Digit_Classification.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Digit_Classification.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/LeoVal1/Digit_Classification/blob/main/Digit_Classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install keras"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r_jaz83HHByn",
        "outputId": "e799038e-b502-4332-e9cc-f8fef4736735"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: keras in /usr/local/lib/python3.7/dist-packages (2.8.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "lDnFmaX0Dnrq"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "import pandas as pd\n",
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "from keras import layers\n",
        "import tensorflow_datasets as tfds\n",
        "from keras.models import Sequential\n",
        "from keras.layers.convolutional import Conv2D\n",
        "from keras.layers.convolutional import MaxPooling2D\n",
        "from keras.layers import Flatten\n",
        "from keras.layers import Dense\n",
        "from tensorflow.python.keras.utils.np_utils import to_categorical\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#LOAD MNIST DATASET"
      ],
      "metadata": {
        "id": "yBc8i_LCE79k"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "mnist = tf.keras.datasets.mnist\n",
        "mnist_df =  mnist.load_data()\n",
        "(x_train, y_train),(x_test, y_test) = mnist_df  # Load data\n",
        "plt.imshow(x_train[0], cmap=\"Accent\")\n",
        "plt.show()\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "Mxn56Rs2FIl_",
        "outputId": "af43d3d1-187f-45d7-d467-a691f9ac3440"
      },
      "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"
          ]
        },
        {
          "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": "markdown",
      "source": [
        "##PREPROCESS THE DATA INTO NUMPY ARRAYS"
      ],
      "metadata": {
        "id": "awQr1r3QN-6w"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x_train_expand = x_train.reshape((x_train.shape[0], 28, 28, 1))\n",
        "x_test_expand = x_test.reshape((x_test.shape[0], 28, 28, 1))\n",
        "\n",
        "x_train_expand = x_train_expand.astype('float16')\n",
        "x_test_expand = x_test_expand.astype('float16')\n",
        "\n",
        "print(x_train_expand.shape)\n",
        "print(x_test_expand.shape)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WAXRVn4HOPXy",
        "outputId": "20b24761-9026-4d93-de70-4e51013317f3"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(60000, 28, 28, 1)\n",
            "(10000, 28, 28, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        ""
      ],
      "metadata": {
        "id": "nLxUo48QPCiP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# normalize inputs from 0-255 to 0-1\n",
        "x_train_norm = x_train_expand / 255\n",
        "x_test_norm = x_test_expand / 255\n",
        "x_train_expand = x_train_expand.reshape(-1, 28, 28, 1)\n",
        "x_test_expand = x_test_expand.reshape(-1, 28, 28, 1)\n",
        "# one hot encode outputs\n",
        "y_train = to_categorical(y_train)\n",
        "y_test = to_categorical(y_test)\n",
        "print(x_train_expand.shape, x_test_expand.shape)\n",
        "print(y_train.shape)\n",
        "num_classes = y_test.shape[1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eEe1MXTSPDie",
        "outputId": "01046493-2d77-4bad-e3dc-13360cd93d66"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(60000, 28, 28, 1) (10000, 28, 28, 1)\n",
            "(60000, 10)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(num_classes)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oEazbgnQNR0R",
        "outputId": "55b0e383-01d5-437e-8b65-85444b47a2ac"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "10\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##BUILD A MODEL"
      ],
      "metadata": {
        "id": "4VgqyOUQJ9Nt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential(\n",
        "    [\n",
        "layers.Conv2D(filters=8,kernel_size=(5,5),strides=1,padding='same',activation='relu',input_shape=(28,28,1)),\n",
        "layers.MaxPooling2D(2),\n",
        "layers.Conv2D(filters=16,kernel_size=(3,3),strides=1,padding='same',activation='relu',input_shape=(14,14,8)),\n",
        "layers.Flatten(),\n",
        "layers.Dense(32, activation='relu'),\n",
        "layers.Dense(num_classes, activation='softmax'),\n",
        "  ])"
      ],
      "metadata": {
        "id": "WaLjkpqPKBpV"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary(),\n",
        "model.reset_states(),\n",
        "model.reset_metrics(),"
      ],
      "metadata": {
