04. MNIST Clothing TF.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jYysdyb-CaWM"
      },
      "source": [
        "# Basic classification: Classify images of clothing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FbVhjPpzn6BM"
      },
      "source": [
        "This guide trains a neural network model to classify images of clothing, like sneakers and shirts."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dzLKpmZICaWN",
        "outputId": "c6a93d05-0780-43e7-88e2-a83c8553eb34"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.15.0\n"
          ]
        }
      ],
      "source": [
        "# TensorFlow and tf.keras\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "\n",
        "# Helper libraries\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "print(tf.__version__)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yR0EdgrLCaWR"
      },
      "source": [
        "## Import the Fashion MNIST dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7MqDQO0KCaWS",
        "outputId": "30acfcae-e49e-4e36-9764-7ee65dc14abf"
      },
      "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 [==============================] - 0s 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 [==============================] - 0s 0us/step\n"
          ]
        }
      ],
      "source": [
        "fashion_mnist = keras.datasets.fashion_mnist\n",
        "\n",
        "(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "IjnLH5S2CaWx"
      },
      "outputs": [],
      "source": [
        "class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',\n",
        "               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zW5k_xz1CaWX",
        "outputId": "91029ddf-9dc6-4ea8-f79c-b198b3344231"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60000, 28, 28)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "train_images.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TRFYHB2mCaWb",
        "outputId": "6d5bee7d-859d-42a8-dfdb-17de27d819f4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "60000"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "len(train_labels)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XKnCTHz4CaWg",
        "outputId": "04884a17-5be7-44c2-9ff3-ac88dae1af4a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([9, 0, 0, ..., 3, 0, 5], dtype=uint8)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "train_labels"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2KFnYlcwCaWl",
        "outputId": "fb928ddd-9baf-41f0-eca2-75b51373d2a3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000, 28, 28)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "test_images.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iJmPr5-ACaWn",
        "outputId": "697828d6-9063-4810-9f27-658681df8a76"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10000"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "len(test_labels)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "m4VEw8Ud9Quh",
        "outputId": "d567652e-bf7a-4178-c358-23de50115ee8"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure()\n",
        "plt.imshow(train_images[0])\n",
        "plt.colorbar()\n",
        "plt.grid(False)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "bW5WzIPlCaWv"
      },
      "outputs": [],
      "source": [
        "train_images = train_images / 255.0\n",
        "\n",
        "test_images = test_images / 255.0"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 826
        },
        "id": "oZTImqg_CaW1",
        "outputId": "431c33e0-308b-49de-bd15-36c2abb04e65"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 25 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "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",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "9ODch-OFCaW4"
      },
      "outputs": [],
      "source": [
        "model = keras.Sequential([\n",
        "    keras.layers.Flatten(input_shape=(28, 28)),\n",
        "    keras.layers.Dense(128, activation='relu'),\n",
        "    keras.layers.Dense(10)\n",
        "])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "Lhan11blCaW7"
      },
      "outputs": [],
      "source": [
        "model.compile(optimizer='adam',\n",
        "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
        "              metrics=['accuracy'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 392
        },
        "id": "xvwvpA64CaW_",
        "outputId": "e557c347-be13-4e7d-cf94-258619621291"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.4961 - accuracy: 0.8243\n",
            "Epoch 2/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3736 - accuracy: 0.8642\n",
            "Epoch 3/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3355 - accuracy: 0.8783\n",
