ciafr10ann.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNFA2PLucdU2OT3ZfGz3du6",
      "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/Adarsh-N123/CIFAR-10-ANN/blob/main/ciafr10ann.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": "ZWUl7k_QVAIx"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tf.config.experimental.list_physical_devices()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_MYJI2IoWUgH",
        "outputId": "b17643ad-39db-4503-adc3-6bb6668670ff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU'),\n",
              " PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tf.print(tf.__version__)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OTIk45i3We4q",
        "outputId": "da411641-a6bd-419b-8cfa-d34ea26819f7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.9.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tf.test.is_built_with_cuda()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pIfmevLYWq7v",
        "outputId": "d15634db-9eae-47e3-b8f5-6c2b378ddb37"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "(x_train,y_train),(x_test,y_test) = tf.keras.datasets.cifar10.load_data()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SDs1YzTRYFXL",
        "outputId": "6980081a-7b79-4d5c-ad3c-7d26ce4f4e54"
      },
      "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",
            "170498071/170498071 [==============================] - 15s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N58ZiVn9YpmV",
        "outputId": "8b11ae4b-b6ea-46a6-d271-aeff2679800e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(50000, 32, 32, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9PdkQwgkY6Ar",
        "outputId": "eed7bbdb-fbba-4ae6-cdb3-0f8032e66c78"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(50000, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def plotsample(index):\n",
        "  plt.figure(figsize=(20,2))\n",
        "  plt.imshow(x_train[index])"
      ],
      "metadata": {
        "id": "OfvwIupiY-dF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plotsample(4)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 159
        },
        "id": "hsB67XWDZAPA",
        "outputId": "01b71d00-430b-48ab-ddd4-0576964e768b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x144 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "classes=[\"airplane\",\"automobile\",\"bird\",\"cat\",\"deer\",\"dog\",\"frog\",\"horse\",\"ship\",\"truck\"]"
      ],
      "metadata": {
        "id": "z0yPThUhZl2G"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "classes[y_train[4][0]]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "I0GBJDNTZ8eW",
        "outputId": "70d9f3e3-3a23-4594-9f7b-db790756b9fe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'automobile'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train = x_train/255\n",
        "x_test = x_test/255"
      ],
      "metadata": {
        "id": "7gXiNHSbaHe5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_train_cat = keras.utils.to_categorical(\n",
        "    y_train, num_classes=10,dtype=\"float32\"\n",
        ")"
      ],
      "metadata": {
        "id": "-koq-SMnaT1g"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_test_cat = keras.utils.to_categorical(\n",
        "    y_test, num_classes=10,dtype=\"float32\"\n",
        ")"
      ],
      "metadata": {
        "id": "DisriNKCawEW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_test_cat"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tFYUSysFa-Jg",
        "outputId": "cacaf385-1d19-4078-d20a-4d09fc58074b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0., 0., 0., ..., 0., 0., 0.],\n",
              "       [0., 0., 0., ..., 0., 1., 0.],\n",
              "       [0., 0., 0., ..., 0., 1., 0.],\n",
              "       ...,\n",
              "       [0., 0., 0., ..., 0., 0., 0.],\n",
              "       [0., 1., 0., ..., 0., 0., 0.],\n",
              "       [0., 0., 0., ..., 1., 0., 0.]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "uA9wSRjobWzq"
      },
      "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(1000,activation=\"relu\"),\n",
        "    keras.layers.Dense(10,activation=\"sigmoid\")\n",
        "])\n",
        "\n",
        "model.compile(\n",
        "    optimizer=\"SGD\",\n",
        "    loss=\"categorical_crossentropy\",\n",
        "    metrics=[\"accuracy\"]\n",
        "\n",
        ")\n",
        "model.fit(x_train,y_train_cat,epochs=50)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wgYIGDmVa_xK",
        "outputId": "ace708a0-2bcf-4b83-e55d-2a68ea3e56ed"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "1563/1563 [==============================] - 8s 4ms/step - loss: 1.8103 - accuracy: 0.3546\n",
            "Epoch 2/50\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 1.6256 - accuracy: 0.4258\n",
            "Epoch 3/50\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 1.5430 - accuracy: 0.4557\n",
            "Epoch 4/50\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 1.4825 - accuracy: 0.4779\n",
            "Epoch 5/50\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 1.4335 - accuracy: 0.4952\n",
            "Epoch 6/50\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 1.3892 - accuracy: 0.5118\n",
