Image_Classifaction_with_Convolution_Neural_Network.ipynb

{
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  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "authorship_tag": "ABX9TyPMPTfAxUGt5NRo3TRltJSP",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/MudassirABBASSi/Age-Prediction/blob/main/Image_Classifaction_with_Convolution_Neural_Network.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Lfo72jj-qDPR"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from tensorflow.keras import layers"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "(x_train, y_train), (x_test, y_test) =tf.keras.datasets.cifar10.load_data()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PJW-Z30lqqo8",
        "outputId": "0b596044-ae53-450a-fe8c-fbb0a18ebf34"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\n",
            "\u001b[1m170498071/170498071\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "plHr30fjrLPU",
        "outputId": "8707b04e-9cb2-48d9-94e0-b52f5bc9dc15"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(50000, 32, 32, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GSWXwKi7ratH",
        "outputId": "6c07a30e-22c0-4565-d58b-5ea47c3e4fd3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(50000, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PLngZuWHrcFW",
        "outputId": "7dcedab6-2d14-4539-f3ed-299dfe884dfd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000, 32, 32, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uJ0zvMuorfNY",
        "outputId": "c9e71446-9858-4f5d-cfbd-9a5e31c55485"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# y_trian is in 2D array in shape\n",
        "y_train[:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cuWNHjLWuy5Z",
        "outputId": "1220dded-7bb7-4d29-a876-0ecf0384b8b7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[6],\n",
              "       [9],\n",
              "       [9],\n",
              "       [4],\n",
              "       [1]], dtype=uint8)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#  we have to  convert it into 1D array\n",
        "y_train = y_train.reshape(-1)\n",
        "y_train[:5]\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GhAVx29rrwXC",
        "outputId": "91b79c32-b3d6-4bfd-c281-ccafa064045d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([6, 9, 9, 4, 1], dtype=uint8)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# same for y_test\n",
        "y_test  = y_test.reshape(-1)\n",
        "y_test[:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SSMPMlT3uidl",
        "outputId": "6057c235-516b-4d89-ec6e-5d05586fa978"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([3, 8, 8, 0, 6], dtype=uint8)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def simple(index):\n",
        "  plt.figure(figsize=(2,2))\n",
        "  plt.imshow(x_train[index]) # Use indexing correctly: x_train[index]\n",
        "  plt.show()\n",
        "\n"
      ],
      "metadata": {
        "id": "xRPYJVszvWwN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "simple(1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 217
        },
        "id": "-_zUPBE8vqR0",
        "outputId": "768f919a-7f41-4bcd-a637-4cf245d5b65a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 200x200 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train =  x_train/255\n",
        "x_test = x_test/255"
      ],
      "metadata": {
        "id": "p0eScjNWvrjW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = keras.Sequential([\n",
        "    keras.layers.Flatten(input_shape=(32, 32, 3)),\n",
        "    keras.layers.Dense(3000, activation='relu'),\n",
        "    keras.layers.Dense(10, activation='softmax')\n",
        "])\n",
        "\n",
        "model.compile(optimizer='SGD',\n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "model.fit(x_train, y_train, epochs=10)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nlj5ZwmSwExZ",
        "outputId": "be671001-605b-43e9-9ddc-8c9530519606"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.11/dist-packages/keras/src/layers/reshaping/flatten.py:37: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
            "  super().__init__(**kwargs)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 9ms/step - accuracy: 0.3028 - loss: 1.9482\n",
            "Epoch 2/10\n",
            "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - accuracy: 0.4177 - loss: 1.6694\n",
            "Epoch 3/10\n",
            "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 4ms/step - accuracy: 0.4452 - loss: 1.5838\n",
