Number_Recognition.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOvu9cIbNEkQ4Vr5iHeFZlE",
      "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/Premrufus/BharatIntenRepo/blob/main/Number_Recognition.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **DATA SCIENCE INTERN - BHARAT INTERN**"
      ],
      "metadata": {
        "id": "BfLl9Uz1ehU7"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **TASK 3 - Number Recognition**"
      ],
      "metadata": {
        "id": "keJn8uCbiIza"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Problem Statement:** Handwritten Digit Recognition Using  MNIST Dataset with the Help of Neural Network."
      ],
      "metadata": {
        "id": "7eC6_EBsiS1B"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **About Dataset**\n",
        "\n",
        "The MNIST dataset is a large database of handwritten digits that is commonly used for training various image processing systems. The database is also widely used for training and testing in the field of machine learning. It contains 60,000 training images and 10,000 testing images. Each image is a 28x28 pixel grayscale image of a handwritten digit from 0 to 9. The images have been size-normalized and centered in a fixed-size image.\n",
        "\n",
        "The MNIST dataset is a popular dataset for machine learning because it is relatively simple to understand and implement. The dataset is also large enough to train a variety of machine learning models, and it is diverse enough to capture the different ways that people write digits.\n",
        "\n",
        "The MNIST dataset can be used to train a variety of machine learning models, such as:\n",
        "* Supervised learning models, such as support vector machines (SVMs) and decision trees.\n",
        "* Unsupervised learning models, such as k-means clustering and principal component analysis (PCA).\n",
        "* Deep learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs).\n",
        "\n",
        "The MNIST dataset has been used to develop a variety of applications, such as:\n",
        "\n",
        "* Handwritten digit recognition systems.\n",
        "* Fraud detection systems.\n",
        "* Image classification systems.\n",
        "* Natural language processing systems.\n",
        "\n",
        "The MNIST dataset is a valuable resource for researchers and practitioners in the field of machine learning. It is a simple, yet effective dataset for training and testing machine learning models"
      ],
      "metadata": {
        "id": "qqW2Y7u4p-P4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Importing Libraries and Dataset to used"
      ],
      "metadata": {
        "id": "j-zeuwkPrcEI"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "QK6YKg0ufI_F"
      },
      "outputs": [],
      "source": [
        "# Importing Libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from numpy import unique , argmax\n",
        "\n",
        "# TensorFlow already contain MNIST data set which can be loaded using Keras\n",
        "import tensorflow as tf # installing tenserflow\n",
        "from tensorflow import keras"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# To Load the MNIST dataset from the Keras API provided by TensorFlow.\n",
        "mnist = tf.keras.datasets.mnist"
      ],
      "metadata": {
        "id": "dl4ZZCkItR3H"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The Above Code Reflects that the Dataset Contains :\n",
        "\n",
        "\n",
        "1.   An array of 60,000 images, each represented as a 28x28 NumPy array, with pixel values ranging from 0 to 255.\n",
        "2.   An array of 60,000 labels, each representing the correct digit (0-9) for the 1.\n",
        "3. An array of 10,000 images, each represented as a 28x28 NumPy array, with pixel values ranging from 0 to 255.\n",
        "4. An array of 10,000 labels, each representing the correct digit (0-9) for the 3.\n",
        "\n"
      ],
      "metadata": {
        "id": "CM8uWa6xtWUR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Dividing the data into train and test data"
      ],
      "metadata": {
        "id": "TzKB8f00u8R2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "(x_train, y_train), (x_test, y_test) = mnist.load_data()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hDof2pQIu7S0",
        "outputId": "7292ab60-363c-4be9-e345-cb27e185c07e"
      },
      "execution_count": 3,
      "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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(x_train.shape)\n",
        "print(y_train.shape)\n",
        "print(x_test.shape)\n",
        "print(y_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "erKXqFwgvTEX",
        "outputId": "87000698-eacc-44b9-c0ed-b3c28f95ece3"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(60000, 28, 28)\n",