            "Epoch 4/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3127 - accuracy: 0.8843\n",
            "Epoch 5/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2936 - accuracy: 0.8913\n",
            "Epoch 6/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2810 - accuracy: 0.8955\n",
            "Epoch 7/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2669 - accuracy: 0.8992\n",
            "Epoch 8/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2566 - accuracy: 0.9039\n",
            "Epoch 9/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2465 - accuracy: 0.9087\n",
            "Epoch 10/10\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.2377 - accuracy: 0.9104\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f3c96d86fd0>"
            ]
          },
          "execution_count": 16,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model.fit(train_images, train_labels, epochs=10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "id": "VflXLEeECaXC",
        "outputId": "1a96fcbe-5ca5-493b-9429-6789183a2021"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "313/313 - 0s - loss: 0.3560 - accuracy: 0.8758\n",
            "\n",
            "Test accuracy: 0.8758000135421753\n"
          ]
        }
      ],
      "source": [
        "test_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)\n",
        "\n",
        "print('\\nTest accuracy:', test_acc)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "DnfNA0CrQLSD"
      },
      "outputs": [],
      "source": [
        "probability_model = tf.keras.Sequential([model,\n",
        "                                         tf.keras.layers.Softmax()])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gl91RPhdCaXI",
        "outputId": "d3386d75-3d73-4a32-adbd-368c90b31842"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 3s 7ms/step\n"
          ]
        }
      ],
      "source": [
        "predictions = probability_model.predict(test_images)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x9Kk1voUCaXJ"
      },
      "source": [
        "Here, the model has predicted the label for each image in the testing set. Let's take a look at the first prediction:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3DmJEUinCaXK",
        "outputId": "9ee1cec0-4c50-4ab1-ee4c-895866a71d03"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.11987902, 0.04939557, 0.07405893, 0.07521173, 0.12232985,\n",
              "       0.11038887, 0.05158726, 0.1599657 , 0.14585146, 0.09133165],\n",
              "      dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "predictions[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qsqenuPnCaXO",
        "outputId": "78d74590-8679-47c0-dce5-5313a525702c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "np.argmax(predictions[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Sd7Pgsu6CaXP",
        "outputId": "16453f58-354a-4483-baee-fcc83d261fef"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "test_labels[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "DvYmmrpIy6Y1"
      },
      "outputs": [],
      "source": [
        "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",
        "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')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 291
        },
        "id": "HV5jw-5HwSmO",
        "outputId": "71c690a4-5ae3-4432-c686-0c55bcbca7f7"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x300 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "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()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 291
        },
        "id": "Ko-uzOufSCSe",
        "outputId": "00a673f9-b25b-4136-f25d-cabeb850c307"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x300 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "i = 12\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()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 947
        },
        "id": "hQlnbqaw2Qu_",
        "outputId": "eb30348d-0ac9-4941-b4f1-9edbb5a537e4"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1000 with 30 Axes>"
            ],
