47af314a6c64488a44659b709d49513",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=27000.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "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",
            "\rtfds.core.DatasetInfo(\n",
            "    name='eurosat',\n",
            "    version=2.0.0,\n",
            "    description='EuroSAT dataset is based on Sentinel-2 satellite images covering 13 spectral\n",
            "bands and consisting of 10 classes with 27000 labeled and\n",
            "geo-referenced samples.\n",
            "\n",
            "Two datasets are offered:\n",
            "- rgb: Contains only the optical R, G, B frequency bands encoded as JPEG image.\n",
            "- all: Contains all 13 bands in the original value range (float32).\n",
            "\n",
            "URL: https://github.com/phelber/eurosat\n",
            "',\n",
            "    homepage='https://github.com/phelber/eurosat',\n",
            "    features=FeaturesDict({\n",
            "        'filename': Text(shape=(), dtype=tf.string),\n",
            "        'image': Image(shape=(64, 64, 3), dtype=tf.uint8),\n",
            "        'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=10),\n",
            "    }),\n",
            "    total_num_examples=27000,\n",
            "    splits={\n",
            "        'train': 27000,\n",
            "    },\n",
            "    supervised_keys=('image', 'label'),\n",
            "    citation=\"\"\"@misc{helber2017eurosat,\n",
            "        title={EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification},\n",
            "        author={Patrick Helber and Benjamin Bischke and Andreas Dengel and Damian Borth},\n",
            "        year={2017},\n",
            "        eprint={1709.00029},\n",
            "        archivePrefix={arXiv},\n",
            "        primaryClass={cs.CV}\n",
            "    }\"\"\",\n",
            "    redistribution_info=,\n",
            ")\n",
            "\n",
            "Train:    18900\n",
            "Validate: 5400\n",
            "Test:     2700\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "iAwNcMGvNV6X",
        "colab": {}
      },
      "source": [
        "class_names = [\"annual crop\", \"forest\", \"herbaceous vegetation\", \"highway\", \"industrial\", \"pasture\", \"permanent crop\", \"residential\", \"river\", \"sea & lake\"]\n",
        "\n",
        "def label_to_string(label):\n",
        "  return class_names[label]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "LcvN5iQ2G3dL"
      },
      "source": [
        "## Create a Deep Neural Network to solve our classification problem - Convolutional Neural Network.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "5gyY4qHYG3eI"
      },
      "source": [
        "# Some pointers :\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "> Some data visualisations of the train, validation and test data.\n",
        "\n",
        "> Exploratory Data Analysis of the dataset.\n",
        "\n",
        "> Accuracy plots\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1Zsacd8QuB4c",
        "colab_type": "text"
      },
      "source": [
        "Notebook contains\n",
        "\n",
        "\n",
        "1.   Some Visualizations of Dataset\n",
        "2.   Preprocessing\n",
        "3.   Convolutional Neural Network\n",
        "4.   Convolutional Neural Network with Dropout\n",
        "5.   CNNs With Augumentation and Dropout\n",
        "6.   Accuracy Plots of all the three cases\n",
        "7.   Transfer Learning\n",
        "8.   Accuracy Plot\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YYISn7tUbP8b",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "4ba30fa0-9b73-4317-de9d-68c33c7fcf44"
      },
      "source": [
        "# Shape of Image\n",
        "for image, label in dataset_train_original:\n",
        "  print(image.shape,label)\n",
        "  break"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(64, 64, 3) tf.Tensor(4, shape=(), dtype=int64)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ixustXvwhYUV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 534
        },
        "outputId": "a28a984a-5b24-42aa-baf0-e8a27437d368"
      },
      "source": [
        "ds, info = tfds.load('eurosat/rgb', split='train', with_info=True)\n",
        "fig = tfds.show_examples(info,ds)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 648x648 with 9 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aLWKRlRugYZ7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Preprocessing \n",
        "def normalization(image, label):\n",
        "   return tf.cast(image, tf.float32) / 255.0, label\n",
        "\n",
        "## FOR TRAINING DATA\n",
        "#To run on multiple threads\n",
        "dataset_train_original = dataset_train_original.map(normalization,num_parallel_calls=tf.data.experimental.AUTOTUNE)\n",
        "# For better performance\n",
        "dataset_train_original = dataset_train_original.cache()\n",
        "dataset_train_original = dataset_train_original.shuffle(info.splits['train[:70%]'].num_examples)\n",
        "dataset_train_original = dataset_train_original.batch(32)\n",
        "dataset_train_original = dataset_train_original.prefetch(tf.data.experimental.AUTOTUNE)\n",
        "\n",
        "## FOR VALIDATION DATA\n",
        "#To run on multiple threads\n",
        "dataset_validate_original = dataset_validate_original.map(normalization,num_parallel_calls=tf.data.experimental.AUTOTUNE)\n",
        "# For better performance\n",
        "dataset_validate_original = dataset_validate_original.cache()\n",
        "dataset_validate_original = dataset_validate_original.shuffle(info.splits['train[70%:90%]'].num_examples)\n",
        "dataset_validate_original = dataset_validate_original.batch(32)\n",
        "dataset_validate_original = dataset_validate_original.prefetch(tf.data.experimental.AUTOTUNE)\n",
        "\n",
        "## FOR TEST DATA\n",
        "#To run on multiple threads\n",
        "dataset_test_original = dataset_test_original.map(normalization,num_parallel_calls=tf.data.experimental.AUTOTUNE)\n",
        "# For better performance\n",
        "dataset_test_original = dataset_test_original.cache()\n",
        "dataset_test_original = dataset_test_original.batch(32)\n",
        "dataset_test_original = dataset_test_original.prefetch(tf.data.experimental.AUTOTUNE)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "stt_HbueskzD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# HYPERPARAMETERS\n",
