fashion_mnist_tf.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iYXMaVyBFq8Z",
        "outputId": "b8ffe0f1-73fb-4bcd-d083-c1b52eaccefb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: tensorflow_datasets in /usr/local/lib/python3.10/dist-packages (4.9.4)\n",
            "Requirement already satisfied: absl-py in /usr/local/lib/python3.10/dist-packages (from tensorflow_datasets) (1.4.0)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.10/dist-packages (from tensorflow_datasets) (8.1.7)\n",
            "Requirement already satisfied: dm-tree in /usr/local/lib/python3.10/dist-packages (from tensorflow_datasets) (0.1.8)\n",
            "Requirement already satisfied: etils[enp,epath,etree]>=0.9.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow_datasets) (1.7.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from tensorflow_datasets) (1.25.2)\n",
            "Requirement already satisfied: promise in /usr/local/lib/python3.10/dist-packages (from tensorflow_datasets) (2.3)\n",
            "Requirement already satisfied: protobuf>=3.20 in /usr/local/lib/python3.10/dist-packages (from tensorflow_datasets) (3.20.3)\n",
            "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from tensorflow_datasets) (5.9.5)\n",
            "Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow_datasets) (2.31.0)\n",
            "Requirement already satisfied: tensorflow-metadata in /usr/local/lib/python3.10/dist-packages (from tensorflow_datasets) (1.15.0)\n",
            "Requirement already satisfied: termcolor in /usr/local/lib/python3.10/dist-packages (from tensorflow_datasets) (2.4.0)\n",
            "Requirement already satisfied: toml in /usr/local/lib/python3.10/dist-packages (from tensorflow_datasets) (0.10.2)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from tensorflow_datasets) (4.66.4)\n",
            "Requirement already satisfied: wrapt in /usr/local/lib/python3.10/dist-packages (from tensorflow_datasets) (1.14.1)\n",
            "Requirement already satisfied: array-record>=0.5.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow_datasets) (0.5.1)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from etils[enp,epath,etree]>=0.9.0->tensorflow_datasets) (2023.6.0)\n",
            "Requirement already satisfied: importlib_resources in /usr/local/lib/python3.10/dist-packages (from etils[enp,epath,etree]>=0.9.0->tensorflow_datasets) (6.4.0)\n",
            "Requirement already satisfied: typing_extensions in /usr/local/lib/python3.10/dist-packages (from etils[enp,epath,etree]>=0.9.0->tensorflow_datasets) (4.11.0)\n",
            "Requirement already satisfied: zipp in /usr/local/lib/python3.10/dist-packages (from etils[enp,epath,etree]>=0.9.0->tensorflow_datasets) (3.18.1)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->tensorflow_datasets) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->tensorflow_datasets) (3.7)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->tensorflow_datasets) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->tensorflow_datasets) (2024.2.2)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from promise->tensorflow_datasets) (1.16.0)\n"
          ]
        }
      ],
      "source": [
        "!pip install -U tensorflow_datasets"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf"
      ],
      "metadata": {
        "id": "l8RquNscF9Ef"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow_datasets as tfds\n",
        "tfds.disable_progress_bar()\n",
        "\n",
        "import math\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "fFCPG6fWHCe6"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import tqdm\n",
        "import tqdm.auto\n",
        "tqdm.tqdm = tqdm.auto.tqdm\n",
        "\n",
        "print(tf.__version__)\n",
        "\n",
        "#tf.enable_eager_execution()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ig5vn6aQHVb5",