            "image/png": 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22SIpKSLDh4ssWhR+v8mTRR54wF997rpL5NxzbX9Tuw9y/bQknEMAzaqdLFpkP2SrVom88YadAdiz51DXK7iKCj1eVCRy/PEi997rvu/cufaEY/x4kfnzRRYssCcc7f7/wq/HHxf56itb7uqrRX78Y9vYi4isXy/yxBO2YW6IMSKPPCJy5ZXe24qLRV58UeQ3vxH5178aPlZb1LmzHRR97LFDXRMATeG000S2bbM/H35oB1POOqv5H7ddO/tF+o03bF/41FN28uBnP6sp89hjIr/9rf2i/c039iRk8mSR//7X3l5dbfuCn/3M9g1ffmn7ipBp0+xtOTkN1+eVV+xA1dix4fEjjrCvzYYNtg97/HGRX/0q4JM/xEIDcRMnisyZY19bAK1DqD3fts2umkxLC4/9+tdN/5iZmXaQxqX24L/L66+LnHNO09XJr1mzbL8yb56dtKiosOciRUXesn/5iz6wc9ddIgUFIgsX2nO6q66quW3ePJEvvvC30qu42K6oDZ2j/PrX4X+77GyRO+4Ij9Xm53U+FKqqbH/NOQTQvMwnn5h6iRjzxBPGnHeeMUlJxvTvb8zrr4eXWbbMmNNOMyY52ZiuXY35yU+M2bWr5va33zZm7Fhj0tONycw05swzjVmzpub29evt4yxaZH+vrDTm8suNGTTImI0bbey114wZPtyYhARj+vY15rbbjKmoCK/no48ac/bZxnToYMz06fU/r7qPWdsxxxhzyy3u+157rTFTp9r/Fxfb4+zcaX8/9VRjZsyo/7FDFiwwpl07Y/Lzvbc99ZQxxx5rzP799vnk5YXfPmmSMeeea8z99xvTvbt9Xa+7zpjy8poyOTnGPPhgze9PPGH/Bh98YH8fN86YKVNqbi8tNeZXvzKmRw/7mKNHG/Pxx/U/h9DrftppxiQm2r/Nyy+Hl1m61Jjvfc/enplpzFVXGVNQUHN7VZUxt99uTM+exsTHGzNsmH3P1H6M2j/jxtXc9vTTxmRn119HANEv1KbVNnt2ePtqjDG/+Y0xAwbY/qhvX9tW1273jDHmzjuN6dLFmJQUY6680rbXw4ZFVp+HHgpvW8aMMebXvw4vc+ONtm8zxpgdO2xdS0pq6nnddfb/n31mzMiRtm/z48wzvY81fbr3OVx1lW3/jdFfvylTwtvLum1+3T5i40ZjzjnH9uWpqcZceKEx27fb23Jz7fNbsSL8Mf78Z2P69av5vaHvA+PGGTN5sq1Hp07GnHhizW3f+179fS+AluvJJ+130IZ8/LExRx9tv4empxtz3HHGbNhgbwu1g888Y9uvtDRjJkwI/x6ttXN33GHMJZfYdm3SpPq/V+bl2e+iBw7Y+9Yul5NTU+7RR23bFxdnzMCBtk61+fl+3JCdO+1xZs0Kjy9aZL8zb9tmb585s+a200835rHH7P+XL7evozG2nxw2zJ57+PHyy7Yfdanbf7ja9k8+sX/P+HjbX02dGn7+Vvc4xth6hs7jqqvt/3v1ssfIyjLm5z+vKdvQuUvofff668YMGWJMbKw9BzSGcwigGbWT116zl3/V5/bbRS66yF62cMYZdsZx71572/799rKy4cPtzO0774js2GHLhxQVidx4o739ww/trPH559uR5LrKykQuvNAuYZ09217KN3u2yKWXikyZIrJ8ucjf/25nmuuuFrrtNnvcZctErriicaNuO3faUf6uXe0lbN26iYwbZ2dYQ4YNs7+XlIi8+65IVpYdEX/+eZHERFsHP2bPtpdPaDMu//ynXaqbnm6Xwz71lLfMxx+LrF1r/336aVtGKycict99dib9vfdETj5ZL3P99XYG/sUX7d/6wgvtyoLVq+t/HrfeKvKDH4gsWWLfGxdfLLJihb2tqMjOFGRk2BVhL79sVwhcf33N/R96yC7d/dOf7OOeeqqdQQo97vz59t8PPrAzJTNm1Nx39GiRzZtb5/XiQFtWWCjy3HN2z7pOnWriqam2nVu+3LYdTzwh8uCDNbc//7ztG+6916447d078pnKrVttOzNuXE2srMy277UlJdn2qaJCpEsX2xe8956dAZ49W+Soo+xt115r+63YWH+PP2eOyKhRDZdLSmq62eLqaruaa+9eO5v+/vv2MoIJE+ztAwfaOj3/fPj9nn/eruAS8fd9QMT2V/Hx9tL8v/2tJj56tH3dALRNlZX2ErFx4+z3wdAVA7VX7Kxday8Pe/NN+zNrlsg999R/3D/9yX53X7TIfmet73tl6KqOtDT7vVVE5MknbbnQ7zNn2nOSX/1K5Ouv7fYWl19uv4/XVt/3Yz8OHLD/ZmbWxIqLbZv717/abU7qGjZM5KOP7Gv57ru2HxKx5wEnnuivbxGxbfHIkf7rKuJt27dsseeNRx9tX4PHHrPnN3/4g/9jvvqq7eP//nd7XvDaayJHHllzu59zl+Ji+53gH/+wq2W7drVxziGA5mMyMuwI+3HHGfPb3xqzZEn4MJRI+MxiYaGNhVaa3HmnMePHh99n0yZbJjdXH9ratcvevmyZ/T20qmj2bGNOPtmY44+3K3hCTj7ZmD/+MfwYzz5rR7Nr1/OGG3yPrjlXMs2da+OZmcb861/GLFxojxsfb8yqVbZMebmdoe7Tx5hRo2y99+yxMxp5ecbcfLMxhx1mX5fNm911mDLFmJNO8sZXrbIzI6HZ35kz7QxIdXVNmUmT7Oh/7ZnxCy+0MzohodmB3/zGvlZffx3+OLVnezZutKP7W7aElzn5ZPu+cBEx5mc/C48dc4xd7WWMMY8/bkxGhn3fhPzvf3YFV2iGvEcPY+66K/wYRx9dswqgvlVnBw7Y2xpakdeMHnnkEZOTk2MSEhLM6NGjzRdffOH7vrNmzTJnnXWWycrKMiJiZtaejfLpj3/8oxk1apRJSUkxXbp0Meeee65ZuXJlRMd49NFHzZFHHmlSU1NNamqqOfbYY81bb70VcV1C7r77biMiZkrt2UQfpk+fbkQk7GfQoEERP/7mzZvNxIkTTWZmpklMTDRDhw41C/zO3hljcnJyPPUQEXNd6D3ZgMrKSnPLLbeYPn36mMTERNOvXz9zxx13mOran2Ef8vPzzZQpU0zv3r1NYmKiGTNmjJk/f35Ex2gxJk2ybVBysv0Rse3WV1/Vf7/777erhEKOOcbOptY2dqy/lUwXX2xXSInYVbGhVUnG2Hawe3djvvzStsULFhjTrZstu3WrLTN7tu0T+vSpWVl6xx22nf36a9vPDhxozMMPu+uwb5895qefhsfrrmT68ktjOnc25oc/tL8HXcn03nv29a+9avabb2xdQu+5Bx+0fVtI3dVNfr4PjBtnVyVrHnrIvnatFH2FF31FG+or/Kxk2rOn/u9006fb1Sq1Vy7ddJNt90O0du6888KPU9/3yu9/35hHHqn5ve5KIWNsW37VVeGxCy805owzwu9X3/fjhlRV2VWtodWyIVdfbVfouuq3f78xP/qRMb17G3PCCbYdX7XKrgDevduYa66x5xQXXhh+rlXXuecac8UV7tu1lUx12/bf/c5elVL7/fzXv9pVxlVV+nGMCV/J9MADtt+su2LZGH/nLk8+aV+jxYu994+CcwgNfYUXfUXL6yvaydatdtT+tNNEPvlEZMQI72qY0Ci4SM1G2Dt32t+XLLEj9ykpNT+DB9vb1q61/65eLfKjH4n062fv26ePjeflhT/Oj35kV768955dwROyZIm97rf2Y1x1lZ1VKC6uKed3dL4+odVVoVmJ4cPtCPqgQTV7I8XF2RmE9evtrMbxx9vZjF/8ws6SvPaarfOxx9qYS0mJd2ZcxD7Oqafa1VEidhbgwAE7M1HbEUeEz4xnZdX8XUIeeMDO9M+ZY8u7LFtmr1MeODD8dZ41q+bv6DJmjPf30EzNihV2ViU5ueb2sWPt65ybazfW3brVu/fI2LH+ZnuSkuy/td8HB9FLL70kN954o0yfPl0WLlwow4YNk1NPPVV21v07OBQVFcmwYcPkr3/9a6PrMGvWLJk8ebLMmzdP3n//famoqJDx48dLkXYNv0N2drbcc8898tVXX8mXX34pJ510kpx77rnyTSP2R1mwYIH8/e9/l6NqtxsROOKII2Tbtm3f/sypvYrQh3379snYsWMlLi5O3n77bVm+fLk88MADkpGR4fsYCxYsCKvD+++/LyIiF154oa/733vvvfLYY4/JI488IitWrJB7771X7rvvPnn44Ycjei4//elP5f3335dnn31Wli1bJuPHj5dTTjlFtmzZEtFxWozvfc+uYl282M40n3qqXclZe7Psl16y7UP37raNuuWW8L4kN9fOTtZW93eXBx+0+1i8/rpt9268sea2W2+1dTn2WNsHnHuuyKRJ9rbQfn3HH2/7hPXra/qIZ56xs7aXXGJn5GfPtv2Za0PzUIIJrW9Ytsw+56Qk+5zGjLH7+jWFFSvshue9etXEDj9cpGPHmrb44ovtjO+8efb355+33xlCfb6f7wMi7tnxpKRD1pY3N/oKL/qKNt5X5OWFtxV//KNdsXPZZbbtP/tsu1q17l4/ffqEXwGgffety+/5QX6+/d7b0H5MK1b4+95a3/fjhkyebFdJvfhiTeyNN+y5QH1ZQdPTRf79b9tvzppl2/FrrrFJiZ5/3q5Qzc0V6dDB9kUurnOU+tRt21essM+59kq0sWPtSuXNm/0d88ILbV369bPnfjNn2lVaIv7PXeLjw89lQw7xOYSGvsKLvqKF9hWeYacrr7Sj3yHaCH56uh0ZNsZea3zBBcasXu39Ca1eGTTIzm5+8IG9Pvjrr8OPG5pRuPpqO0Px4Yfhj5eYaMy99+qPERoJ1+pZH9csxrp1Nv7ss+Hxiy4y5sc/1o/10Ud25U1lpTG//KWdVTHGPs/MTHcdfvc7u89HbZWVdvY+JsaOzod+RMIf38+sdU6Onc1ISzPm7ru9j197tufFF+3jrFzpfY23bXM/BxF7TXNtN9xQcy32L38ZvueGMXbmJHSNuWsW4YYb7P4cxtQ/47R9u70tgtHkpjR69GgzudaqiaqqKtOjRw9zt/Z6N0AaOeNQ186dO42ImFl1r+GPUEZGhvnHP/4R0X0KCgrMgAEDzPvvv2/GjRvXqBmHYZHunVPH1KlTzfHHHx/oGHVNmTLFHHbYYb5nDM4880xzRZ0ZwAsuuMBMnDjR92MWFxeb2NhY8+abb4bFR4wYYW6++Wbfx2kxtDatstKuago9388/t+3UH/5gP/OrVtmVQrVnxzt29LZJv/xl5HsyhfaDCq1SCikvt6tzKivtfhupqTX9UF0nnmj3gQi1c0VFNv7DHxrzf/+n