Handwritten_Digit_Classification_with_MNIST_dataset.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Handwritten Digit Classification with MNIST dataset.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6PC-KcgdAfXZ",
        "outputId": "9863c601-af66-4c24-e402-8f9b214728e7"
      },
      "source": [
        "import tensorflow as tf\n",
        "\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "print(tf.__version__)"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.7.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fD4PM76pCgiN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7e067c98-c84f-4205-86c1-0f4050591309"
      },
      "source": [
        "mnist = tf.keras.datasets.mnist\n",
        "\n",
        "(train_images, train_labels),(test_images, test_labels) = mnist.load_data()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "11493376/11490434 [==============================] - 0s 0us/step\n",
            "11501568/11490434 [==============================] - 0s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2kGpJfAIIs_J"
      },
      "source": [
        "class_names = ['zero', 'one', 'two', 'three', 'four', 'five', 'six', 'seven', 'eight', 'nine']"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gCoJgX2eX56h",
        "outputId": "09f65ef0-1c4a-4edd-9075-fa126d7f7df2"
      },
      "source": [
        "train_images.shape"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60000, 28, 28)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5C82hVRkYMd3",
        "outputId": "c6bc34bc-9b5c-4e0a-abb8-ff63bdbdd779"
      },
      "source": [
        "len(train_labels)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "60000"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kUuSxMT4YbSh",
        "outputId": "88130f48-371b-43e8-a1b2-aaaa0fa699fb"
      },
      "source": [
        "train_labels"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([5, 0, 4, ..., 5, 6, 8], dtype=uint8)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NqwBn6YaYfer",
        "outputId": "cdd2a724-df83-4134-d4f9-076b57705249"
      },
      "source": [
        "test_images.shape"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000, 28, 28)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D8aNtBFGYrdn",
        "outputId": "2d55cb84-333a-49d1-f717-4c4b338ec367"
      },
      "source": [
        "len(test_labels)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10000"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "purcM1tFYw0L",
        "outputId": "e4de8d91-54fa-4538-80b5-030e8109b8a9"
      },
      "source": [
        "plt.figure()\n",
        "plt.imshow(train_images[0])\n",
        "plt.colorbar()\n",
        "plt.grid(False)\n",
        "plt.show()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qDeo37IkZLtN"
      },
      "source": [
        "train_images = train_images / 255.0\n",
