Task_Number_Recognition.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UxaQRMhATjn7"
   },
   "source": [
    "# Name : TANISA DE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bC8rLUYQAzAJ"
   },
   "source": [
    "# Handwritten Digit Recognition Using MNIST Dataset With The Help Of Neural Network."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yEwrLyd2BcYq"
   },
   "source": [
    "# Dataset Used : MNIST Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dboT_BxhTfa5"
   },
   "source": [
    "# About Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vqKNQwE3T2Rv"
   },
   "source": [
    "MNIST is a commonly used dataset in machine learning and computer vision research, which consists of a set of 70,000 images of handwritten digits (0-9), each of size 28x28 pixels.\n",
    "The dataset is split into two sets: a training set of 60,000 images and a test set of 10,000 images. The training set is used to train a machine learning model, while the test set is used to evaluate the model's performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "id": "ieahWoNmsihl"
   },
   "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",
   "execution_count": 27,
   "metadata": {
    "id": "MLWgqoSP8BlX"
   },
   "outputs": [],
   "source": [
    "# To Load the MNIST dataset from the Keras API provided by TensorFlow.\n",
    "mnist = tf.keras.datasets.mnist"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "o4_zAVHZUwwR"
   },
   "source": [
    "The Above Code Reflects that the Dataset Contains :\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."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rAIe08hFJ02A"
   },
   "source": [
    "# Dividing the data into train and test data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "id": "VeB1nqygJwN2"
   },
   "outputs": [],
   "source": [
    "(x_train, y_train), (x_test, y_test) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "pD36dm78DGxW",
    "outputId": "c9948824-bbd1-4c43-baee-09732b0fdf2c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 28, 28)\n",
      "(60000,)\n",
      "(10000, 28, 28)\n",
      "(10000,)\n"
     ]
    }
   ],
   "source": [
    "print(x_train.shape)\n",
    "print(y_train.shape)\n",
    "print(x_test.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "z3hDselGDXl0",
    "outputId": "ef3fc442-6f12-4511-819c-1269de0c6037"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "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"
     ]
    }
   ],
   "source": [
    "print(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0M_e6959VAhp",
    "outputId": "613f6375-da7d-4d2d-9301-d410a30ca2a8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "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"
     ]
    }
   ],
   "source": [
    "print(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "k1cDS-mwDebm",
    "outputId": "cb837b47-1e8a-4643-c1d0-aa881652fe6e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 28, 28, 1)\n",
      "(10000, 28, 28, 1)\n",
      "uint8\n",
      "uint8\n"
     ]
    }
   ],
   "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "03cEBHDdMfin",
    "outputId": "c670777d-98f1-4db3-c1a7-25bc9597e22b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "float32\n",
      "float32\n"
     ]
    }
   ],
   "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 218
    },
    "id": "3IQpTDt7MfmB",
    "outputId": "93487693-95ec-499d-b31e-74ea68c749fc"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x300 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "r8tSDEJMMfpG",
    "outputId": "5ba5b7c2-65a7-4c88-c64e-dc88e1e59e5e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28, 28, 1)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# showing shape of single image\n",
    "img_shape= x_train.shape[1:]\n",
    "img_shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vM7k_7G_Mo8t"
   },
   "source": [
    "# BUILDING NEURAL NETWORK THAT CAN READ HANDWRITTEN DIGITS.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "id": "jPdZrt0TMfsP"
   },
   "outputs": [],
   "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",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "seo7yrceMfvd",
    "outputId": "2acfe992-0542-47b5-c994-d8a8a466e3df"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " flatten_1 (Flatten)         (None, 784)               0         \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 128)               100480    \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 128)               0         \n",
      "                                                                 \n",
      " dense_3 (Dense)             (None, 10)                1290      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 101,770\n",