            "Epoch 4/10\n",
            "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - accuracy: 0.4692 - loss: 1.5216\n",
            "Epoch 5/10\n",
            "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.4823 - loss: 1.4825\n",
            "Epoch 6/10\n",
            "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 5ms/step - accuracy: 0.4955 - loss: 1.4428\n",
            "Epoch 7/10\n",
            "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 5ms/step - accuracy: 0.5144 - loss: 1.4058\n",
            "Epoch 8/10\n",
            "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 4ms/step - accuracy: 0.5181 - loss: 1.3786\n",
            "Epoch 9/10\n",
            "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 3ms/step - accuracy: 0.5320 - loss: 1.3522\n",
            "Epoch 10/10\n",
            "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 3ms/step - accuracy: 0.5365 - loss: 1.3281\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.history.History at 0x7b13da933750>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ann = keras.Sequential([\n",
        "    keras.layers.Flatten(input_shape=(32, 32, 3)),\n",
        "    keras.layers.Dense(3000, activation='relu'),\n",
        "    keras.layers.Dense(10, activation='softmax')\n",
        "])\n",
        "\n",
        "ann.compile(\n",
        "    optimizer='adam',\n",
        "    loss='sparse_categorical_crossentropy',\n",
        "    metrics=['accuracy']\n",
        ")\n",
        "\n",
        "ann.fit(x_train, y_train, epochs=10, batch_size=32, validation_split=0.1)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9-58HJOrwwVd",
        "outputId": "3fd26720-4e15-4077-8ddd-11707d9b957d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.11/dist-packages/keras/src/layers/reshaping/flatten.py:37: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
            "  super().__init__(**kwargs)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "\u001b[1m1407/1407\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 5ms/step - accuracy: 0.2826 - loss: 2.5315 - val_accuracy: 0.3390 - val_loss: 1.8085\n",
            "Epoch 2/10\n",
            "\u001b[1m1407/1407\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 7ms/step - accuracy: 0.3806 - loss: 1.7210 - val_accuracy: 0.4258 - val_loss: 1.6340\n",
            "Epoch 3/10\n",
            "\u001b[1m1407/1407\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 4ms/step - accuracy: 0.4220 - loss: 1.6299 - val_accuracy: 0.4290 - val_loss: 1.6054\n",
            "Epoch 4/10\n",
            "\u001b[1m1407/1407\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 4ms/step - accuracy: 0.4369 - loss: 1.5800 - val_accuracy: 0.4336 - val_loss: 1.5757\n",
            "Epoch 5/10\n",
            "\u001b[1m1407/1407\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 4ms/step - accuracy: 0.4506 - loss: 1.5364 - val_accuracy: 0.4570 - val_loss: 1.5476\n",
            "Epoch 6/10\n",
            "\u001b[1m1407/1407\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 5ms/step - accuracy: 0.4636 - loss: 1.5178 - val_accuracy: 0.4554 - val_loss: 1.5269\n",
            "Epoch 7/10\n",
            "\u001b[1m1407/1407\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 4ms/step - accuracy: 0.4691 - loss: 1.4915 - val_accuracy: 0.4718 - val_loss: 1.5011\n",
            "Epoch 8/10\n",
            "\u001b[1m1407/1407\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 5ms/step - accuracy: 0.4808 - loss: 1.4666 - val_accuracy: 0.4720 - val_loss: 1.4977\n",
            "Epoch 9/10\n",
            "\u001b[1m1407/1407\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 4ms/step - accuracy: 0.4853 - loss: 1.4366 - val_accuracy: 0.4526 - val_loss: 1.5562\n",
            "Epoch 10/10\n",
            "\u001b[1m1407/1407\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 4ms/step - accuracy: 0.4904 - loss: 1.4309 - val_accuracy: 0.4694 - val_loss: 1.5050\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.history.History at 0x7b13d9a4fcd0>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix, classification_report\n",
        "\n",
        "y_pred = model.predict(x_test)\n",
        "y_pred_classes = [tf.argmax(element).numpy() for element in y_pred]\n",
        "\n",
        "print(\"Classification Report: \\n\", classification_report(y_test, y_pred_classes))\n",
        "\n",
        "conf_mat = confusion_matrix(y_test, y_pred_classes)\n",
        "print(\"Confusion Matrix: \\n\", conf_mat)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TAYAStN_zxQY",
