Hidden_Layer_Extraction.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 5,
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.11"
    },
    "colab": {
      "name": "Hidden Layer Extraction.ipynb",
      "provenance": []
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5f60f731"
      },
      "source": [
        "## Imports"
      ],
      "id": "5f60f731"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "be016e58"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ],
      "id": "be016e58",
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "732d10bc"
      },
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import Sequential\n",
        "from tensorflow.keras.layers import Dense, Flatten"
      ],
      "id": "732d10bc",
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "46e3fffc"
      },
      "source": [
        "## Importing the dataset"
      ],
      "id": "46e3fffc"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6c621bf3"
      },
      "source": [
        "from tensorflow.keras.datasets import mnist"
      ],
      "id": "6c621bf3",
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b3897d84"
      },
      "source": [
        "(X_train, y_train), (X_test, y_test) = mnist.load_data()"
      ],
      "id": "b3897d84",
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6f1e99dd"
      },
      "source": [
        "## Data visualization and preprocessing"
      ],
      "id": "6f1e99dd"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d0f238d5",
        "outputId": "d2a1796e-3dd3-4080-fb6d-9f29ec968934"
      },
      "source": [
        "print(f'X_train shape: {X_train.shape}')\n",
        "print(f'y_train shape: {y_train.shape}')\n",
        "print(f'X_test shape : {X_test.shape}')\n",
        "print(f'y_test shape : {y_test.shape}')"
      ],
      "id": "d0f238d5",
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X_train shape: (60000, 28, 28)\n",
            "y_train shape: (60000,)\n",
            "X_test shape : (10000, 28, 28)\n",
            "y_test shape : (10000,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "rgwi0CK8ltNq",
        "outputId": "c7ab27e3-342d-4844-8150-f21f7bad432a"
      },
      "source": [
        "plt.figure()\n",
        "plt.imshow(X_train[0])"
      ],
      "id": "rgwi0CK8ltNq",
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f61f1951ed0>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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LJX1U0jpJmyT9l6SeLurtPyQ9JelJDQVrdod6W6ihQ/QnJW2o/Z3b6feu0Fdb3jculwWS4AQdkARhB5Ig7EAShB1IgrADSRB2IAnCDiTx/65XcTNOWsh5AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e38e7421",
        "outputId": "41a25976-fb6a-4c7a-8931-d86d0f63769a"
      },
      "source": [
        "y_train[0]"
      ],
      "id": "e38e7421",
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b04f6eea"
      },
      "source": [
        "X_train = X_train/255.0\n",
        "X_test = X_test/255.0"
      ],
      "id": "b04f6eea",
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d10aca2b",
        "outputId": "81616dfe-251f-47d7-d26e-cd0b82461ec6"
      },
      "source": [
        "np.unique(y_train)"
      ],
      "id": "d10aca2b",
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=uint8)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "47333321"
      },
      "source": [
        "## Question - 1\n",
        "##### Implement a fully connected neural network with one hidden layer in keras/tensorflow. Set the number of neurons in the hidden layer to 100. "
      ],
      "id": "47333321"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "39fb890d"
      },
      "source": [
        "model1 = Sequential()\n",
        "model1.add(Flatten(input_shape=X_train[0].shape))\n",
        "model1.add(Dense(100, activation='relu'))\n",
        "model1.add(Dense(10, activation='softmax'))"
      ],
      "id": "39fb890d",
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ba882108",
        "outputId": "ec981767-422e-4091-feea-71f07ea04849"
      },
      "source": [
        "model1.summary()"
      ],
      "id": "ba882108",