        "\n",
        "test_images = test_images / 255.0"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 589
        },
        "id": "7hWfIDt-ZaZ2",
        "outputId": "119e454e-b565-4e64-abd4-d9904f43cf18"
      },
      "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()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 25 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4k8Zf-Wib5Re"
      },
      "source": [
        "model = tf.keras.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",
        "])"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CmT7nk9NdJWb"
      },
      "source": [
        "model.compile(optimizer='adam',\n",
        "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TIokzImbig5p",
        "outputId": "e55af2a8-58a9-403d-bc42-52501f07aee7"
      },
      "source": [
        "model.fit(train_images, train_labels, epochs=10)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2559 - accuracy: 0.9281\n",
            "Epoch 2/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1158 - accuracy: 0.9655\n",
            "Epoch 3/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0797 - accuracy: 0.9764\n",
            "Epoch 4/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0586 - accuracy: 0.9822\n",
            "Epoch 5/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0461 - accuracy: 0.9855\n",
            "Epoch 6/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0352 - accuracy: 0.9884\n",
            "Epoch 7/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0293 - accuracy: 0.9903\n",
            "Epoch 8/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0233 - accuracy: 0.9926\n",
            "Epoch 9/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0189 - accuracy: 0.9942\n",
            "Epoch 10/10\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0158 - accuracy: 0.9950\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fb01df89d90>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tErzx0fmlAiX",
        "outputId": "05bffcde-e43a-4c1b-d3c4-cb123422781a"
      },
      "source": [
        "test_loss, test_acc  = model.evaluate(test_images, test_labels, verbose=2)\n",
        "print('\\nTest Accuracy:', test_acc)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 - 0s - loss: 0.0782 - accuracy: 0.9782 - 455ms/epoch - 1ms/step\n",
            "\n",
            "Test Accuracy: 0.9782000184059143\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xigyJPjsly_z"
      },
      "source": [
        "probability_model = tf.keras.Sequential([model, tf.keras.layers.Softmax()])"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b8DDy0mCmZy1"
      },
      "source": [
        "predictions = probability_model.predict(test_images)"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kPAC9u9dmo88",
        "outputId": "b5d69fea-1960-4b10-aaac-6bd27a09cc0e"
