wardrobe_classifier_model.ipynb

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      "authorship_tag": "ABX9TyNWw0APEuUyViogDDlyLVcD",
      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "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/chiragtyagi2003/wardrobe_classifier/blob/main/wardrobe_classifier_model.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Improting Libraries"
      ],
      "metadata": {
        "id": "0ATptVY0Fh3W"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "print(tf.__version__)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JXAqvfSjFxf7",
        "outputId": "bc73c104-3bb0-489e-db9c-f54341ce9123"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.12.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Downloading the Dataset"
      ],
      "metadata": {
        "id": "bSFSzV1UOGBi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# fashion mnist dataset\n",
        "fashion_mnist = tf.keras.datasets.fashion_mnist\n",
        "(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n581yI5pF7tc",
        "outputId": "4b0e882e-922b-4922-9740-703aae87c27a"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n",
            "29515/29515 [==============================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n",
            "26421880/26421880 [==============================] - 2s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n",
            "5148/5148 [==============================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n",
            "4422102/4422102 [==============================] - 1s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# labels\n",
        "# 0 => T-shirt/Top\n",
        "# 1 => Trouser\n",
        "# 2 => Pullover\n",
        "# 3 => Dress\n",
        "# 4 => Coat\n",
        "# 5 => Sandal\n",
        "# 6 => Shirt\n",
        "# 7 => Sneaker\n",
        "# 8 => Bag\n",
        "# 9 => Ankle boot"
      ],
      "metadata": {
        "id": "M3LkVsg8GStk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class_names = ['T-shirt/Top', 'Trouser', 'Pullover', 'Dress', 'Coat',\n",
        "               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']"
      ],
      "metadata": {
        "id": "tRjVfmGJGrTe"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# explore training data\n",
        "\n",
        "# 1 => no of samples\n",
        "# 2,3 => pixel dims\n",
        "train_images.shape\n",
        "\n",
        "len(train_labels)\n",
        "\n",
        "train_labels"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vjyuF8gqG-TV",
        "outputId": "ea90dd84-ee81-42a1-d5b5-cd613cec622f"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([9, 0, 0, ..., 3, 0, 5], dtype=uint8)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "BMRTQh1MHQCP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# explore test data\n",
        "\n",
        "test_images.shape\n",
        "\n",
        "len(test_labels)\n",
        "\n",
        "test_labels"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MwZ_eV0yHQEF",
        "outputId": "e7a83e55-39c5-4ec5-d150-69a464209d15"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([9, 2, 1, ..., 8, 1, 5], dtype=uint8)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Data PreProcessing"
