MNIST_Custom_Image_cv2_np.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "MNIST Custom Image cv2 np",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BC6fDkA-tl2I",
        "outputId": "1a95a269-70e5-46a0-ba24-734dc923f24d"
      },
      "source": [
        "import tensorflow as tf\n",
        "mnist = tf.keras.datasets.mnist\n",
        "\n",
        "(x_train, y_train),(x_test, y_test) = mnist.load_data()\n",
        "x_train, x_test = x_train / 255.0, x_test / 255.0\n",
        "\n",
        "model = tf.keras.models.Sequential([\n",
        "  tf.keras.layers.Flatten(input_shape=(28, 28)),\n",
        "  tf.keras.layers.Dense(128, activation='relu'),\n",
        "  tf.keras.layers.Dropout(0.2),\n",
        "  tf.keras.layers.Dense(10, activation='softmax')\n",
        "])\n",
        "\n",
        "model.compile(optimizer='adam',\n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "model.fit(x_train, y_train, epochs=5)\n",
        "model.evaluate(x_test, y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "11493376/11490434 [==============================] - 0s 0us/step\n",
            "Epoch 1/5\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.2934 - accuracy: 0.9143\n",
            "Epoch 2/5\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1408 - accuracy: 0.9583\n",
            "Epoch 3/5\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1057 - accuracy: 0.9678\n",
            "Epoch 4/5\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0881 - accuracy: 0.9727\n",
            "Epoch 5/5\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0740 - accuracy: 0.9771\n",
            "313/313 [==============================] - 1s 1ms/step - loss: 0.0704 - accuracy: 0.9781\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.07039346545934677, 0.9781000018119812]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "7fakuz-Awaxc",
        "outputId": "95908fa5-13aa-4fec-ea52-7ddd24092ac8"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.imshow(x_train[0])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fa454ba9850>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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LJX1U0jpJmyT9l6SeLurtPyQ9JelJDQVrdod6W6ihQ/QnJW2o/Z3b6feu0Fdb3jculwWS4AQdkARhB5Ig7EAShB1IgrADSRB2IAnCDiTx/65XcTNOWsh5AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": "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",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 89
        },
        "id": "Y4PTIz8wuozO",
        "outputId": "4018cfa7-3e68-412a-c9e8-b8210a495f02"
      },
      "source": [
        "from google.colab import files\n",
        "\n",
        "uploaded = files.upload()\n",
        "\n",
        "for fn in uploaded.keys():\n",
        "  print('User uploaded file \"{name}\" with length {length} bytes'.format(\n",
        "      name=fn, length=len(uploaded[fn])))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-2e924b93-5750-47e9-9923-965d3ac678bc\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-2e924b93-5750-47e9-9923-965d3ac678bc\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving 2021_07_16_0tj_Kleki.png to 2021_07_16_0tj_Kleki.png\n",
            "User uploaded file \"2021_07_16_0tj_Kleki.png\" with length 734 bytes\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SQljC6DRv91f"
      },
      "source": [
        "import cv2\n",
        "img = cv2.imread(\"2021_07_16_0tj_Kleki.png\",0)\n",
        "img = cv2.resize(img, (28, 28))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "xhK8PgtTu--s",
        "outputId": "25be0a32-f694-4e82-f299-cdf5fcc02b07"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.imshow(img)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fa446b02ad0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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PS9om6d8kzeii3v5B0nOSntVIsObW1Nv5GvmI/qykTcXPirrfu0RfHXnf+LoskAkO0AGZIOxAJgg7kAnCDmSCsAOZIOxAJgg7kIn/B+kjOi+pTvmeAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FEdKVUWOvJ08"
      },
      "source": [
        "img = img.astype('float32')\n",
        "img = 255-img\n",
        "img /= 255"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "B7Gn9FGmxDuF",
        "outputId": "c1f2cf39-4552-4c47-bef6-f605e3607c24"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.imshow(img)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fa445aba750>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kZx_2o5VwPmT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1d0ac6ca-0bad-4162-a9e0-7c6ccb5e694f"
      },
      "source": [
        "import numpy as np\n",
        "img = np.reshape(img, [1, 28, 28, 1])\n",
        "print(np.argmax(model.predict(img)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "9\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WVK9wc-QOcC0"
      },
      "source": [
        "https://stackoverflow.com/questions/54519303/keras-mnist-test-own-images-bad-prediction\n",
        "\n",
        "https://stackoverflow.com/questions/54265959/predicting-my-own-image-in-cnn-using-keras"
      ]
    }
  ]
}