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "flatten (Flatten)            (None, 784)               0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 100)               78500     \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 10)                1010      \n",
            "=================================================================\n",
            "Total params: 79,510\n",
            "Trainable params: 79,510\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d6611fdd"
      },
      "source": [
        "model1.compile(optimizer='adam', metrics=['accuracy'], loss='sparse_categorical_crossentropy')"
      ],
      "id": "d6611fdd",
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8d714ee1",
        "outputId": "160b95a0-7f97-41a1-f01b-d8aae3c27852"
      },
      "source": [
        "history1 = model1.fit(X_train, y_train, batch_size=100, epochs=25)"
      ],
      "id": "8d714ee1",
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/25\n",
            "600/600 [==============================] - 2s 3ms/step - loss: 0.3508 - accuracy: 0.9038\n",
            "Epoch 2/25\n",
            "600/600 [==============================] - 2s 3ms/step - loss: 0.1601 - accuracy: 0.9545\n",
            "Epoch 3/25\n",
            "600/600 [==============================] - 2s 3ms/step - loss: 0.1182 - accuracy: 0.9656\n",
            "Epoch 4/25\n",
            "600/600 [==============================] - 2s 3ms/step - loss: 0.0932 - accuracy: 0.9733\n",
            "Epoch 5/25\n",
            "600/600 [==============================] - 2s 3ms/step - loss: 0.0747 - accuracy: 0.9789\n",
            "Epoch 6/25\n",
            "600/600 [==============================] - 2s 3ms/step - loss: 0.0631 - accuracy: 0.9822\n",
            "Epoch 7/25\n",
            "600/600 [==============================] - 2s 3ms/step - loss: 0.0536 - accuracy: 0.9847\n",
            "Epoch 8/25\n",
            "600/600 [==============================] - 2s 3ms/step - loss: 0.0459 - accuracy: 0.9867\n",
            "Epoch 9/25\n",
            "600/600 [==============================] - 2s 3ms/step - loss: 0.0393 - accuracy: 0.9890\n",
            "Epoch 10/25\n",
            "600/600 [==============================] - 2s 3ms/step - loss: 0.0332 - accuracy: 0.9905\n",
            "Epoch 11/25\n",
            "600/600 [==============================] - 2s 3ms/step - loss: 0.0285 - accuracy: 0.9925\n",
            "Epoch 12/25\n",
            "600/600 [==============================] - 2s 3ms/step - loss: 0.0253 - accuracy: 0.9932\n",
            "Epoch 13/25\n",
            "600/600 [==============================] - 2s 3ms/step - loss: 0.0207 - accuracy: 0.9947\n",
            "Epoch 14/25\n",
            "600/600 [==============================] - 2s 3ms/step - loss: 0.0188 - accuracy: 0.9955\n",
            "Epoch 15/25\n",
            "600/600 [==============================] - 2s 3ms/step - loss: 0.0161 - accuracy: 0.9959\n",
            "Epoch 16/25\n",
            "600/600 [==============================] - 2s 3ms/step - loss: 0.0138 - accuracy: 0.9967\n",
            "Epoch 17/25\n",
            "600/600 [==============================] - 2s 3ms/step - loss: 0.0118 - accuracy: 0.9974\n",
            "Epoch 18/25\n",
            "600/600 [==============================] - 2s 3ms/step - loss: 0.0107 - accuracy: 0.9976\n",
            "Epoch 19/25\n",
            "600/600 [==============================] - 2s 3ms/step - loss: 0.0091 - accuracy: 0.9984\n",
            "Epoch 20/25\n",
            "600/600 [==============================] - 2s 3ms/step - loss: 0.0083 - accuracy: 0.9983\n",
            "Epoch 21/25\n",
            "600/600 [==============================] - 2s 3ms/step - loss: 0.0071 - accuracy: 0.9987\n",
            "Epoch 22/25\n",
            "600/600 [==============================] - 2s 3ms/step - loss: 0.0061 - accuracy: 0.9991\n",
            "Epoch 23/25\n",
            "600/600 [==============================] - 2s 3ms/step - loss: 0.0074 - accuracy: 0.9982\n",
            "Epoch 24/25\n",
            "600/600 [==============================] - 2s 3ms/step - loss: 0.0058 - accuracy: 0.9987\n",
