Convolutional_Neural_Network_for_Classification_of_handwritten_digits.ipynb

{
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  "metadata": {
    "colab": {
      "name": "Convolutional Neural Network for Classification of handwritten digits.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8sow9f0r1dUe"
      },
      "source": [
        "# Import necessary packages"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l1Pf0_JzJERA"
      },
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras import layers,models\n",
        "from tensorflow import keras\n",
        "import numpy as np"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dUimr__51gb1"
      },
      "source": [
        "# Loading dataset "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZXAgaoRSJOYY"
      },
      "source": [
        "(X_train, y_train) , (X_test, y_test) = keras.datasets.mnist.load_data()"
      ],
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tcnqpvNL1jna"
      },
      "source": [
        "# Scaling the train and test dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t9PM9BiXJa0b"
      },
      "source": [
        "X_train = X_train / 255\n",
        "X_test = X_test / 255"
      ],
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G0hLV_JCJgib"
      },
      "source": [
        "X_train[0]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SFdJ6dQpbuze",
        "outputId": "85cd5262-44d3-49db-f312-d7436d14f114"
      },
      "source": [
        "X_train.shape"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60000, 28, 28)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_oEAnGmoic_a",
        "outputId": "767b51d8-a470-4318-8d7c-51b389b69196"
      },
      "source": [
        "X_train = X_train.reshape(-1,28,28,1)\n",
        "X_train.shape"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60000, 28, 28, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d-6Fx5x5ifSQ",
        "outputId": "8f90402d-2dc4-4a12-a4cc-9bd35b476de4"
      },
      "source": [
        "X_test = X_test.reshape(-1,28,28,1)\n",
        "X_test.shape"
      ],
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000, 28, 28, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JnYFf6la1pY9"
      },
      "source": [
        "# Creating and Training a Convolutional Neural Network"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fk5dFInaJvQe"
      },
      "source": [
        "convolutional_neural_network = models.Sequential([\n",
        "    layers.Conv2D(filters=25, kernel_size=(3, 3), activation='relu', input_shape=(28,28,1)),\n",
        "    layers.MaxPooling2D((2, 2)),\n",
        "    \n",
        "    layers.Conv2D(filters=64, kernel_size=(3, 3), activation='relu'),\n",
        "    layers.MaxPooling2D((2, 2)),\n",
        "\n",
        "    layers.Conv2D(filters=64, kernel_size=(3, 3), activation='relu'),\n",
        "    layers.MaxPooling2D((2, 2)),\n",
        "    \n",
        "    layers.Flatten(),\n",
        "    layers.Dense(64, activation='relu'),\n",
        "    layers.Dense(10, activation='softmax')\n",
        "])"
      ],
      "execution_count": 94,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9_9Lq7Pmd3m9",
        "outputId": "b6d5227e-5a38-4648-e48c-b158d9b40385"
      },
      "source": [
        "convolutional_neural_network.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
        "convolutional_neural_network.fit(X_train, y_train, epochs=10)"
      ],
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "1875/1875 [==============================] - 56s 30ms/step - loss: 0.2263 - accuracy: 0.9293\n",
            "Epoch 2/10\n",
            "1875/1875 [==============================] - 55s 30ms/step - loss: 0.0767 - accuracy: 0.9765\n",
            "Epoch 3/10\n",
            "1875/1875 [==============================] - 56s 30ms/step - loss: 0.0552 - accuracy: 0.9828\n",
            "Epoch 4/10\n",
            "1875/1875 [==============================] - 55s 29ms/step - loss: 0.0442 - accuracy: 0.9859\n",
            "Epoch 5/10\n",
            "1875/1875 [==============================] - 55s 30ms/step - loss: 0.0358 - accuracy: 0.9884\n",
            "Epoch 6/10\n",
            "1875/1875 [==============================] - 55s 30ms/step - loss: 0.0295 - accuracy: 0.9904\n",
            "Epoch 7/10\n",
            "1875/1875 [==============================] - 55s 30ms/step - loss: 0.0244 - accuracy: 0.9920\n",
            "Epoch 8/10\n",
            "1875/1875 [==============================] - 55s 29ms/step - loss: 0.0201 - accuracy: 0.9934\n",
            "Epoch 9/10\n",
            "1875/1875 [==============================] - 55s 29ms/step - loss: 0.0182 - accuracy: 0.9936\n",
            "Epoch 10/10\n",
            "1875/1875 [==============================] - 55s 30ms/step - loss: 0.0157 - accuracy: 0.9947\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f2189125ed0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KL_LvtQt3kKZ"
      },
      "source": [
        "# Evaluating the CNN model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0J7O7tT9J2x7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "acceefbe-707f-4e3f-fef9-a6f83e3a4100"
      },
      "source": [
        "convolutional_neural_network.evaluate(X_test, y_test)"
      ],
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "313/313 [==============================] - 3s 10ms/step - loss: 0.0565 - accuracy: 0.9847\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.05653385445475578, 0.9847000241279602]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hxaNlM7V3tko"
      },
      "source": [
        "# Making Predictions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "olPSi4-3J5fR",
        "outputId": "cfd07fad-a03c-43af-a39a-7d2428a668d4"
      },
      "source": [
        "y_predicted_by_model = convolutional_neural_network.predict(X_test)\n",
        "y_predicted_by_model[0]   #getting probability score for each class digits"
      ],
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1.2530975e-07, 1.2117318e-06, 4.6432956e-07, 6.7424160e-08,\n",
              "       6.1667479e-06, 1.0952139e-08, 1.5022253e-12, 9.9998653e-01,\n",
              "       8.2066890e-08, 5.3035865e-06], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C_TUt8iYJ_uv",
        "outputId": "3f6c5565-fa90-4d30-ec39-5efe85581801"
      },
      "source": [
        "np.argmax(y_predicted[0])"
      ],
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pu62ekGXKC5g"
      },
      "source": [
        "y_predicted_labels = [np.argmax(i) for i in y_predicted]"
      ],
      "execution_count": 101,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5cD33oTFKGDA",
        "outputId": "fab861a9-3ba1-4e27-f963-17a326e6c94c"
      },
      "source": [
        "y_predicted_labels[:5]"
      ],
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[7, 2, 1, 0, 4]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 102
        }
      ]
    }
  ]
}