36eszLb/774bHp8+3e4nsXq1bRPLysJvv/xyu59Sbddd538lk2sVUd3X8/vfr9kPo39/O8sc4uf7QN061HbPPcYccYR+WwtHXxGOvsJqU31F3ZVMFRXhbcSePTW3LVxor2AYM8auepk718a1vekefDB8r6SG9p4zxv298sUXjfnOd8Jj2vlFRobdO7W2v/zFrhCqfb/6vh/XZ/Jku1fQunXh8SlT9PODdu3C2/ra/vUvY84/3/7//PPtSiJjjHnzTWNGjHDX4cc/tucQLtpKprqf4/PPN+ayy8JjixfbOof23O3b1+7tV9vhh4fvrVtcbMwbb9i+p3t3+74oL/d37lLfCrpDfA6hoa8IR19htcS+op1n1Onww/UsBi4jRtjrW/v0sXtn1P5JTraZ6nJz7WzzySfb9M/79unHuvZae131OefYUejaj5Gb6z1+//41M8hNpU8fm8a6bsrQVav0rEClpXa2IbTfRlVVTWa7igr7u8vw4SIrV9ZkphMReestmxVi0aKaGf3Fi0VeeMFeM75/f2TPZ/RokbfftjNEf/pT/XWpqrKzQXVfY+2a79pCs9q1fx8yxP5/yBA7u137PfXZZ/bvNmiQXdnWo4eN1fbZZ/a9KGJnIET01/Lrr+2qgvpWaTWT8vJy+eqrr+SUU075NtauXTs55ZRTZO7cuQe9PiEH/v81/Jm1r+GPQFVVlbz44otSVFQkY+rOwjVg8uTJcuaZZ4a9JpFavXq19OjRQ/r16ycTJ06UvLorHhvwxhtvyKhRo+TCCy+Url27yvDhw+WJJ55odH3Ky8vlueeekyuuuEJifKboPe644+TDDz+UVatWiYjIkiVLZM6cOXL66af7ftzKykqpqqqSxDoziUlJSRHPwrRYMTG2rQit7vn8c9sO33yznZkeMCB8lZOIbVdC+2aE1P3dj9Cq1rp7FsbF2aw6sbF2hvmss/R+6J//tLPy55xT03b56Rvi423bt3y5flv//rafCrWLIV26eGf8Xem5NUOGiGzaZH9Cli+3fU6oLRax+4q89JLdA2PdOru6KaSh7wMN+fpr2xe1MvQVXvQVVpvuK9q3D28jar8Phw+32Tw//1xk6FC7Mqcpub5Xvv66XaVaW1yct9yQIfV/bw2p7/uxxhi7x9DMmXbFUt++4bdPm2ZXwdY+PxCxq3CffNJ7vF277Gql0GqHSM9RtH4oEkOG2L6i9nnOZ5/ZlWjZ2fb3un1Xfr5dBVxbUpJd2fZ//2evupk7165iCnLuInJIzyE09BVe9BVWi+wrzLPP2n2Y1q0z5j//sXtM1B4pa2gl05YtNvvAD39o921Ys8aYd96xI9eVlXaGt1Mnm2Fm9Wq7Sunoo/WVTKEZhQcftDMXs2fb3995x5j27W1Gua+/tquhXnihZnbbVU/Nnj32cf73P3ufF1+0v9derfPgg3b1z8sv2zrfcotdTVU7I17I735nsxqEvPSSXQm2ZIldFVb7+uy6du+2ey+F9qYyxs7k195XKaSqyo7eh64T97uSKTTLMHu2fU3rm3WYONHOZL/6qn0/fPGFnUmqM+IZRsTuC/LPf9o9N37/ezuj8s039vaiIrsy6wc/sM/zo4/s3lWTJtUcI/R6v/iinY2YOtW+LqE9sCoq7D4pf/iDnXWofQ359On6vlYHwZYtW4yImM8//zwsftNNN5nRo0dHfDxpghmHqqoqc+aZZ5qxda/h92Hp0qUmOTnZxMbGmvT0dPO///0vovu/8MILZujQoabk/+9j05gZh7feesv85z//MUuWLDHvvPOOGTNmjOndu7fJ1zIwOiQkJJiEhATz29/+1ixcuND8/e9/N4mJieapurOOPr300ksmNjbWbKl7zX89qqqqzNSpU01MTIxp3769iYmJMX+su6+cD2PGjDHjxo0zW7ZsMZWVlebZZ5817dq1MwMHDoz4WFFv0iS7EmbbNvuzfLldiRMTU5Mp5vXXbV/wwgu2PX7oIbtatPYs5XPP2fbiqadsG3LnnbZ9qTs7Xdv//mdne5cts/3Rm2/aVUO1P0e5uXaF66pVtm2cMME+dihLTW07dti2tPZ7ZsgQ24d9/rlti+u7Bv7GG22bWZs2g1/bO+/Y1+rpp20df/97+7z9rmSqrrav0Xe/a/fB+uILu9dV3dnx/Hz7+g4bZve9qK2h7wNaHWrLyfFmaGoF6CvC0VfUaFN9hZ89mdatM2baNNtObthgV3R26mRXjRrTdCuZtO+VFRV25WbdfQAHDLD7KG3bZszevTY2c6b9nvroo7a9feABu6Kmdlazhr4fa6691r5Gn3xS0xdu22ZX8rjUd/7z4x+H7wF47722XV++3Gahq28/mKVLbX8bes51+VnJtHmzvUJl8mS7d99rr9nXpPYqpWnT7PnNp5/axzzvPNtHhso8+aQx//iH7Z/XrrXnZElJ9hzKmIbPXep73x3CcwgNfUU4+ooaLbGvEDNihP3wdehgL2u75ZbwxqyhQSZjbAN7/vm2cU5KMmbwYLskNLQE7P337RfshARjjjrKNp71DTIZYxvs1FSb+tkY+0X1uOPs8dPSbIrKxx+vv56a0AZwdX9qN3jG2MvLsrPt6zJmTM2AV23LltnLBWpval1VZTuJtDQ7mLZ6df31uegi28AaYzu69u3tYJ/m2mtrNtWLdJDJGHt5WnJyzWUadTuE8nLbCfbpYzvPrCz7d1261F1/Ebv09vvft3/fPn3sQFttS5faS98SE+1J2VVXGVNQUHN7VZU9+erZ0z7usGE1G8uHPPGETV9ad0nwoEH2hPMQiMbO4Gc/+5nJyckxmzZtivi+ZWVlZvXq1ebLL78006ZNM507dzbf1PdlqJa8vDzTtWtXs6RW4oDGdAZ17du3z6SlpUW0vDYuLs6MqXMZ6s9//nNz7LHHNqoO48ePN2eddVZE93nhhRdMdna2eeGFF8zSpUvNM888YzIzMyPukNasWWNOOOEEIyImNjbWHH300WbixIlm8ODBER2nRaibVjo11bahr7wSXu6mm+xJR0qKHeh58EHvF8g77rBfZFNS7KTJL35hTH1//48+su18erptpwYMsIPd+/bVlFm+3A7ChPqgc8+1g+Kaiy/2bu79xRe2b8zMNOb22+t/Lb75xj5O3QH1hpZ8//73dqIoPd1eInj99f4HmYyxly+cc47tJ1JT7cawoQQNtV10kf0b/etf3tsa+j7gGmT6/HN7n/pOploo+ooa9BXh2lRf4WeQaft2O8iQlWUT7uTk2HYtdElyUw0yGeP9XvnBB3o6+zfesN/127cPf5xHH7WTpnFxdmPqugPkfr4f16Wdn4iEn3Np99Hag3fesedKtS/nLiqy7Xpqqp0k2LGj/vqMHm3M3/6m3+ZnkMkYe8539NH279m9u+1bKypqbj9wwPblaWn27/HUU+Ebf8+caTdMT0uzfdOxx9q/VUhD5y71ve8O4TmEhr6iBn1FuJbYV3j3ZMLBtWSJMV27hg+6wJ+33rKDl7U7q4OorKzMxMbGehrwSy+91JxTd28UH4J2BpMnTzbZ2dlmXd1r+Bvp5JNPNldffbWvsjNnzvy2wQr9iIiJiYkxsbGxprJ2FsQIjRo1ykwLDcT60Lt3b3Nl7cwrxma56NGjR8SPvWHDBtOuXTvz2muvRXS/7Oxs80jt7DTGmDvvvLNRGS2MMaawsNBs/f97A1100UXmjPpWSMLrlFPsatqW5Ic/9GZVbc0uusibZbSVoK+oQV8Rjr4iivz85/4zv/kR6V6x0Si0qte172BLdojPITT0FTXoK8K1xL6iiTc0QsSOOkrk3nu91x+jYUVF9hr09u0PycPHx8fLyJEj5cMPP/w2Vl1dLR9++GHE1xwHYYyR66+/XmbOnCkfffSR9K17DX8jVVdXS1nd/WgcTj75ZFm2bJksXrz4259Ro0bJxIkTZfHixRJbOwtiBAoLC2Xt2rWSlZXl+z5jx46V3Dp7qq1atUpytD3VGvDkk09K165d5cwzz4zofsXFxdKuzj49sbGxUh3a5ydCycnJkpWVJfv27ZN3331Xzq27ZwRqFBeL/PnPdm+glStFpk8X+eCDmkxwLcX999ssOW1BebnIkUeK/PKXh7omzYK+ogZ9RTj6iigydKjdGxY1zjzTZkWN5iyFjXWIzyE09BU16CvCtci+osmHrYA25MUXXzQJCQnmqaeeMsuXLzdXX3216dixo9muXWKiKCgoMIsWLTKLFi0yImL+/Oc/m0WLFpmNoawbPlx77bUmPT3dfPLJJ2bbtm3f/hRHcNnJtGnTzKxZs8z69evN0qVLzbRp00xMTIx57733fB+jrsYsa/3Vr35lPvnkE7N+/Xrz2WefmVNOOcV07tzZ7Ny50/cx5s+fb9q3b2/uuusus3r1avP888+bDh06mOeeey6iulRVVZnevXubqVOnRnQ/Y4yZNGmS6dmzp3nzzTfN+vXrzYwZM0znzp3Nb37zm4iO884775i3337brFu3zrz33ntm2LBh5phjjjHl5eUR16nNKC62lwFkZtrLnYcPt3s1AIcQfYUbfQV9RavUGlYy4aCjr3Cjr2hZfQWDTEBADz/8sOndu7eJj483o0ePNvPmzfN9348//tiIiOdnUu2N0Rug3V9EzJP1XcNfxxVXXGFycnJMfHy86dKlizn55JMDdQTGNK4zmDBhgsnKyjLx8fGmZ8+eZsK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\n"
          },
          "metadata": {}