            "Epoch 25/25\n",
            "600/600 [==============================] - 2s 3ms/step - loss: 0.0043 - accuracy: 0.9991\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1cff5d38",
        "outputId": "df3fda09-7913-440d-e89c-3e7e6615fd2a"
      },
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "y_pred_train = np.argmax(model1.predict(X_train), axis=-1)\n",
        "y_pred_test = np.argmax(model1.predict(X_test), axis=-1)\n",
        "print('Training accuracy:', 100*accuracy_score(y_train, y_pred_train))\n",
        "print('Testing accuracy:', 100*accuracy_score(y_test, y_pred_test))"
      ],
      "id": "1cff5d38",
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training accuracy: 99.93833333333333\n",
            "Testing accuracy: 97.75\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "id": "df489328",
        "outputId": "de3ab5c5-3303-469c-b5fa-500e7f04c64b"
      },
      "source": [
        "plt.figure()\n",
        "plt.plot(history1.history['accuracy'])\n",
        "plt.title('Model Accuracy')\n",
        "plt.xlabel('No. of Epochs')\n",
        "plt.ylabel('Accuracy')"
      ],
      "id": "df489328",
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Accuracy')"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "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": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "id": "bc611639",
        "outputId": "0f0973b4-5798-4292-b45a-f29cd38624f0"
      },
      "source": [
        "plt.figure()\n",
        "plt.plot(history1.history['loss'])\n",
        "plt.title('Model Loss')\n",
        "plt.xlabel('No. of Epochs')\n",
        "plt.ylabel('Loss')"
      ],
      "id": "bc611639",
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Loss')"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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bzApPtoHxMeBTwH8kHdfHAg/nrqzRqWFCOaXF8hmGmRWkrDq9I+IR4BEASUXA9oj4aC4LG42KisQxNZ4Xw8wKU1ZnGJLullSdXC31LLBC0idzW9roNM33YphZgcq2SWpBRLQC7yQ93tMc0ldKFZwmT9VqZgUq28AolVRKOjAWJ/dfRO7KGr0aa1Nsbu2gt68gD9/MCli2gXErsAaoAh5NJjpqzVVRo1ljbQW9fcG2ts6Db2xmNo5kFRgR8bWIaIqISyNtLfCWHNc2KvXfi+FmKTMrNNl2etdI+kr/3BOSvkz6bKPgNPnmPTMrUNk2Sd0BtAHvTh6twDdzVdRoNq3GU7WaWWHK6j4MYG5E/FHG+7+RtDQXBY12E1OlTEyVODDMrOBke4bRLunc/jeSzgEK9i9m+tJa34thZoUl2zOM64BvS6pJ3u8CrspNSaNfY20Fm3YXbF6aWYHK9iqpZRFxKnAKcEpEnA5ckNPKRrHGWg8PYmaF55Bm3IuI1uSOb4AbclDPmNBYW8Gufd3s6ezJdylmZiPmSKZo1VGrYoxZOGsSAN9dsi7PlZiZjZwjCYyCHRvjrDmTOG9ePbc8vIq2jmFnqTUzGzeGDQxJbZJaB3m0AY0jVOOo9Mm3H8/OvV3c/tgr+S7FzGxEDBsYETExIqoHeUyMiINeYSXpYkkrJa2SdOMg68+X9FtJPZKuGLCuV9LS5LH40A8tt06ZXss7Tp7G7Y+tZvsejytlZuPfkTRJDUtSMXALcAmwALhS0oIBm70KXA3cPchHtEfEacnjslzVeSRuuGg+HT193PLwqnyXYmaWczkLDOAsYFVErI6ILuAe4PLMDSJiTUQsB/pyWEfOzG2YwLvOmM7/feJV1u3cl+9yzMxyKpeB0QRkXka0PlmWrVQy0OETkt452AaSru0fEHHbtm1HUuth+/ML54Hg5gdfysv3m5mNlFwGxpGaFRELgfcCN0uaO3CDiLgtIhZGxMKGhoaRr5D0lK1Xnz2bHzy9npWb2/JSg5nZSMhlYGwAZmS8n54sy0pEbEieVwO/BE4/msUdTf/9zXOZUFbCl36+Mt+lmJnlTC4DYwkwT9IcSWXAIiCrq50k1UkqT17XA+cAK3JW6RGqqyrjw28+lgdWbOGptbvyXY6ZWU7kLDAioge4HrgfeB64NyKek3STpMsAJJ0paT3wLuBWSc8lu58INEtaBjwMfCEiRm1gAHzwnDnUTyjnH372AhEFe0+jmY1j2Y5We1gi4j7gvgHLPpPxegnppqqB+z0OnJzL2o62qvISPvrW4/jMD5/jkRe38XvHT8l3SWZmR9Vo7vQecxadOZMZkyr44s9W0tfnswwzG18cGEdRWUkRn3jb8azY1MqPn9mU73LMzI4qB8ZRdtmpjZxwzES+/POVdPeOyfsRzcwG5cA4yoqKxF9cfDxrd+zz8OdmNq44MHLgLcdP4czZdXztoZdo7+rNdzlmZkeFAyMHJPEXF5/A1rZO7nx8Tb7LMTM7KhwYOXLm7Em89YQp/OsvV7F7nydZMrOxz4GRQ//z7cfT1tnDvz36cr5LMTM7Yg6MHDpxWjWXn9rIN3/9CltaO/JdjpnZEXFg5NgNbzuent7gaw95+HMzG9scGDk2c3Il733DTO5Zso5Xtu/NdzlmZofNgTECrr/gOMqKi/jcT1Z4yBAzG7McGCNgysQUn7hoPg8+v5Uv/OyFfJdjZnZYcjparb3mmnPnsG7nPm57dDVTq1Ncc+6cfJdkZnZIHBgjRBKf+W+vY0trJ3/3kxUcU53iHadMy3dZZmZZc5PUCCouEjcvOo0zZtbx8e8u5YnVO/JdkplZ1hwYIyxVWsztVy1kxqQKPvTtZlZubst3SWZmWXFg5EFtZRnf+pOzqCgt5upvPsmm3e35LsnM7KAcGHkyva6SOz94Fm0dPVx9xxJ2t3u8KTMb3XIaGJIulrRS0ipJNw6y/nxJv5XUI+mKAeuukvRS8rgql3Xmy4LGam59/xms3r6HD9/VTGePh0I3s9ErZ4EhqRi4BbgEWABcKWnBgM1eBa4G7h6w7yTgs8AbgLOAz0qqy1Wt+XTOcfX84xWn8sTqnXzi3mW+sc/MRq1cnmGcBayKiNUR0QXcA1yeuUFErImI5cDAuUzfDjwQETsjYhfwAHBxDmvNq3ee3sSNl5zAj5dv4u/vez7f5ZiZDSqX92E0AZlzlK4nfcZwuPs2DdxI0rXAtQAzZ848vCpHiQ+ffyybd3dw+69e4ZiaFH963rH5LsnM7ABjutM7Im6LiIURsbChoSHf5RwRSfzv31/AJScdw9/95Hl+tGxjvksyMztALgNjAzAj4/30ZFmu9x2ziovEP73nNM6cXccn7l3Gb172jX1mNnrkMjCWAPMkzZFUBiwCFme57/3ARZLqks7ui5Jl416qtJhvfGAhMydXcu1dzTy7YXe+SzIzA3IYGBHRA1xP+g/988C9EfGcpJskXQYg6UxJ64F3AbdKei7Zdyfwt6RDZwlwU7KsIPTf2DexvIR33/ob7n9uc75LMjNDEePjMs6FCxdGc3Nzvss4qra2dvChu55i+foW/uLtJ3Ddm49FUr7LMrNxRNJTEbEwm23HdKf3eDelOsV3r30jv39KI//wsxf4xPeW+eY+M8sbD28+yqVKi/naotOYP2UCX37gRdbu2Met7z+D+gnl+S7NzAqMzzDGAEn82Vvn8fU/fj3PbdzN5f/ya57f1JrvssyswDgwxpBLT57G9z58Nj19fVzxr4/z4Iot+S7JzAqIA2OMOXl6DYuvP5e5UybwobuaufWRlxkvFy6Y2ejmwBiDplan+O61b+LSk6fx+Z++wCe/v9yd4WaWc+70HqMqyor5lytP57iGCXz1oZdYu2Mv//a+M5jsznAzyxGfYYxhkvj42+bztStPZ/n63Vx+y6895auZ5YwDYxy47NRGvvvhN9HV08cffP3X3Pzgi7R1eAY/Mzu6HBjjxGkzavnh9edw3rx6bn7wJc7/4sN849HVdHS7b8PMjg4PDTIOLV/fwpd+/iKPvriNqdXlXH/BPN6zcAZlJf73gZkd6FCGBnFgjGNPrN7Bl+5fSfPaXcyYVMHHL5zP5ac1UVzk8ajMLM1jSRkAbzx2Mt+77k1884NnUp0q5YZ7l3HxzY/ys2c3+d4NMztkDoxxThJvOX4KP7r+XL7+x6+nL4LrvvNbLvuXX/PIi9scHGaWNQdGgSgqEpeePI37P3Y+X3rXqeza18VVdzzJe259gsdf3u7gMLODch9Ggerq6eO7S17la79Yxba2Tk44ZiJXnT2by09rpLLM93OaFQp3elvWOrp7Wbx0I3c+voYVm1qpTpXwnjNn8P43zmbm5Mp8l2dmOebAsEMWETSv3cW3Hl/Dz57dTG8EFxw/hQ+cPZvzjqunyFdWmY1LhxIYbnswIN05fubsSZw5exKbd3dw95Ovcvd/vcpVdzzJsfVVvP9Ns7jijOlMTJXmu1QzyxOfYdiQOnt6+dmzm7nz8TU8/WoLVWXF/NEZ0/nAm2Zx3JSJ+S7PzI6CUdMkJeli4KtAMXB7RHxhwPpy4NvAGcAO4D0RsUbSbOB5YGWy6RMRcd1w3+XAyK3l61v41uNr+dHyjXT19HHGrDouO7WRS0+eRsNEj5BrNlaNisCQVAy8CLwNWA8sAa6MiBUZ2/wP4JSIuE7SIuAPIuI9SWD8OCJOyvb7HBgjY8eeTu5tXs8Pl27ghc1tFAnOnlvPZac28vaTjqGmwk1WZmPJaAmMNwF/HRFvT95/CiAiPp+xzf3JNr+RVAJsBhqAWTgwRr0Xt7Txo2UbWbxsI2t37KOsuIg3H9/Afzu1kQtPnOLLc83GgNHS6d0ErMt4vx54w1DbRESPpN3A5GTdHElPA63ApyPisYFfIOla4FqAmTNnHt3q7aDmT53IJy46nhveNp/l63ezeNlGfrx8Iw+s2EJlWTEXnjiVy05t5Pz5DR740GwcGK3/BNwEzIyIHZLOAP5T0usiojVzo4i4DbgN0mcYeajTSF9hdeqMWk6dUctfXXoiT67ZyeJlG/npM5tYvGwj1akSLj7pGC5acAznHFdPRVlxvks2s8OQy8DYAMzIeD89WTbYNuuTJqkaYEek28k6ASLiKUkvA/MBtzmNckVF4o3HTuaNx07mby57Hb9atZ0fLd3Ifc9s5t7m9ZSXFHHucfVccOIU3nrCVI6pSeW7ZDPLUi4DYwkwT9Ic0sGwCHjvgG0WA1cBvwGuAH4RESGpAdgZEb2SjgXmAatzWKvlQGlxEW85fgpvOX4KXT19PPnKTh58fgsPvbCFh17Yyl/xLCc1VfPWE6Zy4YlTOampGsk3CJqNVrm+rPZS4GbSl9XeERGfk3QT0BwRiyWlgLuA04GdwKKIWC3pj4CbgG6gD/hsRPxouO9yp/fYERG8tHVPOjye38pvX91FBEytLueCE6Zy4YlTOOe4elKlbroyy7VRcZXUSHNgjF079nTyy5XbeOiFLTz64nb2dPaQKi3i7Ln1nDevnvPmNTC3ocpnH2Y54MCwMaurp4//emUHDz2/lUde3MYr2/cC0FiT4rx5DZw3v55z5tZTV1WW50rNxofRclmt2SErKylKB8O8BgDW7dzHYy9t57GXtvHTZzfx3eZ1SHByU83+s4/Xz6zzZbtmI8BnGDZm9PYFy9e37A+Q377aQm9fUFlWzBuPncx58+o5a84kjp86kZJiB4hZNtwkZQWhraObJ1bv5LGXtvHYS9v3N1+lSos4qbGG05J7Q06bUcv0ugr3gZgNwoFhBWndzn08va6FZetaWLquhWc37Kazpw+AyVVl+8Pj1Bm1nDq9htpK94OYuQ/DCtKMSZXMmFTJZac2AtDd28fKzW0szQiRh1dupf/fSHPqqzi5qYa5DROYXV/JnPoqZk2u8gCKZkPwGYYVlLaObp7ZsHt/iDy7oZUNLe0HbDOpqoxZkyuZMzkdILPrK5k9uYrZ9Q4TG398hmE2hImpUs6eW8/Zc+v3L+vo7uXVnft4Zfte1u7Yyyvb97F2x16eWL2DHzx94Gg2dZWlHDdlAic31XLqjBpOmV7L7MmV7h+xguDAsIKXKi1m/tSJzJ/6u7MI9ofJmu17WbNjL2t27GPl5jbufnItd/w63T9SU1HKKdNrkkctp06v9RhZNi45MMyGMVSY9PT28eKWPSxf38Ky9btZvr6FWx9ZTU9fuol3ysTy/Z3r6bOQKiZPKKOyrNhnIzZmOTDMDkNJcRELGqtZ0FjNorPSyzq6e3luYyvL17ewfP1ulq1v4YEVWw7YL1VaxOSqciZPKGNyVRmTJ5Qnz2UZy8upn1hGw4Ry309io4oDw+woSZUWc8asOs6YVbd/WWtHN8+u382GlnZ27O1ix57O5LmLbXs6Wbm5je17u+hKLv/NVFwkjqlOMb2ugqa6CqbXVjC9rpKmugqaaiuYVpuivMQDNNrIcWCY5VB1qpSzj6sfdpuIYE9nDzv2dO0PlW17OtnY0s6GXe1saGnnNy/vYEtrB30ZFzVK6aavptoKmuoqaaxNcUx1+jGlOsUxNSmmTCyn1GcpdpQ4MMzyTBITU6VMTJUyu75qyO26e/vYvLuDdbv27Q+S9bvSobJsXQs/e7ad7t4DL5OX0jctTq1O7X8cU51ianU5U2tSNEwop2FiOZOqyhwsdlAODLMxorS4aP/NiYPp6wt27etic2sHW1s72dzawZb9j0427+5g2boWduztGnT/uspS6ieUpx8Ty6mfUEb9hHIaJqT7VPrXTUiVUFVWQnGRO+8LjQPDbJwoKlK6E31COa9rHHq7rp4+tralg2RbWyfb9nSxva2T7Xv6H10sX9/C9rZO9nb1Dvk5FaXFVJWXMKE8/VxVVkJVef+ykvSy8hImlpdQU1lKbUUpdVVl1FaUJu/LPMrwGOPAMCswZSVFTK+rZHrd4Gcqmdq7eg8Iku17Otnb2cOezp7kuZe9+1/3sH1PF2t37Nu/frjAAagqK6a2soyailJqK/sfZUwoL6G8pIhUaTHlJUWUlxaTGuQ5VVpMeWkRqZJ0UE1Mpffzpcu54cAwsyFVlBUP2wx2MH19wZ6uHnbv62Z3ezct+7rZta+LlvZudu/romVfN4RodxEAAAjWSURBVC3t3bQkr1/csoeWfV3s7eylo6eXwxm5qLQ43Sc0IQmQ/ucDlqXSZz6VZSVUlhVTUZYOnP6zpsqy4uQxeNNbb1/Q1tFNW0cPu9u7aU1et7Z309rRQ1tHN63tPezp7KayrIS6yjLqqtJhOKmyjNrKUiZVlVFXWUZF2di50s2BYWY5U1QkqlOlVKdKmXGI+0YE3b1BR08vHd29dHb30dnTS0fGc0d3L509fbR39bK3q4e2jvRjT2f6D/ie5P2Glg72dLbtX9/bl30SlZcU7Q+Pvojk83sOul+6Wa6YfV29tHUMvX2qtIi6yrJ0mCShUlOR/m9WU/G7j+qKEmoq0hdJjHQ/Uk4DQ9LFwFeBYuD2iPjCgPXlwLeBM4AdwHsiYk2y7lPANUAv8NGIuD+XtZrZ6CKJshJRVlJEderoDfoYEXR099HW2U17Vy/7unrZ19XDvq5e9nb20t6dfr2vc8C6rh6KlARgRQnVqVImpkqoTv64T0yV7P9DPyF14JlJd29f+mxqXxc793axq/91cma1c28XLfvSy5/f1Epre/qMbOBVbwf+90mHUk1FKa+fWcfXrjz9qP03GkrOAkNSMXAL8DZgPbBE0uKIWJGx2TXArog4TtIi4B+A90haACwCXgc0Ag9Kmh8RwzeImpkdhCQqkmaokVJaXETDxPQlzNmKCNq7e9NNXu3ppq+Bj9bkMa12ZMYuy+UZxlnAqohYDSDpHuByIDMwLgf+Onn9feBflO6tuhy4JyI6gVckrUo+7zc5rNfMbNSQlPSxlDCtJt/VpOXymrYmYF3G+/XJskG3iYgeYDcwOct9kXStpGZJzdu2bTuKpZuZ2UBj+iLoiLgtIhZGxMKGhoZ8l2NmNq7lMjA2wAEXRkxPlg26jaQSoIZ053c2+5qZ2QjKZWAsAeZJmiOpjHQn9uIB2ywGrkpeXwH8ItJzxi4GFkkqlzQHmAc8mcNazczsIHLW6R0RPZKuB+4nfVntHRHxnKSbgOaIWAz8H+CupFN7J+lQIdnuXtId5D3AR3yFlJlZfikO51bKUWjhwoXR3Nyc7zLMzMYUSU9FxMJsth3Tnd5mZjZyHBhmZpaVcdMkJWkbsPYIPqIe2H6UyhlrfOyFq5CPv5CPHV47/lkRkdV9CeMmMI6UpOZs2/HGGx97YR47FPbxF/Kxw+Edv5ukzMwsKw4MMzPLigPjNbflu4A88rEXrkI+/kI+djiM43cfhpmZZcVnGGZmlhUHhpmZZaXgA0PSxZJWSlol6cZ81zPSJK2R9IykpZLG9dgqku6QtFXSsxnLJkl6QNJLyXNdPmvMpSGO/68lbUh+/6WSLs1njbkiaYakhyWtkPScpD9Plo/733+YYz/k376g+zCSaWRfJGMaWeDKAdPIjmuS1gALI2Lc38Ak6XxgD/DtiDgpWfZFYGdEfCH5B0NdRPxlPuvMlSGO/6+BPRHxpXzWlmuSpgHTIuK3kiYCTwHvBK5mnP/+wxz7uznE377QzzD2TyMbEV1A/zSyNg5FxKOkR0XOdDnwreT1t0j/jzQuDXH8BSEiNkXEb5PXbcDzpGfxHPe//zDHfsgKPTCymgp2nAvg55KeknRtvovJg6kRsSl5vRmYms9i8uR6ScuTJqtx1yQzkKTZwOnAf1Fgv/+AY4dD/O0LPTAMzo2I1wOXAB9Jmi0KUjJ5V6G10f4rMBc4DdgEfDm/5eSWpAnAvwMfi4jWzHXj/fcf5NgP+bcv9MAo+KlgI2JD8rwV+A/SzXSFZEvSxtvf1rs1z/WMqIjYEhG9EdEHfINx/PtLKiX9B/P/RsQPksUF8fsPduyH89sXemBkM43suCWpKukEQ1IVcBHw7PB7jTuZ0wRfBfwwj7WMuP4/lok/YJz+/pJEeobP5yPiKxmrxv3vP9SxH85vX9BXSQEkl5LdzGvTyH4uzyWNGEnHkj6rgPR0vXeP5+OX9P+A3yM9rPMW4LPAfwL3AjNJD4//7ogYlx3DQxz/75FukghgDfDhjDb9cUPSucBjwDNAX7L4f5Fuyx/Xv/8wx34lh/jbF3xgmJlZdgq9ScrMzLLkwDAzs6w4MMzMLCsODDMzy4oDw8zMsuLAsHFPUkj6csb7/5kMune0v+f/JcMsfHzA8oGjgi6VVHsUv/dOSVccrc8zG0pJvgswGwGdwB9K+nyuRuWVdAxwZkQcN8Qm/zTeR4S18c9nGFYIekjPX/zxgSskzZb0i+TM4CFJM4f7IEkpSd9M5hB5WtJbklU/B5qSs4fzsilK0tWSfijpl8l8DJ/NWHeDpGeTx8cyln8gqXWZpLsyPu58SY9LWt1/tiFpmqRHk5qezbYus6H4DMMKxS3A8mT+i0z/DHwrIr4l6U+ArzH8ENcfIT1O3cmSTiA90u984DLgxxFx2hD7fVzS+5LXuyKiP2jOAk4C9gFLJP2E9J23HwTeAAj4L0mPAF3Ap4GzI2K7pEkZnz8NOBc4gfRwF98H3gvcHxGfS+Z+qRzmuMwOyoFhBSEiWiV9G/go0J6x6k3AHyav7wIGBspA55IOGSLiBUlrgflA67B7Dd0k9UBE7ACQ9IPk8wP4j4jYm7H8vGT59/qb1QYMYfGfySByKyT1D9G9BLgjGXjuPyNi6UFqNBuWm6SskNwMXANU5buQDAPH5jncsXo6M14L9k+YdD7pEZjvlPSBw/xsM8CBYQUk+Rf5vaRDo9/jpEcpBvhj0oO0DeexZDuSpqiZwMojKOttSs8rXUG6KezXyXe8U1JlMorwHyTLfgG8S9Lk5PsnDfWhyfpZwJaI+AZwO/D6I6jTzE1SVnC+DFyf8f7PgG9K+iSwjXTfAZKuA4iIfxuw/9eBf5X0DOnO9KsjojM9gvSwMvsw4LV+kidJz1MwHfhORDQn339nsg7g9oh4Oln+OeARSb3A06TnpB7K7wGflNRNei5vn2HYEfFotWZ5IulqYGFEXH+wbc1GAzdJmZlZVnyGYWZmWfEZhpmZZcWBYWZmWXFgmJlZVhwYZmaWFQeGmZll5f8DnQEjDdL1JVgAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J1vubCFSoXBF"
      },
      "source": [
        "## Question - 2\n",
        "##### Implement a deep neural network that has 10 hidden layers and each layer has 10 neurons. Now compare the performance of this network with the previous net.  "
      ],
      "id": "J1vubCFSoXBF"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "abmNMaOvoBSX"
      },
      "source": [
        "model2 = Sequential()\n",
        "model2.add(Flatten(input_shape=X_train[0].shape))\n",
        "model2.add(Dense(10, activation='relu'))\n",
        "model2.add(Dense(10, activation='relu'))\n",
        "model2.add(Dense(10, activation='relu'))\n",
        "model2.add(Dense(10, activation='relu'))\n",
        "model2.add(Dense(10, activation='relu'))\n",
        "model2.add(Dense(10, activation='relu'))\n",
        "model2.add(Dense(10, activation='relu'))\n",
        "model2.add(Dense(10, activation='relu'))\n",
        "model2.add(Dense(10, activation='relu'))\n",
        "model2.add(Dense(10, activation='relu'))\n",
        "model2.add(Dense(10, activation='softmax'))"
      ],
      "id": "abmNMaOvoBSX",
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1e4PEx67ongd",
        "outputId": "143a0abd-f81f-42ff-b43c-65011fc26762"
      },
      "source": [
