MNIST_Train.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "DevlUp",
      "provenance": [],
      "mount_file_id": "15NkwPt9Ntmwq0rZ93jJoPOvsGDOxpfg6",
      "authorship_tag": "ABX9TyNRN4S3cDRPQFj6LsA37uXN",
      "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/KD-3/DevlUp_Webinar_DL_Deployment/blob/master/MNIST_Train.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cW0K3dSOPPtw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "4e7c4dd6-b010-4b2d-a07c-9da1d9504e2a"
      },
      "source": [
        "# TensorFlow and tf.keras\n",
        "import tensorflow as tf\n",
        "\n",
        "from tensorflow import keras\n",
        "\n",
        "# Helper libraries\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "print(tf.__version__)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.3.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JJ-i4zb0PX66",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "68918077-dc90-4c22-ac87-cdc903c41c9a"
      },
      "source": [
        "mnist = tf.keras.datasets.mnist\n",
        "(train_images, train_labels), (test_images, test_labels) = mnist.load_data()"
      ],
      "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"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LQAGzs63PrxG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class_names = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3Ou0wiIIQG0T",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "a1a49daa-6ba6-4d31-d1a2-a07dcbf62bac"
      },
      "source": [
        "train_images.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60000, 28, 28)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rGIfXSSsQKiz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "759b270f-e2c7-4e92-80f3-8bc24cb3a487"
      },
      "source": [
        "plt.figure()\n",
        "plt.imshow(train_images[1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fbce1d48cc0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        },
        {
          "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": "cwzEpmZ6QW1-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_images = train_images / 255.0\n",
        "test_images = test_images / 255.0"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RvQ15vfaQ6zL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = tf.keras.Sequential([\n",
        "                          tf.keras.layers.Flatten(input_shape=(28, 28)),\n",
        "                          tf.keras.layers.Dense(128, activation=tf.nn.relu),\n",
        "                          tf.keras.layers.Dense(10, activation=tf.nn.softmax)\n",
        "])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gwOTK9CiR_0E",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.compile(optimizer='adam',\n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "boTCWePLSVdA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "a59c50e9-b6a1-4f18-b012-d0101cbcefdd"
      },
      "source": [
        "model.fit(train_images, train_labels, epochs=5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.2601 - accuracy: 0.9264\n",
            "Epoch 2/5\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.1164 - accuracy: 0.9663\n",
            "Epoch 3/5\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.0794 - accuracy: 0.9767\n",
            "Epoch 4/5\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0595 - accuracy: 0.9821\n",
            "Epoch 5/5\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0473 - accuracy: 0.9852\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f640e523ef0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HPbPrsL2STCx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "outputId": "c3ed2521-112a-485f-dc04-3e711f76450e"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "flatten (Flatten)            (None, 784)               0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 128)               100480    \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 10)                1290      \n",
            "=================================================================\n",
            "Total params: 101,770\n",
            "Trainable params: 101,770\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iPiHnW4iSyt0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "9aad1fa1-077c-4c29-fc2c-d509f542db47"
      },
      "source": [
        "test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "313/313 - 0s - loss: 0.0794 - accuracy: 0.9774\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "unU7QMDaTLqp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "predictions = model.predict(test_images)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PjjiJosSTUkT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "0cce71db-8061-4fd1-9cb3-482b1bb161e8"
      },
      "source": [
        "np.argmax(predictions[0])\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ODWkw7WZTaTP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "fea293cc-780f-4b8a-ec50-2cb3c5671069"
      },
      "source": [
        "test_labels[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IvpLzdyDTdRK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model_json = model.to_json()\n",
        "with open(\"model.json\", \"w\") as json_file:\n",
        "    json_file.write(model_json)\n",
        "# serialize weights to HDF5\n",
        "model.save_weights(\"model.h5\")\n",
        "# model.save(\"modelf.h5\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n7zz0SVbTlu1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "25a697a1-614f-45f8-b671-979b4f3a7a98"
      },
      "source": [
        "!ls drive\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "'My Drive'\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V2V74m9f1KjI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "73ad43cb-6200-49e3-dca3-7b75121180ab"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "mv: cannot create regular file 'drive/model.h5': Operation not supported\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e0dPGmN91j7a",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}