      },
      "source": [
        "predictions[0]"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([6.75963174e-11, 1.79520756e-11, 1.34761855e-08, 5.68055439e-05,\n",
              "       9.32383375e-15, 3.01925207e-10, 2.50643217e-16, 9.99942183e-01,\n",
              "       2.32964759e-09, 9.81474273e-07], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LPT0FLSVmthM",
        "outputId": "27c6cc82-898a-4a5d-ffba-585b98e3ac1d"
      },
      "source": [
        "np.argmax(predictions[0])"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VDFEYuHDmzqP",
        "outputId": "f8536057-8826-41b1-866c-b5da2350bc6a"
      },
      "source": [
        "test_labels[0]"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V6YVYnzBm6TQ"
      },
      "source": [
        "# Graph \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(predicted_label),\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')"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 211
        },
        "id": "w53r42aJGHta",
        "outputId": "6b700e4d-45f8-4ff5-8e4e-467d0bd0b500"
      },
      "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()"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 211
        },
        "id": "FtrWfHDsGsWp",
        "outputId": "55e26d51-da77-4e89-84c5-483760fd09d3"
      },
      "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()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAADCCAYAAAB3whgdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAOw0lEQVR4nO3dfZBfVX3H8fdXlockMKZDUoaAGESgwQwPKWGwFIgNQsAM0QqVdsDGVirFxqbV0to20FIoMIqjUwqMEqykIg8xtjxkTOpAoNMqNQ8QYmkdhIAQLInIg32C0G//uDe45t7N3t38NiebvF8zv8nd757zu+duks+ePff8fhuZiSRpx3tT6QFI0u7KAJakQgxgSSrEAJakQgxgSSrEAJakQvpKD0AqbcKECTl58uTSw9BO5pFHYPPm7u37+uCYY5r1VatWbcrMia19hjs4aVcxefJkVq5cWXoY2slEDK395s3Q9s8oIp4aqI9LEJJUiAEsSYUYwJJUyJDWgL1ZoZG0fv16Nm3aNMSVN2n0GlIAe7NCI+n4448vPQRph3IJQpIKMYAlqRADWJIKMYAlqRADWJIKMYAlqRADWJIKMYAlqRADWJIKMYAlqRADWJIKMYAlqRADWJIKMYAlqRADWJIKMYAlqRADWJIKMYAlqRADWJIKMYAlqRADWJIKMYAlqRADWJIKMYAlqRADWJIKMYAlqRADWJIKMYAlqRADWJIKMYAlqRADWJIKMYAlqRADWJIKMYAlqRADWJIK6Ss9gNFozZo1jdqCBQsataVLl7b2z8xGLSJa25577rmN2pVXXtna9sADD2zU7r///ta2M2fObNTGjBnT2lbSyHAGLEmFGMCSVIgBLEmFGMCSVIgBLEmFuAui9tprrzVqDzzwQGvbuXPnNmrPPfdcozbQzoY2A7VdvHhxozbQboWnn366UVuxYkVr21tuuaVRO//887cxQkm95gxYkgoxgCWpEANYkgoxgCWpEG/C1VavXt2onXHGGZ37T5o0qVG77rrrWtuOHTu28/M+9dRTnfvPmzevUdt7771b27a9bFnSjuUMWJIKMYAlqRADWJIKMYAlqRADWJIK2e12Qaxbt661fvbZZ3d+jtNOO61Ru+qqqxq1adOmdR/YADZs2NCozZkzp7Xtiy++2KhdcsklrW3b3pBd0o7lDFiSCjGAJakQA1iSCjGAJamQ3e4m3BVXXNFa37hxY6M2e/bs1rbXXntto3b44Ydv38AG0HbTsO1l0wOZNWtWL4cjqYecAUtSIQawJBViAEtSIQawJBViAEtSIbv0LogLL7ywUbvjjjta2+67776N2tVXX93adiR2PLT9VmZof4lzZra2nTFjRqN26qmnbte4JI0cZ8CSVIgBLEmFGMCSVIgBLEmF7NI34VauXNmoRURr23HjxjVqRx11VM/HBO033BYsWNDa9sEHH2zUBrqGSy+9dPsGJmmHcgYsSYUYwJJUiAEsSYUYwJJUyC59E6609evXt9avv/76