MNIST.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "MNIST.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyOWg90T1ZyE4XaKE6EcW6ZK",
      "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/mart-anthony-stark/Machine-Learning-and-Deep-Learning/blob/main/MNIST.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "id": "b3jyq6sNb5Ct"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mnist = tf.keras.datasets.mnist\n",
        "\n",
        "(X_train, y_train), (X_test, y_test) = mnist.load_data()\n",
        "\n",
        "x_train = tf.keras.utils.normalize(X_train, axis=1)\n",
        "x_test = tf.keras.utils.normalize(X_test, axis=1)\n",
        "\n",
        "model = tf.keras.models.Sequential()\n",
        "model.add(tf.keras.layers.Flatten())\n",
        "model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu))\n",
        "model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu))\n",
        "model.add(tf.keras.layers.Dense(10, activation=tf.nn.softmax))\n",
        "\n",
        "model.compile(optimizer='adam',\n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "model.fit(x_train, y_train, epochs=3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JdwroNCicvZD",
        "outputId": "bbf5a2f4-d6b1-45ef-b658-20f104342749"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/3\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2627 - accuracy: 0.9231\n",
            "Epoch 2/3\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1064 - accuracy: 0.9671\n",
            "Epoch 3/3\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0735 - accuracy: 0.9767\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7efffe78a9d0>"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "val_loss, val_acc = model.evaluate(x_test, y_test)\n",
        "val_loss, val_acc"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q6dWfZeem2Sf",
        "outputId": "c8ba7da5-5ee0-49d0-d6c9-3a7362da9250"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 2ms/step - loss: 0.0838 - accuracy: 0.9730\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.08375442773103714, 0.9729999899864197)"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.imshow(x_train[0], cmap=plt.cm.binary)\n",
        "plt.show()\n",
        "# print(x_train[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "ApilUSu_egPj",
        "outputId": "1d2bb53e-a17a-41e7-a737-ef51d7c1abab"
      },
      "execution_count": 26,
      "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": "markdown",
      "source": [
        "## Save model and download as zip"
      ],
      "metadata": {
        "id": "K8SaPYxBn3w4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.save('number_identifier.model')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gvK0er6WgKdX",
        "outputId": "9ca6d432-fbd3-49b7-9f38-0ed98eccdcab"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:Assets written to: number_identifier.model/assets\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!zip -r /content/file.zip /content/number_identifier.model"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y1gBlpXJnrda",
        "outputId": "cbeafab6-a19a-4974-9cb3-8f0163b54d46"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  adding: content/number_identifier.model/ (stored 0%)\n",
            "  adding: content/number_identifier.model/saved_model.pb (deflated 87%)\n",
            "  adding: content/number_identifier.model/assets/ (stored 0%)\n",
            "  adding: content/number_identifier.model/variables/ (stored 0%)\n",
            "  adding: content/number_identifier.model/variables/variables.index (deflated 63%)\n",
            "  adding: content/number_identifier.model/variables/variables.data-00000-of-00001 (deflated 13%)\n",
            "  adding: content/number_identifier.model/keras_metadata.pb (deflated 87%)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "files.download(\"/content/file.zip\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "bIqf6KYynwOK",
        "outputId": "5c44f662-691a-457c-9983-036e7d6b0533"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_716b68ef-1e23-4e39-996a-67b0157b4d3e\", \"file.zip\", 1259222)"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Load Saved Model"
      ],
      "metadata": {
        "id": "hDFlz-H3n-m4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "new_model = tf.keras.models.load_model('number_identifier.model')"
      ],
      "metadata": {
        "id": "1d8GvN86oAvN"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = new_model.predict(x_test)\n",
        "predictions"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XI5Rb9whoK6J",
        "outputId": "9ebb2094-c7ae-4529-b174-22662d296752"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.0533824e-08, 1.1157860e-07, 2.6750863e-06, ..., 9.9998736e-01,\n",
              "        4.0394369e-08, 5.4591305e-07],\n",
              "       [6.4649188e-09, 1.2650849e-05, 9.9998569e-01, ..., 7.8212498e-10,\n",
              "        1.3895908e-07, 1.4286658e-13],\n",
              "       [2.0141543e-07, 9.9976164e-01, 2.8832976e-05, ..., 1.6514056e-04,\n",
              "        2.8462158e-05, 4.5256368e-07],\n",
              "       ...,\n",
              "       [6.5477512e-09, 1.8249106e-07, 1.7241289e-08, ..., 2.6076523e-05,\n",
              "        5.3550448e-06, 2.0404134e-04],\n",
              "       [1.5842759e-06, 4.9608298e-07, 1.7724783e-06, ..., 4.0201618e-07,\n",
              "        1.1953812e-03, 3.6031945e-08],\n",
              "       [2.6380278e-08, 1.6839801e-08, 3.9277722e-08, ..., 3.6581680e-12,\n",
              "        3.0605804e-08, 8.5057961e-10]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(np.argmax(predictions[2]))\n",
        "plt.imshow(x_test[2], cmap=plt.cm.binary)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "1ZLz0O3VoVDz",
        "outputId": "6721ede7-f6d8-4554-e81e-0cda8a2c21ef"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1\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": "code",
      "source": [
        "print(np.argmax(predictions[4]))\n",
        "plt.imshow(x_test[4], cmap=plt.cm.binary)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "WNnb2n-2o5kT",
        "outputId": "5f29d0c9-a70c-43ee-e99f-93205781c5fb"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4\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"
          }
        }
      ]
    }
  ]
}