New digit recognition play ground.ipynb

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  "metadata": {
    "colab": {
      "name": "New digit recognition play ground.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n",
        "import cv2\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "eSqvAy-h7pCd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Model / data parameters\n",
        "num_classes = 10\n",
        "input_shape = (28, 28, 1)\n",
        "\n",
        "# the data, split between train and test sets\n",
        "(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()\n",
        "\n",
        "# Scale images to the [0, 1] range\n",
        "x_train = x_train.astype(\"float32\") / 255\n",
        "x_test = x_test.astype(\"float32\") / 255\n",
        "# Make sure images have shape (28, 28, 1)\n",
        "x_train = np.expand_dims(x_train, -1)\n",
        "x_test = np.expand_dims(x_test, -1)\n",
        "print(\"x_train shape:\", x_train.shape)\n",
        "print(x_train.shape[0], \"train samples\")\n",
        "print(x_test.shape[0], \"test samples\")\n",
        "\n",
        "\n",
        "# convert class vectors to binary class matrices\n",
        "y_train = keras.utils.to_categorical(y_train, num_classes)\n",
        "y_test = keras.utils.to_categorical(y_test, num_classes)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C1EG0cZ_7qGb",
        "outputId": "11b1b97b-21bd-439f-97fd-ac0686f48279"
      },
      "execution_count": null,
      "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",
            "x_train shape: (60000, 28, 28, 1)\n",
            "60000 train samples\n",
            "10000 test samples\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = keras.Sequential(\n",
        "    [\n",
        "        keras.Input(shape=input_shape),\n",
        "        layers.Conv2D(32, kernel_size=(3, 3), activation=\"relu\"),\n",
        "        layers.MaxPooling2D(pool_size=(2, 2)),\n",
        "        layers.Conv2D(64, kernel_size=(3, 3), activation=\"relu\"),\n",
        "        layers.MaxPooling2D(pool_size=(2, 2)),\n",
        "        layers.Flatten(),\n",
        "        layers.Dropout(0.5),\n",
        "        layers.Dense(num_classes, activation=\"softmax\"),\n",
        "    ]\n",
        ")\n",
        "\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "anuYXGhm7soj",
        "outputId": "68650f75-183c-4bb2-b555-c34e447a06e9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d (Conv2D)             (None, 26, 26, 32)        320       \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2D  (None, 13, 13, 32)       0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 11, 11, 64)        18496     \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPooling  (None, 5, 5, 64)         0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 1600)              0         \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 1600)              0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 10)                16010     \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 34,826\n",
            "Trainable params: 34,826\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "batch_size = 128\n",
        "epochs = 20\n",
        "\n",
        "model.compile(loss=\"categorical_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])\n",
        "\n",
        "model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, validation_split=0.1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 718
        },
        "id": "m0VZT-do7vJN",
        "outputId": "8890fa26-020d-4ee8-cf95-a58300be66aa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "422/422 [==============================] - 44s 103ms/step - loss: 0.0192 - accuracy: 0.9933 - val_loss: 0.0281 - val_accuracy: 0.9925\n",
            "Epoch 2/20\n",
            "422/422 [==============================] - 44s 105ms/step - loss: 0.0183 - accuracy: 0.9940 - val_loss: 0.0278 - val_accuracy: 0.9937\n",
            "Epoch 3/20\n",
            "422/422 [==============================] - 44s 103ms/step - loss: 0.0176 - accuracy: 0.9941 - val_loss: 0.0289 - val_accuracy: 0.9930\n",
            "Epoch 4/20\n",
            "422/422 [==============================] - 44s 105ms/step - loss: 0.0186 - accuracy: 0.9935 - val_loss: 0.0252 - val_accuracy: 0.9932\n",
            "Epoch 5/20\n",
            "422/422 [==============================] - 43s 101ms/step - loss: 0.0166 - accuracy: 0.9943 - val_loss: 0.0277 - val_accuracy: 0.9933\n",
            "Epoch 6/20\n",
            "422/422 [==============================] - 43s 101ms/step - loss: 0.0174 - accuracy: 0.9942 - val_loss: 0.0277 - val_accuracy: 0.9932\n",
            "Epoch 7/20\n",