      ],
      "metadata": {
        "id": "dU5U8sGvOLQs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# preprocess the data\n",
        "train_images[:1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xo2Z6MHNHXgb",
        "outputId": "569c21c4-4fea-47d2-c6d5-69f1eaa56364"
      },
      "execution_count": 11,
      "outputs": [
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          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure()\n",
        "plt.imshow(train_images[0])\n",
        "plt.colorbar()\n",
        "plt.grid(False)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "NzOjfC7sH4SW",
        "outputId": "64f123c1-3a9f-46a8-e871-410055410419"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# scaling the pixel values\n",
        "train_images = train_images/255.0\n",
        "test_images = test_images/255.0"
      ],
      "metadata": {
        "id": "go_bBqvtIFjL"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# displaying some images\n",
        "plt.figure(figsize = (10,10))\n",
        "for i in range(25):\n",
        "  plt.subplot(5,5,i+1)\n",
        "  plt.xticks([])\n",
        "  plt.yticks([])\n",
        "  plt.grid(False)\n",
        "  plt.imshow(train_images[i], cmap = plt.cm.binary)\n",
        "  plt.xlabel(class_names[train_labels[i]])\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 826
        },
        "id": "d4uUyZrnIPt_",
        "outputId": "b4748bf0-d3e8-4848-fb07-811c7598743e"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 25 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Building the Model"
      ],
      "metadata": {
        "id": "uKJYububOPqj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# building the model\n",
        "model = tf.keras.models.Sequential([\n",
        "    tf.keras.layers.Flatten(input_shape=(28,28)),\n",
        "    tf.keras.layers.Dense(128, activation = 'relu'),\n",
        "    tf.keras.layers.Dense(10)\n",
        "])"
      ],
      "metadata": {
        "id": "H-hG9XHlJVvO"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Compiling the Model"
      ],
      "metadata": {
        "id": "xCYHO8iMOWxF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer = 'adam', loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits = True), metrics = ['accuracy'])"
      ],
      "metadata": {
        "id": "42SxkWnpKIYc"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Training the Model"
      ],
      "metadata": {
        "id": "HLx6c3fiOUEv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# training the model\n",
        "model.fit(train_images, train_labels, epochs = 10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KYFUX7BbKYR4",
        "outputId": "dc71b242-821e-42f4-d40a-e8baafc308f1"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "1875/1875 [==============================] - 10s 4ms/step - loss: 0.4965 - accuracy: 0.8259\n",
            "Epoch 2/10\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.3738 - accuracy: 0.8641\n",
            "Epoch 3/10\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.3340 - accuracy: 0.8778\n",