Rq3tPYYHMmnSpNb6scce2/k5JJXnDFiSCjGAJakQA1iSCjGAJakQA1iSCtmld0FMmTKlUVu7dm1r2xdeeKFRO+6447br/G2/aRlgw4YNjdpALy9uM3PmzNb6+PHjOz+HpPKcAUtSIQawJBViAEtSIQawJBWyS9+EW7hwYaP2yiuvtLa99957G7WBbthtr7vuuqtRW7RoUWvbxYsXN2oXXXRRz8ckacdzBixJhRjAklSIASxJhRjAklSIASxJhezSuyDGjBnTqN19992tbVesWNGotf1W5YG0/Qbls846q7XtxRdf3KjdeeedrW2PPPLIRu2www7rPC5JOy9nwJJUiAEsSYUYwJJUiAEsSYXs0jfhhmLGjBmdar1w4403NmoDvR/w9OnTG7WJEyf2fEySdjxnwJJUiAEsSYUYwJJUiAEsSYUYwJJUiLsgRtD69es7t91vv/1a6/Pnz+/RaCTtbJwBS1IhBrAkFWIAS1IhBrAkFeJNuBF0+eWXd247e/bs1vq0adN6NRxJOxlnwJJUiAEsSYUYwJJUiAEsSYUYwJJUiLsgemTdunWN2pIlSzr3nzVrVi+HI2kUcAYsSYUYwJJUiAEsSYUYwJJUiDfhemTNmjWN2ssvv9zatu03IO+zzz49H5OknZszYEkqxACWpEIMYEkqxACWpEIMYEkqxF0QPbJx48ZGrW23A8DUqVMbtXPOOafnY5K0c3MGLEmFGMCSVIgBLEmFGMCSVIg34Xpk0aJFndtecMEFIzgSSaOFM2BJKsQAlqRCDGBJKsQAlqRCDGBJKsRdED0yZcqURm3t2rUFRiJptHAGLEmFGMCSVIgBLEmFGMCSVIg34XrkzDPPbNSeeOKJ1rbTp08f6eFIGgWcAUtSIQawJBViAEtSIQawJBViAEtSIe6C6JG2N1n3jdclbYszYEkqxACWpEIMYEkqxACWpEKGdBNu1apVmyLiqZEajHZ7by09AGlHGlIAZ+bEkRqIJO1uXIKQpEIMYEkqxACWpEJ2qQCO4PIITuvRc10Twbr68YF+9UMjeCiCxyO4PYK96vre9ceP15+fXNdPimBtBCsjOLyujY9gecTAX/8IFkfwtkHGeFEEHxzm9U2M4OvD6SupNyIzS49hpxPBe4D5wJnA3sAKYGYmL0dwB7Akk9siuBF4JJMbIrgYODqTiyI4D3hfJh+IYAnwMWByXft4BJ8G7slkxQDnfwdwRSbvG+Hr/CJwUyb/NJLn2dlFxEZgqLt7JgCbhnE6++0859xR/d464AaGzBxVD8jJkI9BfgHyO5DLIcfUn/sbyHPq4/WQfw65GvJRyJ+r6+Mgb4b8F8g1kHNazvEHkAv6fbwQ8lcgA3ITZF9dfyfksvp4GeQ76+O+ul1A3g55BOTpkFdDHgZ5+yDX+JeQc/t9/GPIKyEfgfwW5AF1/c8gP1Efr4C8pr6u70KeXNf3gPwU5Lch10J+pN/zzoG8vvTf6Wh8ACvt17t+o2ms23ONWz9G6xLE4cBfZ/IO4EXg/QO025TJNOAG4BN17U+A+zI5AXgX8KkIxm3V7xFgVgRjI5hQt3sLsD/wYiab63bPAAfVxwcB3weoP/9S3f4q4Bbgk8B1wJXAnw5yfScBq/p9PA74VibHAA8CFw7Qr6++rvnAZXXtN4GXMpkOTAcujODQ+nMrgZMHGYukETJa3w3tyUwero9XUf1432ZJvza/XB+fDpwd8UYg7wMcAjy2pVMmyyOYDvwzsBH4JvD6cAZaj/NEgAhOAZ4DIoLbgdeAj2fyH1t1O7A+7xavAvf0u5Z3D3C6/tc7uT4+HTg6gnPqj99M9Q3sSeB5YNLQr0pSL4zWAP7ffsevA2MGafc6P7nWAN6fyb9v6wSZXEk1WyWCW4HvAj8ExkfQV89yDwaerbs8SzVLfiaCPqqg++GW54sgqGa+5wF/BVxCFZIfo5qV9/ffVN8Ytngtky2L9f2vpev1zstkWUv7fepzaeg+b7+e9itxzhLX+FNG6xLE9lgGzKsDkQiO27pBBHtEsH99fDRwNLC8DsH74Y3Z5K8Df18f31V/TP35+/qFJsAHgaWZvACMBf6vfoxtGeNjwNuHfYU/bRnw2xHsWV/PEf2WXI4A1vXoPLuVzBzWf0L77TznLHGNWxutM+Dt8RfAZ4G19TawJ4HZW7XZE/jHCABeBs7vt+77h8BtEVwBrAEW1vWFwKIIHgdeoJrpAhDBWGAu1XIAwGeApVRLC7/WMsZ7gRnAN4Z5jf3dRDXTXl1/09kIvLf+3Lvqc0kqwG1oO6EIxlDNtE/KHN7ac8fzPAjMyeRHI3WOXU1EzAI+B+wB3JSZV3fsdzPVN/rnM3PqEM73FqqbuAcACXw+Mz/Xod8+VDds96aaaC3OzMu23eun+u9BdZP22czceoIyUJ/1wCtUS2CbM/P4jv3GU00UplJd429k5jcH6XMkcHu/0tuASzPzsx3O93vAh+tzPQp8KDP/p0O/36W6AR7AF7qca1C92k7ho7cPyDMgDxnB558I+d7S1zmaHlSh+z2q/+x7Ue2WOapj31OAacC6IZ7zQGBafbwf1b2IQc9Zh8S+9fGewEPAiUM47+8DtwL3DKHPemDCML6uXwI+XB/vBYwfxt/LD6j22w7W9iCqn3rrravcAczt0G8q1XLdWKpvaN8A3r69/6Z2xzXgUSGTZZk8PYLPvzGTvxup599FnQA8nplPZOarwG3AnC4dM/NBqqWpIcnM5zJzdX38CtX9gYO23Quy8uP6wz3rR6cfdyPiYOA9VLPSERURb6b65rQQIDNfzcwXh/g0M4HvZWbXF9P0AWMioo8qUDd06DMFeCgz/yszNwMP8JOdVcNmAEvdvbHXu9Z/H/iIi4jJwHFUs9ku