Shallow_Net.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "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": "3.6.5"
    },
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/cassianojr/deep-learning/blob/main/Shallow_Net.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mXKu_OA55qBw"
      },
      "source": [
        "\n",
        "\n",
        "```\n",
        "# Isto está formatado como código\n",
        "```\n",
        "\n",
        "# Shallow Neural Network in Keras"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MSSGZcQr5qBx"
      },
      "source": [
        "Build a shallow neural network to classify MNIST digits"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RywMXPrN5qBx"
      },
      "source": [
        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/the-deep-learners/deep-learning-illustrated/blob/master/notebooks/shallow_net_in_keras.ipynb)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0EiWsnlu5qBy"
      },
      "source": [
        "#### Load dependencies"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5WEeta6o5qBy"
      },
      "source": [
        "import tensorflow as tf\n",
        "import keras\n",
        "from keras.datasets import mnist\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "from tensorflow.keras.optimizers import SGD\n",
        "from matplotlib import pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oouoP7Y05qB2"
      },
      "source": [
        "#### Load data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "wur5LCHi5qB5"
      },
      "source": [
        "(X_train, y_train), (X_valid, y_valid) = mnist.load_data()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DlVPFnMA5qB_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "64222847-4cbf-47f2-85df-2d6f334098d2"
      },
      "source": [
        "X_train.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60000, 28, 28)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yzYNa9aD5qCJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "776d842d-d37a-4121-c374-3f5278d2acfc"
      },
      "source": [
        "y_train.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60000,)"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5KrHGosu5qCL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0422e643-2fca-44e8-f4b3-6dfe33881a4e"
      },
      "source": [
        "y_train[0:12]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([5, 0, 4, 1, 9, 2, 1, 3, 1, 4, 3, 5], dtype=uint8)"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tzwwOqW85qCN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bbd95732-1eb8-41cb-8131-e05b2d6778c5"
      },
      "source": [
        "plt.figure(figsize=(5,5))\n",
        "for k in range(12):\n",
        "    plt.subplot(3, 4, k+1)\n",
        "    plt.imshow(X_train[k], cmap='Greys')\n",
        "    plt.axis('off')\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 12 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "73ncBdsn5qCQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5de51654-aed6-4207-ec45-864e4b427540"
      },
      "source": [
        "X_valid.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000, 28, 28)"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IjYFIYXl5qCS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "103376ed-36cb-41ec-8b1e-fbe2e14e89ab"
      },
      "source": [
        "y_valid.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000,)"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LoGjfd_e5qCU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f5ef8c6f-b7d7-4083-cb30-c79a57bd3221"
      },
      "source": [
        "plt.imshow(X_valid[0], cmap='Greys')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fa28b97dd50>"
            ]
          },
          "metadata": {},
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P1mndEwG5qCX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2e59fb3b-bf07-4925-ff83-85b0a747264f"
      },
      "source": [
        "X_valid[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   0,  84, 185, 159, 151,  60,  36,   0,\n",
              "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   0, 222, 254, 254, 254, 254, 241, 198,\n",
              "        198, 198, 198, 198, 198, 198, 198, 170,  52,   0,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   0,  67, 114,  72, 114, 163, 227, 254,\n",
              "        225, 254, 254, 254, 250, 229, 254, 254, 140,   0,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,  17,  66,\n",
              "         14,  67,  67,  67,  59,  21, 236, 254, 106,   0,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0,   0,   0,   0,  83, 253, 209,  18,   0,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0,   0,   0,  22, 233, 255,  83,   0,   0,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0,   0,   0, 129, 254, 238,  44,   0,   0,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0,   0,  59, 249, 254,  62,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0,   0, 133, 254, 187,   5,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0,   9, 205, 248,  58,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0, 126, 254, 182,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,  75, 251, 240,  57,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "         19, 221, 254, 166,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   3,\n",
              "        203, 254, 219,  35,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,  38,\n",
