cnn_mnist.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "cnn_mnist.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyP7eC6Lmhe+IIEkxDoUZ4S+",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/vrajasekaran/ai-3d-labs/blob/master/cnn_mnist.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ARIeKf-AWHtZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import keras as K\n",
        "from keras.models import Sequential\n",
        "from keras.datasets import mnist\n",
        "\n",
        "\n",
        "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hk9CKOnRoL3s",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "7ec0ada5-f881-47be-a50e-b558530e3551"
      },
      "source": [
        "print(x_train.shape)\n",
        "print(x_train.size)\n",
        "print(x_train.data)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(60000, 28, 28)\n",
            "47040000\n",
            "<memory at 0x7f9de744ed68>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8YEQlc4HquWa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "1acba084-1447-4099-bf82-8e6de0ab308f"
      },
      "source": [
        "print(y_train.shape)\n",
        "print(y_train.size)\n",
        "print(y_train.data)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(60000,)\n",
            "60000\n",
            "<memory at 0x7f9de727e708>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yzs1RtHMqvH5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "10ac44c4-2edb-4204-e422-b813aa571f08"
      },
      "source": [
        "print(x_test.shape)\n",
        "print(x_test.size)\n",
        "print(x_test.data)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(10000, 28, 28)\n",
            "7840000\n",
            "<memory at 0x7f9de744ed68>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4qWMu086qvhr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "3cd69433-7dd4-4b01-c91e-0c60172d4ef7"
      },
      "source": [
        "print(y_test.shape)\n",
        "print(y_test.size)\n",
        "print(y_test.data)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(10000,)\n",
            "10000\n",
            "<memory at 0x7f9de727e708>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ipDTP5dSr562",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e474895c-ec19-4a18-ee35-c0339fe4b929"
      },
      "source": [
        "print(y_test[0])"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "7\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GQAbhnYqsMEN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "bef37275-a34a-4b5b-d5bf-9eb63e4c06fe"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "# Display the data for index in range(len(x_train)):\n",
        "for index in range(0,5):\n",
        "  plt.imshow(x_train[index], cmap=plt.get_cmap('gray'))\n",
        "  plt.show()\n",
        "  plt.imshow(x_train[index], cmap=plt.get_cmap('Oranges'))\n",
        "  plt.show()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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XejtWo0/Rn5K0uvo5udvHrtBXR44bb5cFkuACHZAEYQeSIOxAEoQdSIKwA0kQdiAJwg4k8f+AEiJIx8qySQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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I826NkY1TiQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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8SY9Vlyd63Zuk1Rp9W/eGRo9tnC1phqS7JT0j6S5Jh/ZRbzdJWivpcY0Ga3aPejtOo2/RH5f0aHU5udevXaGvrrxufF0WSIIDdEAShB1IgrADSRB2IAnCDiRB2IEkCDuQxP8Dp3I5UwCa3roAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "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"
          }
        },
        {
          "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"
          }
        },
        {
          "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"
          }
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "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"
          }
        },
        {
          "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": "markdown",
      "metadata": {
        "id": "M5ulcatd_663",
        "colab_type": "text"
      },
      "source": [
        "Prepare for Training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0n8ijL98_zN1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "b34d7b3d-52f2-477e-85cd-f93e89e54443"
      },
      "source": [
        "#Reshape to add 4th dimension for Keras\n",
        "img_rows = x_train[0].shape[0]\n",
        "img_cols = x_train[0].shape[1]\n",
        "\n",
        "x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)\n",
        "x_train = x_train.astype('float32')\n",
        "x_train /= 255\n",
        "print(x_train.shape)\n",
        "print(x_train.size)\n",
        "print(x_train.data)\n",
        "\n",
        "x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)\n",
        "x_test = x_test.astype('float32')\n",
        "x_test /= 255\n",
        "print(x_test.shape)\n",
        "print(x_test.size)\n",
        "print(x_test.data)\n",
        "\n",
        "input_shape = (img_rows, img_cols, 1)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(60000, 28, 28, 1)\n",
            "47040000\n",
            "<memory at 0x7f9fd70e2258>\n",
            "(10000, 28, 28, 1)\n",
            "7840000\n",
            "<memory at 0x7f9fd70e2258>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dZ26AF-WB8O3",
        "colab_type": "text"
      },
      "source": [
        "Hot One Encoding\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_YXv4YzWB_hP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "08851564-30fd-472d-f776-be691e2c9f52"
      },
      "source": [
        "from keras.utils import np_utils\n",
        "\n",
        "y_train = np_utils.to_categorical(y_train)\n",