            "422/422 [==============================] - 44s 105ms/step - loss: 0.0172 - accuracy: 0.9939 - val_loss: 0.0291 - val_accuracy: 0.9932\n",
            "Epoch 8/20\n",
            "422/422 [==============================] - 43s 103ms/step - loss: 0.0173 - accuracy: 0.9939 - val_loss: 0.0275 - val_accuracy: 0.9930\n",
            "Epoch 9/20\n",
            "422/422 [==============================] - 43s 101ms/step - loss: 0.0174 - accuracy: 0.9939 - val_loss: 0.0283 - val_accuracy: 0.9925\n",
            "Epoch 10/20\n",
            " 74/422 [====>.........................] - ETA: 34s - loss: 0.0118 - accuracy: 0.9960"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-167-1b9a72e89cf9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"categorical_crossentropy\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"adam\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetrics\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"accuracy\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidation_split\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
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            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/util/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    148\u001b[0m     \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    151\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    913\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    914\u001b[0m       \u001b[0;32mwith\u001b[0m \u001b[0mOptionalXlaContext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jit_compile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 915\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    916\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    917\u001b[0m       \u001b[0mnew_tracing_count\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexperimental_get_tracing_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    945\u001b[0m       \u001b[0;31m# In this case we have created variables on the first call, so we run the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    946\u001b[0m       \u001b[0;31m# defunned version which is guaranteed to never create variables.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 947\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateless_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=not-callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    948\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateful_fn\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    949\u001b[0m       \u001b[0;31m# Release the lock early so that multiple threads can perform the call\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2955\u001b[0m        filtered_flat_args) = self._maybe_define_function(args, kwargs)\n\u001b[1;32m   2956\u001b[0m     return graph_function._call_flat(\n\u001b[0;32m-> 2957\u001b[0;31m         filtered_flat_args, captured_inputs=graph_function.captured_inputs)  # pylint: disable=protected-access\n\u001b[0m\u001b[1;32m   2958\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2959\u001b[0m   \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1852\u001b[0m       \u001b[0;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1853\u001b[0m       return self._build_call_outputs(self._inference_function.call(\n\u001b[0;32m-> 1854\u001b[0;31m           ctx, args, cancellation_manager=cancellation_manager))\n\u001b[0m\u001b[1;32m   1855\u001b[0m     forward_backward = self._select_forward_and_backward_functions(\n\u001b[1;32m   1856\u001b[0m         \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m    502\u001b[0m               \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    503\u001b[0m               \u001b[0mattrs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattrs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 504\u001b[0;31m               ctx=ctx)\n\u001b[0m\u001b[1;32m    505\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    506\u001b[0m           outputs = execute.execute_with_cancellation(\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     53\u001b[0m     \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0;32m---> 55\u001b[0;31m                                         inputs, attrs, num_outputs)\n\u001b[0m\u001b[1;32m     56\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GFGT3bNL6cMQ"
      },
      "outputs": [],
      "source": [
        "from IPython.display import HTML, Image\n",
        "from google.colab.output import eval_js\n",
        "from base64 import b64decode\n",
        "from PIL import Image, ImageOps\n",
        "\n",
        "canvas_html = \"\"\"\n",
        "<canvas width=%d height=%d></canvas>\n",
        "<button>Finish</button>\n",
        "<script>\n",