            "(60000,)\n",
            "(10000, 28, 28)\n",
            "(10000,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(x_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5VUASFXYvWBo",
        "outputId": "1316eda2-fecf-4635-9df1-b7d781e5bc1f"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[[0 0 0 ... 0 0 0]\n",
            "  [0 0 0 ... 0 0 0]\n",
            "  [0 0 0 ... 0 0 0]\n",
            "  ...\n",
            "  [0 0 0 ... 0 0 0]\n",
            "  [0 0 0 ... 0 0 0]\n",
            "  [0 0 0 ... 0 0 0]]\n",
            "\n",
            " [[0 0 0 ... 0 0 0]\n",
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            "  [0 0 0 ... 0 0 0]\n",
            "  ...\n",
            "  [0 0 0 ... 0 0 0]\n",
            "  [0 0 0 ... 0 0 0]\n",
            "  [0 0 0 ... 0 0 0]]\n",
            "\n",
            " [[0 0 0 ... 0 0 0]\n",
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            "  [0 0 0 ... 0 0 0]\n",
            "  ...\n",
            "  [0 0 0 ... 0 0 0]\n",
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            "  [0 0 0 ... 0 0 0]]\n",
            "\n",
            " ...\n",
            "\n",
            " [[0 0 0 ... 0 0 0]\n",
            "  [0 0 0 ... 0 0 0]\n",
            "  [0 0 0 ... 0 0 0]\n",
            "  ...\n",
            "  [0 0 0 ... 0 0 0]\n",
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            "  [0 0 0 ... 0 0 0]]\n",
            "\n",
            " [[0 0 0 ... 0 0 0]\n",
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            "  [0 0 0 ... 0 0 0]\n",
            "  ...\n",
            "  [0 0 0 ... 0 0 0]\n",
            "  [0 0 0 ... 0 0 0]\n",
            "  [0 0 0 ... 0 0 0]]\n",
            "\n",
            " [[0 0 0 ... 0 0 0]\n",
            "  [0 0 0 ... 0 0 0]\n",
            "  [0 0 0 ... 0 0 0]\n",
            "  ...\n",
            "  [0 0 0 ... 0 0 0]\n",
            "  [0 0 0 ... 0 0 0]\n",
            "  [0 0 0 ... 0 0 0]]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(x_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OeX11vetvYAX",
        "outputId": "d2889728-4a5c-433a-c297-e446c92edc8c"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[[0 0 0 ... 0 0 0]\n",
            "  [0 0 0 ... 0 0 0]\n",
            "  [0 0 0 ... 0 0 0]\n",
            "  ...\n",
            "  [0 0 0 ... 0 0 0]\n",
            "  [0 0 0 ... 0 0 0]\n",
            "  [0 0 0 ... 0 0 0]]\n",
            "\n",
            " [[0 0 0 ... 0 0 0]\n",
            "  [0 0 0 ... 0 0 0]\n",
            "  [0 0 0 ... 0 0 0]\n",
            "  ...\n",
            "  [0 0 0 ... 0 0 0]\n",
            "  [0 0 0 ... 0 0 0]\n",
            "  [0 0 0 ... 0 0 0]]\n",
            "\n",
            " [[0 0 0 ... 0 0 0]\n",
            "  [0 0 0 ... 0 0 0]\n",
            "  [0 0 0 ... 0 0 0]\n",
            "  ...\n",
            "  [0 0 0 ... 0 0 0]\n",
            "  [0 0 0 ... 0 0 0]\n",
            "  [0 0 0 ... 0 0 0]]\n",
            "\n",
            " ...\n",
            "\n",
            " [[0 0 0 ... 0 0 0]\n",
            "  [0 0 0 ... 0 0 0]\n",
            "  [0 0 0 ... 0 0 0]\n",
            "  ...\n",
            "  [0 0 0 ... 0 0 0]\n",
            "  [0 0 0 ... 0 0 0]\n",
            "  [0 0 0 ... 0 0 0]]\n",
            "\n",
            " [[0 0 0 ... 0 0 0]\n",
            "  [0 0 0 ... 0 0 0]\n",
            "  [0 0 0 ... 0 0 0]\n",
            "  ...\n",
            "  [0 0 0 ... 0 0 0]\n",
            "  [0 0 0 ... 0 0 0]\n",
            "  [0 0 0 ... 0 0 0]]\n",
            "\n",
            " [[0 0 0 ... 0 0 0]\n",
            "  [0 0 0 ... 0 0 0]\n",
            "  [0 0 0 ... 0 0 0]\n",
            "  ...\n",
            "  [0 0 0 ... 0 0 0]\n",
            "  [0 0 0 ... 0 0 0]\n",
            "  [0 0 0 ... 0 0 0]]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Reshaping the input Data which is used as a input in CNN in Tenserflow\n",
        "# CNN takes the input Data in 4D Format with the shape (num_samples, image_height, image_width, num_channels)\n",
        "# Here (num_channels) is set to 1 which means input image is Grayscale.\n",
        "\n",
        "x_train = x_train.reshape((x_train.shape[0] , x_train.shape[1] , x_train.shape[2],1))\n",
        "x_test = x_test.reshape((x_test.shape[0] , x_test.shape[1] , x_test.shape[2],1))\n",
        "print(x_train.shape)\n",
        "print(x_test.shape)\n",
        "print(x_train.dtype)\n",
        "print(x_test.dtype)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pRmrNo_8vawm",
        "outputId": "fc62c37c-12bd-42ae-bee3-cad1842d4c6c"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(60000, 28, 28, 1)\n",
            "(10000, 28, 28, 1)\n",
            "uint8\n",
            "uint8\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Normalizing Pixel Values\n",
        "\n",