        "\n",
        "num_classses = len(class_names)\n",
        "epochs = 50"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l7bl7KX1liEL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 459
        },
        "outputId": "ac334db7-2fc8-416f-9e20-c58aa19e5709"
      },
      "source": [
        "# Convolution Neural Network\n",
        "\n",
        "model = models.Sequential([\n",
        "                           Input(shape=(64,64,3)),\n",
        "                           layers.Conv2D(16,3,padding='same',activation='relu'),\n",
        "                           layers.MaxPooling2D(pool_size=(3,3)),\n",
        "                           layers.Conv2D(32,3,padding='same',activation='relu'),\n",
        "                           layers.MaxPooling2D(pool_size=(3,3)),\n",
        "                           layers.Conv2D(64,3,padding='same',activation='relu'),\n",
        "                           layers.MaxPooling2D(pool_size =(3,3)),\n",
        "                           layers.Flatten(),\n",
        "                           layers.Dense(128,activation='relu'),\n",
        "                           layers.Dense(num_classses,activation='softmax')\n",
        "\n",
        "])\n",
        "\n",
        "model.compile(optimizer='adam', loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
        "              metrics=['accuracy'])\n",
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d (Conv2D)              (None, 64, 64, 16)        448       \n",
            "_________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D) (None, 21, 21, 16)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 21, 21, 32)        4640      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 7, 7, 32)          0         \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 7, 7, 64)          18496     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 2, 2, 64)          0         \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 256)               0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 128)               32896     \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 10)                1290      \n",
            "=================================================================\n",
            "Total params: 57,770\n",
            "Trainable params: 57,770\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5CbFsxyXtMct",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "28864714-2ccc-49cf-e397-3031ddbdc796"
      },
      "source": [
        "history = model.fit(dataset_train_original,\n",
        "          validation_data=dataset_validate_original,\n",
        "          epochs = epochs)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "591/591 [==============================] - 5s 8ms/step - loss: 1.7411 - accuracy: 0.7194 - val_loss: 1.7548 - val_accuracy: 0.7067\n",
            "Epoch 18/50\n",
            "591/591 [==============================] - 5s 8ms/step - loss: 1.7346 - accuracy: 0.7259 - val_loss: 1.7442 - val_accuracy: 0.7154\n",
            "Epoch 19/50\n",
            "591/591 [==============================] - 5s 8ms/step - loss: 1.7358 - accuracy: 0.7258 - val_loss: 1.7444 - val_accuracy: 0.7170\n",
            "Epoch 20/50\n",
            "591/591 [==============================] - 5s 8ms/step - loss: 1.7243 - accuracy: 0.7351 - val_loss: 1.7386 - val_accuracy: 0.7194\n",
            "Epoch 21/50\n",
            "591/591 [==============================] - 5s 8ms/step - loss: 1.7197 - accuracy: 0.7414 - val_loss: 1.7316 - val_accuracy: 0.7269\n",
            "Epoch 22/50\n",
            "591/591 [==============================] - 5s 8ms/step - loss: 1.7010 - accuracy: 0.7598 - val_loss: 1.6814 - val_accuracy: 0.7785\n",
            "Epoch 23/50\n",
            "591/591 [==============================] - 5s 8ms/step - loss: 1.7003 - accuracy: 0.7604 - val_loss: 1.6853 - val_accuracy: 0.7748\n",
            "Epoch 24/50\n",
            "591/591 [==============================] - 5s 8ms/step - loss: 1.6882 - accuracy: 0.7732 - val_loss: 1.7175 - val_accuracy: 0.7413\n",
            "Epoch 25/50\n",
            "591/591 [==============================] - 5s 8ms/step - loss: 1.6797 - accuracy: 0.7815 - val_loss: 1.6984 - val_accuracy: 0.7628\n",
            "Epoch 26/50\n",
            "591/591 [==============================] - 5s 8ms/step - loss: 1.6854 - accuracy: 0.7766 - val_loss: 1.6768 - val_accuracy: 0.7828\n",
            "Epoch 27/50\n",
            "591/591 [==============================] - 5s 8ms/step - loss: 1.6725 - accuracy: 0.7885 - val_loss: 1.6573 - val_accuracy: 0.8019\n",
            "Epoch 28/50\n",
            "591/591 [==============================] - 5s 8ms/step - loss: 1.6614 - accuracy: 0.7989 - val_loss: 1.6870 - val_accuracy: 0.7735\n",
            "Epoch 29/50\n",
            "591/591 [==============================] - 5s 8ms/step - loss: 1.6599 - accuracy: 0.8016 - val_loss: 1.6866 - val_accuracy: 0.7743\n",
            "Epoch 30/50\n",
            "591/591 [==============================] - 5s 8ms/step - loss: 1.6545 - accuracy: 0.8063 - val_loss: 1.6843 - val_accuracy: 0.7735\n",
            "Epoch 31/50\n",
            "591/591 [==============================] - 5s 8ms/step - loss: 1.6604 - accuracy: 0.8011 - val_loss: 1.6859 - val_accuracy: 0.7731\n",
            "Epoch 32/50\n",
            "591/591 [==============================] - 5s 8ms/step - loss: 1.6430 - accuracy: 0.8177 - val_loss: 1.6496 - val_accuracy: 0.8113\n",
            "Epoch 33/50\n",
            "591/591 [==============================] - 5s 8ms/step - loss: 1.6496 - accuracy: 0.8116 - val_loss: 1.6563 - val_accuracy: 0.8046\n",
            "Epoch 34/50\n",
            "591/591 [==============================] - 5s 8ms/step - loss: 1.6457 - accuracy: 0.8148 - val_loss: 1.6755 - val_accuracy: 0.7844\n",
            "Epoch 35/50\n",
            "591/591 [==============================] - 5s 8ms/step - loss: 1.6383 - accuracy: 0.8226 - val_loss: 1.6668 - val_accuracy: 0.7919\n",
            "Epoch 36/50\n",
            "591/591 [==============================] - 5s 8ms/step - loss: 1.6327 - accuracy: 0.8295 - val_loss: 1.6558 - val_accuracy: 0.8054\n",
            "Epoch 37/50\n",
            "591/591 [==============================] - 5s 8ms/step - loss: 1.6399 - accuracy: 0.8210 - val_loss: 1.6607 - val_accuracy: 0.8000\n",
            "Epoch 38/50\n",
