tensorflow_tut1_mnist.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/SahilMalavi/DS_Program/blob/main/tensorflow_tut1_mnist.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import Sequential\n",
        "from tensorflow.keras.layers import Flatten,Dense"
      ],
      "metadata": {
        "id": "EJFejQ8aKQWv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JAhkKFvvt5ke"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UIeUOSB2105E",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b93f5a58-effd-4f36-dae4-b018cddcab5c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "11490434/11490434 [==============================] - 0s 0us/step\n"
          ]
        }
      ],
      "source": [
        "mnist_data=tf.keras.datasets.mnist\n",
        "\n",
        "(x_train,y_train),(x_test,y_test) = mnist_data.load_data()\n",
        "x_train, x_test = x_train / 255.0, x_test / 255.0"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "E6ks8r3e4CTR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5a465c74-3d3a-47a3-b07e-c146b22e90e1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(60000, 28, 28) (10000, 28, 28)\n"
          ]
        }
      ],
      "source": [
        "print(x_train.shape,x_test.shape)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.imshow(x_train[5])\n",
        "print(y_train[5])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "iDjVJk03HWJq",
        "outputId": "d4499f16-0ed7-4c4f-db6a-1f0a55d0516a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model= Sequential()"
      ],
      "metadata": {
        "id": "Ocr9TPw6IqrX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.add(Flatten(input_shape=(28,28)))\n",
        "model.add(Dense(128,activation='relu'))\n",
        "model.add(Dense(10,activation='softmax'))"
      ],
      "metadata": {
        "id": "Xe9ZMr4YKvWy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YFwqQGpLLlit",
        "outputId": "54889468-bc55-4f8e-e376-6a3c8c0b6e74"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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",
            "=================================================================\n",
            "Total params: 101,770\n",
            "Trainable params: 101,770\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer='Adam',loss='sparse_categorical_crossentropy',metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "WOmradDdNIMY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history=model.fit(x_train,y_train,epochs=10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0B1Mvsi_P7_G",
        "outputId": "a7a75570-4194-44ba-bbc3-fc4dbf8358be"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.2546 - accuracy: 0.9255\n",
            "Epoch 2/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1119 - accuracy: 0.9669\n",
            "Epoch 3/10\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0776 - accuracy: 0.9766\n",
            "Epoch 4/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0590 - accuracy: 0.9823\n",
            "Epoch 5/10\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0448 - accuracy: 0.9865\n",
            "Epoch 6/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0347 - accuracy: 0.9894\n",
            "Epoch 7/10\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0287 - accuracy: 0.9910\n",
            "Epoch 8/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0237 - accuracy: 0.9927\n",
            "Epoch 9/10\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0176 - accuracy: 0.9946\n",
            "Epoch 10/10\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0150 - accuracy: 0.9953\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.evaluate(x_test,y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yVsAg6hSQqc3",
        "outputId": "f8a17def-844b-4243-e2fb-2064f0435b4a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 2ms/step - loss: 0.0856 - accuracy: 0.9775\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.0856463834643364, 0.9775000214576721]"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred = model.predict(x_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yq48wCZ9S_Yz",
        "outputId": "0ec0f3f0-ddb3-4db6-d673-ac0374343747"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 2ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred_list = np.argmax(pred,axis=1)"
      ],
      "metadata": {
        "id": "Fj3ONiFtXq46"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def prediction(num):\n",
        "  print(\"\\nPredicted Digit:\",pred_list[num],\"\\n\\n\")\n",
        "  plt.imshow(x_test[num])"
      ],
      "metadata": {
        "id": "ECBE1vTwSMXZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "prediction(1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        },
        "id": "E-8qjvwra7hA",
        "outputId": "883dd4b6-d40c-4707-cb14-5771d969da16"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Predicted Digit: 2 \n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig = plt.figure(figsize=(12, 35))\n",
        "rows = 20\n",
        "columns = 5\n",
        "\n",
        "#first 10 images digit prediction\n",
        "for i in range(0,10):\n",
        "  i=i+1\n",
        "  fig.add_subplot(rows, columns, i)\n",
        "  plt.imshow(x_test[i])\n",
        "  plt.axis('off')\n",
        "  plt.title(str(i)+\") Predicted Digit:\"+str(pred_list[i]))\n"
      ],
      "metadata": {
        "id": "GqecGyepbzhF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 308
        },
        "outputId": "4b2be860-cdde-4b2b-c9f5-8ba72b3c4a92"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x3500 with 10 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyMLsuSr2uuDyZcmTCv/nxkO",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}