              "        254, 254,  77,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,  31, 224,\n",
              "        254, 115,   1,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0, 133, 254,\n",
              "        254,  52,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,  61, 242, 254,\n",
              "        254,  52,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0, 121, 254, 254,\n",
              "        219,  40,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0, 121, 254, 207,\n",
              "         18,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0]], dtype=uint8)"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G9tmt7es5qCZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d653e16f-959c-465f-9c92-3a546abbe0b8"
      },
      "source": [
        "y_valid[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UvlV-jqg5qCb"
      },
      "source": [
        "#### Preprocess data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QHvPQuhD5qCc"
      },
      "source": [
        "X_train = X_train.reshape(60000, 784).astype('float32')\n",
        "X_valid = X_valid.reshape(10000, 784).astype('float32')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n5GyKPjt5qCe"
      },
      "source": [
        "X_train /= 255\n",
        "X_valid /= 255"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7hUsmolr5qCg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3a3f7d36-1d03-4ef8-a209-502a6f9c2ae2"
      },
      "source": [
        "X_valid[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.32941177, 0.7254902 , 0.62352943,\n",
              "       0.5921569 , 0.23529412, 0.14117648, 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.87058824, 0.99607843, 0.99607843, 0.99607843, 0.99607843,\n",
              "       0.94509804, 0.7764706 , 0.7764706 , 0.7764706 , 0.7764706 ,\n",
              "       0.7764706 , 0.7764706 , 0.7764706 , 0.7764706 , 0.6666667 ,\n",
              "       0.20392157, 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.2627451 , 0.44705883,\n",
              "       0.28235295, 0.44705883, 0.6392157 , 0.8901961 , 0.99607843,\n",
              "       0.88235295, 0.99607843, 0.99607843, 0.99607843, 0.98039216,\n",
              "       0.8980392 , 0.99607843, 0.99607843, 0.54901963, 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.06666667, 0.25882354, 0.05490196, 0.2627451 ,\n",
              "       0.2627451 , 0.2627451 , 0.23137255, 0.08235294, 0.9254902 ,\n",
              "       0.99607843, 0.41568628, 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.3254902 , 0.99215686, 0.81960785, 0.07058824,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.08627451, 0.9137255 ,\n",
              "       1.        , 0.3254902 , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.5058824 , 0.99607843, 0.93333334, 0.17254902,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.23137255, 0.9764706 ,\n",
              "       0.99607843, 0.24313726, 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.52156866, 0.99607843, 0.73333335, 0.01960784,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.03529412, 0.8039216 ,\n",
              "       0.972549  , 0.22745098, 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.49411765, 0.99607843, 0.7137255 , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.29411766, 0.9843137 ,\n",
              "       0.9411765 , 0.22352941, 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.07450981, 0.8666667 , 0.99607843, 0.6509804 , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.01176471, 0.79607844, 0.99607843,\n",
              "       0.85882354, 0.13725491, 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.14901961, 0.99607843, 0.99607843, 0.3019608 , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.12156863, 0.8784314 , 0.99607843,\n",
              "       0.4509804 , 0.00392157, 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.52156866, 0.99607843, 0.99607843, 0.20392157, 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.23921569, 0.9490196 , 0.99607843,\n",
              "       0.99607843, 0.20392157, 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.4745098 , 0.99607843, 0.99607843, 0.85882354, 0.15686275,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.4745098 , 0.99607843,\n",
              "       0.8117647 , 0.07058824, 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        ], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KDS3wGYt5qCi"
      },
      "source": [
        "n_classes = 10\n",
        "y_train = tf.keras.utils.to_categorical(y_train, n_classes)\n",
        "y_valid = tf.keras.utils.to_categorical(y_valid, n_classes)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IpujSGOl5qCk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "68313ce0-c4e6-49b1-b95f-d49573d11b59"
      },
      "source": [
        "y_valid[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0., 0., 0., 0., 0., 0., 0., 1., 0., 0.], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ySWHP3j-5qCm"
      },
      "source": [
        "#### Design neural network architecture"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lAK0YRm95qCm"
      },
      "source": [
        "model = Sequential()\n",
        "model.add(Dense(64, activation='sigmoid', input_shape=(784,)))\n",