        "model2.summary()"
      ],
      "id": "1e4PEx67ongd",
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "flatten_1 (Flatten)          (None, 784)               0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 10)                7850      \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 10)                110       \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 10)                110       \n",
            "_________________________________________________________________\n",
            "dense_5 (Dense)              (None, 10)                110       \n",
            "_________________________________________________________________\n",
            "dense_6 (Dense)              (None, 10)                110       \n",
            "_________________________________________________________________\n",
            "dense_7 (Dense)              (None, 10)                110       \n",
            "_________________________________________________________________\n",
            "dense_8 (Dense)              (None, 10)                110       \n",
            "_________________________________________________________________\n",
            "dense_9 (Dense)              (None, 10)                110       \n",
            "_________________________________________________________________\n",
            "dense_10 (Dense)             (None, 10)                110       \n",
            "_________________________________________________________________\n",
            "dense_11 (Dense)             (None, 10)                110       \n",
            "_________________________________________________________________\n",
            "dense_12 (Dense)             (None, 10)                110       \n",
            "=================================================================\n",
            "Total params: 8,950\n",
            "Trainable params: 8,950\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KuE3z8kxopDH"
      },
      "source": [
        "model2.compile(optimizer='adam', metrics=['accuracy'], loss='sparse_categorical_crossentropy')"
      ],
      "id": "KuE3z8kxopDH",
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DNJNdH4CoutZ",
        "outputId": "504cdc04-17d4-40f1-d37a-08eb3354057c"
      },
      "source": [
        "history2 = model2.fit(X_train, y_train, batch_size=100, epochs=25)"
      ],
      "id": "DNJNdH4CoutZ",
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/25\n",
            "600/600 [==============================] - 2s 3ms/step - loss: 1.2690 - accuracy: 0.5607\n",
            "Epoch 2/25\n",
            "600/600 [==============================] - 2s 3ms/step - loss: 0.5926 - accuracy: 0.8299\n",
            "Epoch 3/25\n",
            "600/600 [==============================] - 2s 3ms/step - loss: 0.4712 - accuracy: 0.8675\n",
            "Epoch 4/25\n",
            "600/600 [==============================] - 2s 3ms/step - loss: 0.4281 - accuracy: 0.8787\n",
            "Epoch 5/25\n",
            "600/600 [==============================] - 2s 3ms/step - loss: 0.4006 - accuracy: 0.8873\n",
            "Epoch 6/25\n",
            "600/600 [==============================] - 2s 3ms/step - loss: 0.3780 - accuracy: 0.8936\n",
            "Epoch 7/25\n",
            "600/600 [==============================] - 2s 3ms/step - loss: 0.3629 - accuracy: 0.8978\n",
            "Epoch 8/25\n",
            "600/600 [==============================] - 2s 3ms/step - loss: 0.3486 - accuracy: 0.9015\n",
            "Epoch 9/25\n",
            "600/600 [==============================] - 2s 3ms/step - loss: 0.3332 - accuracy: 0.9063\n",
            "Epoch 10/25\n",
            "600/600 [==============================] - 2s 3ms/step - loss: 0.3230 - accuracy: 0.9093\n",
            "Epoch 11/25\n",
            "600/600 [==============================] - 2s 3ms/step - loss: 0.3151 - accuracy: 0.9111\n",
            "Epoch 12/25\n",
            "600/600 [==============================] - 2s 3ms/step - loss: 0.3068 - accuracy: 0.9138\n",
            "Epoch 13/25\n",