            "591/591 [==============================] - 5s 8ms/step - loss: 1.6374 - accuracy: 0.8234 - val_loss: 1.6817 - val_accuracy: 0.7780\n",
            "Epoch 39/50\n",
            "591/591 [==============================] - 5s 8ms/step - loss: 1.6363 - accuracy: 0.8246 - val_loss: 1.6439 - val_accuracy: 0.8170\n",
            "Epoch 40/50\n",
            "591/591 [==============================] - 5s 8ms/step - loss: 1.6240 - accuracy: 0.8367 - val_loss: 1.6660 - val_accuracy: 0.7948\n",
            "Epoch 41/50\n",
            "591/591 [==============================] - 5s 8ms/step - loss: 1.6210 - accuracy: 0.8398 - val_loss: 1.6468 - val_accuracy: 0.8144\n",
            "Epoch 42/50\n",
            "591/591 [==============================] - 5s 8ms/step - loss: 1.6227 - accuracy: 0.8384 - val_loss: 1.6475 - val_accuracy: 0.8109\n",
            "Epoch 43/50\n",
            "591/591 [==============================] - 5s 8ms/step - loss: 1.6159 - accuracy: 0.8444 - val_loss: 1.6338 - val_accuracy: 0.8267\n",
            "Epoch 44/50\n",
            "591/591 [==============================] - 5s 8ms/step - loss: 1.6175 - accuracy: 0.8441 - val_loss: 1.6583 - val_accuracy: 0.8052\n",
            "Epoch 45/50\n",
            "591/591 [==============================] - 5s 8ms/step - loss: 1.6199 - accuracy: 0.8408 - val_loss: 1.6391 - val_accuracy: 0.8220\n",
            "Epoch 46/50\n",
            "591/591 [==============================] - 5s 8ms/step - loss: 1.6145 - accuracy: 0.8467 - val_loss: 1.6528 - val_accuracy: 0.8063\n",
            "Epoch 47/50\n",
            "591/591 [==============================] - 5s 8ms/step - loss: 1.6115 - accuracy: 0.8494 - val_loss: 1.6616 - val_accuracy: 0.7985\n",
            "Epoch 48/50\n",
            "591/591 [==============================] - 5s 8ms/step - loss: 1.6047 - accuracy: 0.8564 - val_loss: 1.6274 - val_accuracy: 0.8350\n",
            "Epoch 49/50\n",
            "591/591 [==============================] - 5s 8ms/step - loss: 1.6107 - accuracy: 0.8498 - val_loss: 1.6207 - val_accuracy: 0.8404\n",
            "Epoch 50/50\n",
            "591/591 [==============================] - 5s 8ms/step - loss: 1.6105 - accuracy: 0.8503 - val_loss: 1.6346 - val_accuracy: 0.8265\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E2-QeyW6us-V",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "72133e96-bd72-45fd-e2b8-5ef2bda05a10"
      },
      "source": [
        "# WITH DROPOUT\n",
        "\n",
        "model = models.Sequential([\n",
        "                           Input(shape=(64,64,3)),\n",
        "                           layers.Conv2D(16,3,padding='same',activation='relu'),\n",
        "                           layers.MaxPooling2D(pool_size=(3,3)),\n",
        "                           layers.Dropout(0.2),\n",
        "                           layers.Conv2D(32,3,padding='same',activation='relu'),\n",
        "                           layers.MaxPooling2D(pool_size=(3,3)),\n",
        "                           layers.Dropout(0.3),\n",
        "                           layers.Conv2D(64,3,padding='same',activation='relu'),\n",
        "                           layers.MaxPooling2D(pool_size =(3,3)),\n",
        "                           layers.Flatten(),\n",
        "                           layers.Dense(128,activation='relu'),\n",
        "                           layers.Dense(num_classses,activation='softmax')\n",
        "\n",
        "])\n",
        "\n",
        "model.compile(optimizer='adam', loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
        "              metrics=['accuracy'])\n",
        "model.summary()\n",
        "history1 = model.fit(dataset_train_original,\n",
        "          validation_data=dataset_validate_original,\n",
        "          epochs = epochs)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_3 (Conv2D)            (None, 64, 64, 16)        448       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2 (None, 21, 21, 16)        0         \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 21, 21, 16)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_4 (Conv2D)            (None, 21, 21, 32)        4640      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_4 (MaxPooling2 (None, 7, 7, 32)          0         \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 7, 7, 32)          0         \n",
            "_________________________________________________________________\n",
            "conv2d_5 (Conv2D)            (None, 7, 7, 64)          18496     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_5 (MaxPooling2 (None, 2, 2, 64)          0         \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 256)               0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 128)               32896     \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 10)                1290      \n",
            "=================================================================\n",
            "Total params: 57,770\n",
            "Trainable params: 57,770\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Epoch 1/50\n",
            "591/591 [==============================] - 3s 5ms/step - loss: 2.0874 - accuracy: 0.3631 - val_loss: 1.9673 - val_accuracy: 0.4965\n",
            "Epoch 2/50\n",
            "591/591 [==============================] - 3s 4ms/step - loss: 1.9480 - accuracy: 0.5112 - val_loss: 1.8763 - val_accuracy: 0.5856\n",
            "Epoch 3/50\n",
            "591/591 [==============================] - 3s 4ms/step - loss: 1.8827 - accuracy: 0.5779 - val_loss: 1.8389 - val_accuracy: 0.6206\n",
            "Epoch 4/50\n",
            "591/591 [==============================] - 3s 4ms/step - loss: 1.8500 - accuracy: 0.6097 - val_loss: 1.7594 - val_accuracy: 0.7067\n",
            "Epoch 5/50\n",
            "591/591 [==============================] - 3s 4ms/step - loss: 1.8098 - accuracy: 0.6510 - val_loss: 1.7581 - val_accuracy: 0.7019\n",
            "Epoch 6/50\n",
            "591/591 [==============================] - 3s 4ms/step - loss: 1.8139 - accuracy: 0.6460 - val_loss: 1.7509 - val_accuracy: 0.7102\n",
            "Epoch 7/50\n",
            "591/591 [==============================] - 3s 4ms/step - loss: 1.7838 - accuracy: 0.6756 - val_loss: 1.7757 - val_accuracy: 0.6819\n",
            "Epoch 8/50\n",
            "591/591 [==============================] - 3s 4ms/step - loss: 1.7761 - accuracy: 0.6831 - val_loss: 1.7388 - val_accuracy: 0.7207\n",
            "Epoch 9/50\n",