        "model.add(Dense(10, activation='softmax'))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xECwTk1U5qCo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a82127e6-67f0-4e9e-e3b9-c5a61c4494cf"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense (Dense)               (None, 64)                50240     \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 10)                650       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 50,890\n",
            "Trainable params: 50,890\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-XkK02CP5qCq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9f3dfe19-c165-4649-a097-90377640d361"
      },
      "source": [
        "(64*784)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "50176"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2FTyzUSd5qCs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9096b1ac-a06f-4566-b1e1-4fb332c36fa6"
      },
      "source": [
        "(64*784)+64"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "50240"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xlrq-5_e5qCv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "536ee78f-3223-4832-f1d1-08c6709735aa"
      },
      "source": [
        "(10*64)+10"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "650"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cpf4osO75qCy"
      },
      "source": [
        "#### Configure model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8HxNpWUt5qCy"
      },
      "source": [
        "model.compile(loss='mean_squared_error', optimizer=SGD(learning_rate=0.1), metrics=['accuracy'])\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xQQ03Qpi5qC0"
      },
      "source": [
        "#### Train!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iJGJCv8v5qC0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a0cb36f6-110c-4850-e4ea-9820991959ff"
      },
      "source": [
        "number_of_epochs = 200\n",
        "\n",
        "history = model.fit(X_train, y_train, batch_size=128, epochs=number_of_epochs, verbose=1, validation_data=(X_valid, y_valid))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/200\n",
            "469/469 [==============================] - 4s 4ms/step - loss: 0.0900 - accuracy: 0.2123 - val_loss: 0.0882 - val_accuracy: 0.2706\n",
            "Epoch 2/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0866 - accuracy: 0.3220 - val_loss: 0.0847 - val_accuracy: 0.3590\n",
            "Epoch 3/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0826 - accuracy: 0.3943 - val_loss: 0.0800 - val_accuracy: 0.4708\n",
            "Epoch 4/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0778 - accuracy: 0.4751 - val_loss: 0.0751 - val_accuracy: 0.4862\n",
            "Epoch 5/200\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0730 - accuracy: 0.5047 - val_loss: 0.0703 - val_accuracy: 0.5248\n",
            "Epoch 6/200\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0681 - accuracy: 0.5526 - val_loss: 0.0654 - val_accuracy: 0.5756\n",
            "Epoch 7/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0634 - accuracy: 0.5979 - val_loss: 0.0608 - val_accuracy: 0.6232\n",
            "Epoch 8/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0589 - accuracy: 0.6389 - val_loss: 0.0563 - val_accuracy: 0.6620\n",
            "Epoch 9/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0547 - accuracy: 0.6760 - val_loss: 0.0522 - val_accuracy: 0.7066\n",
            "Epoch 10/200\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0508 - accuracy: 0.7146 - val_loss: 0.0484 - val_accuracy: 0.7393\n",
            "Epoch 11/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0473 - accuracy: 0.7432 - val_loss: 0.0450 - val_accuracy: 0.7602\n",
            "Epoch 12/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0442 - accuracy: 0.7625 - val_loss: 0.0421 - val_accuracy: 0.7814\n",
            "Epoch 13/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0414 - accuracy: 0.7769 - val_loss: 0.0394 - val_accuracy: 0.7941\n",
            "Epoch 14/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0390 - accuracy: 0.7888 - val_loss: 0.0371 - val_accuracy: 0.8087\n",
            "Epoch 15/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0369 - accuracy: 0.8035 - val_loss: 0.0351 - val_accuracy: 0.8194\n",
            "Epoch 16/200\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0350 - accuracy: 0.8170 - val_loss: 0.0332 - val_accuracy: 0.8331\n",
            "Epoch 17/200\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0333 - accuracy: 0.8291 - val_loss: 0.0316 - val_accuracy: 0.8413\n",
            "Epoch 18/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0317 - accuracy: 0.8379 - val_loss: 0.0301 - val_accuracy: 0.8504\n",
            "Epoch 19/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0303 - accuracy: 0.8464 - val_loss: 0.0288 - val_accuracy: 0.8570\n",
            "Epoch 20/200\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0291 - accuracy: 0.8530 - val_loss: 0.0276 - val_accuracy: 0.8623\n",
            "Epoch 21/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0280 - accuracy: 0.8579 - val_loss: 0.0265 - val_accuracy: 0.8663\n",
            "Epoch 22/200\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0269 - accuracy: 0.8619 - val_loss: 0.0256 - val_accuracy: 0.8714\n",
            "Epoch 23/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0260 - accuracy: 0.8652 - val_loss: 0.0247 - val_accuracy: 0.8751\n",
            "Epoch 24/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0252 - accuracy: 0.8680 - val_loss: 0.0239 - val_accuracy: 0.8793\n",