        "var canvas = document.querySelector('canvas')\n",
        "var ctx = canvas.getContext('2d')\n",
        "ctx.lineWidth = %d\n",
        "var button = document.querySelector('button')\n",
        "var mouse = {x: 0, y: 0}\n",
        "var Touch = {x: 0, y: 0}\n",
        "canvas.addEventListener('mousemove', function(e) {\n",
        "  mouse.x = e.pageX - this.offsetLeft\n",
        "  mouse.y = e.pageY - this.offsetTop\n",
        "})\n",
        "canvas.addEventListener('touchmove', function(e) {\n",
        "  Touch.x = e.pageX - this.offsetLeft\n",
        "  Touch.y = e.pageY - this.offsetTop\n",
        "})\n",
        "canvas.onmousedown = ()=>{\n",
        "  ctx.beginPath()\n",
        "  ctx.moveTo(mouse.x, mouse.y)\n",
        "  canvas.addEventListener('mousemove', onPaint)\n",
        "}\n",
        "canvas.ontouchstart = ()=>{\n",
        "  ctx.beginPath()\n",
        "  ctx.moveTo(Touch.x, Touch.y)\n",
        "  canvas.addEventListener('touchmove', onPaint2)\n",
        "}\n",
        "canvas.onmouseup = ()=>{\n",
        "  canvas.removeEventListener('mousemove', onPaint)\n",
        "}\n",
        "canvas.ontouchend = ()=>{\n",
        "  canvas.removeEventListener('touchmove', onPaint2)\n",
        "}\n",
        "var onPaint = ()=>{\n",
        "  ctx.lineTo(mouse.x, mouse.y)\n",
        "  ctx.stroke()\n",
        "}\n",
        "var onPaint2 = ()=>{\n",
        "  ctx.lineTo(Touch.x, Touch.y)\n",
        "  ctx.stroke()\n",
        "}\n",
        "var data = new Promise(resolve=>{\n",
        "  button.onclick = ()=>{\n",
        "    resolve(canvas.toDataURL('image/png'))\n",
        "  }\n",
        "})\n",
        "</script>\n",
        "\"\"\"\n",
        "\n",
        "def draw(filename='drawing.png', w=280, h=280, line_width=5):\n",
        "  display(HTML(canvas_html % (w, h, line_width)))\n",
        "  data = eval_js(\"data\")\n",
        "  binary = b64decode(data.split(',')[1])\n",
        "  with open(filename, 'wb') as f:\n",
        "    f.write(binary)\n",
        "\n",
        "  img = Image.open(filename)\n",
        "  img = img.resize((np.array(img.size)/10).astype(int))\n",
        "  img = np.array(img)\n",
        "  img = img[:,:,3]\n",
        "  plt.imshow(img)\n",
        "  img = np.reshape(img, (1,28,28,1))\n",
        "  print(img.shape)\n",
        "  print(np.argmax(model.predict(img)))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "draw()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        },
        "id": "sXvIvRWL7CGG",
        "outputId": "c6a96cdc-c7b8-436b-d671-ebf330ed1e52"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "<canvas width=280 height=280></canvas>\n",
              "<button>Finish</button>\n",
              "<script>\n",
              "var canvas = document.querySelector('canvas')\n",
              "var ctx = canvas.getContext('2d')\n",
              "ctx.lineWidth = 5\n",
              "var button = document.querySelector('button')\n",
              "var mouse = {x: 0, y: 0}\n",
              "var Touch = {x: 0, y: 0}\n",
              "canvas.addEventListener('mousemove', function(e) {\n",
              "  mouse.x = e.pageX - this.offsetLeft\n",
              "  mouse.y = e.pageY - this.offsetTop\n",
              "})\n",
              "canvas.addEventListener('touchmove', function(e) {\n",
              "  Touch.x = e.pageX - this.offsetLeft\n",
              "  Touch.y = e.pageY - this.offsetTop\n",
              "})\n",
              "canvas.onmousedown = ()=>{\n",
              "  ctx.beginPath()\n",
              "  ctx.moveTo(mouse.x, mouse.y)\n",
              "  canvas.addEventListener('mousemove', onPaint)\n",
              "}\n",
              "canvas.ontouchstart = ()=>{\n",
              "  ctx.beginPath()\n",
              "  ctx.moveTo(Touch.x, Touch.y)\n",
              "  canvas.addEventListener('touchmove', onPaint2)\n",
              "}\n",
              "canvas.onmouseup = ()=>{\n",
              "  canvas.removeEventListener('mousemove', onPaint)\n",
              "}\n",
              "canvas.ontouchend = ()=>{\n",
              "  canvas.removeEventListener('touchmove', onPaint2)\n",
              "}\n",
              "var onPaint = ()=>{\n",
              "  ctx.lineTo(mouse.x, mouse.y)\n",
              "  ctx.stroke()\n",
              "}\n",
              "var onPaint2 = ()=>{\n",
              "  ctx.lineTo(Touch.x, Touch.y)\n",
              "  ctx.stroke()\n",
              "}\n",
              "var data = new Promise(resolve=>{\n",
              "  button.onclick = ()=>{\n",
              "    resolve(canvas.toDataURL('image/png'))\n",
              "  }\n",
              "})\n",
              "</script>\n"
            ]
          },
          "metadata": {}
        }
      ]
    }
  ]
}