            "591/591 [==============================] - 3s 4ms/step - loss: 1.7779 - accuracy: 0.6824 - val_loss: 1.7329 - val_accuracy: 0.7304\n",
            "Epoch 10/50\n",
            "591/591 [==============================] - 3s 5ms/step - loss: 1.7660 - accuracy: 0.6942 - val_loss: 1.7511 - val_accuracy: 0.7080\n",
            "Epoch 11/50\n",
            "591/591 [==============================] - 3s 4ms/step - loss: 1.7614 - accuracy: 0.6996 - val_loss: 1.7504 - val_accuracy: 0.7083\n",
            "Epoch 12/50\n",
            "591/591 [==============================] - 3s 5ms/step - loss: 1.7488 - accuracy: 0.7114 - val_loss: 1.8202 - val_accuracy: 0.6380\n",
            "Epoch 13/50\n",
            "591/591 [==============================] - 3s 4ms/step - loss: 1.7499 - accuracy: 0.7103 - val_loss: 1.7974 - val_accuracy: 0.6620\n",
            "Epoch 14/50\n",
            "591/591 [==============================] - 3s 4ms/step - loss: 1.7372 - accuracy: 0.7253 - val_loss: 1.7544 - val_accuracy: 0.7057\n",
            "Epoch 15/50\n",
            "591/591 [==============================] - 3s 4ms/step - loss: 1.7315 - accuracy: 0.7289 - val_loss: 1.7206 - val_accuracy: 0.7394\n",
            "Epoch 16/50\n",
            "591/591 [==============================] - 2s 4ms/step - loss: 1.7205 - accuracy: 0.7399 - val_loss: 1.6960 - val_accuracy: 0.7656\n",
            "Epoch 17/50\n",
            "591/591 [==============================] - 3s 4ms/step - loss: 1.7248 - accuracy: 0.7334 - val_loss: 1.7748 - val_accuracy: 0.6831\n",
            "Epoch 18/50\n",
            "591/591 [==============================] - 3s 4ms/step - loss: 1.7087 - accuracy: 0.7512 - val_loss: 1.6800 - val_accuracy: 0.7783\n",
            "Epoch 19/50\n",
            "591/591 [==============================] - 3s 4ms/step - loss: 1.6988 - accuracy: 0.7625 - val_loss: 1.7408 - val_accuracy: 0.7189\n",
            "Epoch 20/50\n",
            "591/591 [==============================] - 3s 4ms/step - loss: 1.7155 - accuracy: 0.7452 - val_loss: 1.7277 - val_accuracy: 0.7335\n",
            "Epoch 21/50\n",
            "591/591 [==============================] - 3s 4ms/step - loss: 1.7014 - accuracy: 0.7587 - val_loss: 1.6900 - val_accuracy: 0.7700\n",
            "Epoch 22/50\n",
            "591/591 [==============================] - 3s 4ms/step - loss: 1.6982 - accuracy: 0.7624 - val_loss: 1.7119 - val_accuracy: 0.7489\n",
            "Epoch 23/50\n",
            "591/591 [==============================] - 3s 5ms/step - loss: 1.6889 - accuracy: 0.7713 - val_loss: 1.7120 - val_accuracy: 0.7487\n",
            "Epoch 24/50\n",
            "591/591 [==============================] - 3s 5ms/step - loss: 1.6851 - accuracy: 0.7756 - val_loss: 1.6614 - val_accuracy: 0.7991\n",
            "Epoch 25/50\n",
            "591/591 [==============================] - 3s 4ms/step - loss: 1.6841 - accuracy: 0.7772 - val_loss: 1.6666 - val_accuracy: 0.7933\n",
            "Epoch 26/50\n",
            "591/591 [==============================] - 3s 5ms/step - loss: 1.6828 - accuracy: 0.7777 - val_loss: 1.7029 - val_accuracy: 0.7585\n",
            "Epoch 27/50\n",
            "591/591 [==============================] - 3s 5ms/step - loss: 1.6826 - accuracy: 0.7786 - val_loss: 1.7879 - val_accuracy: 0.6700\n",
            "Epoch 28/50\n",
            "591/591 [==============================] - 3s 5ms/step - loss: 1.6759 - accuracy: 0.7839 - val_loss: 1.6771 - val_accuracy: 0.7831\n",
            "Epoch 29/50\n",
            "591/591 [==============================] - 3s 4ms/step - loss: 1.6696 - accuracy: 0.7910 - val_loss: 1.6528 - val_accuracy: 0.8080\n",
            "Epoch 30/50\n",
            "591/591 [==============================] - 3s 4ms/step - loss: 1.6688 - accuracy: 0.7911 - val_loss: 1.7468 - val_accuracy: 0.7148\n",
            "Epoch 31/50\n",
            "591/591 [==============================] - 3s 4ms/step - loss: 1.6619 - accuracy: 0.7997 - val_loss: 1.6723 - val_accuracy: 0.7881\n",
            "Epoch 32/50\n",
            "591/591 [==============================] - 3s 4ms/step - loss: 1.6694 - accuracy: 0.7918 - val_loss: 1.6764 - val_accuracy: 0.7854\n",
            "Epoch 33/50\n",
            "591/591 [==============================] - 3s 4ms/step - loss: 1.6635 - accuracy: 0.7961 - val_loss: 1.6997 - val_accuracy: 0.7594\n",
            "Epoch 34/50\n",
            "591/591 [==============================] - 3s 4ms/step - loss: 1.6673 - accuracy: 0.7935 - val_loss: 1.6535 - val_accuracy: 0.8072\n",
            "Epoch 35/50\n",
            "591/591 [==============================] - 2s 4ms/step - loss: 1.6583 - accuracy: 0.8021 - val_loss: 1.7042 - val_accuracy: 0.7570\n",
            "Epoch 36/50\n",
            "591/591 [==============================] - 2s 4ms/step - loss: 1.6668 - accuracy: 0.7931 - val_loss: 1.7484 - val_accuracy: 0.7126\n",
            "Epoch 37/50\n",
            "591/591 [==============================] - 3s 4ms/step - loss: 1.6611 - accuracy: 0.7998 - val_loss: 1.7099 - val_accuracy: 0.7504\n",
            "Epoch 38/50\n",
            "591/591 [==============================] - 2s 4ms/step - loss: 1.6567 - accuracy: 0.8033 - val_loss: 1.6838 - val_accuracy: 0.7761\n",
            "Epoch 39/50\n",
            "591/591 [==============================] - 3s 4ms/step - loss: 1.6520 - accuracy: 0.8089 - val_loss: 1.6552 - val_accuracy: 0.8072\n",
            "Epoch 40/50\n",
            "591/591 [==============================] - 3s 4ms/step - loss: 1.6434 - accuracy: 0.8170 - val_loss: 1.6263 - val_accuracy: 0.8343\n",
            "Epoch 41/50\n",
            "591/591 [==============================] - 3s 4ms/step - loss: 1.6484 - accuracy: 0.8124 - val_loss: 1.6831 - val_accuracy: 0.7783\n",
            "Epoch 42/50\n",
            "591/591 [==============================] - 3s 4ms/step - loss: 1.6544 - accuracy: 0.8051 - val_loss: 1.7350 - val_accuracy: 0.7248\n",
            "Epoch 43/50\n",
            "591/591 [==============================] - 3s 4ms/step - loss: 1.6368 - accuracy: 0.8239 - val_loss: 1.6742 - val_accuracy: 0.7867\n",
            "Epoch 44/50\n",
            "591/591 [==============================] - 3s 4ms/step - loss: 1.6415 - accuracy: 0.8194 - val_loss: 1.6951 - val_accuracy: 0.7641\n",
            "Epoch 45/50\n",
            "591/591 [==============================] - 3s 4ms/step - loss: 1.6387 - accuracy: 0.8212 - val_loss: 1.6940 - val_accuracy: 0.7657\n",
            "Epoch 46/50\n",