            "Epoch 25/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0245 - accuracy: 0.8711 - val_loss: 0.0232 - val_accuracy: 0.8818\n",
            "Epoch 26/200\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0238 - accuracy: 0.8735 - val_loss: 0.0226 - val_accuracy: 0.8830\n",
            "Epoch 27/200\n",
            "469/469 [==============================] - 2s 5ms/step - loss: 0.0232 - accuracy: 0.8758 - val_loss: 0.0220 - val_accuracy: 0.8858\n",
            "Epoch 28/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0227 - accuracy: 0.8775 - val_loss: 0.0215 - val_accuracy: 0.8865\n",
            "Epoch 29/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0221 - accuracy: 0.8798 - val_loss: 0.0210 - val_accuracy: 0.8876\n",
            "Epoch 30/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0217 - accuracy: 0.8813 - val_loss: 0.0206 - val_accuracy: 0.8897\n",
            "Epoch 31/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0213 - accuracy: 0.8827 - val_loss: 0.0202 - val_accuracy: 0.8913\n",
            "Epoch 32/200\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0209 - accuracy: 0.8839 - val_loss: 0.0198 - val_accuracy: 0.8933\n",
            "Epoch 33/200\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0205 - accuracy: 0.8855 - val_loss: 0.0195 - val_accuracy: 0.8942\n",
            "Epoch 34/200\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0202 - accuracy: 0.8867 - val_loss: 0.0192 - val_accuracy: 0.8956\n",
            "Epoch 35/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0199 - accuracy: 0.8879 - val_loss: 0.0189 - val_accuracy: 0.8970\n",
            "Epoch 36/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0196 - accuracy: 0.8883 - val_loss: 0.0186 - val_accuracy: 0.8977\n",
            "Epoch 37/200\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0193 - accuracy: 0.8893 - val_loss: 0.0183 - val_accuracy: 0.8991\n",
            "Epoch 38/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0191 - accuracy: 0.8904 - val_loss: 0.0181 - val_accuracy: 0.8999\n",
            "Epoch 39/200\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0188 - accuracy: 0.8914 - val_loss: 0.0179 - val_accuracy: 0.9003\n",
            "Epoch 40/200\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0186 - accuracy: 0.8922 - val_loss: 0.0177 - val_accuracy: 0.9014\n",
            "Epoch 41/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0184 - accuracy: 0.8929 - val_loss: 0.0174 - val_accuracy: 0.9015\n",
            "Epoch 42/200\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0182 - accuracy: 0.8939 - val_loss: 0.0173 - val_accuracy: 0.9024\n",
            "Epoch 43/200\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0180 - accuracy: 0.8944 - val_loss: 0.0171 - val_accuracy: 0.9033\n",
            "Epoch 44/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0178 - accuracy: 0.8953 - val_loss: 0.0169 - val_accuracy: 0.9035\n",
            "Epoch 45/200\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0176 - accuracy: 0.8960 - val_loss: 0.0167 - val_accuracy: 0.9035\n",
            "Epoch 46/200\n",
            "469/469 [==============================] - 2s 5ms/step - loss: 0.0175 - accuracy: 0.8964 - val_loss: 0.0166 - val_accuracy: 0.9041\n",
            "Epoch 47/200\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0173 - accuracy: 0.8973 - val_loss: 0.0164 - val_accuracy: 0.9042\n",
            "Epoch 48/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0172 - accuracy: 0.8980 - val_loss: 0.0163 - val_accuracy: 0.9049\n",
            "Epoch 49/200\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0170 - accuracy: 0.8984 - val_loss: 0.0162 - val_accuracy: 0.9054\n",
            "Epoch 50/200\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0169 - accuracy: 0.8989 - val_loss: 0.0160 - val_accuracy: 0.9058\n",
            "Epoch 51/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0167 - accuracy: 0.8995 - val_loss: 0.0159 - val_accuracy: 0.9063\n",
            "Epoch 52/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0166 - accuracy: 0.9000 - val_loss: 0.0158 - val_accuracy: 0.9064\n",
            "Epoch 53/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0165 - accuracy: 0.9008 - val_loss: 0.0157 - val_accuracy: 0.9066\n",
            "Epoch 54/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0164 - accuracy: 0.9014 - val_loss: 0.0156 - val_accuracy: 0.9071\n",
            "Epoch 55/200\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0163 - accuracy: 0.9018 - val_loss: 0.0154 - val_accuracy: 0.9074\n",
            "Epoch 56/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0162 - accuracy: 0.9024 - val_loss: 0.0153 - val_accuracy: 0.9082\n",
            "Epoch 57/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0160 - accuracy: 0.9026 - val_loss: 0.0152 - val_accuracy: 0.9085\n",
            "Epoch 58/200\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0159 - accuracy: 0.9032 - val_loss: 0.0151 - val_accuracy: 0.9089\n",
            "Epoch 59/200\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0158 - accuracy: 0.9038 - val_loss: 0.0151 - val_accuracy: 0.9091\n",
            "Epoch 60/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0157 - accuracy: 0.9040 - val_loss: 0.0150 - val_accuracy: 0.9095\n",
            "Epoch 61/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0157 - accuracy: 0.9044 - val_loss: 0.0149 - val_accuracy: 0.9094\n",
