Cifar_10.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Cifar - 10.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyPVB1FaSOdi1fFQQn1UJMkh",
      "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/diazameer/Image-Classification/blob/main/Cifar_10.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9B8t5SN37JEU",
        "outputId": "f6f7c98a-a88a-4f67-c85e-c99fb7da481e"
      },
      "source": [
        "import tensorflow as tf\n",
        "(x_train, y_train), (x_test, y_test) = tf.keras.datasets.cifar10.load_data()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\n",
            "170500096/170498071 [==============================] - 4s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hpgrvMSv75_4",
        "outputId": "fe1459b4-e055-4e6a-cea6-6889d126ce8c"
      },
      "source": [
        "x_train[0].shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(32, 32, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ojapJkYK8DCd"
      },
      "source": [
        "classes = [\"airplane\",\"automobile\",\"bird\",\"cat\",\"deer\",\"dog\",\"frog\",\"horse\",\"ship\",\"truck\"]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yuxW-k938N_0"
      },
      "source": [
        "# Assignment: Create an ANN model for this dataset and get to know the accuracy/loss"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "atgaTkmH8XTn"
      },
      "source": [
        "# Normalization\n",
        "x_train = x_train/255\n",
        "x_test = x_test/255\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "Q33U248phcn3",
        "outputId": "8376ac3f-c47d-4076-8025-8ae49a99ffb3"
      },
      "source": [
        "# Displaying Image\n",
        "# Lets see some of the random images and its respective labels\n",
        "import random\n",
        "import matplotlib.pyplot as plt\n",
        "i = random.randint(1,50000)\n",
        "plt.imshow(x_train[i])\n",
        "classes[y_train[i][0]]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'cat'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0AA0-ztriPb3",
        "outputId": "6660876a-134e-4326-b723-dfdafdc4c6b2"
      },
      "source": [
        "32*32"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1024"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AWx8oMSrhqbu"
      },
      "source": [
        "# ANN Network\n",
        "\n",
        "# I/p Layer - Flatten\n",
        "# Hidden layer - 2048, AF = 'RELU'\n",
        "# O/p Layer - 10 , AF-Softmax"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jyrAN7U3iAu6"
      },
      "source": [
        "from tensorflow import keras\n",
        "from keras.layers import Dense\n",
        "from keras.layers import Flatten\n",
        "ann = keras.Sequential()\n",
        "ann.add(Flatten(input_shape=(32,32,3)))\n",
        "ann.add(Dense(2048,activation='relu'))\n",
        "ann.add(Dense(10,activation='softmax'))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tj6dcejRgI8q"
      },
      "source": [
        "ann.compile(loss='sparse_categorical_crossentropy',optimizer='adam',metrics=['accuracy'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N8tzHdBIgSrP",
        "outputId": "e6dc23f7-1ab3-48d2-80d7-77d40164a354"
      },
      "source": [
        "history = ann.fit(x_train,y_train,epochs=10,validation_data=(x_test,y_test))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "1563/1563 [==============================] - 9s 4ms/step - loss: 2.2943 - accuracy: 0.2856 - val_loss: 1.7235 - val_accuracy: 0.3793\n",
            "Epoch 2/10\n",
            "1563/1563 [==============================] - 5s 3ms/step - loss: 1.7081 - accuracy: 0.3864 - val_loss: 1.6362 - val_accuracy: 0.4142\n",
            "Epoch 3/10\n",
            "1563/1563 [==============================] - 5s 3ms/step - loss: 1.6311 - accuracy: 0.4233 - val_loss: 1.5590 - val_accuracy: 0.4438\n",
            "Epoch 4/10\n",
            "1563/1563 [==============================] - 5s 3ms/step - loss: 1.5676 - accuracy: 0.4440 - val_loss: 1.5936 - val_accuracy: 0.4333\n",
            "Epoch 5/10\n",
            "1563/1563 [==============================] - 5s 3ms/step - loss: 1.5382 - accuracy: 0.4515 - val_loss: 1.5635 - val_accuracy: 0.4459\n",
            "Epoch 6/10\n",
            "1563/1563 [==============================] - 5s 3ms/step - loss: 1.5158 - accuracy: 0.4629 - val_loss: 1.5336 - val_accuracy: 0.4622\n",
            "Epoch 7/10\n",
            "1563/1563 [==============================] - 5s 3ms/step - loss: 1.4833 - accuracy: 0.4743 - val_loss: 1.5175 - val_accuracy: 0.4666\n",
            "Epoch 8/10\n",
            "1563/1563 [==============================] - 5s 3ms/step - loss: 1.4654 - accuracy: 0.4791 - val_loss: 1.5002 - val_accuracy: 0.4718\n",
            "Epoch 9/10\n",
            "1563/1563 [==============================] - 5s 3ms/step - loss: 1.4425 - accuracy: 0.4886 - val_loss: 1.5021 - val_accuracy: 0.4764\n",