        }
      ],
      "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()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yRJ7JU7JCaXT",
        "outputId": "1aa96ae3-c30f-44b4-dbad-4348b4f52425"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(28, 28)\n"
          ]
        }
      ],
      "source": [
        "# Grab an image from the test dataset.\n",
        "img = test_images[1]\n",
        "\n",
        "print(img.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lDFh5yF_CaXW",
        "outputId": "a64c7ef9-95f7-4335-aaa8-4fb533f3c62a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1, 28, 28)\n"
          ]
        }
      ],
      "source": [
        "# Add the image to a batch where it's the only member.\n",
        "img = (np.expand_dims(img,0))\n",
        "\n",
        "print(img.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o_rzNSdrCaXY",
        "outputId": "cb7e333e-c249-48fb-bb62-5ff0cedbeaa5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 22ms/step\n",
            "[[0.05280713 0.02005382 0.01190997 0.0447735  0.08005878 0.0352441\n",
            "  0.05017499 0.25802732 0.3899189  0.05703156]]\n"
          ]
        }
      ],
      "source": [
        "predictions_single = probability_model.predict(img)\n",
        "\n",
        "print(predictions_single)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "id": "6Ai-cpLjO-3A",
        "outputId": "fb27d4d6-8654-4e19-85e9-7a677e022544"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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RPr/7mjZtmgIDA9W1a1cdPnzYp7X8XWvWrFGGDBmUKFEiLVy40Hnd19tUklauXKmUKVPqww8/1I0bN3xdzh+6c+eOJGno0KHq1auXgoOD1aVLF+3bt8/5jD9sz3/jnXfe8asWgrlz56pevXqaNm2aWrVqpfLly2vevHnO+wm1hSDufjJ27Fi5XC61bNlSv/32m89q6tevn5544glNmjRJw4cPV4UKFZQnTx59/PHHPqvJW65fv65Dhw5p+vTpKlq0qDp06KDdu3d75XcTBuJ47bXX5HK5FBISomPHjnm8N3v2bCVJkkSHDh3yWj3uA3X//v364YcfPJqux48fryxZsujVV1/V0aNHvVbTw7BhwwblyJFDdevW1ZYtW5zXfXkBu3Xrlrp06aLevXtLki5duqTNmzcrPDxcr7/+un766Sef1fZX5syZo6CgIHXu3Nmjzh07dviwqr/m/nvv2bNH33zzjZYvX+7x/ttvv+3TQOCu75dfflFAQIDee+89SdLmzZvVrFkzlS9fXvPnz3c+v2LFCp05c8Yntf5d7n/b3SFm9OjRcrlcGj58uC5fvuz1un755ReFhoZq8eLFzmvbt29Xhw4dlDNnTs2ZM8frNcUn99/h6NGj2rt3rw4ePOi8N3XqVBUtWlSdOnVyAsGbb77psW0eJmvDwB8l+XHjxsnlcumNN97QuXPnnNc3bdqk0NBQ/fzzz16pz72TLFy4UKGhoQoNDVX+/PlVrFgxHT9+3Kk1a9aseu211/yyhcD9b9i5c6eWLFmiWbNm6ezZs5KkdevWKSQkRC1bttS2bdt8WaajRYsWKlq0qA4fPqzWrVuratWqKlu2rB5//HE1atTIp7W5t+XmzZs1c+ZMvf/++zp69KizH8+ZM8dpIVi/fr3eeOMNuVwuXbhwwS9bCdw1LVq0SMHBwSpQoIBSp06thg0berRwvP3220qePLlGjx7tkzrXr1+v6dOna8CAAR6vb9myRc8++6zKly+vMWPGaMiQIXK5XDpx4oRP6vw73Nt+zZo16tGjh1588UUNGjTIeX/UqFFyuVwaMWKE1wPB8ePHFRgYqE8++cTj9R9//FE5cuRQ5syZ9emnn3q1pvgS9xyfL18+Zc6cWblz51b9+vV169YtSbGBoFSpUqpcubKeffZZuVwubd26NV7qsTIMxA0Chw4d0u7duz0u/O4Tae/evRUREaHdu3erZs2aKlmypFebA9etW6eAgABNnTpVN2/e1Lp16+RyuTR58mTnM+PHj1eKFCn05ptvKioqymu1PagFCxYoe/bsKlasmMqUKaOAgACtXr1akhQREaGQkBC1bt1aP/zwg1frch+IW7ZscVpcvvvuOxUtWlTJkydX06ZNtWjRIkmxF6wiRYrowoULXq3x7loXLlyo9OnTq2rVqsqUKZOqV6+ujz/+2Hls8NlnnylfvnwqWLCggoODvb5NH0TcYLJy5UqlS5dOU6dOlRTbYuRyuVSnTh3t2rXL+dygQYP02GOP6eLFi/FaW3h4uEaOHOl8ffnyZdWsWVMul0sNGzaUJI9j7Mcff1THjh0VGhqqAgUKeLRy+btFixYpICBAXbt2VZ8+fZQ7d24VKVJEt2/flhTbQpAsWTINGTJEV65ciZca3PtC3P8/d+6cnnnmGfXu3dvjnCxJTZs2VcWKFVWyZEmtWrUqXmrytrVr1yplypSaNGmSVq9erQULFihnzpx6+umnneN63rx56tGjhxo1auRxXDxs1oWBuCejAQMGqFChQkqRIoXKlSunl156yXlv2LBhcrlccrlceuGFF9SoUSPnQPFWIBg9erRT06FDh5Q9e3Z16dLlns9NnDhR+/fv90pNf8emTZsUGBjonOz37Nkjl8ult99+29mGERERSpMmjTp06KCbN296pa64F9fg4GC98sorOnnypKKiohQZGXnPRbR79+6qVauWIiMjvVLf/URERChTpkyaNm2aJGnXrl1KkiSJSpUqpcmTJzvbc9euXfr++++d1iN/sWjRIu3du1dS7Pa/cuWKunfvriFDhkiK3b9z5sypVq1aKUuWLKpSpYp27Njh/K3uvjA8bHfu3NG0adPuaaX69ttv1bhxY6VJk8Y5xtznASn2cdKvv/7qtHglBCdPnlTBggU1fvx4SdLhw4f1xBNPqH379h6fGzp0qAIDA+Nl28c9h8bdnpL07rvvKl26dBo3bpyzXa9cuaImTZpo4sSJKleunF599dWHXpMvDB069J5Wx4MHDypHjhxq2rSpx+t3b6eHzbow4DZixAilT59eX3zxhdauXas333xTBQsW9PjDvP/++3K5XBo/frwuXbok6ffOW97w3HPPqW3btjp//ryCg4PVsWNH5+Q4ffp0jRo1ymu1/BOzZs1Sy5YtJcWe7N3N2G5Xr16VFNsUe+DAAa/W9n//939KmTKlpkyZ8oedBbds2aLevXsrXbp0Pn3+HhUVpeHDhys8PFxS7MnCfeGsWbOmcubMqWnTpnl13/w7du7cqcKFC6thw4bOBfXWrVtavHix9u/frwsXLqh48eJq166dJOmLL76Qy+VS+fLltWfPHq/X+9VXX2nw4MHO15s3b1aVKlUUHBysX375RZL8shXuQe3du1d58uTR7du3deLECQUFBalTp07O+1988YXz3+fPn3/ovz9uEJg4caKaNWum5s2bO0PpJOn1119XxowZVa9ePXXs2FFlypRRsWLFJMWeF6tVq+aXj7/+rhdeeEElSpRwvnbvVx9//LEKFCjg1VBvTRiIu+NcvnxZderU0bvvvuu8FhkZqblz5yp//vz63//+57z+1ltvyeVyaezYsfHaTBn3Duj69euSYu+mwsLC9Pjjj6tDhw6SYg+k6Ohode3aVS+99JLzWX9w98E5dOhQVa1aVUePHlW2bNnUsWNH50SwaNEihYeH+6T+mzdvqnXr1urbt6+k2P1h27ZtGjBggIYOHarz589r586d6tatm4oWLeoXHfH27dunvXv36tq1aypXrpxefPFFSdKRI0eULl06FShQwGk18EcfffSRKleurCZNmjgtBO6WoPnz56tUqVJO59wlS5aoVq1aypcvn9f7wsTExGjixIlyuVx68803ndc3b96ssLAw5ciRw6nTX8PXH9m9e7eio6N1+vRpVapUSQsXLlS2bNnUqVMn5yK0f/9+Pffcc/rmm28kxW+nXveogVdffVWvvPKKQkJC1LZtW+f9mTNnqmfPnqpRo4a6dOni7C+NGjVSeHh4gh3BEdeXX36pXLlyae7cuR6vL1myRCEhITp58qTXarEiDNy908TExKhYsWIejwWk2GaYxo0bq0WLFh6vjxgxQi6XSxMnToyXg8P9M5ctW6YaNWpo1apVio6O1s8//6zy5csrV65cWrFihaTYZsmBAwfqiSee8Oho5S++/fZbJ+F/9913qly5stKnT+8c5O6/RXh4uFq2bBlvzyP/SsuWLVWhQgX98ssvatu2rapWraoSJUro8ccfd1ozdu/e7ZPe4ffbx9wn6/Xr16tgwYLOHfPmzZtVrVo1tW7d2i9HlcS9g/7ggw9Uu3ZtNW3a1KPX9MiRI5UrVy5nWw8YMEDvvPOOz+6+r1+/rilTpihRokTOYwxJ+uGHH1S7dm2lSZPGLzvsxnX3ttu1a5eCgoJ07NgxXbx4UZUrV1aiRInUqlUrj8+98sorevrpp+N9v589e7aefPJJff/995JiA2GqVKmcTqRucc/dZ8+e1cCBA5U+fXonUCYU7mP65MmTOnjwoNP/6Ndff1XDhg1Vp04dZ6TE7du31b9/f5UqVcqr/ZQe+TCwefNmZ8xs3759nZnaunXrprCwsHt2qsGDB6t69eq6efOmx444ZsyYeN0B3R163njjDacpUpK2bt2qokWLqmDBggoNDVX16tWVJUsWv+mBH9etW7cUHh6uqlWrSop9zud+Bjxt2jRFRUXp9OnTGjBggDJkyOC1JuD7XVyXLl2qkiVLKnHixGratKkWLFggKfbxS8mSJX3WP8Bd67fffqsRI0aof//++vrrr527olWrVikkJERLly5VdHS0Bg8erHbt2jmPXPzN3T3XCxYsqMSJE6tZs2bOI4P9+/crTZo0euqpp1SxYkWlTZtW27dv90p97rv7EydO3DNPw4QJE+4JBN99950aN27s9cdaf8eoUaPUpEkTj31448aNCg0NdZ4779mzRxkyZFCdOnX0ySefaOXKlXr55ZeVNm3aeGkJu3Xrlkc9kyZN0uuvvy4p9iYoMDBQY8eO1dSpU5U4cWLnkZHb+fPn1bZtW+XMmVM//vjjQ68vPsUdOZMnTx6FhIQobdq06tatmw4dOqTDhw+rSZMmyp49u/LmzasqVaooMDDQ6+f4RzoMnD17Vi6XS926dVOnTp2UOnVqpzfm9u3blSlTJrVu3doZqnH16lVVrlzZaZKXvNNZ8PDhw8qVK5fef/9953fevn1bmzZt0o0bN3T+/HktW7ZMffv21Zw5c7w618HftWXLFiVPntxJuRcvXlSdOnVUqFAhpUuXTuXLl1dISIjXdvS4F9chQ4aof//+zrClq1ev6rvvvvP4fJcuXVSvXj2fTjq0YMECBQQEqFKlSipdurRcLpdeeeUVHT9+XOfPn3cmYcmfP79PThp/18qVK+VyuTRmzBh9/vnn6tevnwoVKqQmTZo4F+Bdu3apQ4cO6tOnT7yHxIkTJ2rNmjXO3fP8+fMVHBzsDHFcs2aNc9F0B4K4jwz8eUIqKbY/TPLkyT1C4vLly1W4cGFJv5/Ttm7dqqpVqyp79uzKly+f02nzYVuwYIEaNWqkokWL6o033nBeP3TokM6dO6dixYpp+PDhkqQDBw4oa9ascrlc6tevn8fPOXr06D3zvyQUERERSpkypcaMGaOtW7fqvffeU5kyZdSwYUMdPnxY586d0/r16/XKK6/ovffe80mH8Ec2DKxbt06HDx/W1q1blTx5cqVMmVJr1qyR9PvdwPfff6+QkBAVK1ZMBQsWVOnSpVWwYEHnRBDfHVTcP/+nn35S8eLFtXXrVp07d06jRo1SpUqVlDZtWlWsWFEbNmyI1zr+qbjbJzo62vm6V69eqlatmnPgRkZGasuWLZo8ebLWrl3r9Z7uCxcuVNq0adWyZUu9+OKLCgwMvOdR0O7du9WrVy+lS5dOO3fu9Gp9cR04cEDZsmXT1KlTne05Z84cZciQwZkQ6ejRo5oyZYrGjRvnl6NI3GJiYhQdHa327durefPmHu998MEHypcvn5o1a+bcZd+5cydejzn3z86bN6+yZcum7777Tjt37lRISIhGjRqltWvXKiwsTNmyZdP8+fOdsd6TJ092xt0nFGvXrlVAQIDatm2r6OhoLVmyREWKFJHkedzevHlTZ86c0dmzZ+OldWny5MlKkyaNevbsqfDwcCVOnFgTJkxw3t+0aZOyZcvm7McHDhxQy5YttWrVKo8+GQm1s6C77t69e3s8/pB+b510913ytUcyDFy5ckXt2rVT3759tXHjRqVIkUKJEiVSz549derUKUm//5EOHDigOXPmqF+/fnr//feduwVvPK90Py8/evSo0qdPr7CwMGXKlEkNGjTQO++8oxUrVihfvnx+MyXr/axatUqLFy/26Fy5dOlS5c6d2+mE5EvunvfuVpcDBw4offr06tixo/OZTZs2qXPnzipcuLDXmqfdzp49q82bNzutU7t27VLOnDm1fft2jxPgrFmzlChRIq1fv96r9T0MXbt2VfXq1e8ZGhUeHq4UKVIoLCws3ifzuruFr1KlSgoNDdWMGTPUp08fj/caN258TyCYNm1agntOvXr1agUEBKh79+767LPPVKZMGa1cuVIRERHas2ePtm7dqmXLlun06dPx8vunTp2qpEmTesyY16JFC40fP97pk3Dw4EHlypVL3bp10969exUWFqaGDRs6+35C66T5R3r16qXq1avrzp07HvviyJEjlSFDBr94zPdIhgEp9uSZPXt2p7/AqlWrlChRInXt2vUvd35v7IDbt29X8uTJtXHjRkmxU8f2799fo0eP9ui8U716dY0bNy7e6/knrl+/rm7dusnlcqlBgwZOU58ktW7d2mPIjK9s27ZNTz31lKTY0OWettdt8+bNkmIfb7iDorfs2bNH5cqVU82aNdWoUSPduXNHmzdvVtKkSZ2OVXHnXihYsKDHSJeEYtSoUfd9NPTJJ5+oUKFCatGiRby2FrlPvocPH9Z7773n9MkpVaqUXC6XwsLC7gkqjRs3Vq5cuTRz5sx4H9/9MN19B/3111/rP//5j1KlSqVcuXIpJCREmTNnVt68eRUUFKQsWbLEy2PHtWvXyuVyaejQoR6vFy5cWE899ZRSp06tcuXKafz48Ro9erSCgoKUPXt2lS5d2msts940ZswYBQQEONMKu/9tK1euVP78+f1iBdpHLgzE3YGee+45NWnSxJlS8/PPP1eiRInUvXt3Z9rQJk2aeCw44i1HjhxR3bp1lSZNGm3atEmS54n/zp07GjBggDJlyuTRodAffffdd3r11VeVKVMmlSpVSuPHj9eiRYtUvXp1ff75516txf33X7t2rb7++mvt3btXZcuW1apVq+4ZRrVjxw4999xzPll3YPfu3UqXLp2ztkTcu4WmTZsqf/78Hj3ub926peLFi+uDDz7weq0Pyr3t9+3bpx07dng8bilZsqQKFCigzZs3Ox3J+vbtqwEDBsTLWHY393bduXOnnnzySTVs2NDjTvWZZ55RYGCgVq9efc9NwDPPPKNChQr5bMTL3+He9leuXNG1a9c83lu3bp0ef/xx1a5dW8eOHdP58+d1+fJlnTt3zpk/5WHbv3+/KlSooPr16zuBu1GjRsqdO7fmzZun5cuXq0CBAipRooR27NihkydPauPGjc7fK6HO4+Cu++DBg9q3b5/HucU9NHXHjh3OMRAeHq5ixYrF29/h73hkwsD9OvqtXbtWDRs2dO6+pdgJNZInT64aNWqoWLFievLJJ72S/OOGlLiLUzRr1kwpU6Z0pjKNjo7WRx99pAYNGihr1qx+1TnMXfeOHTu0ePFizZ8/32PmtbNnz6pDhw6qVq2aUqZM6Uzp7I2EH/d3rF27VqlSpdKiRYt08OBBlShRQilSpNALL7zg8T29evVSlSpVvL5C2/nz51W+fHl1797d43X3Pvztt9+qZs2ayps3r1avXq1169Zp4MCBypAhg0dA8Efz589XxowZFRwcrFy5cjnDTG/cuKFSpUopJCREJUuWVI0aNZQsWTKvjCjZt2+fAgMD1b9///uO2y5Xrpxy5Mihb7755p7ziL/N5Hg/7n3/yy+/VOXKlVWsWDFVrFhRu3fvdh5zrFmzRqlSpVLnzp29NrfH/v37VbNmTdWpU0flypVTsWLFPIZkbt26VS6XS0uXLvX4voQ2f8CMGTOcWVal2BUug4ODlTFjRuXOnVvNmjXT7du3dfbsWdWsWVOpU6dWyZIlVbVqVaVNm9ZvRkc8EmEgbjPXu+++6/QQj4qKUr169dS4cWOPz0dERKhnz57q06ePV/sIrFu3zqnVfQAfOXJEzZo1U6pUqZydYteuXerZs6fXFkX6O9wn+zx58ihbtmx67LHH9Pnnnzs9rGNiYnTy5EmNHDlShQsXjte5tO/nxIkTGjVqlIYNG+a89tVXXylJkiTq2LGjVqxYoS1btig8PNxnnQX37NmjXLlyad26dX944vvhhx/UqlUrJU+eXLlz51aBAgX8KhjG5d6Xz58/r9DQUH388cdas2aN3nnnHSVNmtRjEZyJEydq4MCB6tOnj1eewd+4cUNNmzZV165dPV6/ffu2Dh065ITZmjVrKlu2bNqwYUOCuxhJsf10UqdOrYEDB2r16tUqW7asChcurK+++soJBKtXr5bL5VLXrl291gS/f/9+Va9eXWnTptVnn30m6ffOxlu3blX+/Pn17bffeqWW+HD27FnVrVtXpUuX1ty5c3Xq1CmFhIRo0qRJWrNmjebOnaugoCBVrVrV2eZTp07VsGHDNGzYML/qAJzgw8D27dvlcrm0ZMkS9ejRQ+nTp/cYA3z69GmFhoY68wvcr2OKN4LA5cuXVb16dWXIkMFJx3GXKC5SpIgef/xxp0nNH5vJtm3bpsDAQH388cc6c+aMzpw5o/bt2ysgIMBZfjbuScbbY/UPHTokl8ultGnT3tPze968eSpWrJgee+wxFSxYUCVLlvR6Z0G3WbNmKUmSJPddRta9X0ZGRmrfvn367bffdPToUZ+uL/8gvv76a/Xv318vv/yyc/G5evWq3n//fSVOnPieueS9dTGKiopShQoVnOWHpdihd+Hh4UqTJo2CgoLUpEkTSbGBIG3atE5/jYTi0KFDKlGihMaMGSNJ+u233xQSEqKMGTMqY8aM+uqrr5xHkOvWrfP6ZGW//PKLwsLCVKtWLY8OsHXr1lXlypUTZPiKa/v27XruuedUpUoV9ezZU61atfJobd63b5+yZMmi5557zodV/rUEHwak2GlvU6ZMqYCAAI87vTt37igqKkpDhw7Vyy+/rOvXr/t0x9u4caNq1aqlkJCQezrtvPDCC0qUKJEyZ86sGzdu+PwAWbly5T0dLRcvXqxixYrp4sWLHifztm3bKnPmzM6IgrtXI4svkZGR+u2337R27VqnD8js2bPlcrnUrFmzexaPOXPmjPbt26dDhw7F+wp4f2bDhg1KkSKFM9HR/YwfP17PPPOM1xZv+jdu3bqlgQMHKnHixCpevLjHe+5AkCJFCmdopOS9MHD58mWFhoaqQ4cO+umnn/T2228rb968aty4scaNG6cPP/xQ2bNnd+YRqFatml9PKHQ/P//8s0aMGKFr167p1KlTyp07t7MGSMmSJVW4cGEtWbLECWm+4H5kULt2bX3zzTdq1KiRxyNaX5/v/q3t27erVatWCgkJ0dNPP+287r6p+/DDD5U/f34dPXrUa+fHvyvBhoG4O497hcEkSZJo4cKF93x2w4YNypQpk7MAhzefYd++fdujQ8+uXbtUrVo1hYSE6MiRI87r4eHh+uyzz3zeq9Q9DbK7OTHuBXXq1KlKlSqVs4O7L1QHDhxQUFCQsxSwN/z88896/vnnFRoaqhQpUih16tRq0aKFTp48qUWLFjlzy/tDx5y7nThxQhkzZlT9+vU99oG4+2Xv3r3Vv39/vzthxBW3tiNHjmjo0KHOtN1xXbt2TaNGjdJjjz2m3377zev/ptWrVytJkiTKnj27UqdOrcmTJzsX/Nu3b6tGjRr3zDuR0Lj/PS+99JIaN27sDFVr3bq1XC6XnnzyyXs6Fnrb/v37VadOHSVNmlR58+Z1goA/toL+E7t27VLz5s2VKlUqj2XmpdhZFoOCgvxyynC3BBsG3IYOHaqOHTtq9+7dGjp0qJImTaqZM2dK8gwMkydPVpEiRbwyg1XcDj0NGzZU4cKF1b59e3311VeSYlcNq169ugIDA/Xaa6+pdevWypw5s1/MLOhupl6wYIGSJk2q7t27O0Mdf/vtNxUoUOCe5YbdMyhGRER4pcYdO3Yoc+bM6ty5s6ZPn659+/apX79+CgkJUd68eXXs2DGnheDtt992RpP4k4ULFyp58uRq3bq1Rye6yMhIDRgwQNmzZ/fLPiPS7/v33SfxY8eO6dVXX1VAQMA9J8PIyEivzrN+t2PHjmnLli33PG6Jjo5W06ZNNWjQIGcRMH/m3vYHDx7Uzz//fM8jjdq1a3vM3NezZ0/9+OOPTsuZr+3bt0/dunXzal8tb9q7d69atGih0qVLa9KkSZJiw3CfPn0UGhrq14/7ElwYiPusf+XKlcqTJ48zYYsUu8hJ0qRJnelwpdgDYsaMGWrcuLFWrlzplTo///xzJUuWTD169NAbb7yhEiVKqEyZMs4a4qdOnVKPHj1UokQJPfPMM37Ro/Sjjz7SzJkznc6AixcvdqZz/vXXXxUdHa2xY8eqTJkyatu2rS5fvqwTJ07o9ddfV44cObxywtmxY4dSpUqlAQMG3HMimTdvnp566imVKlVKN2/e1OTJk5U0aVK99tprfhcIoqOjNXnyZCVJkkShoaFq27atunTpovr16ytjxox+31lw9erVatOmjVq2bOlx8Tl+/LgGDhyo1KlTe/Sw9ke3bt3SoEGDlCVLFr/qyPVH4s5xny9fPhUsWFCZMmVSy5YtnfobNGigfPny6aOPPlKXLl2UNm1av70bfdSCgNvOnTvVokULJU+eXEWLFlWLFi0UGhrqjBjzVwkmDNw9pGr27Nnq0aOHevbsKclzx3r11VflcrnUvXt3lS1bVgULFpQU2xPe3UEvvsTExOjy5cuqUqWKxzzcZ8+eVdeuXfX00097NKdfvnzZL+Y6v3PnjkqWLKkiRYpo4cKFzp2/OxC89NJLunbtmm7cuKH33ntPhQoVUtKkSVWwYEFlzZrVI5DFl2PHjilDhgxq2rSp81pMTMw9K+P95z//ccbjv/XWWwoMDNS5c+fivb5/YtOmTWrSpImKFCmiChUqqF+/fn57YYp7MUqTJo06dOigfv36KUeOHKpfv74T1I8fP67XX39dLpdLH3/8sQ8r/mOffvqpunfvrkyZMvlt8LqfNWvWKCAgQFOnTtW1a9e0fPlyuVwuzZ49W1JsC0yFChWUP39+FS5c2C9uMh41D/LMf+/evWrVqpUyZcqkIUOG+HWLgFuCCANt2rRxVg5zD0spV66cXC6XqlWrdt9e2WPHjlVYWJhat24d7xdb9xzsUuysfO4Lq7tTkvu9c+fOqVChQgoPD4/Xev4u9/a7fv26atasqeLFi2v+/Pl/GAhiYmJ0/fp1LViwQBEREV4bi3348GGVLFlS9evXv2eq47gHZsWKFdWgQQPna182Tz8If51y1b3fxj2utm/frieffNLpF3D48GFlzpxZLpdL5cuXd4LZkSNHNGzYMJ9M6PRXfvrpJ1WuXFkNGzZMcFMMDxkyxJlB85dfflHu3Lk9ptZ2O336tN+1hiV07nPM5cuXdfv2bWcyqj8KBT/++KM6duyYIOaqkBJIGFi6dKnT2cT9/DoqKkrNmzdXlixZ9PHHHzsX/Lgnrrgzh8XXxEJxf+6cOXP0/PPP6/Dhw6pYsaLatm3r1OSuKzw8XNWqVfO7C4D7JH79+nVVq1ZNJUqU0Pz58+95ZNC1a1efdnJ090oOCwvzCARxD8jKlSurZcuW933PH91vQipfizuF75QpU/TDDz9Iip2zwd0ad+zYMeXMmVMdOnRw5sFv0KBBgugY9uuvv/pl59I/ExMTozp16ujVV1/VzZs3lTVrVnXs2NHZZ8aPH++0EODhcm/jL774QvXq1VOJEiVUr149LVu27E+/LyGMBnLz6zBw94lx6tSpatasmdOsFxUVpTp16qhIkSKaN2+eM3Tm7gttfJ1gd+3apSFDhig6Olq//fabcubM6awjsGLFCrlcrnvmkm/WrJmzkpi/cG8f9x10ZGSkqlWrdt8WgmTJkqlt27b3DNvzpriBIO6EJdHR0Tp+/Lhq1ap1z7wSeHD3m8LXPRJHkrOIUoMGDdSqVSvFxMTo2rVrKlGihFwul2rUqOGr0h95n3zyicqXL68MGTKoS5cuHq2i7dq1U9euXRPUBSghWbZsmVKkSKERI0Zo/vz5atu2rVwul1dm0fQGvw4Dd5s0aZKeeuopdezY0SMQ1KpVS0WLFtVnn33mtQPBPdnRhAkTtGbNGr355pvq3Lmzx/CdCRMmyOVyqUWLFurVq5c6deqkgIAAr8/K9yA2bdqkZs2aOb2T3YHg7haCefPmKX369B6LKfnCH7UQ9OvXT4ULF04wTXP+6q+m8L106ZIKFy7szPN/8+ZNtW/fXl9++aVfjIpJ6NwX+RMnTuinn35yvt62bZsqVqyo/PnzOzOtXrt2TQMHDlSWLFn8dgRKQuUOxpGRkapXr55GjhwpSTp58qSyZ89+30c0CZVfhoG4z+Dv9uGHH6pYsWJq166dRyCoW7eusmTJotWrV8d7fXv27FHKlCk1ePBgSdJrr73mjOW9eynKNWvWqH79+qpataoaNWrkk+lvH8TMmTNVpEgRPffcc04ny7gtBAsXLnQCgT8styl5BoJt27ZpxIgRCggI8NnMgo+KP5vC98SJE9q/f78iIyNVvHhxNWjQQIcPH9Yrr7yiJ598Mt6Ww7XRggULFBwcrODgYBUoUEBr166VFDtkuWzZssqZM6fKly+vqlWrKnPmzAmqI6Q/Gz16tEe/rpiYGF26dEk5c+bU+vXrdfbsWecRjduMGTMSfBDzyzAQ1xdffKElS5ZozZo1zmtTp051AoG7t+zt27fVu3fveH8Wv2vXLmXIkEH58uVzXjt79qxGjhypRIkSOWNLpd8fV7hbK/xh1ID0x03nc+bMUfny5dW8eXPnGXFkZKTCwsKUK1cuZ0ERf2p6379/v+rWrauMGTMqadKkfj98JyH4qyl8s2fPrho1amjRokXKlSuXsmbNquDgYC5GD4H7JmjPnj3KmTOnRo0apbVr1yosLExBQUHOrJW7du3SjBkz9NJLL2nKlCl+v7JpQnHjxg298847CggI0Guvvea8fufOHbVu3VrDhg1zVj91n9/Pnj2r559/Xp9++qlfnRv/Lr8KA926dfOYsjQ8PFwZM2bUE088oYIFC3qs8jZ16lQVL15cHTt2dJYAdouvQLB9+3alSpVKlStXVpYsWdStWzfnvYsXLzotBJ988omk2Ium+3/ur33lfi0t+/btu+ckMmvWLFWoUEHPPvusE7SuXbumBg0a+G3z708//aT69es7a4Xj33mQKXzz5cun8PBw/frrr/r2229pEfiH7jdiY+PGjZoxY4b69Onj8dnGjRs7gcCXUws/6i5cuKDx48crXbp0GjhwoPN6//795XK5VKtWLY+VH/v376+8efN6zCaaEPlNGLhw4YJ69uyp/Pnz680339SxY8dUtmxZ7dixQ3v37tWYMWOUJ08etWvXzvmeDz/8UMHBwc4yqfF5sd28ebOSJk2qIUOG6M6dO5oyZYoyZMjgEQguXbqkQYMGyeVyObMg+gP3iebEiROaO3euZs2apQULFqhatWrq1KnTPXM4zJgxQ4GBgWrRokWCWbTFG8tQ2+TPpvC9deuWnnnmGT3//PM+rjJhu3vEhvumxt0Rs2bNmvfs140bN1auXLk8JgfDwxH3xu3q1asaM2aM0qVLpwEDBjifadmypTJmzKiXX35ZgwcPVps2bfxqGeJ/w2/CgBTbKWPIkCEqWLCgmjRpojZt2jh3+ZcuXdKkSZOUK1cutW/f3vmeZcuWeWWY3rp16zxaJi5duvSHgWDw4MFyuVyaO3duvNf1V9wnnB07dihnzpzKnz+/kiZNqlKlSqlw4cIKCwtTjx497rnrL1++vDJmzKj27dvrxo0bCbr5C//Mn03h26RJEw0aNMjjBIoHd78RG59//rnzfq1atRQYGKjVq1ffc3575pln9NRTT3kMncY/c79994cfftCxY8d08eJFjR07VoGBgR6zbA4cOFCNGzdWyZIlnanwHwV+EQbiNpGdPHlSgwcPVkhIiMqWLevxuUuXLmny5MnKmzevGjVq5PGeN8ftx5184n6B4MKFC3rrrbd8PqFJ3CCQKlUq9e3bVydPntTSpUtVq1YtVaxYUS+99JKKFCmiHj16OM1cN27cUIcOHfTWW2/RKx8eEtoUvv7sr0ZslCtXTjly5NA333xzz2M+jsuH49SpU5J+77R+8OBBZcqUyemEfOHCBScQ9O3b1/m+W7du6fbt2343X8y/4fMwEHcnd09mc+bMGQ0ePFjp0qXToEGDPD5/+fJljR49Ws2aNfOLsfpxA8HdPVD9wf2m8JVih2kGBgbqxIkTmjBhgkqUKKFnn31WM2bMUL9+/ZQ/f36/ncIXvpFQp/D1R382YuPQoUPOPB41a9ZUtmzZtGHDBr843z1K5s+fr5CQEI9HoefPn1doaKjHOitxA8Hd16NHiU/DQNyd+4033lCDBg2cCRzcgSA0NNQZwufmnhL37p/hK5cvX9bUqVPlcrk8mpP8wR9N4bty5UoFBgZq3759kqTp06erbt26ypo1qwoXLuyVtQaQcCTkKXz90V+N2AgKClKTJk0kxQaCtGnTJpj+OwnFihUrVK9ePZUsWdLpr3Hw4EHlyZPnnhshd6dCl8ulYcOG+aLceOfzlgFJ6tu3rzJnzqwZM2Z4JLJTp05p8ODByps3r4YOHXrP9/nL3bcU+whj+vTpfjnW1D0ev0aNGtq7d6+uXr2qxx9/3KPZS4r9N5w4cYIWAdxXQpzC1189yIiN7NmzO+ubVKtWzenAiYcnIiJCDRs2VNGiRfX999/ryJEjypQp032nXHf3W/PH9TYeBpckGR9atWqVadOmjVm0aJEpXbq0kWQuXrxojh49avLkyWNcLpcZPXq0GTt2rPnf//5nXnzxRV+W+6ckGZfL5esy7uvAgQOmR48e5vr162bnzp3mhRdeMGPGjDHGGHPnzh2TJEkSH1cI2GXNmjUmLCzMZM2a1Vy4cMGMGjXKVKtWzeTOndtERUWZunXrmscee8zMnj3b16U+cuKeqyMiIsy4cePMiRMnTOfOnc2sWbNMWFiYCQkJMTExMSYqKsrcunXLFChQwJQpU8bHlccfn18BLl68aLJkyWJKlSpltm3bZpYuXWpmz55trly5YqpWrWree+89065dOxMUFGReeOEFX5f7p/w1CBhjTJ48ecy4ceNM586dTZo0aUzDhg2d9xInTuzDygA7Va1a1Rw6dMicPXvWZM+e3WTIkMF5L3HixCZt2rQmV65cJiYmxhhjTKJEiXxV6iMn7rm6cuXK5s6dO2bSpEmmf//+5vz58yZNmjRmxowZxuVymWTJkpno6Gjz2Wef+bDi+OfVloEbN26YlClTery2fft2U6xYMVOzZk2zefNmU7duXVOlShWTPHly89JLL5kvvvjCI41FR0dz8foXfvnlF9OtWzcjybz22mumXLlyvi4JQBy3b982b775pvnoo49MRESEyZMnj69LemS4WwS2bdtmfv31VxMTE2Pq1KljjDFm9erV5qOPPjI7d+40M2fONIULF3a+LzIy0vznP//xVdle4bUw8Omnn5qDBw+aAQMGmOTJkxtJJiYmxiROnNhs2LDBLFiwwDz99NOmatWq5vHHHzeRkZGmUqVKZuTIkaZq1areKNEaBw4cML169TLnzp0zY8aMMU8//bSvSwJgjJk5c6bZvHmzmTdvnlm+fLkpWrSor0t65CxcuNC0adPGPPHEE+bUqVOmcePG5pNPPjHGxAaC8ePHmzNnzpjhw4ebKlWqGGP8+xHwQ+ONjglTpkyRy+XS8uXLJXnO9LR582YdPXrU+ezt27d15coV1axZU2XKlHmkxnH6k3379qlJkyYe2x6A7zBiI/64rzeRkZGqWLGiPvnkEx06dEhffvmlHnvsMTVo0MD5bEREhKpWrapKlSpZNctjvLcMfPrpp6Zdu3ZmyZIlpnbt2sb961wul1m0aJHp2LGjWbhwoalUqZKJiooyEyZMMAsWLDC3b982GzZsMEmTJjUxMTE8L4sHt2/fNsmSJfN1GQD+v7Nnz5rkyZObtGnT+rqUR86qVavMp59+ahInTmxGjBhhMmbMaIwxZsOGDaZBgwamfPnyZtGiRcblcplvvvnGhISEmKCgIB9X7T3xeoWdPn26eeGFF0zlypVN7dq1jTHGxMTEGJfLZZYsWWKaNGlihg0bZipVqmSMiQ0IhQsXNtWrVzffffedSZo0qblz5w5BIJ4QBAD/kjFjRoJAPLlw4YJZsGCBWb58uTN6SpIpV66cWbJkidm0aZOpXr26kWQqVKhgVRAwJh7DwNSpU027du1Mu3btzJ49e0yPHj2MMbG9ZCWZqKgoM3nyZNO5c2fne5IkSWKqVKlihgwZYpIkSWKio6MZ8gYAeCDukRf3+7phw4Zm1qxZJjIy0gwaNMgY8/uognLlypnZs2ebY8eOmZMnT3qvYD8SL48Jxo4da3r16mW+/PJLU6tWLTNlyhQzaNAg07JlSzNu3LiH/esAADDGGPPTTz+ZTz/91HTs2NFky5bNo+NfVFSUWbx4sWnTpo1p3769GT9+vMf33m/Emy3iJQysW7fOnD592jRv3twYY8zly5fNvHnzzMCBAz0CAcMEAQAPS1RUlClXrpzZsmWLyZ07t/nvf/9rSpUqZZo2bep85ubNm2bp0qWmTZs2pnPnzs7ka7aLlzZ4dx8A/f/hGGnTpnWCwcCBA40xxowbN84kTpyYQAAAeCiSJk1qmjZtalq0aGEKFixoNmzYYDp16mSWLVtmypQpYzp37mxSpEhhnn32WWOMMS1atDDJkiUzI0aM8HHlvufVSYeuXLli5s6dawYNGmRatWpFIgMAPFQRERHmv//9r1m9erUpUaKEOX36tPnggw/MyJEjTaFChUy7du1MlSpVTO7cuc3ixYtNvnz5TGhoqK/L9jmvr01w5coVM2/ePNOpUyczZswYp2MhAAAPQ58+fczp06fNtGnTTIoUKUzz5s3Njh07TOnSpc3hw4fNxo0bzahRo0z37t0f/cmEHpDXu+qnSZPGNG3a1GTMmNHUrVvX278eAPCIK126tHn33XdNsmTJTPv27U1ERIRZvXq1KVCggPn555/NihUrTLVq1QgCcfh81UJWzAMAPGyVKlUy3377rXniiSfMV1995bHWAO7l89l8CAIAgIfFfX/br18/kzt3bjNhwgRTuHBh4+P7Xr/n8zAAAMDD4m76L168uImJiTFbt271eB33RxgAADxyMmXKZAYPHmzGjBljfvjhB1+X4/cIAwCAR1KVKlVMyZIlTZYsWXxdit/zeQdCAADiy82bN02KFCl8XYbfIwwAAGA5HhMAAGA5wgAAAJYjDAAAYDnCAAAAliMMAABgOcIAAACWIwwAAGA5wgAAAJYjDAAAYLn/BzpSTxAM5NdmAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plot_value_array(1, predictions_single[0], test_labels)\n",
        "_ = plt.xticks(range(10), class_names, rotation=45)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2tRmdq_8CaXb",
        "outputId": "a96860ca-c94f-483c-c782-59a555aa7a1c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "np.argmax(predictions_single[0])"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "toc_visible": true
    },
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.8"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}