        "id": "myu9_4ATNq_B",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5827b402-eefc-4007-f77f-65582f852c11"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d (Conv2D)             (None, 28, 28, 8)         208       \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2D  (None, 14, 14, 8)        0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 14, 14, 16)        1168      \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 3136)              0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 32)                100384    \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 10)                330       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 102,090\n",
            "Trainable params: 102,090\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(None,)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer = \"adam\", loss = 'categorical_crossentropy', metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "MvWvcaQgGjw5"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Without Normalization"
      ],
      "metadata": {
        "id": "IEf8ecCNVSSt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(x = x_train_expand, y=y_train, epochs= 10 ,validation_split=0.33)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0od9NIm-Ic8I",
        "outputId": "bfaff3fc-df7f-41c4-eb0c-ba31aa763292"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "1257/1257 [==============================] - 32s 24ms/step - loss: 0.8212 - accuracy: 0.7515 - val_loss: 0.1747 - val_accuracy: 0.9501\n",
            "Epoch 2/10\n",
            "1257/1257 [==============================] - 29s 23ms/step - loss: 0.1215 - accuracy: 0.9651 - val_loss: 0.1194 - val_accuracy: 0.9649\n",
            "Epoch 3/10\n",
            "1257/1257 [==============================] - 30s 24ms/step - loss: 0.0767 - accuracy: 0.9766 - val_loss: 0.1173 - val_accuracy: 0.9671\n",
            "Epoch 4/10\n",
            "1257/1257 [==============================] - 30s 24ms/step - loss: 0.0545 - accuracy: 0.9831 - val_loss: 0.0949 - val_accuracy: 0.9737\n",
            "Epoch 5/10\n",
            "1257/1257 [==============================] - 31s 25ms/step - loss: 0.0440 - accuracy: 0.9859 - val_loss: 0.0944 - val_accuracy: 0.9749\n",
            "Epoch 6/10\n",
            "1257/1257 [==============================] - 31s 25ms/step - loss: 0.0374 - accuracy: 0.9882 - val_loss: 0.1039 - val_accuracy: 0.9759\n",
            "Epoch 7/10\n",
            "1257/1257 [==============================] - 31s 25ms/step - loss: 0.0332 - accuracy: 0.9895 - val_loss: 0.1016 - val_accuracy: 0.9757\n",
            "Epoch 8/10\n",
            "1257/1257 [==============================] - 31s 25ms/step - loss: 0.0283 - accuracy: 0.9903 - val_loss: 0.1301 - val_accuracy: 0.9748\n",
            "Epoch 9/10\n",
            "1257/1257 [==============================] - 32s 25ms/step - loss: 0.0296 - accuracy: 0.9908 - val_loss: 0.0962 - val_accuracy: 0.9783\n",
            "Epoch 10/10\n",
            "1257/1257 [==============================] - 32s 25ms/step - loss: 0.0235 - accuracy: 0.9930 - val_loss: 0.1044 - val_accuracy: 0.9782\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",
        "print('Traning Accuracy')\n",
        "print(acc)\n",
        "print('Validation Accuracy')\n",
        "print(val_acc)\n",
        "print('Training loss')\n",
        "print(loss)\n",
        "print('Validation loss')\n",
        "print(val_loss)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8Dn-dBo-Vxkh",
        "outputId": "cfb6ee5b-e77d-4653-e8de-e7ea89c9a3d1"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Traning Accuracy\n",
            "[0.7514863610267639, 0.9650986194610596, 0.9765665531158447, 0.9830841422080994, 0.9858951568603516, 0.9882335662841797, 0.9894773364067078, 0.9902734160423279, 0.9908455610275269, 0.9929848909378052]\n",
            "Validation Accuracy\n",
            "[0.9500530362129211, 0.96490079164505, 0.9670723676681519, 0.9736881852149963, 0.9749002456665039, 0.9758598208427429, 0.9757083058357239, 0.9747992753982544, 0.9783344268798828, 0.9782333970069885]\n",
            "Training loss\n",
            "[0.8212416768074036, 0.12148023396730423, 0.07668540626764297, 0.05446462705731392, 0.043997325003147125, 0.03738056868314743, 0.033210866153240204, 0.02825774811208248, 0.02959158830344677, 0.023543311282992363]\n",
            "Validation loss\n",
            "[0.174735888838768, 0.11942752450704575, 0.1172880083322525, 0.09487975388765335, 0.09441342949867249, 0.10391134768724442, 0.10163844376802444, 0.13007493317127228, 0.09622106701135635, 0.10439643263816833]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "epochs = range(10)\n",
        "plt.figure()\n",
        "plt.plot(epochs, acc, 'r', label='Training accuracy')\n",
        "plt.plot(epochs, val_acc, 'bo', label='Validation accuracy')\n",