        "outputId": "b6d6b5b2-dbab-4fde-e8ca-c61b7f03aec3"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.15.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset, metadata = tfds.load('fashion_mnist', as_supervised = True, with_info = True)\n",
        "train_dataset, test_dataset = dataset['train'], dataset['test']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ehF0t1LcH3a_",
        "outputId": "d55713f5-b1f6-4f78-a1dc-28ac57ded3db"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset 29.45 MiB (download: 29.45 MiB, generated: 36.42 MiB, total: 65.87 MiB) to /root/tensorflow_datasets/fashion_mnist/3.0.1...\n",
            "Dataset fashion_mnist downloaded and prepared to /root/tensorflow_datasets/fashion_mnist/3.0.1. Subsequent calls will reuse this data.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',\n",
        "               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']"
      ],
      "metadata": {
        "id": "oBauH-8OJWs2"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "num_train_examples = metadata.splits['train'].num_examples\n",
        "num_test_examples = metadata.splits['test'].num_examples\n",
        "print(num_train_examples)\n",
        "print(num_test_examples)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P454X-yHN_cf",
        "outputId": "e63cc466-c739-4dd2-996a-40d9f886ddb8"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "60000\n",
            "10000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def normalize(images, labels):\n",
        "  images = tf.cast(images, tf.float32)\n",
        "  images /= 255\n",
        "  return images, labels\n",
        "train_dataset = train_dataset.map(normalize)\n",
        "test_dataset = test_dataset.map(normalize)"
      ],
      "metadata": {
        "id": "aL1Gcj5SPOAu"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for image, labels in test_dataset.take(1):\n",
        "  break\n",
        "image = image.numpy().reshape((28, 28))\n",
        "plt.figure()\n",
        "plt.imshow(image, cmap = plt.cm.binary)\n",
        "plt.colorbar()\n",
        "plt.grid(False)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 435
        },
        "id": "NELRCc2URBTh",
        "outputId": "73425469-86c9-4fb7-a7a5-65863c58050d"
      },
      "execution_count": 17,
      "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": [
        "plt.figure(figsize = (10, 10))\n",
        "i = 0\n",
        "for (image, label) in test_dataset.take(25):\n",
        "  image = image.numpy().reshape((28, 28))\n",
        "  plt.subplot(5, 5, i+1)\n",
        "  plt.xticks([])\n",
        "  plt.yticks([])\n",
        "  plt.grid(False)\n",
        "  plt.imshow(image, cmap = plt.cm.binary)\n",
        "  plt.xlabel(class_names[label])\n",
        "  i += 1\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 662
        },
        "id": "rwgVeXIlSUcv",
        "outputId": "8e9fd741-3688-4e4e-86ae-191cf9416fdf"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 25 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Flatten(input_shape = (28, 28, 1)),\n",
        "    tf.keras.layers.Dense(128, activation = tf.nn.relu),\n",
        "    tf.keras.layers.Dense(10, activation = tf.nn.softmax)\n",
        "])"
      ],
      "metadata": {
        "id": "2b7PRLU2TxcY"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer = 'adam',\n",
        "              loss = 'sparse_categorical_crossentropy',\n",
        "              metrics = ['accuracy'])"
      ],
      "metadata": {
        "id": "ujLW2VszVfNS"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "BATCH_SIZE = 32\n",
        "train_dataset = train_dataset.repeat().shuffle(num_train_examples).batch(BATCH_SIZE)\n",
        "test_dataset = test_dataset.batch(BATCH_SIZE)"
      ],
      "metadata": {
