--- a
+++ b/ML_HW_4.ipynb
@@ -0,0 +1,577 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "authorship_tag": "ABX9TyMZW0BiR/e6UoSMS3Ddcvdx",
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/E-Ameke/ML-Class-Project/blob/main/ML_HW_4.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "ML_HOMEWORK_4<BR>\n",
+        "ELIZABETH AMEKE<BR>\n",
+        "662055975"
+      ],
+      "metadata": {
+        "id": "FbTFbAQIILNj"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Question_1<br>\n",
+        "Construct a fully connected neural network model for classifying the CIFAR-10\n",
+        "dataset. Use a 70%-30% split for training and validation data.<br>\n",
+        "(a) Visualize the data by plotting an image from each category from the\n",
+        "CIFAR-10 dataset."
+      ],
+      "metadata": {
+        "id": "GRPZQV1xWLSA"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "##(a)Answer:\n",
+        "\n",
+        "import tensorflow as tf\n",
+        "from tensorflow import keras\n",
+        "import matplotlib.pyplot as plt\n",
+        "from keras.datasets import cifar10\n",
+        "import numpy as np\n",
+        "\n",
+        "(X, y), _ = cifar10.load_data()\n",
+        "\n",
+        "# Visualizing the data\n",
+        "my_arr = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])\n",
+        "my_index = np.ones(10)\n",
+        "for i in my_arr:\n",
+        "    my_index[i] = np.where(y == i)[0][0]\n",
+        "\n",
+        "class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer',\n",
+        "               'dog', 'frog', 'horse', 'ship', 'truck']\n",
+        "\n",
+        "# Plotting the images\n",
+        "plt.figure(figsize=(10, 5))\n",
+        "for m, n in enumerate(my_index):\n",
+        "    plt.subplot(2, 5, m + 1)  # 2 rows, 5 columns\n",
+        "    plt.xticks([])\n",
+        "    plt.yticks([])\n",
+        "    plt.grid(False)\n",
+        "    plt.imshow(X[int(n)])\n",
+        "    plt.xlabel(class_names[y[int(n)][0]])\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 470
+        },
+        "id": "x9sINREULFi3",
+        "outputId": "45c08864-ce78-458d-e7fb-9ddf498464a8"
+      },
+      "execution_count": 19,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x500 with 10 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "."
+      ],
+      "metadata": {
+        "id": "Pt2dbC2pWmwr"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "(b) Demonstrate the tuning of the hyperparameters of the neural network\n",
+        "via grid-search."
+      ],
+      "metadata": {
+        "id": "O-3lWU7OWonh"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "##(b)Answer:\n",
+        "\n",
+        "from keras.datasets import cifar10\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "import tensorflow as tf\n",
+        "from tensorflow import keras\n",
+        "from tensorflow.keras.models import Sequential\n",
+        "from tensorflow.keras.layers import Dense , Flatten\n",
+        "from tensorflow.keras.utils import to_categorical\n",
+        "import matplotlib.pyplot as plt\n",
+        "import numpy as np\n",
+        "(train_images, train_labels), (test_images, test_labels) = cifar10.load_data()"
+      ],
+      "metadata": {
+        "id": "PrHs7KilG41q"
+      },
+      "execution_count": 36,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Creating the 70%- 30% split by combining the data and splitting it\n",
+        "print(train_images.shape)\n",
+        "print(test_images.shape)\n",
+        "my_data= np.vstack((train_images,test_images))\n",
+        "my_labels=np.vstack((train_labels,test_labels))\n",
+        "print(my_data.shape)\n",
+        "print(my_labels.shape)\n",
+        "\n",
+        "#The 70-30 split\n",
+        "X_train, X_test, y_train, y_test = train_test_split( my_data, my_labels, test_size=0.3, random_state=42)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "uLWHgUyXG4no",