            "591/591 [==============================] - 3s 5ms/step - loss: 1.6424 - accuracy: 0.8186 - val_loss: 1.6599 - val_accuracy: 0.8006\n",
            "Epoch 47/50\n",
            "591/591 [==============================] - 3s 4ms/step - loss: 1.6318 - accuracy: 0.8295 - val_loss: 1.7864 - val_accuracy: 0.6741\n",
            "Epoch 48/50\n",
            "591/591 [==============================] - 3s 4ms/step - loss: 1.6368 - accuracy: 0.8238 - val_loss: 1.6604 - val_accuracy: 0.7993\n",
            "Epoch 49/50\n",
            "591/591 [==============================] - 3s 4ms/step - loss: 1.6424 - accuracy: 0.8181 - val_loss: 1.6295 - val_accuracy: 0.8309\n",
            "Epoch 50/50\n",
            "591/591 [==============================] - 3s 4ms/step - loss: 1.6298 - accuracy: 0.8313 - val_loss: 1.6362 - val_accuracy: 0.8252\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "klr9IpyL1IOQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "71fd6c52-8366-4657-fae2-f88a9ee2e325"
      },
      "source": [
        "# WITH AUGUMENTATION\n",
        "\n",
        "# By reading this research paper - https://www.researchgate.net/publication/335844785_DATA_AUGMENTATION_APPROACHES_FOR_SATELLITE_IMAGE_SUPER-RESOLUTION#:~:text=Data%20augmentation%20is%20a%20well,satellite%20image%20super%20resolution%20tasks.\n",
        "\n",
        "augumentation = models.Sequential([\n",
        "                                   Input(shape=(64,64,3)),\n",
        "                                   layers.experimental.preprocessing.RandomFlip(\"horizontal_and_vertical\"),\n",
        "                                   layers.experimental.preprocessing.RandomContrast(0.7, 1.3)\n",
        "])\n",
        "\n",
        "model = models.Sequential([\n",
        "                           augumentation,\n",
        "                           layers.Conv2D(16,3,padding='same',activation='relu'),\n",
        "                           layers.MaxPooling2D(pool_size=(3,3)),\n",
        "                           layers.Dropout(0.2),\n",
        "                           layers.Conv2D(32,3,padding='same',activation='relu'),\n",
        "                           layers.MaxPooling2D(pool_size=(3,3)),\n",
        "                          #  layers.Dropout(0.3),\n",
        "                           layers.Conv2D(64,3,padding='same',activation='relu'),\n",
        "                           layers.MaxPooling2D(pool_size =(3,3)),\n",
        "                           layers.Flatten(),\n",
        "                           layers.Dense(128,activation='relu'),\n",
        "                           layers.Dense(num_classses,activation='softmax')\n",
        "\n",
        "])\n",
        "\n",
        "model.compile(optimizer='adam', loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
        "              metrics=['accuracy'])\n",
        "# model.summary()\n",
        "history2 = model.fit(dataset_train_original,\n",
        "          validation_data=dataset_validate_original,\n",
        "          epochs = epochs)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/50\n",
            "591/591 [==============================] - 3s 5ms/step - loss: 2.1130 - accuracy: 0.3404 - val_loss: 2.0023 - val_accuracy: 0.4550\n",
            "Epoch 2/50\n",
            "591/591 [==============================] - 3s 5ms/step - loss: 1.9970 - accuracy: 0.4612 - val_loss: 1.9549 - val_accuracy: 0.5070\n",
            "Epoch 3/50\n",
            "591/591 [==============================] - 3s 5ms/step - loss: 1.9632 - accuracy: 0.4946 - val_loss: 1.9420 - val_accuracy: 0.5122\n",
            "Epoch 4/50\n",
            "591/591 [==============================] - 3s 5ms/step - loss: 1.9477 - accuracy: 0.5095 - val_loss: 1.9434 - val_accuracy: 0.5130\n",
            "Epoch 5/50\n",
            "591/591 [==============================] - 3s 5ms/step - loss: 1.9208 - accuracy: 0.5352 - val_loss: 1.9300 - val_accuracy: 0.5270\n",
            "Epoch 6/50\n",
            "591/591 [==============================] - 3s 5ms/step - loss: 1.8986 - accuracy: 0.5603 - val_loss: 1.8322 - val_accuracy: 0.6263\n",
            "Epoch 7/50\n",
            "591/591 [==============================] - 3s 5ms/step - loss: 1.8725 - accuracy: 0.5873 - val_loss: 1.7847 - val_accuracy: 0.6791\n",
            "Epoch 8/50\n",
            "591/591 [==============================] - 3s 5ms/step - loss: 1.8226 - accuracy: 0.6381 - val_loss: 1.8545 - val_accuracy: 0.6074\n",
            "Epoch 9/50\n",
            "591/591 [==============================] - 3s 5ms/step - loss: 1.8190 - accuracy: 0.6407 - val_loss: 1.8086 - val_accuracy: 0.6537\n",
            "Epoch 10/50\n",
            "591/591 [==============================] - 3s 5ms/step - loss: 1.7996 - accuracy: 0.6622 - val_loss: 1.7651 - val_accuracy: 0.6941\n",
            "Epoch 11/50\n",
            "591/591 [==============================] - 3s 5ms/step - loss: 1.8124 - accuracy: 0.6467 - val_loss: 1.7995 - val_accuracy: 0.6598\n",
            "Epoch 12/50\n",
            "591/591 [==============================] - 3s 5ms/step - loss: 1.7930 - accuracy: 0.6668 - val_loss: 1.7257 - val_accuracy: 0.7346\n",
            "Epoch 13/50\n",
            "591/591 [==============================] - 3s 5ms/step - loss: 1.7961 - accuracy: 0.6634 - val_loss: 1.7810 - val_accuracy: 0.6804\n",
            "Epoch 14/50\n",
            "591/591 [==============================] - 3s 5ms/step - loss: 1.7859 - accuracy: 0.6729 - val_loss: 1.7661 - val_accuracy: 0.6937\n",
            "Epoch 15/50\n",
            "591/591 [==============================] - 3s 5ms/step - loss: 1.8035 - accuracy: 0.6553 - val_loss: 1.7535 - val_accuracy: 0.7063\n",
            "Epoch 16/50\n",
            "591/591 [==============================] - 3s 5ms/step - loss: 1.7806 - accuracy: 0.6787 - val_loss: 1.7508 - val_accuracy: 0.7111\n",
            "Epoch 17/50\n",
            "591/591 [==============================] - 3s 5ms/step - loss: 1.7874 - accuracy: 0.6716 - val_loss: 1.7718 - val_accuracy: 0.6859\n",
            "Epoch 18/50\n",
            "591/591 [==============================] - 3s 5ms/step - loss: 1.7725 - accuracy: 0.6860 - val_loss: 1.7810 - val_accuracy: 0.6785\n",
            "Epoch 19/50\n",
            "591/591 [==============================] - 3s 5ms/step - loss: 1.7752 - accuracy: 0.6839 - val_loss: 1.7190 - val_accuracy: 0.7407\n",
            "Epoch 20/50\n",