        "outputId": "59d34881-8b6e-4996-9bae-10a46bb5453c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step\n",
            "Classification Report: \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.06      0.01      0.01      1000\n",
            "           1       0.11      0.04      0.05      1000\n",
            "           2       0.06      0.01      0.02      1000\n",
            "           3       0.07      0.14      0.09      1000\n",
            "           4       0.10      0.59      0.17      1000\n",
            "           5       0.12      0.05      0.07      1000\n",
            "           6       0.12      0.00      0.00      1000\n",
            "           7       0.13      0.04      0.06      1000\n",
            "           8       0.02      0.02      0.02      1000\n",
            "           9       0.00      0.00      0.00      1000\n",
            "\n",
            "    accuracy                           0.09     10000\n",
            "   macro avg       0.08      0.09      0.05     10000\n",
            "weighted avg       0.08      0.09      0.05     10000\n",
            "\n",
            "Confusion Matrix: \n",
            " [[  6   9   8 172 762  16   1  14  12   0]\n",
            " [ 31  36  25 256 482  57   1  53  59   0]\n",
            " [  0  24  13 191 630  26   0  27  89   0]\n",
            " [  4  52  31 136 559  72   1  32 113   0]\n",
            " [  3  13  23 142 587  40   1  51 140   0]\n",
            " [  8  83  22 116 573  51   2  29 116   0]\n",
            " [  4  51  39 176 488  49   1  27 165   0]\n",
            " [ 12  29  29 206 568  49   1  42  64   0]\n",
            " [ 16  24  16 281 583  40   0  21  19   0]\n",
            " [ 14  19  21 366 500  39   0  19  22   0]]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.11/dist-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
            "/usr/local/lib/python3.11/dist-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
            "/usr/local/lib/python3.11/dist-packages/sklearn/metrics/_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Convolutional Neural Network"
      ],
      "metadata": {
        "id": "t1GrGyU8xk20"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cnn = keras.Sequential([\n",
        "    layers.Conv2D(filters=32, kernel_size=(3, 3), activation='relu', input_shape=(32, 32, 3)),\n",
        "    layers.MaxPooling2D((2, 2)),\n",
        "    layers.Flatten(),\n",
        "    layers.Dense(3000, activation='relu'),\n",
        "    layers.Dense(10, activation='softmax') # Use softmax for multi-class classification\n",
        "])\n",
        "\n",
        "cnn.compile (\n",
        "    optimizer='adam',\n",
        "    loss='sparse_categorical_crossentropy',\n",
        "    metrics=['accuracy']\n",
        ")\n",
        "\n",
        "cnn.fit(x_train, y_train, epochs=5)\n",
        "\n",
        "y_pred = cnn.predict(x_test)\n",
        "y_pred_classes = [tf.argmax(element).numpy() for element in y_pred]\n",
        "\n",
        "print(\"Classification Report: \\n\", classification_report(y_test, y_pred_classes))\n",
        "\n",
        "conf_mat = confusion_matrix(y_test, y_pred_classes)\n",
        "print(\"Confusion Matrix: \\n\", conf_mat)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3NkWOMcDxkW9",
        "outputId": "42cfcdc3-1b1b-4477-a98a-3a403535f37e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 8ms/step - accuracy: 0.4233 - loss: 1.7102\n",
            "Epoch 2/5\n",
            "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 7ms/step - accuracy: 0.6369 - loss: 1.0333\n",
            "Epoch 3/5\n",
            "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 8ms/step - accuracy: 0.7152 - loss: 0.8095\n",
            "Epoch 4/5\n",
            "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 7ms/step - accuracy: 0.7945 - loss: 0.5895\n",
            "Epoch 5/5\n",
            "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 7ms/step - accuracy: 0.8693 - loss: 0.3897\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step\n",
            "Classification Report: \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.59      0.81      0.68      1000\n",
            "           1       0.76      0.82      0.79      1000\n",
            "           2       0.57      0.52      0.54      1000\n",
            "           3       0.45      0.51      0.48      1000\n",
            "           4       0.74      0.48      0.58      1000\n",
            "           5       0.54      0.57      0.56      1000\n",