+        "outputId": "5af99bba-17f8-48b7-d6d2-9e269b3fb938"
+      },
+      "execution_count": 37,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(50000, 32, 32, 3)\n",
+            "(10000, 32, 32, 3)\n",
+            "(60000, 32, 32, 3)\n",
+            "(60000, 1)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Transforming the label indices to one-hot encoded vectors\n",
+        "y_train = to_categorical(y_train, num_classes=10)\n",
+        "y_test = to_categorical(y_test, num_classes=10)\n",
+        "\n",
+        "#Reshaping the data and normalize\n",
+        "X_train = X_train.reshape(-1, 32*32*3)\n",
+        "X_test = X_test.reshape(-1, 32*32*3)\n",
+        "\n",
+        "X_train = X_train.astype('float32') / 255.0\n",
+        "X_test = X_test.astype('float32') / 255.0\n",
+        "print(X_train.shape)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "LPRNLeM-S-KN",
+        "outputId": "aee61547-e2e6-4904-a85b-991e3c9d2547"
+      },
+      "execution_count": 38,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(42000, 3072)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from keras.backend import categorical_crossentropy\n",
+        "model = Sequential()\n",
+        "model.add(Dense(512, activation='relu', input_shape=(32*32*3,)))\n",
+        "model.add(Dense(512, activation='relu'))\n",
+        "model.add(Dense(256, activation='relu'))\n",
+        "model.add(Dense(128, activation='relu'))\n",
+        "model.add(Dense(10, activation='softmax'))\n",
+        "opt=keras.optimizers.Adam(learning_rate=0.0001)\n",
+        "model.compile(optimizer=opt,\n",
+        "loss=categorical_crossentropy,\n",
+        "metrics=['accuracy'])\n",
+        "model.summary()\n",
+        "history = model.fit(X_train, y_train, batch_size= 10, epochs=50,validation_data=(X_test,y_test))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "tL1256k1S-DT",
+        "outputId": "34268ed0-e03b-4468-821b-06cd67a92add"
+      },
+      "execution_count": 39,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Model: \"sequential_3\"\n",
+            "_________________________________________________________________\n",
+            " Layer (type)                Output Shape              Param #   \n",
+            "=================================================================\n",
+            " dense_12 (Dense)            (None, 512)               1573376   \n",
+            "                                                                 \n",
+            " dense_13 (Dense)            (None, 512)               262656    \n",
+            "                                                                 \n",
+            " dense_14 (Dense)            (None, 256)               131328    \n",
+            "                                                                 \n",
+            " dense_15 (Dense)            (None, 128)               32896     \n",
+            "                                                                 \n",
+            " dense_16 (Dense)            (None, 10)                1290      \n",
+            "                                                                 \n",
+            "=================================================================\n",
+            "Total params: 2001546 (7.64 MB)\n",
+            "Trainable params: 2001546 (7.64 MB)\n",
+            "Non-trainable params: 0 (0.00 Byte)\n",
+            "_________________________________________________________________\n",
+            "Epoch 1/50\n",
+            "4200/4200 [==============================] - 132s 31ms/step - loss: 1.8391 - accuracy: 0.3343 - val_loss: 1.6907 - val_accuracy: 0.3902\n",
+            "Epoch 2/50\n",
+            "4200/4200 [==============================] - 125s 30ms/step - loss: 1.6437 - accuracy: 0.4123 - val_loss: 1.6064 - val_accuracy: 0.4237\n",
+            "Epoch 3/50\n",