            "591/591 [==============================] - 3s 5ms/step - loss: 1.7707 - accuracy: 0.6878 - val_loss: 1.8596 - val_accuracy: 0.5998\n",
            "Epoch 21/50\n",
            "591/591 [==============================] - 3s 5ms/step - loss: 1.7660 - accuracy: 0.6945 - val_loss: 1.8785 - val_accuracy: 0.5807\n",
            "Epoch 22/50\n",
            "591/591 [==============================] - 3s 5ms/step - loss: 1.7647 - accuracy: 0.6953 - val_loss: 1.7214 - val_accuracy: 0.7396\n",
            "Epoch 23/50\n",
            "591/591 [==============================] - 3s 5ms/step - loss: 1.7694 - accuracy: 0.6899 - val_loss: 1.7235 - val_accuracy: 0.7376\n",
            "Epoch 24/50\n",
            "591/591 [==============================] - 3s 5ms/step - loss: 1.7655 - accuracy: 0.6939 - val_loss: 1.7705 - val_accuracy: 0.6891\n",
            "Epoch 25/50\n",
            "591/591 [==============================] - 3s 5ms/step - loss: 1.7635 - accuracy: 0.6955 - val_loss: 1.7198 - val_accuracy: 0.7385\n",
            "Epoch 26/50\n",
            "591/591 [==============================] - 3s 5ms/step - loss: 1.7558 - accuracy: 0.7037 - val_loss: 1.7101 - val_accuracy: 0.7498\n",
            "Epoch 27/50\n",
            "591/591 [==============================] - 3s 5ms/step - loss: 1.7548 - accuracy: 0.7040 - val_loss: 1.7687 - val_accuracy: 0.6885\n",
            "Epoch 28/50\n",
            "591/591 [==============================] - 3s 5ms/step - loss: 1.7688 - accuracy: 0.6898 - val_loss: 1.7477 - val_accuracy: 0.7117\n",
            "Epoch 29/50\n",
            "591/591 [==============================] - 3s 5ms/step - loss: 1.7625 - accuracy: 0.6967 - val_loss: 1.7020 - val_accuracy: 0.7557\n",
            "Epoch 30/50\n",
            "591/591 [==============================] - 3s 5ms/step - loss: 1.7631 - accuracy: 0.6953 - val_loss: 1.7113 - val_accuracy: 0.7496\n",
            "Epoch 31/50\n",
            "591/591 [==============================] - 3s 5ms/step - loss: 1.7521 - accuracy: 0.7069 - val_loss: 1.7294 - val_accuracy: 0.7311\n",
            "Epoch 32/50\n",
            "591/591 [==============================] - 3s 5ms/step - loss: 1.7409 - accuracy: 0.7178 - val_loss: 1.7613 - val_accuracy: 0.6983\n",
            "Epoch 33/50\n",
            "591/591 [==============================] - 3s 5ms/step - loss: 1.7446 - accuracy: 0.7150 - val_loss: 1.7261 - val_accuracy: 0.7326\n",
            "Epoch 34/50\n",
            "591/591 [==============================] - 3s 5ms/step - loss: 1.7499 - accuracy: 0.7101 - val_loss: 1.7210 - val_accuracy: 0.7391\n",
            "Epoch 35/50\n",
            "591/591 [==============================] - 3s 5ms/step - loss: 1.7522 - accuracy: 0.7071 - val_loss: 1.7330 - val_accuracy: 0.7283\n",
            "Epoch 36/50\n",
            "591/591 [==============================] - 3s 5ms/step - loss: 1.7373 - accuracy: 0.7222 - val_loss: 1.8046 - val_accuracy: 0.6526\n",
            "Epoch 37/50\n",
            "591/591 [==============================] - 3s 5ms/step - loss: 1.7415 - accuracy: 0.7189 - val_loss: 1.6942 - val_accuracy: 0.7643\n",
            "Epoch 38/50\n",
            "591/591 [==============================] - 3s 5ms/step - loss: 1.7367 - accuracy: 0.7235 - val_loss: 1.7418 - val_accuracy: 0.7191\n",
            "Epoch 39/50\n",
            "591/591 [==============================] - 3s 5ms/step - loss: 1.7324 - accuracy: 0.7272 - val_loss: 1.7683 - val_accuracy: 0.6917\n",
            "Epoch 40/50\n",
            "591/591 [==============================] - 3s 5ms/step - loss: 1.7494 - accuracy: 0.7092 - val_loss: 1.6959 - val_accuracy: 0.7639\n",
            "Epoch 41/50\n",
            "591/591 [==============================] - 3s 5ms/step - loss: 1.7409 - accuracy: 0.7179 - val_loss: 1.7102 - val_accuracy: 0.7500\n",
            "Epoch 42/50\n",
            "591/591 [==============================] - 3s 5ms/step - loss: 1.7406 - accuracy: 0.7194 - val_loss: 1.7177 - val_accuracy: 0.7433\n",
            "Epoch 43/50\n",
            "591/591 [==============================] - 3s 5ms/step - loss: 1.7368 - accuracy: 0.7220 - val_loss: 1.6973 - val_accuracy: 0.7628\n",
            "Epoch 44/50\n",
            "591/591 [==============================] - 3s 5ms/step - loss: 1.7225 - accuracy: 0.7376 - val_loss: 1.7068 - val_accuracy: 0.7519\n",
            "Epoch 45/50\n",
            "591/591 [==============================] - 3s 5ms/step - loss: 1.7383 - accuracy: 0.7212 - val_loss: 1.6851 - val_accuracy: 0.7761\n",
            "Epoch 46/50\n",
            "591/591 [==============================] - 3s 5ms/step - loss: 1.7361 - accuracy: 0.7242 - val_loss: 1.6766 - val_accuracy: 0.7839\n",
            "Epoch 47/50\n",
            "591/591 [==============================] - 3s 5ms/step - loss: 1.7331 - accuracy: 0.7260 - val_loss: 1.6784 - val_accuracy: 0.7837\n",
            "Epoch 48/50\n",
            "591/591 [==============================] - 3s 5ms/step - loss: 1.7203 - accuracy: 0.7402 - val_loss: 1.7036 - val_accuracy: 0.7569\n",
            "Epoch 49/50\n",
            "591/591 [==============================] - 3s 5ms/step - loss: 1.7389 - accuracy: 0.7202 - val_loss: 1.7223 - val_accuracy: 0.7370\n",
            "Epoch 50/50\n",
            "591/591 [==============================] - 3s 5ms/step - loss: 1.7346 - accuracy: 0.7249 - val_loss: 1.7535 - val_accuracy: 0.7083\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QlHCqgu1ydpv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "outputId": "20f8109c-fa67-4e1f-e612-483a9bcd8ce9"
      },
      "source": [
        "# Without Dropout\n",
        "accuracy = history.history['accuracy']\n",
        "val_acc = history.history['val_accuracy']\n",
        "\n",
        "#With Dropout\n",
        "accuracy_dropout = history1.history['accuracy']\n",
        "val_acc_dropout = history1.history['val_accuracy']\n",
        "\n",
        "#With Augumentation + Dropout\n",
        "accuracy_aug = history2.history['accuracy']\n",
        "val_acc_aug = history2.history['val_accuracy']\n",
        "\n",
        "\n",
        "epochs_range = range(epochs)\n",
        "\n",
        "plt.figure(figsize=(15, 7))\n",
        "plt.subplot(1, 3, 1)\n",
        "plt.plot(epochs_range, accuracy, label='Training Accuracy')\n",
        "plt.plot(epochs_range, val_acc, label='Validation Accuracy')\n",
        "plt.legend(loc='lower right')\n",
        "plt.title('CASE: 1 Training and Validation Accuracy ')\n",
        "\n",
        "plt.subplot(1, 3, 2)\n",