        "plt.title('Training and Validation accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Accuracy Value')\n",
        "plt.ylim([0.9, 1])\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "HxhBd3CwgW2L",
        "outputId": "b75b97e2-2f0e-4b8d-c0a7-288aec05515e"
      },
      "execution_count": 11,
      "outputs": [
        {
          "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",
      "source": [
        ""
      ],
      "metadata": {
        "id": "VCRPTU-QV6pR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "loss = history.history['loss']\n",
        "print(acc)\n",
        "val_loss = history.history['val_loss']\n",
        "print(val_loss)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NHJdMMLcWEPP",
        "outputId": "007b1595-de54-4094-eed5-c1107784205f"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.7514863610267639, 0.9650986194610596, 0.9765665531158447, 0.9830841422080994, 0.9858951568603516, 0.9882335662841797, 0.9894773364067078, 0.9902734160423279, 0.9908455610275269, 0.9929848909378052]\n",
            "[0.174735888838768, 0.11942752450704575, 0.1172880083322525, 0.09487975388765335, 0.09441342949867249, 0.10391134768724442, 0.10163844376802444, 0.13007493317127228, 0.09622106701135635, 0.10439643263816833]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "epochs = range(10)\n",
        "plt.figure()\n",
        "plt.plot(epochs, loss, 'r', label='Training loss')\n",
        "plt.plot(epochs, val_loss, 'bo', label='Validation loss')\n",
        "plt.title('Training and Validation Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Loss Value')\n",
        "plt.ylim([0, 1])\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "KdH4UPSqhQWs",
        "outputId": "c8fbd438-0e04-48d6-eec9-4322ab1a32b8"
      },
      "execution_count": 13,
      "outputs": [
        {
          "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",
      "source": [
        "test_loss, test_acc = model.evaluate(x=x_test_expand, y=y_test)\n",
        "print('\\nTest accuracy:', test_acc)\n",
        "print('\\nTest loss', test_loss)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2SPGVGJFPVaz",
        "outputId": "d056997f-e296-4d7f-ea08-3f828c70890f"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 3s 9ms/step - loss: 0.0930 - accuracy: 0.9797\n",
            "\n",
            "Test accuracy: 0.9797000288963318\n",
            "\n",
            "Test loss 0.0929744765162468\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = model.predict([x_test_expand]) # Make prediction\n",
        "plt.imshow(x_test[565], cmap=\"Accent\")\n",
        "plt.show()\n",
        "print(np.argmax(predictions[565])) # Print out the number"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "gO2iFQFDWMjo",
        "outputId": "626c075c-6178-4f4a-e212-30caadabf6e8"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##With Normalization"
      ],
      "metadata": {
        "id": "un1ZUgF8Wg5o"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "history1 = model.fit(x = x_train_norm, y=y_train, epochs= 10 ,validation_split=0.33)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aS1EBqvCc1Oz",
        "outputId": "e72c8948-f28e-4eee-b717-011fa8a214bc"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "1257/1257 [==============================] - 31s 25ms/step - loss: 0.2906 - accuracy: 0.9191 - val_loss: 0.1039 - val_accuracy: 0.9680\n",
            "Epoch 2/10\n",
            "1257/1257 [==============================] - 30s 24ms/step - loss: 0.0805 - accuracy: 0.9747 - val_loss: 0.0724 - val_accuracy: 0.9776\n",
            "Epoch 3/10\n",
            "1257/1257 [==============================] - 30s 24ms/step - loss: 0.0545 - accuracy: 0.9832 - val_loss: 0.0601 - val_accuracy: 0.9815\n",
            "Epoch 4/10\n",
            "1257/1257 [==============================] - 31s 24ms/step - loss: 0.0403 - accuracy: 0.9878 - val_loss: 0.0564 - val_accuracy: 0.9824\n",
            "Epoch 5/10\n",
            "1257/1257 [==============================] - 31s 24ms/step - loss: 0.0309 - accuracy: 0.9903 - val_loss: 0.0546 - val_accuracy: 0.9838\n",
            "Epoch 6/10\n",
            "1257/1257 [==============================] - 31s 24ms/step - loss: 0.0248 - accuracy: 0.9916 - val_loss: 0.0537 - val_accuracy: 0.9834\n",
            "Epoch 7/10\n",
            "1257/1257 [==============================] - 31s 25ms/step - loss: 0.0200 - accuracy: 0.9938 - val_loss: 0.0470 - val_accuracy: 0.9870\n",
            "Epoch 8/10\n",
            "1257/1257 [==============================] - 31s 25ms/step - loss: 0.0147 - accuracy: 0.9956 - val_loss: 0.0607 - val_accuracy: 0.9844\n",
            "Epoch 9/10\n",
            "1257/1257 [==============================] - 31s 25ms/step - loss: 0.0121 - accuracy: 0.9961 - val_loss: 0.0563 - val_accuracy: 0.9857\n",