        "id": "25NPEy_ZWRuv"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(train_dataset, epochs = 5, steps_per_epoch = math.ceil(num_train_examples/BATCH_SIZE))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fHdsWZuHW8nd",
        "outputId": "14a08e13-2c64-4712-c430-6193612c00ba"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "1875/1875 [==============================] - 16s 4ms/step - loss: 0.4942 - accuracy: 0.8260\n",
            "Epoch 2/5\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.3658 - accuracy: 0.8677\n",
            "Epoch 3/5\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.3345 - accuracy: 0.8775\n",
            "Epoch 4/5\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.3129 - accuracy: 0.8854\n",
            "Epoch 5/5\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.2916 - accuracy: 0.8922\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7a0441e8dea0>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_loss, test_accuracy = model.evaluate(test_dataset, steps = math.ceil(num_test_examples/32))\n",
        "print(\"Accuracy of the test dataset\", test_accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jfZEivV_XLdV",
        "outputId": "5aedcf04-8fdc-48cf-d547-65cc3869757c"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 3s 8ms/step - loss: 0.3543 - accuracy: 0.8728\n",
            "Accuracy of the test dataset 0.8727999925613403\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for test_images, test_labels in test_dataset.take(1):\n",
        "  test_images = test_images.numpy()\n",
        "  test_labels = test_labels.numpy()\n",
        "  predictions = model.predict(test_images)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eZbvaY39X8O7",
        "outputId": "d45faa1e-cd6d-4d0b-e811-a7a24983e75e"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 125ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q0Xe1mtYY43K",
        "outputId": "2c24e3bd-05ef-4521-faa5-49cc4049462a"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(32, 10)"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O6wkOo4kY8w0",
        "outputId": "a79cc4a6-352c-4306-b7ce-bf68bb8a6b4c"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([3.9228613e-05, 5.0742337e-06, 1.0567209e-02, 9.7906404e-06,\n",
              "       9.5926750e-01, 5.4454490e-09, 3.0107182e-02, 4.7821139e-08,\n",
              "       3.9870292e-06, 5.1803720e-08], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.argmax(predictions[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4XlJyzhZZG5q",
        "outputId": "c6266f2c-d8e2-45e3-9850-5b89d8483e45"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_labels[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jAeVoTnWZO2g",
        "outputId": "6d5803a3-4fdb-45b8-f848-d509466da59f"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_image(i, predictions_array, true_labels, images):\n",
        "  predictions_array, true_label, img = predictions_array[i], true_labels[i], images[i]\n",
        "  plt.grid(False)\n",
        "  plt.xticks([])\n",
        "  plt.yticks([])\n",
        "\n",
        "  plt.imshow(img[...,0], 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",
        "def plot_value_array(i, predictions_array, true_label):\n",
        "  predictions_array, true_label = predictions_array[i], true_label[i]\n",
        "  plt.grid(False)\n",
        "  plt.xticks([])\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": "ZUhEICmAZcMH"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "i = 0\n",
        "plt.figure(figsize = (6, 3))\n",
        "plt.subplot(1, 2, 1)\n",
        "plot_image(i, predictions, test_labels, test_images)\n",
        "plt.subplot(1, 2, 2)\n",
        "plot_value_array(i, predictions, test_labels)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 277
        },
        "id": "MI-Q88j-Zxeh",
        "outputId": "77d42637-7a44-4df4-f6c1-ad4d2e98c3ee"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x300 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "i = 12\n",