            "Epoch 4/10\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.3087 - accuracy: 0.8869\n",
            "Epoch 5/10\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.2910 - accuracy: 0.8919\n",
            "Epoch 6/10\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.2780 - accuracy: 0.8975\n",
            "Epoch 7/10\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.2650 - accuracy: 0.9017\n",
            "Epoch 8/10\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.2577 - accuracy: 0.9041\n",
            "Epoch 9/10\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.2468 - accuracy: 0.9080\n",
            "Epoch 10/10\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.2372 - accuracy: 0.9115\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f52d222b220>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Evaluating the Model"
      ],
      "metadata": {
        "id": "P7xW1QpbOZSx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# evaluate the model\n",
        "test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2)\n",
        "\n",
        "print('\\nTest Accuracy ', test_acc)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NFAacfu1K8Ny",
        "outputId": "e914f6a2-7270-4b81-ca58-87c91610d96f"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 - 1s - loss: 0.3376 - accuracy: 0.8833 - 1s/epoch - 4ms/step\n",
            "\n",
            "Test Accuracy  0.8833000063896179\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# making predictions\n",
        "prob_model = tf.keras.Sequential([\n",
        "    model,\n",
        "    tf.keras.layers.Softmax()\n",
        "])"
      ],
      "metadata": {
        "id": "OxZ8TDz5LV72"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = prob_model.predict(test_images)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xj_HAn2_Lg9K",
        "outputId": "779c2685-e322-4116-9b9c-02a741061fac"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 2ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uUObPVpsLllW",
        "outputId": "dd6b33e6-6068-44ef-c378-81bcdeab413b"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([5.9763344e-12, 1.2202493e-11, 2.6525163e-10, 1.6075218e-11,\n",
              "       7.4467781e-13, 3.2393626e-05, 9.3384651e-09, 1.5623596e-03,\n",
              "       6.4123569e-08, 9.9840516e-01], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.argmax(predictions[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hEuCpzN9Lskv",
        "outputId": "cc9a4bd0-0bb4-4b1a-a5e0-41f0ad5d0239"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class_names[np.argmax(predictions[0])]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "y3m1LrKcLsmL",
        "outputId": "b3531a7f-19ee-41cc-e7c0-105e1a7175ae"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Ankle boot'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# function to plot image, with prediction label,accuracy,true label\n",
        "\n",
        "def plot_image(i, predictions_array, true_label, img):\n",
        "  true_label, img = true_label[i], img[i]\n",
        "  plt.grid(False)\n",
        "  plt.xticks([])\n",