            "Epoch 10/10\n",
            "1257/1257 [==============================] - 31s 25ms/step - loss: 0.0089 - accuracy: 0.9975 - val_loss: 0.0543 - val_accuracy: 0.9864\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "acc = history1.history['accuracy']\n",
        "val_acc = history1.history['val_accuracy']\n",
        "loss = history1.history['loss']\n",
        "val_loss = history1.history['val_loss']\n",
        "print(acc)\n",
        "print(val_acc)\n",
        "print(loss)\n",
        "print(val_loss)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5U65WZEMWlFi",
        "outputId": "7f39ebd3-962f-497d-8751-eb12991b42ca"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.9190775752067566, 0.9747008681297302, 0.9831587672233582, 0.9878355264663696, 0.9903480410575867, 0.9916167259216309, 0.9937809109687805, 0.995572030544281, 0.9960695505142212, 0.9975124001502991]\n",
            "[0.9679813981056213, 0.9775769114494324, 0.9814655780792236, 0.9824251532554626, 0.9837886691093445, 0.9833846688270569, 0.9869703650474548, 0.9843947291374207, 0.985657274723053, 0.9864148497581482]\n",
            "[0.2905930280685425, 0.08047281950712204, 0.054462142288684845, 0.040257107466459274, 0.030948836356401443, 0.024776598438620567, 0.01996646076440811, 0.014684043824672699, 0.012072797864675522, 0.008856268599629402]\n",
            "[0.10386267304420471, 0.07241340726613998, 0.06014538183808327, 0.05642043426632881, 0.05463423579931259, 0.053657807409763336, 0.047013331204652786, 0.06065256521105766, 0.056333065032958984, 0.05425802990794182]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "epochs = range(10)\n",
        "plt.figure()\n",
        "plt.plot(epochs, acc, 'r', label='Training accuracy')\n",
        "plt.plot(epochs, val_acc, 'bo', label='Validation accuracy')\n",
        "\n",
        "plt.title('Training and Validation Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Acuuracy Value')\n",
        "plt.ylim([0.9, 1])\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "u1Pfq2pGmjv1",
        "outputId": "2359b27b-23f8-4cf7-d858-1a1dbfa45199"
      },
      "execution_count": 18,
      "outputs": [
        {
          "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",
      "source": [
        ""
      ],
      "metadata": {
        "id": "wRc7pX6nYW_R"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "loss = history.history['loss']\n",
        "print(acc)\n",
        "val_loss = history.history['val_loss']\n",
        "print(val_loss)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pw_x68MzYqjL",
        "outputId": "d7fad93c-8d6a-43dd-ae6c-41ad2dbf5c36"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.9190775752067566, 0.9747008681297302, 0.9831587672233582, 0.9878355264663696, 0.9903480410575867, 0.9916167259216309, 0.9937809109687805, 0.995572030544281, 0.9960695505142212, 0.9975124001502991]\n",
            "[0.174735888838768, 0.11942752450704575, 0.1172880083322525, 0.09487975388765335, 0.09441342949867249, 0.10391134768724442, 0.10163844376802444, 0.13007493317127228, 0.09622106701135635, 0.10439643263816833]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "epochs = range(10)\n",
        "plt.figure()\n",
        "plt.plot(epochs, loss, 'r', label='Training loss')\n",
        "plt.plot(epochs, val_loss, 'bo', label='Validation loss')\n",
        "plt.title('Training and Validation Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Loss Value')\n",
        "plt.ylim([0, 0.25])\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "NyhZE9mcmo_3",
        "outputId": "41c239a0-2897-4614-b257-82d703388218"
      },
      "execution_count": 21,
      "outputs": [
        {
          "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",
      "source": [
        "test_loss, test_acc = model.evaluate(x=x_test_expand, y=y_test)\n",
        "print('\\nTest accuracy:', test_acc)\n",
        "print('\\nTest loss', test_loss)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1WqOVk8PTlia",
        "outputId": "27a527e8-b6b9-4aea-b989-52e1cdb7b30e"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 3s 9ms/step - loss: 9.3832 - accuracy: 0.9853\n",
            "\n",
            "Test accuracy: 0.9853000044822693\n",
            "\n",
            "Test loss 9.383208274841309\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = model.predict([x_test_expand]) # Make prediction\n",
        "plt.imshow(x_test[565], cmap=\"Accent\")\n",
        "plt.show()\n",
        "print(np.argmax(predictions[565])) # Print out the number"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "5BfshfGeYurJ",
        "outputId": "45a3dab1-89b3-488f-b90f-eba2fd4f39f4"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4\n"
          ]
        }
      ]
    }
  ]
}