            "600/600 [==============================] - 2s 3ms/step - loss: 0.2985 - accuracy: 0.9150\n",
            "Epoch 14/25\n",
            "600/600 [==============================] - 2s 3ms/step - loss: 0.2909 - accuracy: 0.9180\n",
            "Epoch 15/25\n",
            "600/600 [==============================] - 2s 3ms/step - loss: 0.2834 - accuracy: 0.9209\n",
            "Epoch 16/25\n",
            "600/600 [==============================] - 2s 3ms/step - loss: 0.2803 - accuracy: 0.9213\n",
            "Epoch 17/25\n",
            "600/600 [==============================] - 2s 3ms/step - loss: 0.2725 - accuracy: 0.9236\n",
            "Epoch 18/25\n",
            "600/600 [==============================] - 2s 3ms/step - loss: 0.2680 - accuracy: 0.9240\n",
            "Epoch 19/25\n",
            "600/600 [==============================] - 2s 3ms/step - loss: 0.2663 - accuracy: 0.9242\n",
            "Epoch 20/25\n",
            "600/600 [==============================] - 2s 3ms/step - loss: 0.2600 - accuracy: 0.9264\n",
            "Epoch 21/25\n",
            "600/600 [==============================] - 2s 3ms/step - loss: 0.2549 - accuracy: 0.9276\n",
            "Epoch 22/25\n",
            "600/600 [==============================] - 2s 3ms/step - loss: 0.2531 - accuracy: 0.9281\n",
            "Epoch 23/25\n",
            "600/600 [==============================] - 2s 3ms/step - loss: 0.2488 - accuracy: 0.9298\n",
            "Epoch 24/25\n",
            "600/600 [==============================] - 2s 3ms/step - loss: 0.2473 - accuracy: 0.9298\n",
            "Epoch 25/25\n",
            "600/600 [==============================] - 2s 3ms/step - loss: 0.2431 - accuracy: 0.9308\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZqAqRcwko9Ss",
        "outputId": "67a60893-c863-40ca-f4a9-74f9a762acca"
      },
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "y_pred_train = np.argmax(model2.predict(X_train), axis=-1)\n",
        "y_pred_test = np.argmax(model2.predict(X_test), axis=-1)\n",
        "print('Training accuracy:', 100*accuracy_score(y_train, y_pred_train))\n",
        "print('Testing accuracy:', 100*accuracy_score(y_test, y_pred_test))"
      ],
      "id": "ZqAqRcwko9Ss",
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training accuracy: 93.45833333333333\n",
            "Testing accuracy: 92.36999999999999\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "id": "O-Yt37LrpydH",
        "outputId": "8273f4d1-37ab-442a-94ca-146f65b05c2c"
      },
      "source": [
        "plt.figure()\n",
        "plt.plot(history2.history['accuracy'])\n",
        "plt.title('Model Accuracy')\n",
        "plt.xlabel('No. of Epochs')\n",
        "plt.ylabel('Accuracy')"
      ],
      "id": "O-Yt37LrpydH",
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Accuracy')"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "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": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "id": "M6KSc8v9pzXA",
        "outputId": "e05af7c2-fe9f-4df1-ca52-f971b2fc7fcd"
      },
      "source": [
        "plt.figure()\n",
        "plt.plot(history2.history['loss'])\n",
        "plt.title('Model loss')\n",
        "plt.xlabel('No. of Epochs')\n",
        "plt.ylabel('Loss')"
      ],
      "id": "M6KSc8v9pzXA",
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Loss')"
            ]
          },
          "metadata": {},
          "execution_count": 23
        },
        {
          "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": "lkctQzIcLqxV"
      },
      "source": [
        "## Question - 3\n",
        "##### Write another program which will assess the quality of the features generated by the 10 layer deep net (in item 2) in its different layers. For this, train the deep net first. Then generate the features from the hidden layer 5, 7 and 10 separately and use it in k-nearest neighbour to classify the images and compare the performances of the features generated from layers 5, 7 and 10. The features from a layer mean the activation values of the neurons in that particular layer. "
      ],