+            "4200/4200 [==============================] - 130s 31ms/step - loss: 1.5527 - accuracy: 0.4442 - val_loss: 1.5279 - val_accuracy: 0.4546\n",
+            "Epoch 4/50\n",
+            "4200/4200 [==============================] - 130s 31ms/step - loss: 1.4878 - accuracy: 0.4670 - val_loss: 1.4781 - val_accuracy: 0.4734\n",
+            "Epoch 5/50\n",
+            "4200/4200 [==============================] - 133s 32ms/step - loss: 1.4350 - accuracy: 0.4878 - val_loss: 1.4567 - val_accuracy: 0.4792\n",
+            "Epoch 6/50\n",
+            "4200/4200 [==============================] - 141s 34ms/step - loss: 1.3906 - accuracy: 0.5052 - val_loss: 1.4483 - val_accuracy: 0.4877\n",
+            "Epoch 7/50\n",
+            "4200/4200 [==============================] - 150s 36ms/step - loss: 1.3469 - accuracy: 0.5188 - val_loss: 1.4336 - val_accuracy: 0.4914\n",
+            "Epoch 8/50\n",
+            "4200/4200 [==============================] - 164s 39ms/step - loss: 1.3067 - accuracy: 0.5363 - val_loss: 1.4019 - val_accuracy: 0.5023\n",
+            "Epoch 9/50\n",
+            "4200/4200 [==============================] - 145s 35ms/step - loss: 1.2654 - accuracy: 0.5486 - val_loss: 1.4168 - val_accuracy: 0.5061\n",
+            "Epoch 10/50\n",
+            "4200/4200 [==============================] - 129s 31ms/step - loss: 1.2230 - accuracy: 0.5653 - val_loss: 1.3945 - val_accuracy: 0.5116\n",
+            "Epoch 11/50\n",
+            "4200/4200 [==============================] - 125s 30ms/step - loss: 1.1876 - accuracy: 0.5791 - val_loss: 1.4199 - val_accuracy: 0.5016\n",
+            "Epoch 12/50\n",
+            "4200/4200 [==============================] - 133s 32ms/step - loss: 1.1494 - accuracy: 0.5926 - val_loss: 1.4239 - val_accuracy: 0.5043\n",
+            "Epoch 13/50\n",
+            "4200/4200 [==============================] - 128s 30ms/step - loss: 1.1110 - accuracy: 0.6044 - val_loss: 1.3589 - val_accuracy: 0.5264\n",
+            "Epoch 14/50\n",
+            "4200/4200 [==============================] - 126s 30ms/step - loss: 1.0748 - accuracy: 0.6185 - val_loss: 1.4119 - val_accuracy: 0.5201\n",
+            "Epoch 15/50\n",
+            "4200/4200 [==============================] - 119s 28ms/step - loss: 1.0357 - accuracy: 0.6286 - val_loss: 1.3748 - val_accuracy: 0.5303\n",
+            "Epoch 16/50\n",
+            "4200/4200 [==============================] - 124s 29ms/step - loss: 0.9993 - accuracy: 0.6440 - val_loss: 1.4434 - val_accuracy: 0.5171\n",
+            "Epoch 17/50\n",
+            "4200/4200 [==============================] - 134s 32ms/step - loss: 0.9584 - accuracy: 0.6580 - val_loss: 1.4510 - val_accuracy: 0.5211\n",
+            "Epoch 18/50\n",
+            "4200/4200 [==============================] - 134s 32ms/step - loss: 0.9196 - accuracy: 0.6724 - val_loss: 1.4734 - val_accuracy: 0.5210\n",
+            "Epoch 19/50\n",
+            "4200/4200 [==============================] - 136s 32ms/step - loss: 0.8776 - accuracy: 0.6882 - val_loss: 1.4634 - val_accuracy: 0.5218\n",
+            "Epoch 20/50\n",
+            "4200/4200 [==============================] - 138s 33ms/step - loss: 0.8411 - accuracy: 0.7024 - val_loss: 1.4860 - val_accuracy: 0.5271\n",
+            "Epoch 21/50\n",
+            "4200/4200 [==============================] - 130s 31ms/step - loss: 0.8038 - accuracy: 0.7137 - val_loss: 1.4898 - val_accuracy: 0.5271\n",
+            "Epoch 22/50\n",
+            "4200/4200 [==============================] - 117s 28ms/step - loss: 0.7607 - accuracy: 0.7289 - val_loss: 1.5443 - val_accuracy: 0.5290\n",
+            "Epoch 23/50\n",
+            "4200/4200 [==============================] - 110s 26ms/step - loss: 0.7248 - accuracy: 0.7411 - val_loss: 1.5701 - val_accuracy: 0.5237\n",
+            "Epoch 24/50\n",
+            "4200/4200 [==============================] - 115s 27ms/step - loss: 0.6864 - accuracy: 0.7572 - val_loss: 1.5913 - val_accuracy: 0.5323\n",
+            "Epoch 25/50\n",