            "Epoch 10/10\n",
            "1563/1563 [==============================] - 5s 3ms/step - loss: 1.4432 - accuracy: 0.4888 - val_loss: 1.5073 - val_accuracy: 0.4711\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D8wymQVKi5uV"
      },
      "source": [
        "# CNN Model\n",
        "from tensorflow import keras\n",
        "from keras.layers import Dense\n",
        "from keras.layers import Flatten\n",
        "from keras.layers import Conv2D\n",
        "from keras.layers import MaxPooling2D\n",
        "from keras.layers import Dropout\n",
        "cnn = keras.Sequential()\n",
        "cnn.add(Conv2D(32,(3,3),activation='relu',input_shape=(32,32,3)))\n",
        "cnn.add(MaxPooling2D((2,2)))\n",
        "cnn.add(Conv2D(64,(3,3),activation='relu'))\n",
        "cnn.add(MaxPooling2D((2,2)))\n",
        "cnn.add(Conv2D(128,(3,3),activation='relu'))\n",
        "cnn.add(MaxPooling2D((2,2)))\n",
        "cnn.add(Flatten())\n",
        "cnn.add(Dense(64,activation='relu'))\n",
        "cnn.add(Dropout(0.3))\n",
        "cnn.add(Dense(10,activation='softmax'))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JBfl7X1knxLY",
        "outputId": "9a018358-3d0d-48da-9307-c467f1f14b08"
      },
      "source": [
        "cnn.compile(loss='sparse_categorical_crossentropy',optimizer='adam',metrics=['accuracy'])\n",
        "history = cnn.fit(x_train,y_train,epochs=10,validation_data=(x_test,y_test))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "1563/1563 [==============================] - 36s 4ms/step - loss: 1.8713 - accuracy: 0.2988 - val_loss: 1.3742 - val_accuracy: 0.5011\n",
            "Epoch 2/10\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 1.3311 - accuracy: 0.5251 - val_loss: 1.1396 - val_accuracy: 0.5967\n",
            "Epoch 3/10\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 1.1451 - accuracy: 0.5969 - val_loss: 1.0090 - val_accuracy: 0.6498\n",
            "Epoch 4/10\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 1.0181 - accuracy: 0.6422 - val_loss: 0.9699 - val_accuracy: 0.6610\n",
            "Epoch 5/10\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 0.9133 - accuracy: 0.6829 - val_loss: 0.9209 - val_accuracy: 0.6754\n",
            "Epoch 6/10\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 0.8449 - accuracy: 0.7037 - val_loss: 0.8742 - val_accuracy: 0.7025\n",
            "Epoch 7/10\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 0.7771 - accuracy: 0.7300 - val_loss: 0.8414 - val_accuracy: 0.7118\n",
            "Epoch 8/10\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 0.7265 - accuracy: 0.7476 - val_loss: 0.8369 - val_accuracy: 0.7187\n",
            "Epoch 9/10\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 0.6953 - accuracy: 0.7580 - val_loss: 0.8208 - val_accuracy: 0.7228\n",
            "Epoch 10/10\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 0.6495 - accuracy: 0.7758 - val_loss: 0.8529 - val_accuracy: 0.7142\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "6Z5flLcbn96l",
        "outputId": "5b394fbc-99f3-4e51-88cf-90a6a1962655"
      },
      "source": [
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f7cd2210f90>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "bq5yrUrkyMEW",
        "outputId": "2a10334b-8ee6-4122-8262-33cbc4ef6d15"
      },
      "source": [
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['val_accuracy'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f7cd21a9d90>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z7WQRVF0ow5L",
        "outputId": "cabf3a55-b910-4d5c-a4b0-dc0716011f9a"
      },
      "source": [
        "cnn.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_1\"\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",
            "conv2d_1 (Conv2D)            (None, 13, 13, 64)        18496     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 6, 6, 64)          0         \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 4, 4, 128)         73856     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 2, 2, 128)         0         \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 64)                32832     \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 64)                0         \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 10)                650       \n",
            "=================================================================\n",
            "Total params: 126,730\n",
            "Trainable params: 126,730\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sNIcvbJbqmw7"
      },
      "source": [
        "# Assignment : \n",
        "\n",
        "# Consider using padding parameter"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n53mbL1ntbNI"
      },
      "source": [
        "cnn.save('demo-model.hdf5')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CU1QCmmRtg3A"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}