        "y_test = np_utils.to_categorical(y_test)\n",
        "\n",
        "print(y_test.shape[1])\n",
        "\n",
        "num_classes = y_test.shape[1]\n",
        "num_pixels = x_train.shape[1] * x_train.shape[2]\n",
        "\n",
        "y_train[0]"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "10\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0., 0., 0., 0., 0., 1., 0., 0., 0., 0.], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YB7lx7XXEEyQ",
        "colab_type": "text"
      },
      "source": [
        "Build Model "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C2OBA__JEIPC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "outputId": "01aa1232-cdc4-4a5b-9968-0e398f92b401"
      },
      "source": [
        "from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D\n",
        "from keras.optimizers import SGD\n",
        "\n",
        "model = Sequential()\n",
        "\n",
        "model.add(Conv2D(64,kernel_size=(3,3), activation='relu', input_shape=input_shape))\n",
        "model.add(Conv2D(64,kernel_size=(3,3), activation='relu'))\n",
        "\n",
        "model.add(MaxPooling2D(pool_size=(2,2)))\n",
        "\n",
        "\n",
        "model.add(Flatten())\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(num_classes, activation='softmax'))\n",
        "\n",
        "model.compile(loss = 'categorical_crossentropy',\n",
        "              optimizer = SGD(0.01),\n",
        "              metrics = ['accuracy'] )\n",
        "model.summary()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d (Conv2D)              (None, 26, 26, 64)        640       \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 24, 24, 64)        36928     \n",
            "_________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D) (None, 12, 12, 64)        0         \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 9216)              0         \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 9216)              0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 10)                92170     \n",
            "=================================================================\n",
            "Total params: 129,738\n",
            "Trainable params: 129,738\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EAyEsf3QV30I",
        "colab_type": "text"
      },
      "source": [
        "Model Training "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wx7IdJWmWvLq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "outputId": "2d8e0fca-1309-48fd-8e4c-f7e0c8b37aa4"
      },
      "source": [
        "batch_size = 64\n",
        "epochs = 5\n",
        "\n",
        "history = model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, verbose=1, validation_data=(x_test, y_test))"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "938/938 [==============================] - 232s 247ms/step - loss: 0.3578 - accuracy: 0.8925 - val_loss: 0.2750 - val_accuracy: 0.9198\n",
            "Epoch 2/5\n",
            "938/938 [==============================] - 233s 248ms/step - loss: 0.2860 - accuracy: 0.9156 - val_loss: 0.2043 - val_accuracy: 0.9422\n",
            "Epoch 3/5\n",
            "938/938 [==============================] - 232s 248ms/step - loss: 0.2152 - accuracy: 0.9376 - val_loss: 0.1482 - val_accuracy: 0.9586\n",
            "Epoch 4/5\n",
            "938/938 [==============================] - 233s 248ms/step - loss: 0.1672 - accuracy: 0.9515 - val_loss: 0.1192 - val_accuracy: 0.9646\n",
            "Epoch 5/5\n",
            "938/938 [==============================] - 232s 248ms/step - loss: 0.1375 - accuracy: 0.9603 - val_loss: 0.0940 - val_accuracy: 0.9725\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lW8t7iH2g21o",
        "colab_type": "text"
      },
      "source": [
        "Model Testing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7kAKSCnZcx7B",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "3d0fb94a-401c-40d7-f1fc-c089520ac1e6"
      },
      "source": [
        "score = model.evaluate(x_test, y_test, verbose = 0)\n",
        "print('Test Loss:', score[0])\n",
        "print('Test Accuracy:', score[1])"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test Loss: 0.09397300332784653\n",
            "Test Accuracy: 0.9725000262260437\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "22MZmXQKg6Ab",
        "colab_type": "text"
      },
      "source": [
        "Plotting Loss"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pco5TajTg0ih",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "196326b2-0252-4f3a-a71e-ffb782f50ceb"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "history_dict = history.history\n",
        "\n",
        "loss_values = history_dict['loss']\n",
        "val_loss_values = history_dict['val_loss']\n",
        "epochs = range(1, len(loss_values)+1)\n",
        "\n",
        "line1 = plt.plot(epochs, val_loss_values, label='Test Loss')\n",
        "line2 = plt.plot(epochs, loss_values, label='Training Loss')\n",
        "plt.setp(line1, linewidth=2.0, marker = '+', markersize=10.0)\n",
        "plt.setp(line2, linewidth=2.0, marker = '4', markersize=10.0)\n",
        "\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "\n",
        "plt.grid(True)\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "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": "markdown",
      "metadata": {
        "id": "qgZqkBc7ir9t",
        "colab_type": "text"
      },
      "source": [
        "Plotting Accuracy"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JLNQPG4piuV-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "ac56fc5b-df38-43fd-dc44-87415f65c516"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "history_dict = history.history\n",
