CIFAR 10(CNN)/CIFAR_10_CNN_.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "ZfooSfcGMKjx"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras import datasets, layers, models\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "(train_imgs, train_labels), (test_imgs, test_labels) = datasets.cifar10.load_data()\n",
        "train_imgs, test_imgs = train_imgs / 255.0, test_imgs / 255.0"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O8z7Ic6nMjXN",
        "outputId": "f6b5fa26-e940-408c-c637-e47368ae1d3a"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\n",
            "170498071/170498071 [==============================] - 3s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = models.Sequential()"
      ],
      "metadata": {
        "id": "WJZ0ozjWMmA1"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)))\n",
        "model.add(layers.MaxPooling2D((2, 2)))\n",
        "\n",
        "model.add(layers.Conv2D(64, (3, 3), activation='relu'))\n",
        "model.add(layers.MaxPooling2D((2, 2)))\n",
        "\n",
        "model.add(layers.Conv2D(128, (3, 3), activation='relu'))\n",
        "model.add(layers.MaxPooling2D((2, 2)))\n",
        "\n",
        "model.add(layers.Flatten())\n",
        "\n",
        "model.add(layers.Dense(512, activation='relu'))\n",
        "model.add(layers.Dropout(0.5))\n",
        "\n",
        "model.add(layers.Dense(256, activation='relu'))\n",
        "model.add(layers.Dropout(0.5))\n",
        "\n",
        "model.add(layers.Dense(10))"
      ],
      "metadata": {
        "id": "2s-C-Y3QMpz1"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xXRsDD9GMrlZ",
        "outputId": "b37c141f-e074-41b8-b7b3-1f361b835d3b"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d (Conv2D)             (None, 30, 30, 32)        896       \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2D  (None, 15, 15, 32)       0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 13, 13, 64)        18496     \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPooling  (None, 6, 6, 64)         0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 4, 4, 128)         73856     \n",
            "                                                                 \n",
            " max_pooling2d_2 (MaxPooling  (None, 2, 2, 128)        0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 512)               0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 512)               262656    \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 512)               0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 256)               131328    \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 256)               0         \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 10)                2570      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 489,802\n",
            "Trainable params: 489,802\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer='adam',\n",
        "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
        "              metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "YpqfwYKoMxXk"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(train_imgs, train_labels, epochs=10,\n",
        "                    validation_data=(test_imgs, test_labels))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4bpRMcbjM1X6",
        "outputId": "c98a9351-183e-4ba8-9ded-c3524469a5bd"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "1563/1563 [==============================] - 104s 66ms/step - loss: 0.8963 - accuracy: 0.6926 - val_loss: 0.9231 - val_accuracy: 0.6772\n",
            "Epoch 2/10\n",
            "1563/1563 [==============================] - 96s 62ms/step - loss: 0.8418 - accuracy: 0.7110 - val_loss: 0.9012 - val_accuracy: 0.6882\n",
            "Epoch 3/10\n",
            "1563/1563 [==============================] - 96s 61ms/step - loss: 0.7944 - accuracy: 0.7263 - val_loss: 0.8695 - val_accuracy: 0.6983\n",
            "Epoch 4/10\n",
            "1563/1563 [==============================] - 96s 62ms/step - loss: 0.7592 - accuracy: 0.7391 - val_loss: 0.8765 - val_accuracy: 0.7054\n",
            "Epoch 5/10\n",
            "1563/1563 [==============================] - 100s 64ms/step - loss: 0.7162 - accuracy: 0.7522 - val_loss: 0.8570 - val_accuracy: 0.7134\n",
            "Epoch 6/10\n",
            "1563/1563 [==============================] - 97s 62ms/step - loss: 0.6836 - accuracy: 0.7657 - val_loss: 0.8493 - val_accuracy: 0.7140\n",
            "Epoch 7/10\n",
            "1563/1563 [==============================] - 94s 60ms/step - loss: 0.6539 - accuracy: 0.7746 - val_loss: 0.8418 - val_accuracy: 0.7155\n",
            "Epoch 8/10\n",
            "1563/1563 [==============================] - 95s 61ms/step - loss: 0.6271 - accuracy: 0.7831 - val_loss: 0.8814 - val_accuracy: 0.7115\n",
            "Epoch 9/10\n",
            "1563/1563 [==============================] - 96s 62ms/step - loss: 0.6147 - accuracy: 0.7880 - val_loss: 0.9273 - val_accuracy: 0.7042\n",
            "Epoch 10/10\n",
            "1563/1563 [==============================] - 94s 60ms/step - loss: 0.5919 - accuracy: 0.7950 - val_loss: 0.8694 - val_accuracy: 0.7163\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(history.history['accuracy'], label='accuracy')\n",
        "plt.plot(history.history['val_accuracy'], label = 'val_accuracy')\n",
        "plt.legend(loc='lower right')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 451
        },
        "id": "vbajmLxwSFCl",
        "outputId": "a5e665ef-3e0e-4cea-d197-bc6ebbe5e2f4"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7e523a352e30>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}