            "Epoch 62/200\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0156 - accuracy: 0.9049 - val_loss: 0.0148 - val_accuracy: 0.9102\n",
            "Epoch 63/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0155 - accuracy: 0.9050 - val_loss: 0.0147 - val_accuracy: 0.9107\n",
            "Epoch 64/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0154 - accuracy: 0.9057 - val_loss: 0.0146 - val_accuracy: 0.9108\n",
            "Epoch 65/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0153 - accuracy: 0.9063 - val_loss: 0.0145 - val_accuracy: 0.9120\n",
            "Epoch 66/200\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0152 - accuracy: 0.9065 - val_loss: 0.0145 - val_accuracy: 0.9121\n",
            "Epoch 67/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0151 - accuracy: 0.9068 - val_loss: 0.0144 - val_accuracy: 0.9127\n",
            "Epoch 68/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0151 - accuracy: 0.9075 - val_loss: 0.0143 - val_accuracy: 0.9129\n",
            "Epoch 69/200\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0150 - accuracy: 0.9077 - val_loss: 0.0143 - val_accuracy: 0.9128\n",
            "Epoch 70/200\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0149 - accuracy: 0.9081 - val_loss: 0.0142 - val_accuracy: 0.9133\n",
            "Epoch 71/200\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0149 - accuracy: 0.9085 - val_loss: 0.0141 - val_accuracy: 0.9140\n",
            "Epoch 72/200\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0148 - accuracy: 0.9088 - val_loss: 0.0141 - val_accuracy: 0.9144\n",
            "Epoch 73/200\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0147 - accuracy: 0.9091 - val_loss: 0.0140 - val_accuracy: 0.9145\n",
            "Epoch 74/200\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0146 - accuracy: 0.9096 - val_loss: 0.0139 - val_accuracy: 0.9144\n",
            "Epoch 75/200\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0146 - accuracy: 0.9098 - val_loss: 0.0139 - val_accuracy: 0.9146\n",
            "Epoch 76/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0145 - accuracy: 0.9103 - val_loss: 0.0138 - val_accuracy: 0.9152\n",
            "Epoch 77/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0145 - accuracy: 0.9107 - val_loss: 0.0138 - val_accuracy: 0.9154\n",
            "Epoch 78/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0144 - accuracy: 0.9110 - val_loss: 0.0137 - val_accuracy: 0.9154\n",
            "Epoch 79/200\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0143 - accuracy: 0.9114 - val_loss: 0.0137 - val_accuracy: 0.9155\n",
            "Epoch 80/200\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0143 - accuracy: 0.9116 - val_loss: 0.0136 - val_accuracy: 0.9159\n",
            "Epoch 81/200\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0142 - accuracy: 0.9121 - val_loss: 0.0135 - val_accuracy: 0.9159\n",
            "Epoch 82/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0142 - accuracy: 0.9122 - val_loss: 0.0135 - val_accuracy: 0.9156\n",
            "Epoch 83/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0141 - accuracy: 0.9126 - val_loss: 0.0134 - val_accuracy: 0.9165\n",
            "Epoch 84/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0140 - accuracy: 0.9130 - val_loss: 0.0134 - val_accuracy: 0.9168\n",
            "Epoch 85/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0140 - accuracy: 0.9132 - val_loss: 0.0133 - val_accuracy: 0.9171\n",
            "Epoch 86/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0139 - accuracy: 0.9134 - val_loss: 0.0133 - val_accuracy: 0.9175\n",
            "Epoch 87/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0139 - accuracy: 0.9138 - val_loss: 0.0133 - val_accuracy: 0.9180\n",
            "Epoch 88/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0138 - accuracy: 0.9142 - val_loss: 0.0132 - val_accuracy: 0.9187\n",
            "Epoch 89/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0138 - accuracy: 0.9143 - val_loss: 0.0132 - val_accuracy: 0.9186\n",
            "Epoch 90/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0137 - accuracy: 0.9148 - val_loss: 0.0131 - val_accuracy: 0.9194\n",
            "Epoch 91/200\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0137 - accuracy: 0.9151 - val_loss: 0.0131 - val_accuracy: 0.9191\n",
            "Epoch 92/200\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0136 - accuracy: 0.9155 - val_loss: 0.0130 - val_accuracy: 0.9195\n",
            "Epoch 93/200\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0136 - accuracy: 0.9154 - val_loss: 0.0130 - val_accuracy: 0.9202\n",
            "Epoch 94/200\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0136 - accuracy: 0.9158 - val_loss: 0.0130 - val_accuracy: 0.9207\n",
            "Epoch 95/200\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0135 - accuracy: 0.9161 - val_loss: 0.0129 - val_accuracy: 0.9202\n",
            "Epoch 96/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0135 - accuracy: 0.9162 - val_loss: 0.0129 - val_accuracy: 0.9202\n",
            "Epoch 97/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0134 - accuracy: 0.9166 - val_loss: 0.0128 - val_accuracy: 0.9204\n",
            "Epoch 98/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0134 - accuracy: 0.9170 - val_loss: 0.0128 - val_accuracy: 0.9206\n",
            "Epoch 99/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0133 - accuracy: 0.9173 - val_loss: 0.0128 - val_accuracy: 0.9210\n",