        "  plt.yticks([])\n",
        "\n",
        "  plt.imshow(img, cmap=plt.cm.binary)\n",
        "\n",
        "  predicted_label = np.argmax(predictions_array)\n",
        "  if predicted_label == true_label:\n",
        "    color = 'blue'\n",
        "  else:\n",
        "    color = 'red'\n",
        "\n",
        "  plt.xlabel(\"{} {:2.0f}% ({})\".format(class_names[predicted_label],\n",
        "                                100*np.max(predictions_array),\n",
        "                                class_names[true_label]),\n",
        "                                color=color)\n",
        "\n",
        "# function to plot the bar graph\n",
        "def plot_value_array(i, predictions_array, true_label):\n",
        "  true_label = true_label[i]\n",
        "  plt.grid(False)\n",
        "  plt.xticks(range(10))\n",
        "  plt.yticks([])\n",
        "  thisplot = plt.bar(range(10), predictions_array, color=\"#777777\")\n",
        "  plt.ylim([0, 1])\n",
        "  predicted_label = np.argmax(predictions_array)\n",
        "\n",
        "  thisplot[predicted_label].set_color('red')\n",
        "  thisplot[true_label].set_color('blue')"
      ],
      "metadata": {
        "id": "a4bRznurMNYJ"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Making Predictions using the Model"
      ],
      "metadata": {
        "id": "488lKVZSOwnw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "i = 0\n",
        "plt.figure(figsize=(6,3))\n",
        "plt.subplot(1,2,1)\n",
        "plot_image(i, predictions[i], test_labels, test_images)\n",
        "plt.subplot(1,2,2)\n",
        "plot_value_array(i, predictions[i],  test_labels)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 291
        },
        "id": "l-8l2qrIMN_-",
        "outputId": "9661d7dd-4fec-472d-f797-81c631f2c023"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x300 with 2 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeQAAAESCAYAAAAsZab9AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAfXElEQVR4nO3deXhUVZ7G8TcEshASdjBhCbJIaARkEQV07BbUQQZxaUUGnomiztjGFqRVXLpFxxaX0R5tF1yaxtEWl1ZwV4yoKAiKaBAE2ZolsrZoyAIEqJz543SZynJPJZXQOcj38zz1QNWv7smpWwVvzr331IkzxhgBAIAG1aihOwAAAAhkAAC8QCADAOABAhkAAA8QyAAAeIBABgDAAwQyAAAeaNzQHQDgp7KyMm3btk2pqamKi4tr6O4ARyxjjIqKipSRkaFGjYLHwQQygGpt27ZNnTp1auhuAD8Z+fn56tixY2CdQAZQrdTUVEn2P5G0tLQG7g1weOTnSwMHSqWlsW2fmCgtWya5fnctLCxUp06dfvw3FYRABlCt8GHqtLQ0Ahk/WaWlsYdx5PY1+ScS7dQPF3UBAOABAhkAAA8QyAAAeIBABgDAAwQyAAAeIJABAPBAjaY98Y09QP2o6Tf2ADj61CiQ+cYeoH5F+8YeAEefGgUy39gD1I+afmMPgKNPjQKZb+wB6henfgBUxkksAAA8QCADAOABAhkAAA8QyAAAeIBABgDAAwQyAAAeIJABAPAAgQwAgAcIZAAAPEAgAwDgAQIZAAAPEMgAAHiAQAYAwAMEMgAAHiCQAQDwAIEMAIAHCGQAADxAIAMA4AECGQAADxDIAAB4gEAGAMADBDIAAB4gkAEA8ACBDACABwhkAAA8QCADAOABAhkAAA8QyAAAeIBABgDAAwQyAAAeIJABAPAAgQwAgAcIZAAAPEAgAwDgAQIZAAAPEMgAAHiAQAYAwAMEMgAAHiCQAQDwAIEMAIAHCGQAADxAIAMA4AECGQAADxDIAAB4gEAGAMADBDIAAB4gkAEA8ACBDACABwhkAAA80LihO4CjQygUCqw1auT+vTAuLi7mn1taWuqsJyYmBtbWrVvn3LZHjx4x9QkAqsMIGQAADxDIAAB4gEAGAMADBDIAAB4gkAEA8ACBDACABwhkAAA8wDzkI4wxpk5115zfrVu3OrddvHhxYG3kyJHObVNSUpz1w8U1zziaOXPmOOtTp06NuW0AqIwRMgAAHiCQAQDwAIEMAIAHCGQAADxAIAMA4AECGQAADzDt6Scm2lKGLh9//LGz/umnnwbWtm3b5tz2mmuuialPdbVr1y5nfd68eYG11NTU+u4OAARihAwAgAcIZAAAPEAgAwDgAQIZAAAPEMgAAHiAQAYAwAMEMgAAHmAe8hEmFAo5640bu9/SpUuXBtZWr17t3LZ9+/aBtXXr1jm3Pe+88wJrLVu2dG67f/9+Zz0zMzOwtnv3bue2hYWFgbUOHTo4twWA+sQIGQAADxDIAAB4gEAGAMADBDIAAB4gkAEA8ACBDACAB5j25KGysrLAWrRpTSUlJc76Sy+9FFhLTEx0buuaflRUVOTc1hgTU60m9a+//jqw1rFjR+e2rilX0aaYAUB9YoQMAIAHCGQAADxAIAMA4AECGQAADxDIAAB4gEAGAMADBDIAAB74Sc9DjjZ/NS4uzll3zQeOtq2rHm1+a3x8vLPu8thjjznrriUUk5KSnNtu3rw5sBZtiUTXzz106JBz22j7OiUlJbAWbW71nj17AmulpaXObV1zvl19AoDqMEIGAMADBDIAAB4gkAEA8ACBDACABwhkAAA8QCADAOAB76c91WXqUrTpMtE0ahT77yuuqU11mdb03HPPOes7duxw1vv37x9Yizb9qKCgILDWqlUr57atW7cOrH333XfObYuLi531aP12cX2+9u7d69x23bp1gbUTTjgh1i4BOEoxQgYAwAMEMgAAHiCQAQDwAIEMAIAHCGQAADxAIAMA4AECGQAAD3g/D7kuc4ldyyfWpO6aLxytX3WZa/znP/85sLZ27Vrntp06dXLWd+/eHViLNud73759gbUOHTo4ty0qKgqsRduXTZs2ddZdSz/WdQlOl3nz5gXWmIcMoLYYIQMA4AECGQAADxDIAAB4gEAGAMADBDIAAB4gkAEA8ACBDACAB/4p85Cjzfd1iTZP1DXPNNp6xnVZ7ziabdu2BdbmzJnj3NY137dHjx7ObaOtHVxaWhpYc81RlqQmTZoE1qK9T9HWFnaJ9j4lJibGvG1KSkpgLdprWrRokbMOALXBCBkAAA8QyAAAeIBABgDAAwQyAAAeIJABAPAAgQwAgAdqNe0pFAopFApVW3MtN3g4pxfVZfm8v//97876pk2bAmtr1qxxbrt9+/bAWkJCgnPbtLS0wFpBQYFz28LCQmf94MGDgTXXlCjJ/R679pUkHTp0KLDWokUL57bR9lfQZ1KKvvxicnJyTO1KUrNmzQJrK1eurPbxaNPSABy9GCEDAOABAhkAAA8QyAAAeIBABgDAAwQyAAAeIJABAPAAgQwAgAdqNQ85Pj7eORc1yM6dO531zZs3B9ZKSkqc27rqrmUMJWnjxo3OumvJwMaN3bsuNTU1sBZtOco9e/YE1qK9pmj9cr0m15xcyb3M4YEDB5zbpqenB9aizZ2OtnRjy5YtA2vR5v1+//33gTXXPGNJ2rFjR63bjfZ5BnD0YoQMAIAHCGQAADxAIAMA4AECGQAADxDIAAB4gEAGAMADtZr25PLee+8F1rZt2+buhGOqTrQlEl1L5EWbolWXqUvRptO4psREWxLQtQyia4qPFH1Klavf0ZYbTElJCaxFmyLkWmIx2ntcF9H2l2tp0GhTzFxTvYI+W9E+cwCOXoyQAQDwAIEMAIAHCGQAADxAIAMA4AECGQAADxDIAAB4gEAGAMADtZoUOX/+/MC5qDNnzgzcLisry9mua2k+11xgyT3vNiEhwblttHm3rvnC0frlmqPqmvsqSUVFRTH1SYo+dzYuLi6wFm1/uOZWR1tic9WqVYG1aEs3RuuXS7T50a7lEJOSkmJuu127dtU+7npvARzdGCEDAOABAhkAAA8QyAAAeIBABgDAAwQyAAAeIJABAPBAraY9DRw4UGlpadXWlixZErjdihUrnO0uXLiwNt2ooEmTJoG1aFOTWrVqFXO9efPmzm1dU3miTV3avXt3YG3NmjXObffu3eusFxYWBtZcU6Ikafny5YG1vn37Orft0qVLYC03N9e5rWs5Sin6NDIX13KIGRkZzm2D/i1IwdOboi3bCeDoxQgZAAAPEMgAAHiAQAYAwAMEMgAAHiCQAQDwAIEMAIAHCGQAADxQq3nILVq0CJx7eeutt8bcCdfczE8//dS5rWte7ieffOLcdtOmTc76V199FVhzLdsnuecaR5vv65pXG23udJ8+fZz1ESNGBNbOPvts57bRliOM1TnnnOOsb9myxVlv3bp1YM01V1hyz1V3zVGWpMTExMDacccdV+3jrnngAI5ujJABAPAAgQwAgAcIZAAAPEAgAwDgAQIZAAAPEMgAAHiAQAYAwAO1mod8uDRr1iywNnz4cOe2rvpVV10Vc5/wz/Paa681dBf+aUKhUEN3AYCnGCEDAOABAhkAAA8QyAAAeIBABgDAAwQyAAAeIJABAPAAgQwAgAcIZAAAPEAgAwDgAQIZAAAPEMgAAHiAQAYAwAMEMgAAHiCQAQDwAIEMAIAHCGQAADxAIAMA4AECGQAADxDIAAB4gEAGAMADBDIAAB4gkAEA8ACBDACABwhkAAA8QCADAOABAhkAAA8QyAAAeIBABgDAAwQyAAAeIJABAPAAgQwAgAcIZAAAPEAgAwDgAQIZAAAPEMgAAHiAQAYAwAMEMgAAHiCQAQDwAIEMAIAHCGQAADxAIAMA4AECGQAADxDIAAB4oHFNnmSMkSQVFhYe1s4AP3Xhf0Phf1MAEFajQC4qKpIkderU6bB2BjhaFBUVqXnz5g3dDQAeqVEgZ2RkKD8/X6mpqYqLizvcfQJ+sowxKioqUkZGRkN3BYBnahTIjRo1UseOHQ93X4CjAiNjANXhoi4AADxAIAMA4AECGQAADxDIAAB4oEED+bbbpBNOCK4/9ZTUokXdfsYll0jnnlu3No528+dLvXpJoVD9tvvhh1JcnFRQUH190yZbz8uL/WfUx2coVgcOSF26SJ9/3jA/H8CRpU6BvHixFB8vjRpVX905cv3859LkydGfN2eOdOaZUuvWwWGzf7+Uk2Of06yZdMEF0s6dFZ+zZYvd702bSu3aSddfLx06VF7/8kupf3+7/ejR0vffl9cOHZIGDpQ++6xmr+2GG6Tf/ta+15H27ZNatZLatJFKS2vW1k9Vdb9cJiRI110nTZ3aED0CcKSp0bSnIDNnSr/+tf1z2zaJqZXRlZRIp5wiXXSRdMUV1T/n2mulN9+U/vpXqXlz6eqrpfPPlxYtsvVQyIbxMcdIn3wibd8u/cd/SE2aSNOn2+dcfrl0+unSCy/Yv0+fLt13n63df780bJg0eHD0/i5cKG3YYH8