        "plt.plot(epochs_range, accuracy_dropout, label='Training Accuracy')\n",
        "plt.plot(epochs_range, val_acc_dropout, label='Validation Accuracy')\n",
        "plt.legend(loc='upper right')\n",
        "plt.title('CASE:2 Training and Validation Accuracy')\n",
        "\n",
        "plt.subplot(1, 3, 3)\n",
        "plt.plot(epochs_range, accuracy_aug, label='Training Accuracy')\n",
        "plt.plot(epochs_range, val_acc_aug, label='Validation Accuracy')\n",
        "plt.legend(loc='upper right')\n",
        "plt.title('CASE:3 Training and Validation Accuracy')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x504 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7AEvNjoYtL7T",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "d06df500-819d-4bc9-dba3-4df608f6051f"
      },
      "source": [
        "# WITH TRANSFER LEARNING\n",
        "\n",
        "augumentation = models.Sequential([\n",
        "                                   layers.experimental.preprocessing.RandomFlip(\"horizontal_and_vertical\"),\n",
        "                                   layers.experimental.preprocessing.RandomContrast(0.7, 1.3)\n",
        "])\n",
        "\n",
        "base_model = tf.keras.applications.DenseNet201(input_shape=(64,64,3),\n",
        "                                               include_top=False,\n",
        "                                               weights='imagenet')\n",
        "base_model.trainable = False\n",
        "\n",
        "inputs = Input(shape=(64,64,3))\n",
        "x = augumentation(inputs)\n",
        "x = base_model(x,training= False)\n",
        "x = layers.GlobalAveragePooling2D()(x)\n",
        "x = layers.Dense(num_classses,activation='softmax')(x)\n",
        "\n",
        "model = models.Model(inputs,x)\n",
        "model.summary()\n",
        "\n",
        "model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate = 0.0001), loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "history_final = model.fit(dataset_train_original,\n",
        "                          validation_data=dataset_validate_original,\n",
        "                          epochs= epochs)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"functional_11\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_13 (InputLayer)        [(None, 64, 64, 3)]       0         \n",
            "_________________________________________________________________\n",
            "sequential_6 (Sequential)    (None, 64, 64, 3)         0         \n",
            "_________________________________________________________________\n",
            "densenet201 (Functional)     (None, 2, 2, 1920)        18321984  \n",
            "_________________________________________________________________\n",
            "global_average_pooling2d_5 ( (None, 1920)              0         \n",
            "_________________________________________________________________\n",
            "dense_7 (Dense)              (None, 10)                19210     \n",
            "=================================================================\n",
            "Total params: 18,341,194\n",
            "Trainable params: 19,210\n",
            "Non-trainable params: 18,321,984\n",
            "_________________________________________________________________\n",
            "Epoch 1/50\n",
            "591/591 [==============================] - 20s 34ms/step - loss: 2.0349 - accuracy: 0.4938 - val_loss: 1.8606 - val_accuracy: 0.6830\n",
            "Epoch 2/50\n",
            "591/591 [==============================] - 18s 31ms/step - loss: 1.8315 - accuracy: 0.7124 - val_loss: 1.7462 - val_accuracy: 0.7944\n",
            "Epoch 3/50\n",
            "591/591 [==============================] - 18s 30ms/step - loss: 1.7662 - accuracy: 0.7597 - val_loss: 1.7037 - val_accuracy: 0.8270\n",
            "Epoch 4/50\n",
            "591/591 [==============================] - 18s 30ms/step - loss: 1.7307 - accuracy: 0.7860 - val_loss: 1.6794 - val_accuracy: 0.8374\n",
            "Epoch 5/50\n",
            "591/591 [==============================] - 18s 30ms/step - loss: 1.7201 - accuracy: 0.7916 - val_loss: 1.6640 - val_accuracy: 0.8500\n",
            "Epoch 6/50\n",
            "591/591 [==============================] - 18s 30ms/step - loss: 1.7018 - accuracy: 0.8062 - val_loss: 1.6520 - val_accuracy: 0.8576\n",
            "Epoch 7/50\n",
            "591/591 [==============================] - 18s 31ms/step - loss: 1.6887 - accuracy: 0.8141 - val_loss: 1.6438 - val_accuracy: 0.8650\n",
            "Epoch 8/50\n",
            "591/591 [==============================] - 18s 31ms/step - loss: 1.6772 - accuracy: 0.8265 - val_loss: 1.6373 - val_accuracy: 0.8654\n",
            "Epoch 9/50\n",
            "591/591 [==============================] - 18s 30ms/step - loss: 1.6692 - accuracy: 0.8319 - val_loss: 1.6311 - val_accuracy: 0.8711\n",
            "Epoch 10/50\n",
            "591/591 [==============================] - 18s 30ms/step - loss: 1.6598 - accuracy: 0.8405 - val_loss: 1.6257 - val_accuracy: 0.8752\n",
            "Epoch 11/50\n",
            "591/591 [==============================] - 18s 30ms/step - loss: 1.6533 - accuracy: 0.8423 - val_loss: 1.6205 - val_accuracy: 0.8793\n",
            "Epoch 12/50\n",
            "591/591 [==============================] - 18s 30ms/step - loss: 1.6498 - accuracy: 0.8461 - val_loss: 1.6177 - val_accuracy: 0.8791\n",
            "Epoch 13/50\n",
            "591/591 [==============================] - 18s 30ms/step - loss: 1.6457 - accuracy: 0.8488 - val_loss: 1.6155 - val_accuracy: 0.8793\n",
            "Epoch 14/50\n",
            "591/591 [==============================] - 18s 30ms/step - loss: 1.6451 - accuracy: 0.8476 - val_loss: 1.6124 - val_accuracy: 0.8819\n",
            "Epoch 15/50\n",
            "591/591 [==============================] - 18s 30ms/step - loss: 1.6370 - accuracy: 0.8569 - val_loss: 1.6097 - val_accuracy: 0.8852\n",
            "Epoch 16/50\n",
            "591/591 [==============================] - 18s 30ms/step - loss: 1.6347 - accuracy: 0.8585 - val_loss: 1.6071 - val_accuracy: 0.8844\n",
            "Epoch 17/50\n",
            "591/591 [==============================] - 18s 30ms/step - loss: 1.6342 - accuracy: 0.8571 - val_loss: 1.6066 - val_accuracy: 0.8844\n",
            "Epoch 18/50\n",
            "591/591 [==============================] - 18s 30ms/step - loss: 1.6277 - accuracy: 0.8626 - val_loss: 1.6039 - val_accuracy: 0.8867\n",