            "Epoch 100/200\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0133 - accuracy: 0.9172 - val_loss: 0.0127 - val_accuracy: 0.9214\n",
            "Epoch 101/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0133 - accuracy: 0.9175 - val_loss: 0.0127 - val_accuracy: 0.9214\n",
            "Epoch 102/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0132 - accuracy: 0.9179 - val_loss: 0.0126 - val_accuracy: 0.9215\n",
            "Epoch 103/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0132 - accuracy: 0.9179 - val_loss: 0.0126 - val_accuracy: 0.9218\n",
            "Epoch 104/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0131 - accuracy: 0.9184 - val_loss: 0.0126 - val_accuracy: 0.9219\n",
            "Epoch 105/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0131 - accuracy: 0.9187 - val_loss: 0.0125 - val_accuracy: 0.9219\n",
            "Epoch 106/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0130 - accuracy: 0.9189 - val_loss: 0.0125 - val_accuracy: 0.9221\n",
            "Epoch 107/200\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0130 - accuracy: 0.9193 - val_loss: 0.0125 - val_accuracy: 0.9224\n",
            "Epoch 108/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0130 - accuracy: 0.9193 - val_loss: 0.0124 - val_accuracy: 0.9227\n",
            "Epoch 109/200\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0129 - accuracy: 0.9197 - val_loss: 0.0124 - val_accuracy: 0.9226\n",
            "Epoch 110/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0129 - accuracy: 0.9198 - val_loss: 0.0124 - val_accuracy: 0.9230\n",
            "Epoch 111/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0129 - accuracy: 0.9203 - val_loss: 0.0123 - val_accuracy: 0.9229\n",
            "Epoch 112/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0128 - accuracy: 0.9202 - val_loss: 0.0123 - val_accuracy: 0.9231\n",
            "Epoch 113/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0128 - accuracy: 0.9205 - val_loss: 0.0123 - val_accuracy: 0.9231\n",
            "Epoch 114/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0128 - accuracy: 0.9208 - val_loss: 0.0122 - val_accuracy: 0.9234\n",
            "Epoch 115/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0127 - accuracy: 0.9210 - val_loss: 0.0122 - val_accuracy: 0.9237\n",
            "Epoch 116/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0127 - accuracy: 0.9212 - val_loss: 0.0122 - val_accuracy: 0.9240\n",
            "Epoch 117/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0126 - accuracy: 0.9213 - val_loss: 0.0122 - val_accuracy: 0.9240\n",
            "Epoch 118/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0126 - accuracy: 0.9217 - val_loss: 0.0121 - val_accuracy: 0.9240\n",
            "Epoch 119/200\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0126 - accuracy: 0.9216 - val_loss: 0.0121 - val_accuracy: 0.9244\n",
            "Epoch 120/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0125 - accuracy: 0.9222 - val_loss: 0.0121 - val_accuracy: 0.9242\n",
            "Epoch 121/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0125 - accuracy: 0.9222 - val_loss: 0.0120 - val_accuracy: 0.9249\n",
            "Epoch 122/200\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0125 - accuracy: 0.9222 - val_loss: 0.0120 - val_accuracy: 0.9241\n",
            "Epoch 123/200\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0125 - accuracy: 0.9224 - val_loss: 0.0120 - val_accuracy: 0.9248\n",
            "Epoch 124/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0124 - accuracy: 0.9226 - val_loss: 0.0120 - val_accuracy: 0.9249\n",
            "Epoch 125/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0124 - accuracy: 0.9230 - val_loss: 0.0119 - val_accuracy: 0.9248\n",
            "Epoch 126/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0124 - accuracy: 0.9230 - val_loss: 0.0119 - val_accuracy: 0.9247\n",
            "Epoch 127/200\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0123 - accuracy: 0.9233 - val_loss: 0.0119 - val_accuracy: 0.9251\n",
            "Epoch 128/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0123 - accuracy: 0.9234 - val_loss: 0.0119 - val_accuracy: 0.9253\n",
            "Epoch 129/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0123 - accuracy: 0.9237 - val_loss: 0.0118 - val_accuracy: 0.9254\n",
            "Epoch 130/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0122 - accuracy: 0.9238 - val_loss: 0.0118 - val_accuracy: 0.9255\n",
            "Epoch 131/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0122 - accuracy: 0.9237 - val_loss: 0.0118 - val_accuracy: 0.9255\n",
            "Epoch 132/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0122 - accuracy: 0.9239 - val_loss: 0.0117 - val_accuracy: 0.9258\n",
            "Epoch 133/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0121 - accuracy: 0.9241 - val_loss: 0.0117 - val_accuracy: 0.9263\n",
            "Epoch 134/200\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0121 - accuracy: 0.9244 - val_loss: 0.0117 - val_accuracy: 0.9262\n",
            "Epoch 135/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0121 - accuracy: 0.9247 - val_loss: 0.0117 - val_accuracy: 0.9266\n",
            "Epoch 136/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0121 - accuracy: 0.9249 - val_loss: 0.0116 - val_accuracy: 0.9266\n",
            "Epoch 137/200\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0120 - accuracy: 0.9251 - val_loss: 0.0116 - val_accuracy: 0.9268\n",