pqOzll6XevSVjpFdekcaOrfXu+MkbP176zW+kr7+2++pIwjf04WhQXFw/bbj+mdT4G/pMjIqKjGnWzJhvvjFm7Fhj7ryzYv2DD4yRjHnvPWMGDjQmOdmYIUPs88OmTTOmX7/y++vXG3Psscbk5BhTVmbMrFnGNG9esd1XXjGmf39jEhPtc2+7zZiDB4P7mZ1tzJgx9nlt2hiTmmrMf/2XMaWl5c/Zv9+YX//amLZtbbvDhhnz2WcV2/nwQ2NOPNGYhARjjjnGmKlTy39udrZ9rZG3jRvd+2/jRvu8L7+s+HhBgTFNmhjz17+WP7Z6tX3u4sX2/ltvGdOokTE7dpQ/Z8YMY9LSyl9XcrLdzhhjHn3UmLPPtn/fsMGYHj2MKSx09y8sJ8eYX/6y+trPf27MY4/Zn33GGVXrkjFPPmnMuefa/nTvbsyrr5bXw5+RH36w90tKjPnXfzVm6FD7WHX7aMUK+5yUFGPatTNmwgRj/v734P6HP0Nz59qfn5hozJlnGrNlS8XnPfqoMV272n1/3HHGPP10xfrmzcacc479uampxlx4Yfn+nzWr6vs/a1b5tr/4hTG//W1wH32Vn59vJHHjxq2ebvn5+c5/czGPkF98UcrKknr2lCZMsIdrb7rJHoaNdMstdkTWtq105ZXSxInlI71IX30lnXWWdNll0u9/X/3P/PhjOxL84x+lU0+1I7f//E9bmzYtuK/z50tJSfac5aZN0qWX2sPBd95p6zfcYEd7//d/UmamdO+9ti/r19tDslu3Smefbc9HP/209M03dnSblGQPVT74oLR2rXT88dJ//7dts23bGu/KCpYtkw4elEaMKH8sK0vq3NmeIjj5ZPtnnz5S+/blzznrLOlXv7Ijsf79pX79pNxcqXt3+/r79rXPu/JK+/pSU2vWn48/lv7936s+vmGD7cecOZIxdlS/ebPdf5Fuv93+vP/5H+mhh+yIcfNmu18jFRTYUX+zZrbfTZtWPbdcUGBH/ZdfLv3v/9pD5lOn2qMN778f/Br27rXv9dNP28PIV10lXXxx+edw7lxp0iTpgQfsfn/jDfsZ6dhR+sUvpLIyacwY27cFC+wh/5wce0Tgww/tnytXSu+8I733nm0z8rs/Bg+2+/FIUx/f0FdYWKhOnTopPz9faWlpde5Tfbbnc9/quz361rDtmZp+Q1+svz0PHWrMAw/Yvx88aEefH3xQXo8cIYe9+aZ9bN8+ez88Ql60yJiWLY25776KP6PyCHn4cGOmT6/4nGeeMSY9Pbif2dnGtGplR19hM2bY0X0oZExxsR0VPftsef3AAWMyMoy59157/+abjenZ047awx55pLwNY4w57TRjJk0K7kdlQSPkZ5+1o/DKTjzRmBtusH+/4go7yotUUmLbe+ste3/lSmP+5V+M6dzZmHHjjNmzx476xowx5ttv7fbduhlzyy3ufjZvXnW0aIzdJ+eeW35/zBj7fkaSKo4Mi4vtY2+/be+HPyOrVxvTt68xF1xQ8chF5X10xx1VX3d+vn3OmjXV9z88el2ypPyx8BGHTz+194cOtfs00oUXlh9VePddY+LjK46qv/7athE+klL5aE+kBx80pkuX6ms/dXv27DGSzJ49e7xrz+e+1Xd79M2f9lxiuqhrzRp7QdC4cfZ+48Z2lDBzZtXnhkdmkpSebv/ctav8sS1bpDPOkG691Z5rc1m+3I5AmzUrv11xhT2Hundv8Hb9+tkRV9iQIfaYf36+HekdPGjPqYY1aWJHNatX2/urV9ttIgcJw4bZNr791t3nhtK7tx3Nbd4szZ5tX+O0adLDD9vz/kOH2v05Z470+uvB7ezbZ48ERAqF7NGECRPKH5swwV7RXFZW8bmR739KipSWVvH9l+z73727Pd+dkBDcl+XLpQ8+qPj+Z2XZ2oYNwds1biydeGL5/awse+V15Psb+f5L9n5kvVMnewv72c8qtuGSnOz+fAKAFONFXTNn2sN2kaNvY6TERPsffuThuiZNyv8eDrTI/7TbtrXtPPecPZztOiJQXGwPgZ5/ftVa5dA4Uh1zjJ0uU1BQcbrOzp22Fn5O5Sukw1dhh59T2ZQp9rRCx472MOvvf28DctQoe3/06Oq3a9NG+uGHio/Nm2cP41e+iCsUsofHzzij/LHI91+yn4HKoT1qlD1lsGqVPRQfpLjY9vOee6rWwr/s+ej772M/hQHg6FHrEfKhQ/Zc3P332yk74dvy5eXBWhvJyfacXVKSPQ/6j5UeqzVggB2dd+9e9dbI8UqWL7cjvbAlS+zoqlMnqVs3OyqLPK998KC0dKkdBUl2Du7ixfaXjrBFi+x52PCaGwkJ9TNPd+BAG2Lz55c/tmaNPZIwZIi9P2SItGJFxZFmbq79ZSbc50jz59uR3NVX2/uhkH2N4dfq6nf//jYoI82cac/BRr7/eXn2seqOkkRz991SdrY0fHjVnxVpwAB7jrxLl6rvf0pK8HaHDlWcC7xmjf2Fp1cve79Xr6rXNSxaVPH9z8+3t7BVq2wb4ee43v+VK+1+PBolJiZq2rRpSkxM9K49n/tW3+3RN3/ac6rtMe65c+05zoKCqrUbbjBm0CD798pX0BpjzwUq4grkyPNuRUXGnHKKvcK5qMg+Vvkc8jvvGNO4sb1ieuVKY1atMua559znQbOz7bnecePseb833zSmfXtjbryx/DmTJtlzxm+/bZ+TnW3PaX//va1/+60xTZvaK45Xr7ZXerdpU/Gc6RVX2PO8Gzfaq37D55Yr273b7ofw+fTnn7f3t28vf86VV9pzv++/b8znn9ur04cMKa8fOmTM8cfb86l5eXa/tG1rzE03Vf15+/YZk5VV8Vz1yJG2v3l5xnTsaMyLLwbvvz/+0V4lH7Zrlz3nHj4PHOmtt+xVzLt32/uS/bxEat68/Arkyp+RyZPtexO+OrzyOeStW+3r/OUv7bnb9evta7/kErtPqjNrlu3v4MH2PPLnnxtz8sn2FjZ3rn3Oo48as3atMfffb88Zh6+JKCsz5oQTjDn1VGOWLbPnngcOtNcNhD37rL0C+8sv7fu/f395LTOz+vPwABCp1oH8b/9WfrFLZZ9+av8DXb689oFsjA3ioUPtxUjFxdVPe3rnHfuc5GQ7zWfwYGOeeCK4v+FpT7feakzr1jacr7ii4n+Y+/bZaU9t2sQ27ckYe1HRySfbfkW+xsqqmyIjVQz3ffuMueoq+0tB06bGnHdexcA2xphNm2ywJifbfv/mN9VP/7rxRluLtG6dfS1pacb86lfBvzwYY8M1Kal8utp99xnTooW98K2y0lJbe/BBe7+2gWyMfR/S0+3+rO7Ct7Vr7f5o0cK+9qwsG+SRF9xFCn+GXn7ZTmtKTDRmxAg7jSlSXaY9GWM/TxdcYPuliGlPn3xiH9u7t/r+AUBYnDHRZirjaHf99XbS++OPN3RPjjxjx9qLCm++uaF7AsB3LC6BqG65xc4vrnwxFtwOHLAXqV17bUP3BMCRgBEyAAAeYIQMAIAHCGQAh80jjzyiLl26KCkpSSeddJI+q+kSY5V89NFHGj16tDIyMhQXF6dXXnkl5j7dddddOvHEE5Wamqp27drp3HPP1Zo1a2Jub8aMGerbt6/S0tKUlpamIUOG6O233465vUh333234uLiNLkmS8lV47bbblNcXFyFW1b423RisHXrVk2YMEGtW7dWcnKy+vTpo89jXF+0S5cuVfoWFxennJycmNoLhUL63e9+p2OPPVbJycnq1q2b7rjjjugLOgQoKirS5MmTlZmZqeTkZA0dOlRLly6Nqa2aIpABHBYvvPCCpkyZomnTpumLL75Qv379dNZZZ2lX5a9qq4GSkhL169dPjzzySJ37tWDBAuXk5GjJkiXKzc3VwYMHdeaZZ6qkpCSm9jp27Ki7775by5Yt0+eff67TTz9dY8aM0ddff12nfi5dulSPP/64+kZ+3V0Mevfure3bt/94W7hwYUzt/PDDDxo2bJiaNGmit99+W6tWrdL999+vli1bxtTe0qVLK/QrNzdXknThhRfG1N4999yjGTNm6OGHH9bq1at1zz336N5779VDDz0UU3uXX365cnNz9cwzz2jFihU688wzNWLECG3dujWm9mqkQa/xBvCTNXjwYJOTk/Pj/VAoZDIyMsxdd91Vp3YlmbmV59PVwa5du4wks2DBgnprs2XLluZPf/pTzNsXFRWZHj16mNzcXHPaaaeZSbX5ovwI06ZNM/2CvmS9lqZOnWpOOeWUemmrOpMmTTLdunUzZUFzGKMYNWqUmThxYoXHzj//fDN+/Phat7V3714THx9v3njjjQqPDxgwwNwSbQGAOmCEDKDeHThwQMuWLdOIiGXLGjVqpBEjRmjx4sUN2LOq9uzZI0lqVXkJshiEQiE9//zzKikp0ZDwV+vFICcnR6NGjaqw/2K1bt06ZWRkqGvXrho/fry2bNkSUzuvvfaaBg0apAsvvFDt2rVT//799eSTT9a5f5L9vPzlL3/RxIkTY15ZbOjQoZo/f77Wrl0rSVq+fLkWLlyokSNH1rqtQ4cOKRQKKanSdzInJyfHfIShJmJefhEAgnz33XcKhUJqH7lGqKT27dvrm2++aaBeVVVWVqbJkydr2LBhOv7442NuZ8WKFRoyZIj279+vZs2aae7cufpZdd9jWwPPP/+8vvjii3o5X3nSSSfpqaeeUs+ePbV9+3bdfvvtOvXUU7Vy5Uql1nQN1n/429/+phkzZmjKlCm6+eabtXTpUl1zzTVKSEhQdnZ2nfr5yiuvqKCgQJdccknMbdx4440qLCxUVlaW4uPjFQqFdOedd2r8+PG1bis1NVVDhgzRHXfcoV69eql9+/Z67rnntHjxYnXv3j3mPkZDIAM4auXk5GjlypV1HvX07NlTeXl52rNnj1566SVlZ2drwYIFtQ7l/Px8TZo0Sbm5uVVGZ7GIHB327dtXJ510kjIzM/Xiiy/qsssuq1VbZWVlGjRokKZPny5J6t+/v1auXKnHHnuszoE8c+ZMjRw5Mvp6wQ4vvviinn32Wc2ePVu9e/dWXl6eJk+erIyMjJj698wzz2jixInq0KGD4uPjNWDAAI0bN07Lli2LuY/REMgA6l2bNm0UHx+vneFlyP5h586dOiZoSbJ/squvvlpvvPGGPvroI3UMrxITo4SEhB9HTgMHDtTSpUv14IMP6vFafr3dsmXLtGvXLg0YMODHx0KhkD766CM9/PDDKi0tVXx8fMz9bNGihY477jitX7++1tump6dX+QWjV69eevnll2PujyRt3rxZ7733nubMmVOndq6//nrdeOONuvjiiyVJffr00ebNm3XXXXfFFMjdunXTggULVFJSosLCQqWnp2vs2LHq2rVrnfrpwjlkAPUuISFBAwcO1PyIZcvKyso0f/78Op1brQ/GGF199dWaO3eu3n//fR177LH1/jPKyspUWlpa6+2GDx+uFStWKC8v78fboEGDNH78eOXl5dUpjCWpuLhYGzZsUHoM65UOGzasyvSwtWvXKjMzs059mjVrltq1a6dRo0bVqZ29e/eqUaVl/+Lj41VWx68YTElJUXp6un744QfNmzdPY8aMqVN7LoyQARwWU6ZMUXZ2tgYNGqTBgwfrgQceUElJiS699NJat1VcXFxhVLdx40bl5eWpVatW6ty5c63aysnJ0ezZs/Xqq68qNTVVO3bskCQ1b95cycnJte7bTTfdpJEjR6pz584qKirS7Nmz9eGHH2revHm1bis1NbXKueyUlBS1bt06pnPc1113nUaPHq3MzExt27ZN06ZNU3x8vMaNG1frtq699loNHTpU06dP10UXXaTPPvtMTzzxhJ544olatxVWVlamWbNmKTs7W40b1y2ORo8erTvvvFOdO3dW79699eWXX+oPf/iDJk6cGFN78+bNkzFGPXv21Pr163X99dcrKysrps9vjR2267cBHPUeeugh07lzZ5OQkGAGDx5slixZElM7H3zwgZFU5ZadnV3rtqprR5KZFV6iq5YmTpxoMjMzTUJCgmnbtq0ZPny4effdd2Nqqzp1mfY0duxYk56ebhISEkyHDh3M2LFjzfr162Puy+uvv26OP/54k5iYaLKysswTrqX2amDevHlGklmzZk2d2jHGmMLCQjNp0iTTuXNnk5SUZLp27WpuueUWU1paGlN7L7zwgunatatJSEgwxxxzjMnJyTEF1a07XI/4LmsAADzAOWQAADxAIAMA4AECGQAADxDIAAB4gEAGAMADBDIAAB4gkAEA8ACBDACABwhkAAA8QCADAOABAhkAAA/8P0x7uX/qLJorAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot the first X test images, their predicted labels, and the true labels.\n",