        "x_train = x_train.astype('float32')/255.0\n",
        "x_test = x_test.astype('float32')/255.0\n",
        "print(x_train.dtype)\n",
        "print(x_test.dtype)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9PecGFHhvee5",
        "outputId": "230e18ca-1821-4f36-8469-442c56b3df45"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "float32\n",
            "float32\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visulaizing Subsets of images in MNIST Dataset along with coressponding labels.\n",
        "\n",
        "fig=plt.figure(figsize=(5,3))\n",
        "for i in range(20):\n",
        "    ax =fig.add_subplot(2,10,i+1, xticks=[], yticks=[])\n",
        "    ax.imshow(np.squeeze(x_train[i]), cmap='Blues')\n",
        "    ax.set_title(y_train[i])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        },
        "id": "jYsxqPigvgVH",
        "outputId": "a93b7950-75ec-4f6f-82cd-a429c6c16bc2"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x300 with 20 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# showing shape of single image\n",
        "img_shape= x_train.shape[1:]\n",
        "img_shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y_LKJQcQvhaI",
        "outputId": "d17228fe-2444-4d83-8b40-e835a2c42d97"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(28, 28, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Building Neural Network that can read handwritten digits"
      ],
      "metadata": {
        "id": "lzrj1VeMvjlR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating aSequential Model in Keras\n",
        "model = tf.keras.models.Sequential([\n",
        "    tf.keras.layers.Flatten(input_shape=(28, 28)),\n",
        "    tf.keras.layers.Dense(128, activation='relu'),\n",
        "    tf.keras.layers.Dropout(0.2),\n",
        "    tf.keras.layers.Dense(10)\n",
        "])"
      ],
      "metadata": {
        "id": "j0TTEXTQvrT5"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PIOWQ0qOv4Kf",
        "outputId": "3d073949-d5a1-4ae5-87b0-5b909f0031c3"
      },
      "execution_count": 12,
      "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",
            " dropout (Dropout)           (None, 128)               0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 10)                1290      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 101770 (397.54 KB)\n",
            "Trainable params: 101770 (397.54 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This summary shows that the model has four layers:\n",
        "\n",
        "1. A Flatten layer that flattens the input images to a 1D array of length 784.\n",
        "2. A Dense layer with 128 units and ReLU activation.\n",
        "3. A Dropout layer that randomly sets 20% of the input units to 0 during training.\n",
        "4. A second Dense layer with 10 units and no activation function.\n",
        "\n",
        "The summary also shows the number of trainable parameters in each layer, as well as the total number of trainable parameters in the model. In this case, the model has a total of 101,770 trainable parameters."
      ],
      "metadata": {
        "id": "jJii1g4cv7rX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Displaying Neural Network Model\n",
        "from tensorflow.keras.utils import plot_model\n",
        "plot_model(model, 'model.jpg', show_shapes = True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 533
        },
        "id": "8i4rK4Jkv6wn",
        "outputId": "f08784c5-41b7-49df-d261-f53034675e8a"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/jpeg": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Making Prediction on Model\n",
        "prediction = model(x_train[:1]).numpy()\n",
        "prediction"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U6AnqznhwGRX",
        "outputId": "52973a10-09ca-4fb8-b43f-b3f39d40dc28"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.66459775,  0.23936616,  0.27803394,  0.36669415,  0.37008065,\n",
              "         0.51092917, -0.04583089, -0.48883477,  0.31083974,  0.19633031]],\n",
              "      dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Applying Softmax() Function to prediction array\n",
        "# This convert an output vector of real numbers into a probability distribution over predicted classes\n",
        "tf.nn.softmax(prediction).numpy()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GFmyiXSgwIMZ",
        "outputId": "24082e9c-c859-4883-e03f-daf41272a4b9"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.04355152, 0.10754491, 0.11178488, 0.12214838, 0.12256273,\n",
              "        0.14110038, 0.08085936, 0.05192018, 0.11551289, 0.1030148 ]],\n",
              "      dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)\n",
        "loss_fn(y_train[:1], prediction).numpy()\n",