7feIiIepthv+Q2Z26kd1j+QSqpvEQ5HA8ohYFRG/1bHPoVT3Jb4YEWsi4qaI2Hpf/mDOA77SaYCZzwKfBp6m2hL6UmYu79B1HXByROwfEWOBs6h2PW0XA1gaBSJiX+CrwPzMfLlLn8x8PTOPpdoueUJEDLr2HBFb1qlXDda2xS9m5jSql/B/NCJO6dCnj2pp5obMPA74T+CPup4wIvYCzgbu7Nj+Z6h+ajmUag/8uIg4f7B+mfkYcA2wHPg68DDDfG1Afwaw1N2Wvd5b9N8HPmIiYk+q8P1yZi4ZrP3W6h/p7wdmdWh+EnB2fUPtNuCXIuJvO57n2frP54GvUS3ZDOYZ4Jl+s/PFVIHc1ZnA6szc+sVMAzkNeDIzN2bma1QvXvqFLh0zc2Fm/nxmngL8iGo9frsYwFJ33wYOj4hD65nXeVT7v0dMRATV+uhjmfmZIfSbWO8uICLGUL168t8G65eZn8zMgzNzMtX13ZeZg84QI2JcROy35Zhqy+Wge8wz8wfA9+tdDVCt5/7rYP36+VU6Lj/UngZOjIix9dd2Jv1eBbstEfGz9Z+HUK3/3jqE87baHfcBS8OSmZsj4neoXtyyB3BzZn6nS9+I+ArV3u4JEfEMcFlmLtx2L6CakV4APFqv5wL8cWYuHaTfgcCX6u1kbwLuyMx7BumzPQ4AvlZlGn3ArZnZ9e1O5wFfrr+pPQF8qEunOujfDXyk6yAz86GIWAysBjZT7eXv+sKKr0bE/lRvIfDRYdwsbHAfsCQV4hKEJBViAEtSIQawJBViAEtSIQawJBViAEtSIQawJBViAEtSIf8PjpGaoSOU3tYAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 729
        },
        "id": "r742D_FNHr7e",
        "outputId": "12370b84-a191-4c6d-fa31-0d7f238ad306"
      },
      "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[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()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x720 with 30 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zuxshIFqIx0Q",
        "outputId": "687f4fdb-3c55-473a-f77b-dbd101ee1cee"
      },
      "source": [
        "img = test_images[1]\n",
        "\n",
        "print(img.shape)"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(28, 28)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0KNfHv-DI-CT",
        "outputId": "e57e4eba-961f-4d38-b95f-f17a14c071c2"
      },
      "source": [
        "img = (np.expand_dims(img, 0))\n",
        "\n",
        "print(img.shape)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1, 28, 28)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7Bu2hURLJF7g",
        "outputId": "9134c667-1d7a-4b68-c435-02304e3b3f58"
      },
      "source": [
        "predictions_single = probability_model.predict(img)\n",
        "\n",
        "print(predictions_single)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[9.8501055e-11 1.2133460e-06 9.9999881e-01 2.7134190e-08 1.8601941e-22\n",
            "  5.2319801e-11 2.4179086e-11 1.1089200e-19 1.1706475e-08 5.4061583e-19]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "DwRj2PfQJdA9",
        "outputId": "d0eb176a-8c9b-4bdc-d879-7fbb7803b2fa"
      },
      "source": [
        "plot_value_array(1, predictions_single[0], test_labels)\n",
        "_ = plt.xticks(range(10), class_names, rotation=45)\n",
        "plt.show()"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PbvzCk0GJ_yy",
        "outputId": "db580ae3-ba66-467e-f4b0-949db8b55e23"
      },
      "source": [
        "np.argmax(predictions_single[0])"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ALP1Ws7JKGIJ"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}