CIFAR-10 Using ANN.ipynb

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  "metadata": {
    "colab": {
      "name": "CIFAR-10 using ANN.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "R6PoDOqitrOp"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import Sequential\n",
        "from tensorflow.keras.layers import Dense, Flatten\n",
        "from tensorflow.keras.optimizers import Adam, SGD"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AwrYCxIZupOK"
      },
      "source": [
        "(X_train ,y_train), (X_test, y_test) = keras.datasets.cifar10.load_data()"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oUlVs1k3uxC4",
        "outputId": "ebd13122-1e6d-443e-8cc4-b8aa9fd6e266"
      },
      "source": [
        "X_train.shape"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(50000, 32, 32, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sKrR5fP2uzod",
        "outputId": "beb11b25-f31e-408f-f264-ae664ce1dfdb"
      },
      "source": [
        "y_train.shape"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(50000, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kuu8hPozu0fu",
        "outputId": "cf7e6b05-8a5a-431e-a96c-0c5c1b08ada1"
      },
      "source": [
        "X_train[0]"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[ 59,  62,  63],\n",
              "        [ 43,  46,  45],\n",
              "        [ 50,  48,  43],\n",
              "        ...,\n",
              "        [158, 132, 108],\n",
              "        [152, 125, 102],\n",
              "        [148, 124, 103]],\n",
              "\n",
              "       [[ 16,  20,  20],\n",
              "        [  0,   0,   0],\n",
              "        [ 18,   8,   0],\n",
              "        ...,\n",
              "        [123,  88,  55],\n",
              "        [119,  83,  50],\n",
              "        [122,  87,  57]],\n",
              "\n",
              "       [[ 25,  24,  21],\n",
              "        [ 16,   7,   0],\n",
              "        [ 49,  27,   8],\n",
              "        ...,\n",
              "        [118,  84,  50],\n",
              "        [120,  84,  50],\n",
              "        [109,  73,  42]],\n",
              "\n",
              "       ...,\n",
              "\n",
              "       [[208, 170,  96],\n",
              "        [201, 153,  34],\n",
              "        [198, 161,  26],\n",
              "        ...,\n",
              "        [160, 133,  70],\n",
              "        [ 56,  31,   7],\n",
              "        [ 53,  34,  20]],\n",
              "\n",
              "       [[180, 139,  96],\n",
              "        [173, 123,  42],\n",
              "        [186, 144,  30],\n",
              "        ...,\n",
              "        [184, 148,  94],\n",
              "        [ 97,  62,  34],\n",
              "        [ 83,  53,  34]],\n",
              "\n",
              "       [[177, 144, 116],\n",
              "        [168, 129,  94],\n",
              "        [179, 142,  87],\n",
              "        ...,\n",
              "        [216, 184, 140],\n",
              "        [151, 118,  84],\n",
              "        [123,  92,  72]]], dtype=uint8)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UH6moot0u2O5",
        "outputId": "31a8094d-15c3-4021-c27d-d1cc5b3ec575"
      },
      "source": [
        "y_train[:5]"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[6],\n",
              "       [9],\n",
              "       [9],\n",
              "       [4],\n",
              "       [1]], dtype=uint8)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hFrUS3NMv_sy"
      },
      "source": [
        "X_train_sc = X_train/255.0\n",
        "X_test_sc = X_test/255.0"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sd7CN6hku3Dz"
      },
      "source": [
        "def show_img(ind):\n",
        "    plt.imshow(X_train[ind])\n",
        "    return plt.show()"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266
        },
        "id": "AGsAWka1u-lx",
        "outputId": "7266eea9-c27b-4c14-8463-210003887102"
      },
      "source": [
        "show_img(1)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-46EvB5hu_qB"
      },
      "source": [
        "def build_model():\n",
        "    model = Sequential([\n",
        "                        Flatten(input_shape = (32,32,3)),\n",
        "                        Dense(32, activation = 'relu'),\n",
        "                        Dense(16, activation = 'relu'),\n",
        "                        Dense(8, activation = 'relu'),\n",
        "                        Dense(10, activation ='sigmoid')\n",
        "    ])\n",
        "\n",
        "    model.compile(\n",
        "        optimizer = Adam(learning_rate = 0.01),\n",
        "        loss = 'sparse_categorical_crossentropy',\n",
        "        metrics = ['accuracy']\n",
        "    )\n",
        "\n",
        "    return model"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pZT7vrG33mNB"
      },
      "source": [
        "model = build_model()"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8_Sxd1YHvvNI",
        "outputId": "bab749ef-d13b-4f0d-80a8-e84643d03020"
      },
      "source": [
        "model.fit(X_train_sc, y_train, epochs = 10, validation_split = 0.2)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "1250/1250 [==============================] - 5s 4ms/step - loss: 2.2491 - accuracy: 0.1277 - val_loss: 2.0840 - val_accuracy: 0.1863\n",
            "Epoch 2/10\n",
            "1250/1250 [==============================] - 4s 3ms/step - loss: 2.0735 - accuracy: 0.1828 - val_loss: 2.0560 - val_accuracy: 0.1890\n",
            "Epoch 3/10\n",
            "1250/1250 [==============================] - 4s 3ms/step - loss: 2.0570 - accuracy: 0.1868 - val_loss: 2.0585 - val_accuracy: 0.1812\n",
            "Epoch 4/10\n",
            "1250/1250 [==============================] - 4s 3ms/step - loss: 2.0508 - accuracy: 0.1907 - val_loss: 2.0477 - val_accuracy: 0.1875\n",
            "Epoch 5/10\n",
            "1250/1250 [==============================] - 4s 3ms/step - loss: 2.0483 - accuracy: 0.1890 - val_loss: 2.0444 - val_accuracy: 0.1868\n",
            "Epoch 6/10\n",
            "1250/1250 [==============================] - 4s 4ms/step - loss: 2.0482 - accuracy: 0.1902 - val_loss: 2.0474 - val_accuracy: 0.1893\n",
            "Epoch 7/10\n",
            "1250/1250 [==============================] - 4s 4ms/step - loss: 2.0436 - accuracy: 0.1891 - val_loss: 2.0489 - val_accuracy: 0.1846\n",
            "Epoch 8/10\n",
            "1250/1250 [==============================] - 4s 3ms/step - loss: 2.0414 - accuracy: 0.1903 - val_loss: 2.0517 - val_accuracy: 0.1874\n",
            "Epoch 9/10\n",
            "1250/1250 [==============================] - 4s 4ms/step - loss: 2.0431 - accuracy: 0.1899 - val_loss: 2.0758 - val_accuracy: 0.1820\n",
            "Epoch 10/10\n",
            "1250/1250 [==============================] - 4s 3ms/step - loss: 2.0402 - accuracy: 0.1884 - val_loss: 2.0743 - val_accuracy: 0.1846\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f7796f21f50>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "id": "iU7rZZkkwIn-",
        "outputId": "43d987cf-576e-47cd-c886-45c8e7028856"
      },
      "source": [
        "plt.plot(model.history.history['accuracy'])\n",
        "plt.plot(model.history.history['val_accuracy'])\n",
        "plt.title('model accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()\n",
        "\n",
        "\n",
        "plt.plot(model.history.history['loss'])\n",
        "plt.plot(model.history.history['val_loss'])\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}