            "Epoch 138/200\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0120 - accuracy: 0.9253 - val_loss: 0.0116 - val_accuracy: 0.9269\n",
            "Epoch 139/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0120 - accuracy: 0.9252 - val_loss: 0.0116 - val_accuracy: 0.9266\n",
            "Epoch 140/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0119 - accuracy: 0.9257 - val_loss: 0.0115 - val_accuracy: 0.9270\n",
            "Epoch 141/200\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0119 - accuracy: 0.9257 - val_loss: 0.0115 - val_accuracy: 0.9271\n",
            "Epoch 142/200\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0119 - accuracy: 0.9259 - val_loss: 0.0115 - val_accuracy: 0.9278\n",
            "Epoch 143/200\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0119 - accuracy: 0.9260 - val_loss: 0.0115 - val_accuracy: 0.9275\n",
            "Epoch 144/200\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0118 - accuracy: 0.9262 - val_loss: 0.0115 - val_accuracy: 0.9273\n",
            "Epoch 145/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0118 - accuracy: 0.9262 - val_loss: 0.0114 - val_accuracy: 0.9272\n",
            "Epoch 146/200\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0118 - accuracy: 0.9263 - val_loss: 0.0114 - val_accuracy: 0.9273\n",
            "Epoch 147/200\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0118 - accuracy: 0.9265 - val_loss: 0.0114 - val_accuracy: 0.9275\n",
            "Epoch 148/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0117 - accuracy: 0.9268 - val_loss: 0.0114 - val_accuracy: 0.9277\n",
            "Epoch 149/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0117 - accuracy: 0.9269 - val_loss: 0.0113 - val_accuracy: 0.9283\n",
            "Epoch 150/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0117 - accuracy: 0.9271 - val_loss: 0.0113 - val_accuracy: 0.9287\n",
            "Epoch 151/200\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0117 - accuracy: 0.9273 - val_loss: 0.0113 - val_accuracy: 0.9283\n",
            "Epoch 152/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0116 - accuracy: 0.9273 - val_loss: 0.0113 - val_accuracy: 0.9285\n",
            "Epoch 153/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0116 - accuracy: 0.9275 - val_loss: 0.0113 - val_accuracy: 0.9287\n",
            "Epoch 154/200\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0116 - accuracy: 0.9276 - val_loss: 0.0112 - val_accuracy: 0.9288\n",
            "Epoch 155/200\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0116 - accuracy: 0.9279 - val_loss: 0.0112 - val_accuracy: 0.9287\n",
            "Epoch 156/200\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0115 - accuracy: 0.9280 - val_loss: 0.0112 - val_accuracy: 0.9286\n",
            "Epoch 157/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0115 - accuracy: 0.9282 - val_loss: 0.0112 - val_accuracy: 0.9286\n",
            "Epoch 158/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0115 - accuracy: 0.9282 - val_loss: 0.0112 - val_accuracy: 0.9285\n",
            "Epoch 159/200\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0115 - accuracy: 0.9284 - val_loss: 0.0111 - val_accuracy: 0.9291\n",
            "Epoch 160/200\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0114 - accuracy: 0.9284 - val_loss: 0.0111 - val_accuracy: 0.9292\n",
            "Epoch 161/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0114 - accuracy: 0.9289 - val_loss: 0.0111 - val_accuracy: 0.9292\n",
            "Epoch 162/200\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0114 - accuracy: 0.9288 - val_loss: 0.0111 - val_accuracy: 0.9293\n",
            "Epoch 163/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0114 - accuracy: 0.9290 - val_loss: 0.0110 - val_accuracy: 0.9292\n",
            "Epoch 164/200\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0113 - accuracy: 0.9292 - val_loss: 0.0110 - val_accuracy: 0.9295\n",
            "Epoch 165/200\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0113 - accuracy: 0.9293 - val_loss: 0.0110 - val_accuracy: 0.9296\n",
            "Epoch 166/200\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0113 - accuracy: 0.9293 - val_loss: 0.0110 - val_accuracy: 0.9298\n",
            "Epoch 167/200\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0113 - accuracy: 0.9294 - val_loss: 0.0110 - val_accuracy: 0.9299\n",
            "Epoch 168/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0112 - accuracy: 0.9295 - val_loss: 0.0110 - val_accuracy: 0.9297\n",
            "Epoch 169/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0112 - accuracy: 0.9296 - val_loss: 0.0109 - val_accuracy: 0.9299\n",
            "Epoch 170/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0112 - accuracy: 0.9300 - val_loss: 0.0109 - val_accuracy: 0.9299\n",
            "Epoch 171/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0112 - accuracy: 0.9300 - val_loss: 0.0109 - val_accuracy: 0.9303\n",
            "Epoch 172/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0112 - accuracy: 0.9301 - val_loss: 0.0109 - val_accuracy: 0.9305\n",
            "Epoch 173/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0111 - accuracy: 0.9300 - val_loss: 0.0109 - val_accuracy: 0.9305\n",
            "Epoch 174/200\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0111 - accuracy: 0.9302 - val_loss: 0.0108 - val_accuracy: 0.9301\n",