            "Epoch 19/50\n",
            "591/591 [==============================] - 18s 30ms/step - loss: 1.6230 - accuracy: 0.8672 - val_loss: 1.6012 - val_accuracy: 0.8883\n",
            "Epoch 20/50\n",
            "591/591 [==============================] - 18s 30ms/step - loss: 1.6229 - accuracy: 0.8667 - val_loss: 1.5993 - val_accuracy: 0.8889\n",
            "Epoch 21/50\n",
            "591/591 [==============================] - 18s 30ms/step - loss: 1.6197 - accuracy: 0.8700 - val_loss: 1.5982 - val_accuracy: 0.8893\n",
            "Epoch 22/50\n",
            "591/591 [==============================] - 18s 30ms/step - loss: 1.6233 - accuracy: 0.8643 - val_loss: 1.5985 - val_accuracy: 0.8881\n",
            "Epoch 23/50\n",
            "591/591 [==============================] - 18s 30ms/step - loss: 1.6211 - accuracy: 0.8679 - val_loss: 1.5956 - val_accuracy: 0.8920\n",
            "Epoch 24/50\n",
            "591/591 [==============================] - 18s 30ms/step - loss: 1.6173 - accuracy: 0.8708 - val_loss: 1.5961 - val_accuracy: 0.8883\n",
            "Epoch 25/50\n",
            "591/591 [==============================] - 18s 30ms/step - loss: 1.6140 - accuracy: 0.8733 - val_loss: 1.5942 - val_accuracy: 0.8915\n",
            "Epoch 26/50\n",
            "591/591 [==============================] - 18s 30ms/step - loss: 1.6101 - accuracy: 0.8775 - val_loss: 1.5924 - val_accuracy: 0.8937\n",
            "Epoch 27/50\n",
            "591/591 [==============================] - 18s 30ms/step - loss: 1.6124 - accuracy: 0.8735 - val_loss: 1.5910 - val_accuracy: 0.8937\n",
            "Epoch 28/50\n",
            "591/591 [==============================] - 18s 31ms/step - loss: 1.6106 - accuracy: 0.8759 - val_loss: 1.5908 - val_accuracy: 0.8933\n",
            "Epoch 29/50\n",
            "591/591 [==============================] - 18s 30ms/step - loss: 1.6090 - accuracy: 0.8787 - val_loss: 1.5895 - val_accuracy: 0.8956\n",
            "Epoch 30/50\n",
            "591/591 [==============================] - 18s 30ms/step - loss: 1.6064 - accuracy: 0.8799 - val_loss: 1.5888 - val_accuracy: 0.8963\n",
            "Epoch 31/50\n",
            "591/591 [==============================] - 18s 30ms/step - loss: 1.6068 - accuracy: 0.8784 - val_loss: 1.5883 - val_accuracy: 0.8957\n",
            "Epoch 32/50\n",
            "591/591 [==============================] - 18s 30ms/step - loss: 1.6054 - accuracy: 0.8799 - val_loss: 1.5872 - val_accuracy: 0.8969\n",
            "Epoch 33/50\n",
            "591/591 [==============================] - 18s 30ms/step - loss: 1.6022 - accuracy: 0.8817 - val_loss: 1.5857 - val_accuracy: 0.8980\n",
            "Epoch 34/50\n",
            "591/591 [==============================] - 18s 30ms/step - loss: 1.6050 - accuracy: 0.8803 - val_loss: 1.5880 - val_accuracy: 0.8943\n",
            "Epoch 35/50\n",
            "591/591 [==============================] - 18s 30ms/step - loss: 1.6007 - accuracy: 0.8843 - val_loss: 1.5851 - val_accuracy: 0.8963\n",
            "Epoch 36/50\n",
            "591/591 [==============================] - 18s 30ms/step - loss: 1.6017 - accuracy: 0.8832 - val_loss: 1.5844 - val_accuracy: 0.8976\n",
            "Epoch 37/50\n",
            "591/591 [==============================] - 18s 30ms/step - loss: 1.5987 - accuracy: 0.8860 - val_loss: 1.5843 - val_accuracy: 0.8985\n",
            "Epoch 38/50\n",
            "591/591 [==============================] - 18s 30ms/step - loss: 1.6017 - accuracy: 0.8836 - val_loss: 1.5829 - val_accuracy: 0.8987\n",
            "Epoch 39/50\n",
            "591/591 [==============================] - 18s 30ms/step - loss: 1.5971 - accuracy: 0.8861 - val_loss: 1.5817 - val_accuracy: 0.8998\n",
            "Epoch 40/50\n",
            "591/591 [==============================] - 18s 30ms/step - loss: 1.5949 - accuracy: 0.8879 - val_loss: 1.5831 - val_accuracy: 0.8991\n",
            "Epoch 41/50\n",
            "591/591 [==============================] - 18s 30ms/step - loss: 1.5967 - accuracy: 0.8879 - val_loss: 1.5809 - val_accuracy: 0.8993\n",
            "Epoch 42/50\n",
            "591/591 [==============================] - 18s 30ms/step - loss: 1.5977 - accuracy: 0.8848 - val_loss: 1.5815 - val_accuracy: 0.8981\n",
            "Epoch 43/50\n",
            "591/591 [==============================] - 18s 30ms/step - loss: 1.5995 - accuracy: 0.8823 - val_loss: 1.5814 - val_accuracy: 0.8993\n",
            "Epoch 44/50\n",
            "591/591 [==============================] - 18s 30ms/step - loss: 1.5957 - accuracy: 0.8869 - val_loss: 1.5802 - val_accuracy: 0.8989\n",
            "Epoch 45/50\n",
            "591/591 [==============================] - 18s 30ms/step - loss: 1.5945 - accuracy: 0.8870 - val_loss: 1.5801 - val_accuracy: 0.8996\n",
            "Epoch 46/50\n",
            "591/591 [==============================] - 18s 30ms/step - loss: 1.5948 - accuracy: 0.8866 - val_loss: 1.5799 - val_accuracy: 0.8994\n",
            "Epoch 47/50\n",
            "591/591 [==============================] - 18s 30ms/step - loss: 1.5898 - accuracy: 0.8919 - val_loss: 1.5801 - val_accuracy: 0.9000\n",
            "Epoch 48/50\n",
            "591/591 [==============================] - 18s 30ms/step - loss: 1.5930 - accuracy: 0.8901 - val_loss: 1.5789 - val_accuracy: 0.9007\n",
            "Epoch 49/50\n",
            "591/591 [==============================] - 18s 30ms/step - loss: 1.5897 - accuracy: 0.8925 - val_loss: 1.5783 - val_accuracy: 0.9004\n",
            "Epoch 50/50\n",
            "591/591 [==============================] - 18s 30ms/step - loss: 1.5923 - accuracy: 0.8887 - val_loss: 1.5790 - val_accuracy: 0.8989\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YI5_wFAZM-V5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "outputId": "80ed5abe-39e6-4747-cca4-47481bf75287"
      },
      "source": [
        "# Transfer Learning\n",
        "accuracy = history_final.history['accuracy']\n",
        "val_acc = history_final.history['val_accuracy']\n",
        "\n",
        "epochs_range = range(epochs)\n",
        "\n",
        "plt.figure(figsize=(15, 7))\n",
        "plt.subplot(1, 1, 1)\n",
        "plt.plot(epochs_range, accuracy, label='Training Accuracy')\n",
        "plt.plot(epochs_range, val_acc, label='Validation Accuracy')\n",
        "plt.legend(loc='lower right')\n",
        "plt.title('Training and Validation Accuracy ')\n",
        "plt.show()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}