      "id": "lkctQzIcLqxV"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U1vZskjjSOaS"
      },
      "source": [
        "from keras import models\n",
        "# Extracts the outputs of the top 5th, 7th and 10th hidden layers\n",
        "layer_outputs = [layer.output for layer in model2.layers] \n",
        "# Creates a model that will return these outputs, given the model input\n",
        "activation_model = models.Model(inputs=model2.input, outputs=layer_outputs) "
      ],
      "id": "U1vZskjjSOaS",
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4nfma1OcaWUa"
      },
      "source": [
        "activations_train = activation_model.predict(X_train) \n",
        "activations_test = activation_model.predict(X_test) "
      ],
      "id": "4nfma1OcaWUa",
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WHxXRZIvaxQ5"
      },
      "source": [
        "n_neighbors = 7\n",
        "from sklearn.neighbors import KNeighborsClassifier"
      ],
      "id": "WHxXRZIvaxQ5",
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b-fsHiW-c0fL"
      },
      "source": [
        "##### Using 5th hidden layer"
      ],
      "id": "b-fsHiW-c0fL"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ok9FsXxaa0el",
        "outputId": "cbcd2a7e-7ec9-491b-d55a-c6a760804677"
      },
      "source": [
        "knn5 = KNeighborsClassifier(n_neighbors=n_neighbors)\n",
        "knn5.fit(activations_train[6], y_train)\n",
        "knn5_train_pred = knn5.predict(activations_train[6])\n",
        "knn5_test_pred = knn5.predict(activations_test[6])\n",
        "print('Training accuracy from 5th hideden layer:', 100*accuracy_score(y_train, knn5_train_pred))\n",
        "print('Testing accuracy from 5th hidden layer:',100*accuracy_score(y_test, knn5_test_pred) )"
      ],
      "id": "Ok9FsXxaa0el",
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training accuracy from 5th hideden layer: 94.39666666666666\n",
            "Testing accuracy from 5th hidden layer: 92.55\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pZdhJ3apeAuO"
      },
      "source": [
        "##### Using 7th hidden layer"
      ],
      "id": "pZdhJ3apeAuO"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GI6AB3D6eAuP",
        "outputId": "a35d1442-e416-441d-c777-089184d7a136"
      },
      "source": [
        "knn7 = KNeighborsClassifier(n_neighbors=n_neighbors)\n",
        "knn7.fit(activations_train[8], y_train)\n",
        "knn7_train_pred = knn7.predict(activations_train[8])\n",
        "knn7_test_pred = knn7.predict(activations_test[8])\n",
        "print('Training accuracy from 7th hidden layer:', 100*accuracy_score(y_train, knn7_train_pred))\n",
        "print('Testing accuracy from 7th hidden layer:',100*accuracy_score(y_test, knn7_test_pred) )"
      ],
      "id": "GI6AB3D6eAuP",
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training accuracy from 7th hidden layer: 94.38666666666666\n",
            "Testing accuracy from 7th hidden layer: 92.42\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fPPbcUH5eD6I"
      },
      "source": [
        "##### Using 10th hidden layer"
      ],
      "id": "fPPbcUH5eD6I"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wsENxo_XeD6J",
        "outputId": "c339d7bf-acf9-4dc4-f7b1-9d1825c6ba54"
      },
      "source": [
        "knn10 = KNeighborsClassifier(n_neighbors=n_neighbors)\n",
        "knn10.fit(activations_train[11], y_train)\n",
        "knn10_train_pred = knn10.predict(activations_train[11])\n",
        "knn10_test_pred = knn10.predict(activations_test[11])\n",
        "print('Training accuracy from 10th layer:', 100*accuracy_score(y_train, knn10_train_pred))\n",
        "print('Testing accuracy from 10th layer:',100*accuracy_score(y_test, knn10_test_pred) )"
      ],
      "id": "wsENxo_XeD6J",
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training accuracy from 10th layer: 94.195\n",
            "Testing accuracy from 10th layer: 92.31\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t9yKAajEej1G"
      },
      "source": [
        ""
      ],
      "id": "t9yKAajEej1G",
      "execution_count": 29,
      "outputs": []
    }
  ]
}