            "Epoch 175/200\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0111 - accuracy: 0.9305 - val_loss: 0.0108 - val_accuracy: 0.9309\n",
            "Epoch 176/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0111 - accuracy: 0.9304 - val_loss: 0.0108 - val_accuracy: 0.9306\n",
            "Epoch 177/200\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0110 - accuracy: 0.9308 - val_loss: 0.0108 - val_accuracy: 0.9306\n",
            "Epoch 178/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0110 - accuracy: 0.9307 - val_loss: 0.0108 - val_accuracy: 0.9309\n",
            "Epoch 179/200\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0110 - accuracy: 0.9308 - val_loss: 0.0107 - val_accuracy: 0.9314\n",
            "Epoch 180/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0110 - accuracy: 0.9309 - val_loss: 0.0107 - val_accuracy: 0.9309\n",
            "Epoch 181/200\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0110 - accuracy: 0.9312 - val_loss: 0.0107 - val_accuracy: 0.9315\n",
            "Epoch 182/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0109 - accuracy: 0.9312 - val_loss: 0.0107 - val_accuracy: 0.9312\n",
            "Epoch 183/200\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0109 - accuracy: 0.9312 - val_loss: 0.0107 - val_accuracy: 0.9318\n",
            "Epoch 184/200\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0109 - accuracy: 0.9313 - val_loss: 0.0107 - val_accuracy: 0.9317\n",
            "Epoch 185/200\n",
            "469/469 [==============================] - 2s 5ms/step - loss: 0.0109 - accuracy: 0.9315 - val_loss: 0.0106 - val_accuracy: 0.9321\n",
            "Epoch 186/200\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0109 - accuracy: 0.9317 - val_loss: 0.0106 - val_accuracy: 0.9320\n",
            "Epoch 187/200\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0108 - accuracy: 0.9319 - val_loss: 0.0106 - val_accuracy: 0.9320\n",
            "Epoch 188/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0108 - accuracy: 0.9319 - val_loss: 0.0106 - val_accuracy: 0.9322\n",
            "Epoch 189/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0108 - accuracy: 0.9320 - val_loss: 0.0106 - val_accuracy: 0.9321\n",
            "Epoch 190/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0108 - accuracy: 0.9322 - val_loss: 0.0106 - val_accuracy: 0.9322\n",
            "Epoch 191/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0108 - accuracy: 0.9323 - val_loss: 0.0105 - val_accuracy: 0.9326\n",
            "Epoch 192/200\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0107 - accuracy: 0.9324 - val_loss: 0.0105 - val_accuracy: 0.9324\n",
            "Epoch 193/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0107 - accuracy: 0.9326 - val_loss: 0.0105 - val_accuracy: 0.9322\n",
            "Epoch 194/200\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0107 - accuracy: 0.9327 - val_loss: 0.0105 - val_accuracy: 0.9327\n",
            "Epoch 195/200\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0107 - accuracy: 0.9327 - val_loss: 0.0105 - val_accuracy: 0.9327\n",
            "Epoch 196/200\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0107 - accuracy: 0.9330 - val_loss: 0.0105 - val_accuracy: 0.9330\n",
            "Epoch 197/200\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0106 - accuracy: 0.9332 - val_loss: 0.0104 - val_accuracy: 0.9330\n",
            "Epoch 198/200\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0106 - accuracy: 0.9332 - val_loss: 0.0104 - val_accuracy: 0.9331\n",
            "Epoch 199/200\n",
            "469/469 [==============================] - 2s 3ms/step - loss: 0.0106 - accuracy: 0.9333 - val_loss: 0.0104 - val_accuracy: 0.9329\n",
            "Epoch 200/200\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0106 - accuracy: 0.9335 - val_loss: 0.0104 - val_accuracy: 0.9338\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "acc = history.history['accuracy']\n",
        "val_acc = history.history['val_accuracy']\n",
        "loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "\n",
        "epochs_range = range(number_of_epochs)\n",
        "\n",
        "plt.figure(figsize=(15, 15))\n",
        "plt.subplot(2, 2, 1)\n",
        "plt.plot(epochs_range, acc, label='Training Accuracy')\n",
        "plt.plot(epochs_range, val_acc, label='Validation Accuracy')\n",
        "plt.legend(loc='lower right')\n",
        "plt.title('Training and Validation Accuracy')\n",
        "\n",
        "plt.subplot(2, 2, 2)\n",
        "plt.plot(epochs_range, loss, label='Training Loss')\n",
        "plt.plot(epochs_range, val_loss, label='Validation Loss')\n",
        "plt.legend(loc='upper right')\n",
        "plt.title('Training and Validation Loss')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 435
        },
        "id": "j1cJEVqd0o-X",
        "outputId": "7f555943-8960-41c4-a19e-bd68a8b15ef3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x1080 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pnJNnC2u5qC5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7f73ac78-43e1-4de6-8d02-d4ae76bc9881"
      },
      "source": [
        "model.evaluate(X_valid, y_valid)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 2ms/step - loss: 0.0104 - accuracy: 0.9338\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.010390992276370525, 0.9337999820709229]"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    }
  ]
}