+            "4200/4200 [==============================] - 109s 26ms/step - loss: 0.6510 - accuracy: 0.7698 - val_loss: 1.6398 - val_accuracy: 0.5293\n",
+            "Epoch 26/50\n",
+            "4200/4200 [==============================] - 108s 26ms/step - loss: 0.6159 - accuracy: 0.7821 - val_loss: 1.7073 - val_accuracy: 0.5210\n",
+            "Epoch 27/50\n",
+            "4200/4200 [==============================] - 109s 26ms/step - loss: 0.5840 - accuracy: 0.7929 - val_loss: 1.6782 - val_accuracy: 0.5287\n",
+            "Epoch 28/50\n",
+            "4200/4200 [==============================] - 106s 25ms/step - loss: 0.5500 - accuracy: 0.8055 - val_loss: 1.7519 - val_accuracy: 0.5258\n",
+            "Epoch 29/50\n",
+            "4200/4200 [==============================] - 111s 27ms/step - loss: 0.5192 - accuracy: 0.8182 - val_loss: 1.8856 - val_accuracy: 0.5249\n",
+            "Epoch 30/50\n",
+            "4200/4200 [==============================] - 111s 26ms/step - loss: 0.4847 - accuracy: 0.8290 - val_loss: 1.8759 - val_accuracy: 0.5249\n",
+            "Epoch 31/50\n",
+            "4200/4200 [==============================] - 112s 27ms/step - loss: 0.4596 - accuracy: 0.8371 - val_loss: 1.8719 - val_accuracy: 0.5325\n",
+            "Epoch 32/50\n",
+            "4200/4200 [==============================] - 116s 28ms/step - loss: 0.4349 - accuracy: 0.8466 - val_loss: 1.9375 - val_accuracy: 0.5277\n",
+            "Epoch 33/50\n",
+            "4200/4200 [==============================] - 111s 27ms/step - loss: 0.4093 - accuracy: 0.8571 - val_loss: 2.0729 - val_accuracy: 0.5317\n",
+            "Epoch 34/50\n",
+            "4200/4200 [==============================] - 116s 28ms/step - loss: 0.3851 - accuracy: 0.8644 - val_loss: 2.1438 - val_accuracy: 0.5296\n",
+            "Epoch 35/50\n",
+            "4200/4200 [==============================] - 117s 28ms/step - loss: 0.3619 - accuracy: 0.8738 - val_loss: 2.2082 - val_accuracy: 0.5180\n",
+            "Epoch 36/50\n",
+            "4200/4200 [==============================] - 111s 26ms/step - loss: 0.3411 - accuracy: 0.8796 - val_loss: 2.3262 - val_accuracy: 0.5229\n",
+            "Epoch 37/50\n",
+            "4200/4200 [==============================] - 112s 27ms/step - loss: 0.3204 - accuracy: 0.8891 - val_loss: 2.3483 - val_accuracy: 0.5256\n",
+            "Epoch 38/50\n",
+            "4200/4200 [==============================] - 106s 25ms/step - loss: 0.3060 - accuracy: 0.8929 - val_loss: 2.4200 - val_accuracy: 0.5298\n",
+            "Epoch 39/50\n",
+            "4200/4200 [==============================] - 105s 25ms/step - loss: 0.2859 - accuracy: 0.9003 - val_loss: 2.4552 - val_accuracy: 0.5271\n",
+            "Epoch 40/50\n",
+            "4200/4200 [==============================] - 107s 25ms/step - loss: 0.2735 - accuracy: 0.9032 - val_loss: 2.5661 - val_accuracy: 0.5238\n",
+            "Epoch 41/50\n",
+            "4200/4200 [==============================] - 114s 27ms/step - loss: 0.2637 - accuracy: 0.9072 - val_loss: 2.7193 - val_accuracy: 0.5174\n",
+            "Epoch 42/50\n",
+            "4200/4200 [==============================] - 112s 27ms/step - loss: 0.2428 - accuracy: 0.9145 - val_loss: 2.6803 - val_accuracy: 0.5186\n",
+            "Epoch 43/50\n",
+            "4200/4200 [==============================] - 112s 27ms/step - loss: 0.2372 - accuracy: 0.9185 - val_loss: 2.7707 - val_accuracy: 0.5239\n",
+            "Epoch 44/50\n",
+            "4200/4200 [==============================] - 111s 27ms/step - loss: 0.2279 - accuracy: 0.9201 - val_loss: 2.8333 - val_accuracy: 0.5201\n",
+            "Epoch 45/50\n",
+            "4200/4200 [==============================] - 108s 26ms/step - loss: 0.2166 - accuracy: 0.9244 - val_loss: 2.8028 - val_accuracy: 0.5225\n",
+            "Epoch 46/50\n",
+            "4200/4200 [==============================] - 108s 26ms/step - loss: 0.2082 - accuracy: 0.9274 - val_loss: 3.0369 - val_accuracy: 0.5277\n",