      "Trainable params: 101,770\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mThkQDYIBAcb"
   },
   "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 533
    },
    "id": "z_yaQqG_Mfy_",
    "outputId": "d2731d7e-30ec-4391-bbea-3a19d949f7c4"
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Displaying Neural Network Model\n",
    "from tensorflow.keras.utils import plot_model\n",
    "plot_model(model, 'model.jpg', show_shapes = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ylgovUfbMf2L",
    "outputId": "f68c0b11-d6cc-4b89-c531-0de23bca54ae"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.08819406,  0.02334788,  0.05840464,  0.06903427,  0.5252824 ,\n",
       "        -0.04078927, -0.22387438, -0.43524188, -0.27941954, -0.03243962]],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Making Prediction on Model\n",
    "prediction = model(x_train[:1]).numpy()\n",
    "prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "F5uwn8CdMf4x",
    "outputId": "03474edd-5ebf-4ce1-cda0-b21fa51ba549"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.10846904, 0.10165843, 0.10528546, 0.10641058, 0.16793098,\n",
       "        0.09534305, 0.07939189, 0.06426588, 0.07510229, 0.09614246]],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "id": "5J_iycOwMf7m"
   },
   "outputs": [],
   "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'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5O-pLKT1NA4w"
   },
   "source": [
    "# Model fitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "22uv89DKMf-s",
    "outputId": "f1377698-0dbd-4c78-bf23-9360ab45b250"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "1875/1875 [==============================] - 8s 4ms/step - loss: 0.2960 - accuracy: 0.9140\n",
      "Epoch 2/5\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1439 - accuracy: 0.9571\n",
      "Epoch 3/5\n",
      "1875/1875 [==============================] - 7s 4ms/step - loss: 0.1089 - accuracy: 0.9672\n",
      "Epoch 4/5\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.0890 - accuracy: 0.9727\n",
      "Epoch 5/5\n",
      "1875/1875 [==============================] - 7s 4ms/step - loss: 0.0757 - accuracy: 0.9764\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f41a1286830>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Training the Model\n",
    "model.fit(x_train, y_train, epochs=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "vpV5HdwMMgBp",
    "outputId": "6d0152fe-3718-4b10-d1b7-3a25e351d359"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 - 1s - loss: 0.0752 - accuracy: 0.9770 - 545ms/epoch - 2ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.07518018782138824, 0.9769999980926514]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluating the Model\n",
    "model.evaluate(x_test, y_test, verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "X3EtESLWNINg",
    "outputId": "c3a995a3-9257-4ff9-d1ea-27d8e59c613a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(5, 10), dtype=float32, numpy=\n",
       "array([[1.5023636e-08, 2.8046278e-08, 8.6892478e-06, 2.1114422e-05,\n",
       "        8.8602803e-10, 4.8365621e-08, 6.1451641e-13, 9.9996769e-01,\n",
       "        2.8051858e-07, 2.1160774e-06],\n",
       "       [3.8681939e-08, 4.1496165e-05, 9.9991894e-01, 1.7614640e-05,\n",
       "        4.3373732e-15, 1.6626309e-05, 3.2137373e-06, 9.0283954e-13,\n",
       "        2.0866528e-06, 7.1583733e-13],\n",
       "       [3.7049006e-07, 9.9759489e-01, 1.6454932e-04, 5.1491074e-06,\n",
       "        3.9168852e-05, 6.7521983e-06, 4.3748798e-05, 1.8154874e-03,\n",
       "        3.2646983e-04, 3.4668781e-06],\n",
       "       [9.9989104e-01, 1.0030488e-07, 3.4515320e-05, 4.4909646e-07,\n",
       "        1.5330463e-05, 2.1342439e-06, 2.7114160e-05, 2.8521930e-05,\n",
       "        7.9536186e-08, 5.7907613e-07],\n",
       "       [4.1292051e-06, 6.2117507e-09, 5.1304491e-05, 1.7004332e-07,\n",
       "        9.9647850e-01, 4.2222570e-07, 3.9773659e-06, 8.5955893e-05,\n",
       "        2.9344408e-06, 3.3726362e-03]], dtype=float32)>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 430
    },
    "id": "_N7VsLJkNIQ8",
    "outputId": "6664dc3b-ae67-4882-83ed-e7c1cdbe65db"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Displaying a Grayscale Image\n",
    "img = x_train[12]\n",
    "plt.imshow(np.squeeze(img) ,cmap='gray')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "SXxmSMlLNIUF",
    "outputId": "978f454a-94ac-44e0-9abb-d88bd7e6691f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 55ms/step\n",
      "predicted : 3\n"
     ]
    }
   ],
   "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": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}