        "plt.figure(figsize=(6,3))\n",
        "plt.subplot(1,2,1)\n",
        "plot_image(i, predictions, test_labels, test_images)\n",
        "plt.subplot(1,2,2)\n",
        "plot_value_array(i, predictions, test_labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 277
        },
        "id": "JLwW-cDOaJ9S",
        "outputId": "655f6b2c-8150-4d66-a37a-ba25aeabf47c"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x300 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "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, test_labels, test_images)\n",
        "  plt.subplot(num_rows, 2*num_cols, 2*i+2)\n",
        "  plot_value_array(i, predictions, test_labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 557
        },
        "id": "Rlx3wzqEaU_K",
        "outputId": "42b3ec80-77b9-45e9-876a-c9eff933b1ad"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1000 with 30 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "img = test_images[0]\n",
        "print(img.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZrRXa-ldakHp",
        "outputId": "5b003962-b398-4fd1-e2ca-9c701c0cb398"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(28, 28, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "img = np.array([img])\n",
        "print(img.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DaF4fUZuazbK",
        "outputId": "1db4cdaf-437a-4e54-a422-bb479696e965"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1, 28, 28, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions_simple = model.predict(img)\n",
        "print(predictions_simple)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Aja9UZjoa4sO",
        "outputId": "105db183-37cc-467c-a8ca-b65cc82ca324"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 27ms/step\n",
            "[[3.92286129e-05 5.07422374e-06 1.05671985e-02 9.79062133e-06\n",
            "  9.59267497e-01 5.44544898e-09 3.01071685e-02 4.78210467e-08\n",
            "  3.98702923e-06 5.18037204e-08]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_value_array(0, predictions_simple, test_labels)\n",
        "_ = plt.xticks(range(10), class_names, rotation = 45)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "id": "md_kzQdFbH14",
        "outputId": "47a762cd-8d1b-4819-c235-918fddae02b9"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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WrVS+fHnNmzfPeT+xthDE30/Gjh0rl8ulli1b6rfffvNZTf369dOTTz6pSZMmafjw4apQoYLy5MmjTz75xGc1ecv169d16NAhTZ8+XUWLFlWHDh20e/dur/xuwkA8r7/+ulwul0JDQ3Xs2DGP92bPnq1kyZLp0KFDXqvHfaDu379fP/zwg0fT9fjx45UlSxa99tprOnr0qNdqehg2btyoHDlyqG7dutqyZYvzui8vYLdu3VKXLl3Uu3dvSdKlS5cUGRmpiIgIvfHGG/rpp598VttfmTNnjoKDg9W5c2ePOnfs2OHDqv6a+++9Z88effvtt1q+fLnH+++8845PA4G7vl9++UWBgYF6//33JUmRkZFq1qyZypcvr/nz5zufX7Fihc6cOeOTWv8u97/t7hAzevRouVwuDR8+XJcvX/Z6Xb/88ovCwsK0ePFi57Xt27erQ4cOypkzp+bMmeP1mhKS++9w9OhR7d27VwcPHnTemzp1qooWLapOnTo5geCtt97y2DYPk7Vh4I+S/Lhx4+RyufTmm2/q3LlzzuubN29WWFiYfv75Z6/U595JFi5cqLCwMIWFhSl//vwqVqyYjh8/7tSaNWtWvf76637ZQuD+N+zcuVNLlizRrFmzdPbsWUnS+vXrFRoaqpYtW2rbtm2+LNPRokULFS1aVIcPH1br1q1VtWpVlS1bVk888YQaNWrk09rc2zIyMlIzZ87UBx98oKNHjzr78Zw5c5wWgg0bNujNN9+Uy+XShQsX/LKVwF3TokWLFBISogIFCih16tRq2LChRwvHO++8o4CAAI0ePdondW7YsEHTp0/XgAEDPF7fsmWLnnvuOZUvX15jxozRkCFD5HK5dOLECZ/U+Xe4t/2aNWvUo0cPvfTSSxo0aJDz/qhRo+RyuTRixAivB4Ljx48rKChIn376qcfrP/74o3LkyKHMmTPrs88+82pNCSX+OT5fvnzKnDmzcufOrfr16+vWrVuS4gJBqVKlVLlyZT333HNyuVzaunVrgtRjZRiIHwQOHTqk3bt3e1z43SfS3r17a926ddq9e7dq1qypkiVLerU5cP369QoMDNTUqVN18+ZNrV+/Xi6XS5MnT3Y+M378eKVIkUJvvfWWoqOjvVbbg1qwYIGyZ8+uYsWKqUyZMgoMDNTq1aslSevWrVNoaKhat26tH374wat1uQ/ELVu2OC0u33//vYoWLaqAgAA1bdpUixYtkhR3wSpSpIguXLjg1RrvrnXhwoVKnz69qlatqkyZMql69er65JNPnMcGn3/+ufLly6eCBQsqJCTE69v0QcQPJitXrlS6dOk0depUSXEtRi6XS3Xq1NGuXbuczw0aNEiPP/64Ll68mKC1RUREaOTIkc7Xly9fVs2aNeVyudSwYUNJ8jjGfvzxR3Xs2FFhYWEqUKCARyuXv1u0aJECAwPVtWtX9enTR7lz51aRIkV0+/ZtSXEtBI899piGDBmiK1euJEgN7n0h/v+fO3dOzz77rHr37u1xTpakpk2bqmLFiipZsqRWrVqVIDV529q1a5UyZUpNmjRJq1ev1oIFC5QzZ04988wzznE9b9489ejRQ40aNfI4Lh4268JA/JPRgAEDVKhQIaVIkULlypXTyy+/7Lw3bNgwuVwuuVwuvfjii2rUqJFzoHgrEIwePdqp6dChQ8qePbu6dOlyz+cmTpyo/fv3e6Wmv2Pz5s0KCgpyTvZ79uyRy+XSO++842zDdevWKU2aNOrQoYNu3rzplbriX1xDQkL06quv6uTJk4qOjlZUVNQ9F9Hu3burVq1aioqK8kp997Nu3TplypRJ06ZNkyTt2rVLyZIlU6lSpTR58mRne+7atUv//e9/ndYjf7Fo0SLt3btXUtz2v3Llirp3764hQ4ZIitu/c+bMqVatWilLliyqUqWKduzY4fyt7r4wPGx37tzRtGnT7mml+u6779S4cWOlSZPGOcbc5wEp7nHSr7/+6rR4JQYnT55UwYIFNX78eEnS4cOH9eSTT6p9+/Yenxs6dKiCgoISZNvHP4fG356S9N577yldunQaN26cs12vXLmiJk2aaOLEiSpXrpxee+21h16TLwwdOvSeVseDBw8qR44catq0qcfrd2+nh826MOA2YsQIpU+fXl9++aXWrl2rt956SwULFvT4w3zwwQdyuVwaP368Ll26JOn3zlve8Pzzz6tt27Y6f/68QkJC1LFjR+fkOH36dI0aNcprtfwTs2bNUsuWLSXFnezdzdhuV69elRTXFHvgwAGv1vZ///d/SpkypaZMmfKHnQW3bNmi3r17K126dD59/h4dHa3hw4crIiJCUtzJwn3hrFmzpnLmzKlp06Z5dd/8O3bu3KnChQurYcOGzgX11q1bWrx4sfbv368LFy6oePHiateunSTpyy+/lMvlUvny5bVnzx6v1/v1119r8ODBzteRkZGqUqWKQkJC9Msvv0iSX7bCPai9e/cqT548un37tk6cOKHg4GB16tTJef/LL790/vv8+fMP/ffHDwITJ05Us2bN1Lx5c2conSS98cYbypgxo+rVq6eOHTuqTJkyKlasmKS482K1atX88vHX3/Xiiy+qRIkSztfu/eqTTz5RgQIFvBrqrQkD8Xecy5cvq06dOnrvvfec16KiojR37lzlz59f//nPf5zX3377bblcLo0dOzZBmynj3wFdv35dUtzdVHh4uJ544gl16NBBUtyBFBMTo65du+rll192PusP7j44hw4dqqpVq+ro0aPKli2bOnbs6JwIFi1apIiICJ/Uf/PmTbVu3Vp9+/aVFLc/bNu2TQMGDNDQoUN1/vx57dy5U926dVPRokX9oiPevn37tHfvXl27dk3lypXTSy+9JEk6cuSI0qVLpwIFCjitBv7o448/VuXKldWkSROnhcDdEjR//nyVKlXK6Zy7ZMkS1apVS/ny5fN6X5jY2FhNnDhRLpdLb731lvN6ZGSkwsPDlSNHDqdOfw1ff2T37t2KiYnR6dOnValSJS1cuFDZsmVTp06dnIvQ/v379fzzz+vbb7+VlLCdet2jBl577TW9+uqrCg0NVdu2bZ33Z86cqZ49e6pGjRrq0qWLs780atRIERERiXYER3xfffWVcuXKpblz53q8vmTJEoWGhurkyZNeq8WKMHD3ThMbG6tixYp5PBaQ4pphGjdurBYtWni8PmLECLlcLk2cODFBDg73z1y2bJlq1KihVatWKSYmRj///LPKly+vXLlyacWKFZLimiUHDhyoJ5980qOjlb/47rvvnIT//fffq3LlykqfPr1zkLv/FhEREWrZsmWCPY/8Ky1btlSFChX0yy+/qG3btqpatapKlCihJ554wmnN2L17t096h99vH3OfrDds2KCCBQs6d8yRkZGqVq2aWrdu7ZejSuLfQX/44YeqXbu2mjZt6tFreuTIkcqVK5ezrQcMGKB3333XZ3ff169f15QpU5QkSRLnMYYk/fDDD6pdu7bSpEnjlx1247t72+3atUvBwcE6duyYLl68qMqVKytJkiRq1aqVx+deffVVPfPMMwm+38+ePVtPPfWU/vvf/0qKC4SpUqVyOpG6xT93nz17VgMHDlT69OmdQJlYuI/pkydP6uDBg07/o19//VUNGzZUnTp1nJESt2/fVv/+/VWqVCmv9lN65MNAZGSkM2a2b9++zkxt3bp1U3h4+D071eDBg1W9enXdvHnTY0ccM2ZMgu6A7g49b775ptMUKUlbt25V0aJFVbBgQYWFhal69erKkiWL3/TAj+/WrVuKiIhQ1apVJcU953M/A542bZqio6N1+vRpDRgwQBkyZPBaE/D9Lq5Lly5VyZIllTRpUjVt2lQLFiyQFPf4pWTJkj7rH+Cu9bvvvtOIESPUv39/ffPNN85d0apVqxQaGqqlS5cqJiZGgwcPVrt27ZxHLv7m7p7rBQsWVNKkSdWsWTPnkcH+/fuVJk0aPf3006pYsaLSpk2r7du3e6U+9939iRMn7pmnYcKECfcEgu+//16NGzf2+mOtv2PUqFFq0qSJxz68adMmhYWFOc+d9+zZowwZMqhOnTr69NNPtXLlSr3yyitKmzZtgrSE3bp1y6OeSZMm6Y033pAUdxMUFBSksWPHaurUqUqaNKnzyMjt/Pnzatu2rXLmzKkff/zxodeXkOKPnMmTJ49CQ0OVNm1adevWTYcOHdLhw4fVpEkTZc+eXXnz5lWVKlUUFBTk9XP8Ix0Gzp49K5fLpW7duqlTp05KnTq10xtz+/btypQpk1q3bu0M1bh69aoqV67sNMlL3uksePjwYeXKlUsffPCB8ztv376tzZs368aNGzp//ryWLVumvn37as6cOV6d6+Dv2rJliwICApyUe/HiRdWpU0eFChVSunTpVL58eYWGhnptR49/cR0yZIj69+/vDFu6evWqvv/+e4/Pd+nSRfXq1fPppEMLFixQYGCgKlWqpNKlS8vlcunVV1/V8ePHdf78eWcSlvz58/vkpPF3rVy5Ui6XS2PGjNEXX3yhfv36qVChQmrSpIlzAd61a5c6dOigPn36JHhInDhxotasWePcPc+fP18hISHOEMc1a9Y4F013IIj/yMCfJ6SS4vrDBAQEeITE5cuXq3DhwpJ+P6dt3bpVVatWVfbs2ZUvXz6n0+bDtmDBAjVq1EhFixbVm2++6bx+6NAhnTt3TsWKFdPw4cMlSQcOHFDWrFnlcrnUr18/j59z9OjRe+Z/SSzWrVunlClTasyYMdq6davef/99lSlTRg0bNtThw4d17tw5bdiwQa+++qref/99n3QIf2TDwPr163X48GFt3bpVAQEBSpkypdasWSPp97uB//73vwoNDVWxYsVUsGBBlS5dWgULFnROBAndQcX983/66ScVL15cW7du1blz5zRq1ChVqlRJadOmVcWKFbVx48YEreOfir99YmJinK979eqlatWqOQduVFSUtmzZosmTJ2vt2rVe7+m+cOFCpU2bVi1bttRLL72koKCgex4F7d69W7169VK6dOm0c+dOr9YX34EDB5QtWzZNnTrV2Z5z5sxRhgwZnAmRjh49qilTpmjcuHF+OYrELTY2VjExMWrfvr2aN2/u8d6HH36ofPnyqVmzZs5d9p07dxL0mHP/7Lx58ypbtmz6/vvvtXPnToWGhmrUqFFau3atwsPDlS1bNs2fP98Z6z158mRn3H1isXbtWgUGBqpt27aKiYnRkiVLVKRIEUmex+3Nmzd15swZnT17NkFalyZPnqw0adKoZ8+eioiIUNKkSTVhwgTn/c2bNytbtmzOfnzgwAG1bNlSq1at8uiTkVg7C7rr7t27t8fjD+n31kl33yVfeyTDwJUrV9SuXTv17dtXmzZtUooUKZQkSRL17NlTp06dkvT7H+nAgQOaM2eO+vXrpw8++MC5W/DG80r38/KjR48qffr0Cg8PV6ZMmdSgQQO9++67WrFihfLly+c3U7Lez6pVq7R48WKPzpVLly5V7ty5nU5IvuTuee9udTlw4IDSp0+vjh07Op/ZvHmzOnfurMKFC3utedrt7NmzioyMdFqndu3apZw5c2r79u0eJ8BZs2YpSZIk2rBhg1frexi6du2q6tWr3zM0KiIiQilSpFB4eHiCT+Z1dwtfpUqVFBYWphkzZqhPnz4e7zVu3PieQDBt2rRE95x69erVCgwMVPfu3fX555+rTJkyWrlypdatW6c9e/Zo69atWrZsmU6fPp0gv3/q1KlKnjy5x4x5LVq00Pjx450+CQcPHlSuXLnUrVs37d27V+Hh4WrYsKGz7ye2Tpp/pFevXqpevbru3LnjsS+OHDlSGTJk8IvHfI9kGJDiTp7Zs2d3+gusWrVKSZIkUdeuXf9y5/fGDrh9+3YFBARo06ZNkuKmju3fv79Gjx7t0XmnevXqGjduXILX809cv35d3bp1k8vlUoMGDZymPklq3bq1x5AZX9m2bZuefvppSXGhyz1tr1tkZKSkuMcb7qDoLXv27FG5cuVUs2ZNNWrUSHfu3FFkZKSSJ0/udKyKP/dCwYIFPUa6JBajRo2676OhTz/9VIUKFVKLFi0StLXIffI9fPiw3n//fadPTqlSpeRyuRQeHn5PUGncuLFy5cqlmTNnJvj47ofp7jvob775Rv/617+UKlUq5cqVS6GhocqcObPy5s2r4OBgZcmSJUEeO65du1Yul0tDhw71eL1w4cJ6+umnlTp1apUrV07jx4/X6NGjFRwcrOzZs6t06dJea5n1pjFjxigwMNCZVtj9b1u5cqXy58/vFyvQPnJhIP4O9Pzzz6tJkybOlJpffPGFkiRJou7duzvThjZp0sRjwRFvOXLkiOrWras0adJo8+bNkjxP/Hfu3NGAAQOUKVMmjw6F/uj777/Xa6+9pkyZMqlUqVIaP368Fi1apOrVq+uLL77wai3uv//atWv1zTffaO/evSpbtqxWrVp1zzCqHTt26Pnnn/fJugO7d+9WunTpnLUl4t8tNG3aVPnz5/focX/r1i0VL15cH374oddrfVDubb9v3z7t2LHD43FLyZIlVaBAAUVGRjodyfr27asBAwYkyFh2N/d23blzp5566ik1bNjQ40712WefVVBQkFavXn3PTcCzzz6rQoUK+WzEy9/h3vZXrlzRtWvXPN5bv369nnjiCdWuXVvHjh3T+fPndfnyZZ07d86ZP+Vh279/vypUqKD69es7gbtRo0bKnTu35s2bp+XLl6tAgQIqUaKEduzYoZMnT2rTpk3O3yuxzuPgrvvgwYPat2+fx7nFPTR1x44dzjEQERGhYsWKJdjf4e94ZMLA/Tr6rV27Vg0bNnTuvqW4CTUCAgJUo0YNFStWTE899ZRXkn/8kBJ/cYpmzZopZcqUzlSmMTEx+vjjj9WgQQNlzZrVrzqHuevesWOHFi9erPnz53vMvHb27Fl16NBB1apVU8qUKZ0pnb2R8OP/jrVr1ypVqlRatGiRDh48qBIlSihFihR68cUXPb6nV69eqlKlitdXaDt//rzKly+v7t27e7zu3oe/++471axZU3nz5tXq1au1fv16DRw4UBkyZPAICP5o/vz5ypgxo0JCQpQrVy5nmOmNGzdUqlQphYaGqmTJkqpRo4Yee+wxr4wo2bdvn4KCgtS/f//7jtsuV66ccuTIoW+//fae84i/zeR4P+59/6uvvlLlypVVrFgxVaxYUbt373Yec6xZs0apUqVS586dvTa3x/79+1WzZk3VqVNH5cqVU7FixTyGZG7dulUul0tLly71+L7ENn/AjBkznFlWpbgVLkNCQpQxY0blzp1bzZo10+3bt3X27FnVrFlTqVOnVsmSJVW1alWlTZvWb0ZHPBJhIH4z13vvvef0EI+Ojla9evXUuHFjj8+vW7dOPXv2VJ8+fbzaR2D9+vVOre4D+MiRI2rWrJlSpUrl7BS7du1Sz549vbYo0t/hPtnnyZNH2bJl0+OPP64vvvjC6WEdGxurkydPauTIkSpcuHCCzqV9PydOnNCoUaM0bNgw57Wvv/5ayZIlU8eOHbVixQpt2bJFERERPussuGfPHuXKlUvr16//wxPfDz/8oFatWikgIEC5c+dWgQIF/CoYxufel8+fP6+wsDB98sknWrNmjd59910lT57cYxGciRMnauDAgerTp49XnsHfuHFDTZs2VdeuXT1ev337tg4dOuSE2Zo1aypbtmzauHFjorsYSXH9dFKnTq2BAwdq9erVKlu2rAoXLqyvv/7aCQSrV6+Wy+VS165dvdYEv3//flWvXl1p06bV559/Lun3zsZbt25V/vz59d1333mlloRw9uxZ1a1bV6VLl9bcuXN16tQphYaGatKkSVqzZo3mzp2r4OBgVa1a1dnmU6dO1bBhwzRs2DC/6gCc6MPA9u3b5XK5tGTJEvXo0UPp06f3GAN8+vRphYWFOfML3K9jijeCwOXLl1W9enVlyJDBScfxlyguUqSInnjiCadJzR+bybZt26agoCB98sknOnPmjM6cOaP27dsrMDDQWX42/knG22P1Dx06JJfLpbRp097T83vevHkqVqyYHn/8cRUsWFAlS5b0emdBt1mzZilZsmT3XUbWvV9GRUVp3759+u2333T06FGfri//IL755hv1799fr7zyinPxuXr1qj744AMlTZr0nrnkvXUxio6OVoUKFZzlh6W4oXcRERFKkyaNgoOD1aRJE0lxgSBt2rROf43E4tChQypRooTGjBkjSfrtt98UGhqqjBkzKmPGjPr666+dR5Dr16/3+mRlv/zyi8LDw1WrVi2PDrB169ZV5cqVE2X4im/79u16/vnnVaVKFfXs2VOtWrXyaG3et2+fsmTJoueff96HVf61RB8GpLhpb1OmTKnAwECPO707d+4oOjpaQ4cO1SuvvKLr16/7dMfbtGmTatWqpdDQ0Hs67bz44otKkiSJMmfOrBs3bvj8AFm5cuU9HS0XL16sYsWK6eLFix4n87Zt2ypz5szOiIK7VyNLKFFRUfrtt9+0du1apw/I7Nmz5XK51KxZs3sWjzlz5oz27dunQ4cOJfgKeH9m48aNSpEihTPR0f2MHz9ezz77rNcWb/pf3Lp1SwMHDlTSpElVvHhxj/fcgSBFihTO0EjJe2Hg8uXLCgsLU4cOHfTTTz/pnXfeUd68edW4cWONGzdOH330kbJnz+7MI1CtWjW/nlDofn7++WeNGDFC165d06lTp5Q7d25nDZCSJUuqcOHCWrJkiRPSfMH9yKB27dr69ttv1ahRI49HtL4+3/2vtm/frlatWik0NFTPPPOM87r7pu6jjz5S/vz5dfToUa+dH/+uRBsG4u887hUGkyVLpoULF97z2Y0bNypTpkzOAhzefIZ9+/Ztjw49u3btUrVq1RQaGqojR444r0dEROjzzz/3ea9S9zTI7ubE+BfUqVOnKlWqVM4O7r5QHThwQMHBwc5SwN7w888/64UXXlBYWJhSpEih1KlTq0WLFjp58qQWLVrkzC3vDx1z7nbixAllzJhR9evX99gH4u+XvXv3Vv/+/f3uhBFf/NqOHDmioUOHOtN2x3ft2jWNGjVKjz/+uH777Tev/5tWr16tZMmSKXv27EqdOrUmT57sXPBv376tGjVq3DPvRGLj/ve8/PLLaty4sTNUrXXr1nK5XHrqqafu6Vjobfv371edOnWUPHly5c2b1wkC/tgK+k/s2rVLzZs3V6pUqTyWmZfiZlkMDg72yynD3RJtGHAbOnSoOnbsqN27d2vo0KFKnjy5Zs6cKckzMEyePFlFihTxygxW8Tv0NGzYUIULF1b79u319ddfS4pbNax69eoKCgrS66+/rtatWytz5sx+MbOgu5l6wYIFSp48ubp37+4Mdfztt99UoECBe5Ybds+guG7dOq/UuGPHDmXOnFmdO3fW9OnTtW/fPvXr10+hoaHKmzevjh075rQQvPPOO85oEn+ycOFCBQQEqHXr1h6d6KKiojRgwABlz57dL/uMSL/v33efxI8dO6bXXntNgYGB95wMo6KivDrP+t2OHTumLVu23PO4JSYmRk2bNtWgQYOcRcD8mXvbHzx4UD///PM9jzRq167tMXNfz5499eOPPzotZ762b98+devWzat9tbxp7969atGihUqXLq1JkyZJigvDffr0UVhYmF8/7kt0YSD+s/6VK1cqT548zoQtUtwiJ8mTJ3emw5XiDogZM2aocePGWrlypVfq/OKLL/TYY4+pR48eevPNN1WiRAmVKVPGWUP81KlT6tGjh0qUKKFnn33WL3qUfvzxx5o5c6bTGXDx4sXOdM6//vqrYmJiNHbsWJUpU0Zt27bV5cuXdeLECb3xxhvKkSOHV044O3bsUKpUqTRgwIB7TiTz5s3T008/rVKlSunmzZuaPHmykidPrtdff93vAkFMTIwmT56sZMmSKSwsTG3btlWXLl1Uv359ZcyY0e87C65evVpt2rRRy5YtPS4+x48f18CBA5U6dWqPHtb+6NatWxo0aJCyZMniVx25/kj8Oe7z5cunggULKlOmTGrZsqVTf4MGDZQvXz59/PHH6tKli9KmTeu3d6OPWhBw27lzp1q0aKGAgAAVLVpULVq0UFhYmDNizF8lmjBw95Cq2bNnq0ePHurZs6ckzx3rtddek8vlUvfu3VW2bFkVLFhQUlxPeHcHvYQSGxury5cvq0qVKh7zcJ89e1Zdu3bVM88849GcfvnyZb+Y6/zOnTsqWbKkihQpooULFzp3/u5A8PLLL+vatWu6ceOG3n//fRUqVEjJkydXwYIFlTVrVo9AllCOHTumDBkyqGnTps5rsbGx96yM969//csZj//2228rKChI586dS/D6/onNmzerSZMmKlKkiCpUqKB+/fr57YUp/sUoTZo06tChg/r166ccOXKofv36TlA/fvy43njjDblcLn3yySc+rPiPffbZZ+revbsyZcrkt8HrftasWaPAwEBNnTpV165d0/Lly+VyuTR79mxJcS0wFSpUUP78+VW4cGG/uMl41DzIM/+9e/eqVatWypQpk4YMGeLXLQJuiSIMtGnTxlk5zD0spVy5cnK5XKpWrdp9e2WPHTtW4eHhat26dYJfbN1zsEtxs/K5L6zuTknu986dO6dChQopIiIiQev5u9zb7/r166pZs6aKFy+u+fPn/2EgiI2N1fXr17VgwQKtW7fOa2OxDx8+rJIlS6p+/fr3THUc/8CsWLGiGjRo4Hzty+bpB+GvU66699v4x9X27dv11FNPOf0CDh8+rMyZM8vlcql8+fJOMDty5IiGDRvmkwmd/spPP/2kypUrq2HDholuiuEhQ4Y4M2j+8ssvyp07t8fU2m6nT5/2u9awxM59jrl8+bJu377tTEb1R6Hgxx9/VMeOHRPFXBVSIgkDS5cudTqbuJ9fR0dHq3nz5sqSJYs++eQT54If/8QVf+awhJpYKP7PnTNnjl544QUdPnxYFStWVNu2bZ2a3HVFRESoWrVqfncBcJ/Er1+/rmrVqqlEiRKaP3/+PY8Munbt6tNOju5eyeHh4R6BIP4BWblyZbVs2fK+7/mj+01I5Wvxp/CdMmWKfvjhB0lxcza4W+OOHTumnDlzqkOHDs48+A0aNEgUHcN+/fVXv+xc+mdiY2NVp04dvfbaa7p586ayZs2qjh07OvvM+PHjnRYCPFzubfzll1+qXr16KlGihOrVq6dly5b96fclhtFAbn4dBu4+MU6dOlXNmjVzmvWio6NVp04dFSlSRPPmzXOGztx9oU2oE+yuXbs0ZMgQxcTE6LffflPOnDmddQRWrFghl8t1z1zyzZo1c1YS8xfu7eO+g46KilK1atXu20Lw2GOPqW3btvcM2/Om+IEg/oQlMTExOn78uGrVqnXPvBJ4cPebwtc9EkeSs4hSgwYN1KpVK8XGxuratWsqUaKEXC6XatSo4avSH3mffvqpypcvrwwZMqhLly4eraLt2rVT165dE9UFKDFZtmyZUqRIoREjRmj+/Plq27atXC6XV2bR9Aa/DgN3mzRpkp5++ml17NjRIxDUqlVLRYsW1eeff+61A8E92dGECRO0Zs0avfXWW+rcubPH8J0JEybI5XKpRYsW6tWrlzp16qTAwECvz8r3IDZv3qxmzZo5vZPdgeDuFoJ58+Ypffr0Hosp+cIftRD069dPhQsXTjRNc/7qr6bwvXTpkgoXLuzM83/z5k21b99eX331lV+Mikns3Bf5EydO6KeffnK+3rZtmypWrKj8+fM7M61eu3ZNAwcOVJYsWfx2BEpi5Q7GUVFRqlevnkaOHClJOnnypLJnz37fRzSJlV+GgfjP4O/20UcfqVixYmrXrp1HIKhbt66yZMmi1atXJ3h9e/bsUcqUKTV48GBJ0uuvv+6M5b17Kco1a9aofv36qlq1qho1auST6W8fxMyZM1WkSBE9//zzTifL+C0ECxcudAKBPyy3KXkGgm3btmnEiBEKDAz02cyCj4o/m8L3xIkT2r9/v6KiolS8eHE1aNBAhw8f1quvvqqnnnoqwZbDtdGCBQsUEhKikJAQFShQQGvXrpUUN2S5bNmyypkzp8qXL6+qVasqc+bMiaojpD8bPXq0R7+u2NhYXbp0STlz5tSGDRt09uxZ5xGN24wZMxJ9EPPLMBDfl19+qSVLlmjNmjXOa1OnTnUCgbu37O3bt9W7d+8Efxa/a9cuZciQQfny5XNeO3v2rEaOHKkkSZI4Y0ul3x9XuFsr/GHUgPTHTedz5sxR+fLl1bx5c+cZcVRUlMLDw5UrVy5nQRF/anrfv3+/6tatq4wZMyp58uR+P3wnMfirKXyzZ8+uGjVqaNGiRcqVK5eyZs2qkJAQLkYPgfsmaM+ePcqZM6dGjRqltWvXKjw8XMHBwc6slbt27dKMGTP08ssva8qUKX6/smlicePGDb377rsKDAzU66+/7rx+584dtW7dWsOGDXNWP3Wf38+ePasXXnhBn332mV+dG/8uvwoD3bp185iyNCIiQhkzZtSTTz6pggULeqzyNnXqVBUvXlwdO3Z0lgB2S6hAsH37dqVKlUqVK1dWlixZ1K1bN+e9ixcvOi0En376qaS4i6b7f+6vfeV+LS379u275yQya9YsVahQQc8995wTtK5du6YGDRr4bfPvTz/9pPr16ztrheN/8yBT+ObLl08RERH69ddf9d1339Ei8A/db8TGpk2bNGPGDPXp08fjs40bN3YCgS+nFn7UXbhwQePHj1e6dOk0cOBA5/X+/fvL5XKpVq1aHis/9u/fX3nz5vWYTTQx8pswcOHCBfXs2VP58+fXW2+9pWPHjqls2bLasWOH9u7dqzFjxihPnjxq166d8z0fffSRQkJCnGVSE/JiGxkZqeTJk2vIkCG6c+eOpkyZogwZMngEgkuXLmnQoEFyuVzOLIj+wH2iOXHihObOnatZs2ZpwYIFqlatmjp16nTPHA4zZsxQUFCQWrRokWgWbfHGMtQ2+bMpfG/duqVnn31WL7zwgo+rTNzuHrHhvqlxd8SsWbPmPft148aNlStXLo/JwfBwxL9xu3r1qsaMGaN06dJpwIABzmdatmypjBkz6pVXXtHgwYPVpk0bv1qG+H/hN2FAiuuUMWTIEBUsWFBNmjRRmzZtnLv8S5cuadKkScqVK5fat2/vfM+yZcu8Mkxv/fr1Hi0Tly5d+sNAMHjwYLlcLs2dOzfB6/or7hPOjh07lDNnTuXPn1/JkydXqVKlVLhwYYWHh6tHjx733PWXL19eGTNmVPv27XXjxo1E3fyFf+bPpvBt0qSJBg0a5HECxYO734iNL774wnm/Vq1aCgoK0urVq+85vz377LN6+umnPYZO45+53777ww8/6NixY7p48aLGjh2roKAgj1k2Bw4cqMaNG6tkyZLOVPiPAr8IA/GbyE6ePKnBgwcrNDRUZcuW9fjcpUuXNHnyZOXNm1eNGjXyeM+b4/bjTz5xv0Bw4cIFvf322z6f0CR+EEiVKpX69u2rkydPaunSpapVq5YqVqyol19+WUWKFFGPHj2cZq4bN26oQ4cOevvtt+mVDw+JbQpff/ZXIzbKlSunHDly6Ntvv73nMR/H5cNx6tQpSb93Wj948KAyZcrkdEK+cOGCEwj69u3rfN+tW7d0+/Ztv5sv5n/h8zAQfyd3T2Zz5swZDR48WOnSpdOgQYM8Pn/58mWNHj1azZo184ux+vEDwd09UP3B/abwleKGaQYFBenEiROaMGGCSpQooeeee04zZsxQv379lD9/fr+dwhe+kVin8PVHfzZi49ChQ848HjVr1lS2bNm0ceNGvzjfPUrmz5+v0NBQj0eh58+fV1hYmMc6K/EDwd3Xo0eJT8NA/J37zTffVIMGDZwJHNyBICwszBnC5+aeEvfun+Erly9f1tSpU+VyuTyak/zBH03hu3LlSgUFBWnfvn2SpOnTp6tu3brKmjWrChcu7JW1BpB4JOYpfP3RX43YCA4OVpMmTSTFBYK0adMmmv47icWKFStUr149lSxZ0umvcfDgQeXJk+eeGyF3p0KXy6Vhw4b5otwE5/OWAUnq27evMmfOrBkzZngkslOnTmnw4MHKmzevhg4des/3+cvdtxT3CGP69Ol+OdbUPR6/Ro0a2rt3r65evaonnnjCo9lLivs3nDhxghYB3FdinMLXXz3IiI3s2bM765tUq1bN6cCJh2fdunVq2LChihYtqv/+9786cuSIMmXKdN8p19391vxxvY2HwSVJxodWrVpl2rRpYxYtWmRKly5tJJmLFy+ao0ePmjx58hiXy2VGjx5txo4da/7zn/+Yl156yZfl/ilJxuVy+bqM+zpw4IDp0aOHuX79utm5c6d58cUXzZgxY4wxxty5c8ckS5bMxxUCdlmzZo0JDw83WbNmNRcuXDCjRo0y1apVM7lz5zbR0dGmbt265vHHHzezZ8/2damPnPjn6nXr1plx48aZEydOmM6dO5tZs2aZ8PBwExoaamJjY010dLS5deuWKVCggClTpoyPK084Pr8CXLx40WTJksWUKlXKbNu2zSxdutTMnj3bXLlyxVStWtW8//77pl27diY4ONi8+OKLvi73T/lrEDDGmDx58phx48aZzp07mzRp0piGDRs67yVNmtSHlQF2qlq1qjl06JA5e/asyZ49u8mQIYPzXtKkSU3atGlNrly5TGxsrDHGmCRJkviq1EdO/HN15cqVzZ07d8ykSZNM//79zfnz502aNGnMjBkzjMvlMo899piJiYkxn3/+uQ8rTnhebRm4ceOGSZkypcdr27dvN8WKFTM1a9Y0kZGRpm7duqZKlSomICDAvPzyy+bLL7/0SGMxMTFcvP4Hv/zyi+nWrZuRZF5//XVTrlw5X5cEIJ7bt2+bt956y3z88cdm3bp1Jk+ePL4u6ZHhbhHYtm2b+fXXX01sbKypU6eOMcaY1atXm48//tjs3LnTzJw50xQuXNj5vqioKPOvf/3LV2V7hdfCwGeffWYOHjxoBgwYYAICAowkExsba5ImTWo2btxoFixYYJ555hlTtWpV88QTT5ioqChTqVIlM3LkSFO1alVvlGiNAwcOmF69eplz586ZMWPGmGeeecbXJQEwxsycOdNERkaaefPmmeXLl5uiRYv6uqRHzsKFC02bNm3Mk08+aU6dOmUaN25sPv30U2NMXCAYP368OXPmjBk+fLipUqWKMca/HwE/NN7omDBlyhS5XC4tX75ckudMT5GRkTp69Kjz2du3b+vKlSuqWbOmypQp80iN4/Qn+/btU5MmTTy2PQDfYcRGwnFfb6KiolSxYkV9+umnOnTokL766is9/vjjatCggfPZdevWqWrVqqpUqZJVszwmeMvAZ599Ztq1a2eWLFliateubdy/zuVymUWLFpmOHTuahQsXmkqVKpno6GgzYcIEs2DBAnP79m2zceNGkzx5chMbG8vzsgRw+/Zt89hjj/m6DAD/39mzZ01AQIBJmzatr0t55Kxatcp89tlnJmnSpGbEiBEmY8aMxhhjNm7caBo0aGDKly9vFi1aZFwul/n2229NaGioCQ4O9nHV3pOgV9jp06ebF1980VSuXNnUrl3bGGNMbGyscblcZsmSJaZJkyZm2LBhplKlSsaYuIBQuHBhU716dfP999+b5MmTmzt37hAEEghBAPAvGTNmJAgkkAsXLpgFCxaY5cuXO6OnJJly5cqZJUuWmM2bN5vq1asbSaZChQpWBQFjEjAMTJ061bRr1860a9fO7Nmzx/To0cMYE9dLVpKJjo42kydPNp07d3a+J1myZKZKlSpmyJAhJlmyZCYmJoYhbwCAB+IeeXG/rxs2bGhmzZploqKizKBBg4wxv48qKFeunJk9e7Y5duyYOXnypPcK9iMJ8phg7NixplevXuarr74ytWrVMlOmTDGDBg0yLVu2NOPGjXvYvw4AAGOMMT/99JP57LPPTMeOHU22bNk8Ov5FR0ebxYsXmzZt2pj27dub8ePHe3zv/Ua82SJBwsD69evN6dOnTfPmzY0xxly+fNnMmzfPDBw40CMQMEwQAPCwREdHm3LlypktW7aY3Llzm3//+9+mVKlSpmnTps5nbt68aZYuXWratGljOnfu7Ey+ZrsEaYN39wHQ/x+OkTZtWicYDBw40BhjzLhx40zSpEkJBACAhyJ58uSmadOmpkWLFqZgwYJm48aNplOnTmbZsmWmTJkypnPnziZFihTmueeeM8YY06JFC/PYY4+ZESNG+Lhy3/PqpENXrlwxc+fONYMGDTKtWrUikQEAHqp169aZf//732b16tWmRIkS5vTp0+bDDz80I0eONIUKFTLt2rUzVapUMblz5zaLFy82+fLlM2FhYb4u2+e8vjbBlStXzLx580ynTp3MmDFjnI6FAAA8DH369DGnT58206ZNMylSpDDNmzc3O3bsMKVLlzaHDx82mzZtMqNGjTLdu3d/9CcTekBe76qfJk0a07RpU5MxY0ZTt25db/96AMAjrnTp0ua9994zjz32mGnfvr1Zt26dWb16tSlQoID5+eefzYoVK0y1atUIAvH4fNVCVswDADxslSpVMt9995158sknzddff+2x1gDu5fPZfAgCAICHxX1/269fP5M7d24zYcIEU7hwYePj+16/5/MwAADAw+Ju+i9evLiJjY01W7du9Xgd90cYAAA8cjJlymQGDx5sxowZY3744Qdfl+P3CAMAgEdSlSpVTMmSJU2WLFl8XYrf83kHQgAAEsrNmzdNihQpfF2G3yMMAABgOR4TAABgOcIAAACWIwwAAGA5wgAAAJYjDAAAYDnCAAAAliMMAABgOcIAAACWIwwAAGC5/wfVgAM29tWmjgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.argmax(predictions_simple[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9iUzTT77bhbH",
        "outputId": "bc4dffc8-a77e-4e63-d73c-20d4cd532bff"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    }
  ]
}