        "\n",
        "loss_values = history_dict['accuracy']\n",
        "val_loss_values = history_dict['val_accuracy']\n",
        "epochs = range(1, len(loss_values)+1)\n",
        "\n",
        "line1 = plt.plot(epochs, val_loss_values, label='Test accuracy')\n",
        "line2 = plt.plot(epochs, loss_values, label='Training accuracy')\n",
        "plt.setp(line1, linewidth=2.0, marker = '+', markersize=10.0)\n",
        "plt.setp(line2, linewidth=2.0, marker = '4', markersize=10.0)\n",
        "\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('accuracy')\n",
        "\n",
        "plt.grid(True)\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "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": "markdown",
      "metadata": {
        "id": "QqZhGRh1i_xH",
        "colab_type": "text"
      },
      "source": [
        "Save the Model\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yBBSltkJjBtK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "f14aea5f-81b2-42e9-e6d8-e50c84dfc282"
      },
      "source": [
        "modelFileName = 'mnist_cnn_5_epochs.h5'\n",
        "model.save(modelFileName)\n",
        "print('Model Saved!')"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model Saved!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_6oCO8QajdMj",
        "colab_type": "text"
      },
      "source": [
        "Load the Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VFNGi8nwjcJz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e418a837-8c59-4643-ba49-c7ee15dc6f83"
      },
      "source": [
        "from keras.models import load_model\n",
        "classifier = load_model(modelFileName)\n",
        "\n"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.engine.sequential.Sequential at 0x7f9fcd0f9898>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DDciZudEnrVM",
        "colab_type": "text"
      },
      "source": [
        "Input Testing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I0FsXUiJntFd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "836c8646-b2c9-4b2c-884f-fedec723cf0d"
      },
      "source": [
        "import cv2\n",
        "import numpy as np\n",
        "from google.colab.patches import cv2_imshow\n",
        "\n",
        "def draw_test(name, pred, input_im):\n",
        "    BLACK = [0,0,0]\n",
        "    expanded_image = cv2.copyMakeBorder(input_im, 0, 0, 0, imageL.shape[0] ,cv2.BORDER_CONSTANT,value=BLACK)\n",
        "    expanded_image = cv2.cvtColor(expanded_image, cv2.COLOR_GRAY2BGR)\n",
        "    cv2.putText(expanded_image, str(pred), (152, 70) , cv2.FONT_HERSHEY_COMPLEX_SMALL,4, (0,255,0), 2)\n",
        "    cv2_imshow(expanded_image)\n",
        "    cv2_imshow(input_im)\n",
        "\n",
        "for i in range(0,10):\n",
        "    rand = np.random.randint(0,len(x_test))\n",
        "    input_im = x_test[rand]\n",
        "\n",
        "    imageL = cv2.resize(input_im, None, fx=4, fy=4, interpolation = cv2.INTER_CUBIC)\n",
        "    input_im = input_im.reshape(1,28,28,1) \n",
        "    \n",
        "    ## Get Prediction\n",
        "    res = str(classifier.predict_classes(input_im, 1, verbose = 0)[0])\n",
        "\n",
        "    draw_test(\"Prediction\", res, imageL) \n",
        "    cv2.waitKey(0)\n",
        "\n",
        "cv2.destroyAllWindows()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=224x112 at 0x7F9FC3804AC8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=L size=112x112 at 0x7F9FC3804AC8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=224x112 at 0x7F9FC36BD048>"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=L size=112x112 at 0x7F9FC3804AC8>"
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          "metadata": {
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        {
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          "metadata": {
            "tags": []
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        {
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          "data": {
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          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
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        },
        {
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          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
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            "text/plain": [
              "<PIL.Image.Image image mode=L size=112x112 at 0x7F9FC37272E8>"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
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          "metadata": {
            "tags": []
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        {
          "output_type": "display_data",
          "data": {
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              "<PIL.Image.Image image mode=L size=112x112 at 0x7F9FC3666D30>"
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          "metadata": {
            "tags": []
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        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C5b5JA5woehX",
        "colab_type": "text"
      },
      "source": [
        "Model Diagram"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qqwk-WDfoiNg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 754
        },
        "outputId": "8de54690-1b08-438f-8237-ddaca53c673a"
      },
      "source": [
        "from tensorflow.keras.utils import plot_model\n",
        "plot_model(model, to_file='mnist_classifier_model_diagram.png', show_shapes = True, show_layer_names= True)\n"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    }
  ]
}