            "           6       0.75      0.73      0.74      1000\n",
            "           7       0.72      0.73      0.73      1000\n",
            "           8       0.84      0.74      0.79      1000\n",
            "           9       0.77      0.72      0.74      1000\n",
            "\n",
            "    accuracy                           0.66     10000\n",
            "   macro avg       0.67      0.66      0.66     10000\n",
            "weighted avg       0.67      0.66      0.66     10000\n",
            "\n",
            "Confusion Matrix: \n",
            " [[805  21  48  26   8   7   6  12  29  38]\n",
            " [ 47 817   5  10   0   4   2   5  29  81]\n",
            " [118  10 517  84  48  88  74  40  11  10]\n",
            " [ 47  23  70 510  36 192  50  40  13  19]\n",
            " [ 59   7 116 106 478  61  69  90  14   0]\n",
            " [ 31   8  59 204  25 573  21  61   9   9]\n",
            " [ 28  16  44 104  21  36 729  10   5   7]\n",
            " [ 38   5  31  53  31  82   4 733   5  18]\n",
            " [121  45  15  25   0  12   3   4 741  34]\n",
            " [ 67 128   5  19   2   8   8  21  25 717]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cnn.evaluate(x_test ,y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iXOLb15f2DgJ",
        "outputId": "8aa48e5f-02bd-4489-e62e-f6271b667471"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.6593 - loss: 1.1005\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1.1169712543487549, 0.6620000004768372]"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cnn1 = keras.Sequential([\n",
        "    layers.Conv2D(filters=32, kernel_size=(3, 3), padding='valid',strides=(2,2), activation='relu', input_shape=(32, 32, 3)),\n",
        "    layers.MaxPooling2D((2, 2)),\n",
        "    layers.Flatten(),\n",
        "    layers.Dense(3000, activation='relu'),\n",
        "    layers.Dense(10, activation='softmax') # Use softmax for multi-class classification\n",
        "])\n",
        "\n",
        "cnn1.compile (\n",
        "    optimizer='adam',\n",
        "    loss='sparse_categorical_crossentropy',\n",
        "    metrics=['accuracy']\n",
        ")\n",
        "\n",
        "cnn1.fit(x_train, y_train, epochs=2)\n",
        "print (\"elevation will be\")\n",
        "cnn1.evaluate(x_test,y_test)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eJsNW00q3Mhu",
        "outputId": "74b06b24-3df1-41f5-e8c2-ebdae42685ed"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/2\n",
            "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 4ms/step - accuracy: 0.4144 - loss: 1.6292\n",
            "Epoch 2/2\n",
            "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 4ms/step - accuracy: 0.5941 - loss: 1.1436\n",
            "elevation will be\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.6085 - loss: 1.1048\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1.1128829717636108, 0.6061000227928162]"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cnn1 = keras.Sequential([\n",
        "    layers.Conv2D(filters=32, kernel_size=(3, 3), padding='same',strides=(2,2), activation='relu', input_shape=(32, 32, 3)),\n",
        "    layers.MaxPooling2D((2, 2)),\n",
        "    layers.Flatten(),\n",
        "    layers.Dense(3000, activation='relu'),\n",
        "    layers.Dense(10, activation='softmax') # Use softmax for multi-class classification\n",
        "])\n",
        "\n",
        "cnn1.compile (\n",
        "    optimizer='adam',\n",
        "    loss='sparse_categorical_crossentropy',\n",
        "    metrics=['accuracy']\n",
        ")\n",
        "\n",
        "cnn1.fit(x_train, y_train, epochs=2)\n",
        "print (\"elevation will be\")\n",
        "cnn1.evaluate(x_test,y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o-o5kXch5Ddt",
        "outputId": "924ed0d5-06d3-4ffe-c532-a5cdfe4e037b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/2\n",
            "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 4ms/step - accuracy: 0.4100 - loss: 1.6453\n",
            "Epoch 2/2\n",
            "\u001b[1m1563/1563\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 4ms/step - accuracy: 0.6120 - loss: 1.0979\n",
            "elevation will be\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.6283 - loss: 1.0475\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1.0609538555145264, 0.6245999932289124]"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "4PyLHCd_63qf"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}