        "model.compile(optimizer='adam',loss=loss_fn,metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "GdCnyTIewJ1Q"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model Fitting"
      ],
      "metadata": {
        "id": "S9qxk7FWwMHI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Training the Model\n",
        "model.fit(x_train, y_train, epochs=10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5kkNUCIXwN83",
        "outputId": "0c4f72d8-f3de-4966-944a-1d7d883d24cd"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "1875/1875 [==============================] - 9s 4ms/step - loss: 0.3004 - accuracy: 0.9130\n",
            "Epoch 2/10\n",
            "1875/1875 [==============================] - 11s 6ms/step - loss: 0.1422 - accuracy: 0.9576\n",
            "Epoch 3/10\n",
            "1875/1875 [==============================] - 10s 5ms/step - loss: 0.1057 - accuracy: 0.9677\n",
            "Epoch 4/10\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.0885 - accuracy: 0.9725\n",
            "Epoch 5/10\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.0749 - accuracy: 0.9768\n",
            "Epoch 6/10\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0655 - accuracy: 0.9791\n",
            "Epoch 7/10\n",
            "1875/1875 [==============================] - 12s 6ms/step - loss: 0.0612 - accuracy: 0.9801\n",
            "Epoch 8/10\n",
            "1875/1875 [==============================] - 12s 6ms/step - loss: 0.0521 - accuracy: 0.9830\n",
            "Epoch 9/10\n",
            "1875/1875 [==============================] - 8s 4ms/step - loss: 0.0505 - accuracy: 0.9830\n",
            "Epoch 10/10\n",
            "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0458 - accuracy: 0.9851\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7c39fc1efd90>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluating the Model\n",
        "model.evaluate(x_test, y_test, verbose=2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5bFLgCE2wRi6",
        "outputId": "2dd4d7b4-6d13-4aa6-ac6b-fa0024e5a371"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 - 1s - loss: 0.0726 - accuracy: 0.9797 - 650ms/epoch - 2ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.07260674983263016, 0.9797000288963318]"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating a new sequential model which includes both previously trained model and softmax layer.\n",
        "probability_model = tf.keras.Sequential([ model,tf.keras.layers.Softmax() ])\n",
        "probability_model(x_test[:5])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sCsigH8-wTJj",
        "outputId": "7de4bb43-d4b6-4f87-f03a-e436422caca3"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(5, 10), dtype=float32, numpy=\n",
              "array([[1.47790957e-09, 3.02284933e-11, 1.38890607e-06, 8.95034289e-04,\n",
              "        5.03831765e-14, 6.05575508e-07, 1.09643915e-16, 9.99066412e-01,\n",
              "        6.81612050e-07, 3.58629150e-05],\n",
              "       [1.56267308e-11, 7.45208035e-06, 9.99992371e-01, 1.59527175e-07,\n",
              "        3.45062957e-21, 2.41979752e-08, 3.23012950e-10, 2.25757557e-14,\n",
              "        1.44254386e-10, 2.39982108e-20],\n",
              "       [1.37093226e-09, 9.99680638e-01, 2.52759310e-05, 2.03951998e-07,\n",
              "        6.04806155e-06, 1.82417708e-07, 4.11134261e-06, 1.61444696e-04,\n",
              "        1.22185229e-04, 8.37922620e-09],\n",
              "       [9.99994874e-01, 8.33524572e-16, 1.22725987e-06, 9.46486015e-11,\n",
              "        2.63308753e-08, 1.14323335e-07, 4.29416929e-07, 1.16442372e-07,\n",
              "        2.92381341e-10, 3.26244344e-06],\n",
              "       [8.33211988e-09, 2.30600278e-11, 8.68923866e-09, 7.88196244e-11,\n",
              "        9.99633431e-01, 4.59924898e-09, 7.85368641e-08, 9.37142318e-07,\n",
              "        1.11242615e-09, 3.65425483e-04]], dtype=float32)>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Displaying a Grayscale Image\n",
        "img = x_train[12]\n",
        "plt.imshow(np.squeeze(img) ,cmap='gray')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "hhKO7WjzwUuJ",
        "outputId": "3d0bc4de-eae1-4dbf-b3ef-26934e504d17"
      },
      "execution_count": 20,
      "outputs": [
        {
          "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": [
        "# Predicting the Result\n",
        "img= img.reshape(1, img.shape[0],img.shape[1],img.shape[2])\n",
        "p= model.predict([img])\n",
        "print(\"predicted : {}\".format(argmax(p)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gZv5mbaFw4yv",
        "outputId": "c7e78cac-b296-40d7-d49b-8930857f74c6"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 182ms/step\n",
            "predicted : 3\n"
          ]
        }
      ]
    }
  ]
}