+            "Epoch 47/50\n",
+            "4200/4200 [==============================] - 113s 27ms/step - loss: 0.2026 - accuracy: 0.9283 - val_loss: 3.0108 - val_accuracy: 0.5219\n",
+            "Epoch 48/50\n",
+            "4200/4200 [==============================] - 110s 26ms/step - loss: 0.1907 - accuracy: 0.9333 - val_loss: 3.0341 - val_accuracy: 0.5201\n",
+            "Epoch 49/50\n",
+            "4200/4200 [==============================] - 112s 27ms/step - loss: 0.1815 - accuracy: 0.9366 - val_loss: 3.0841 - val_accuracy: 0.5261\n",
+            "Epoch 50/50\n",
+            "4200/4200 [==============================] - 112s 27ms/step - loss: 0.1811 - accuracy: 0.9371 - val_loss: 3.0651 - val_accuracy: 0.5248\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "test_loss, test_acc = model.evaluate(X_test, y_test)\n",
+        "print('Test accuracy:', test_acc)\n",
+        "print('Test loss:', test_loss)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "e0E1Qj10S98U",
+        "outputId": "75c16094-e07d-45b3-f9cf-42b622b566bc"
+      },
+      "execution_count": 40,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "563/563 [==============================] - 4s 7ms/step - loss: 3.0651 - accuracy: 0.5248\n",
+            "Test accuracy: 0.5247777700424194\n",
+            "Test loss: 3.065103769302368\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Final training accuracy: 0.9371<br>\n",
+        "Validation accuracy: 0.5248"
+      ],
+      "metadata": {
+        "id": "PABDuvMVtX9H"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "."
+      ],
+      "metadata": {
+        "id": "BndXIlHgWHnz"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "(c) Calculate and plot the training and validation losses of the tuned network."
+      ],
+      "metadata": {
+        "id": "Haz9UQhfV9WH"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "##(c)Anwer:\n",
+        "\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "# Plotting the model loss\n",
+        "plt.plot(history.history['loss'], label='Train')\n",
+        "plt.plot(history.history['val_loss'], label='Validation')\n",
+        "plt.title('Model Loss')\n",
+        "plt.ylabel('Loss')\n",
+        "plt.xlabel('Epoch')\n",
+        "plt.legend()\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 472
+        },
+        "id": "VZtRlgfjuPOI",
+        "outputId": "eb3c689e-96a7-4034-89cb-c7d266bf4b29"
+      },
+      "execution_count": 43,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "."
+      ],
+      "metadata": {
+        "id": "A-WMSOYLXcja"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "(d) Calculate and plot the training and validation accuracies of the tuned\n",
+        "network."
+      ],
+      "metadata": {
+        "id": "AyqzKWB5Xh1H"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "## (d)Answer\n",
+        "\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "# Plotting the model accuracy\n",
+        "plt.plot(history.history['accuracy'], label='Train')\n",
+        "plt.plot(history.history['val_accuracy'], label='Validation')\n",
+        "plt.title('Model Accuracy')\n",
+        "plt.ylabel('Accuracy')\n",
+        "plt.xlabel('Epoch')\n",
+        "plt.legend()\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 472
+        },
+        "id": "e23DltDvXRYJ",
+        "outputId": "c879cb50-8080-4b39-a415-ba0b41980c50"
+      },
+      "execution_count": 44,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "5oxshb51XRN_"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "7UEA2rwTXREn"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "JhhaMXhUXQ7p"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "wqh_P4jYXQ0o"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "RI1eeL8AXQtd"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "gVQdEXc6XQlS"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file