            "Epoch 7/50\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 1.3505 - accuracy: 0.5255\n",
            "Epoch 8/50\n",
            "1563/1563 [==============================] - 7s 4ms/step - loss: 1.3174 - accuracy: 0.5367\n",
            "Epoch 9/50\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 1.2852 - accuracy: 0.5496\n",
            "Epoch 10/50\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 1.2531 - accuracy: 0.5592\n",
            "Epoch 11/50\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 1.2244 - accuracy: 0.5702\n",
            "Epoch 12/50\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 1.1961 - accuracy: 0.5793\n",
            "Epoch 13/50\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 1.1677 - accuracy: 0.5921\n",
            "Epoch 14/50\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 1.1433 - accuracy: 0.6001\n",
            "Epoch 15/50\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 1.1127 - accuracy: 0.6129\n",
            "Epoch 16/50\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 1.0892 - accuracy: 0.6205\n",
            "Epoch 17/50\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 1.0613 - accuracy: 0.6297\n",
            "Epoch 18/50\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 1.0376 - accuracy: 0.6356\n",
            "Epoch 19/50\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 1.0093 - accuracy: 0.6491\n",
            "Epoch 20/50\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 0.9862 - accuracy: 0.6566\n",
            "Epoch 21/50\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 0.9550 - accuracy: 0.6704\n",
            "Epoch 22/50\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 0.9368 - accuracy: 0.6779\n",
            "Epoch 23/50\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 0.9086 - accuracy: 0.6851\n",
            "Epoch 24/50\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 0.8819 - accuracy: 0.6968\n",
            "Epoch 25/50\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 0.8589 - accuracy: 0.7047\n",
            "Epoch 26/50\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 0.8339 - accuracy: 0.7122\n",
            "Epoch 27/50\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 0.8086 - accuracy: 0.7218\n",
            "Epoch 28/50\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 0.7808 - accuracy: 0.7338\n",
            "Epoch 29/50\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 0.7601 - accuracy: 0.7396\n",
            "Epoch 30/50\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 0.7338 - accuracy: 0.7496\n",
            "Epoch 31/50\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 0.7076 - accuracy: 0.7601\n",
            "Epoch 32/50\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 0.6804 - accuracy: 0.7706\n",
            "Epoch 33/50\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 0.6564 - accuracy: 0.7782\n",
            "Epoch 34/50\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 0.6353 - accuracy: 0.7857\n",
            "Epoch 35/50\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 0.6081 - accuracy: 0.7966\n",
            "Epoch 36/50\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 0.5861 - accuracy: 0.8048\n",
            "Epoch 37/50\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 0.5605 - accuracy: 0.8130\n",
            "Epoch 38/50\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 0.5379 - accuracy: 0.8224\n",
            "Epoch 39/50\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 0.5134 - accuracy: 0.8300\n",
            "Epoch 40/50\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 0.4944 - accuracy: 0.8381\n",
            "Epoch 41/50\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 0.4706 - accuracy: 0.8489\n",
            "Epoch 42/50\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 0.4517 - accuracy: 0.8542\n",
            "Epoch 43/50\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 0.4299 - accuracy: 0.8628\n",
            "Epoch 44/50\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 0.4108 - accuracy: 0.8699\n",
            "Epoch 45/50\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 0.3874 - accuracy: 0.8785\n",
            "Epoch 46/50\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 0.3738 - accuracy: 0.8828\n",
            "Epoch 47/50\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 0.3526 - accuracy: 0.8899\n",
            "Epoch 48/50\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 0.3321 - accuracy: 0.8984\n",
            "Epoch 49/50\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 0.3132 - accuracy: 0.9061\n",
            "Epoch 50/50\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 0.2965 - accuracy: 0.9095\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fa7d4050310>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.evaluate(x_test,y_test_cat)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jyHOlDvnca3W",
        "outputId": "af076cf9-d3f0-4313-9716-60fed1ec2852"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 3ms/step - loss: 1.5922 - accuracy: 0.5583\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1.5921870470046997, 0.5583000183105469]"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "QNlhrn7sf_nk"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}