        "# Color correct predictions in blue and incorrect predictions in red.\n",
        "num_rows = 5\n",
        "num_cols = 3\n",
        "num_images = num_rows*num_cols\n",
        "plt.figure(figsize=(2*2*num_cols, 2*num_rows))\n",
        "for i in range(num_images):\n",
        "  plt.subplot(num_rows, 2*num_cols, 2*i+1)\n",
        "  plot_image(i, predictions[i], test_labels, test_images)\n",
        "  plt.subplot(num_rows, 2*num_cols, 2*i+2)\n",
        "  plot_value_array(i, predictions[i], test_labels)\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "TBGXkLOyMeWt",
        "outputId": "e556ff83-a1c9-4ffd-b4dd-f877ede10829"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1000 with 30 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Prediction for a single image"
      ],
      "metadata": {
        "id": "lvF5SFqtOdwf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# using the trained model to make prediction on single image\n",
        "\n",
        "img = test_images[1]\n",
        "\n",
        "print(img.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7QiEVGcuMn8X",
        "outputId": "cf73db82-3382-498a-8d95-a23d2e6490c2"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(28, 28)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# keras models make predictions on batches\n",
        "\n",
        "img = (np.expand_dims(img, 0))\n",
        "\n",
        "print(img.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yunlBg4TMtYu",
        "outputId": "2cc7dbcf-1045-481e-d89e-788b838f497d"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1, 28, 28)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred_single = prob_model.predict(img)\n",
        "\n",
        "print(pred_single)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ton4Nt79NA4w",
        "outputId": "f3ed1281-d380-4c30-be48-4fa9b1585001"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 22ms/step\n",
            "[[8.8928664e-06 3.3565561e-12 9.9686855e-01 5.0970023e-10 2.8062689e-03\n",
            "  6.5809175e-14 3.1639109e-04 1.3118537e-16 2.9602063e-09 2.4287810e-15]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# pred_single is a list of list\n",
        "# one list for each image of the batch\n",
        "plot_value_array(1, pred_single[0], test_labels)\n",
        "_ = plt.xticks(range(10), class_names, rotation=45)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "id": "d7ZWSynwNKX_",
        "outputId": "e660edbd-8b01-4f00-89d3-a3312fc004f4"
      },
      "execution_count": 36,
      "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": "iwhJIpF8Nt2e"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.argmax(pred_single[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G_yxu5jONeU0",
        "outputId": "1a334d15-a159-4f77-c5de-dd24bb4bcdb4"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class_names[np.argmax(pred_single[0])]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "gVvKJrPzNhYi",
        "outputId": "4e35569d-a396-4c22-be42-fb8c2459d5cc"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Pullover'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    }
  ]
}