firstproject_on_cnn.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "authorship_tag": "ABX9TyN/QZk1123e17vEcyyc3y8T",
      "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/darshbaxi/Tensorflow-Practice/blob/main/firstproject_on_cnn.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": "WmPG690X3HRM"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import tensorflow as tf\n",
        "import keras\n",
        "from tensorflow.keras import datasets,layers,models\n",
        "import cv2"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## LOADING **DATA**"
      ],
      "metadata": {
        "id": "_r7qe2ktOywm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "(x_train,y_train),(x_test,y_test)=datasets.cifar10.load_data()"
      ],
      "metadata": {
        "id": "WwEZFcddByFu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "91c97cc3-0d4c-457b-f1f0-b58aaff7685b"
      },
      "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 [==============================] - 2s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        " x_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yM_OzD15DSV9",
        "outputId": "cae89398-46c7-431d-895d-9db73c0f036c"
      },
      "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"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X-sZsO6haine",
        "outputId": "e8fbcdbf-f47a-4e75-c5f1-9d232be6b5ef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[6],\n",
              "       [9],\n",
              "       [9],\n",
              "       ...,\n",
              "       [9],\n",
              "       [1],\n",
              "       [1]], dtype=uint8)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Reshaping Y_TRAIN"
      ],
      "metadata": {
        "id": "5gMoD6E8PAID"
      }
    },
    {
      "cell_type": "code",
      "source": [
        " y_train.reshape(-1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Cl82ZT2GNpXh",
        "outputId": "c83c7379-26e3-45c5-ce96-4426772abb8a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([6, 9, 9, ..., 9, 1, 1], dtype=uint8)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "classes=[\"airplane\",\"automobile\",\"bird\",\"cat\",\"deer\",\"dog\",\"frog\",\"horse\",\"ship\",\"truck\"]"
      ],
      "metadata": {
        "id": "6qeXBM8JO7kJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "gYE7ONOgOvo7"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## EXPLORING DATA"
      ],
      "metadata": {
        "id": "-vWDilw8PUfK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "plt.imshow(x_train[1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "ivnY-cogbJ8G",
        "outputId": "345253fe-2840-4709-94e1-74440ac44ddd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f00d44f7b80>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "HKqZanjrPZMm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Normalization"
      ],
      "metadata": {
        "id": "_sf-RXbiPakU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "x_train=x_train/255.0\n"
      ],
      "metadata": {
        "id": "T5LSNriXPbXY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## CNN MODEL"
      ],
      "metadata": {
        "id": "qDv5ZqCvPhv3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cnn = models.Sequential([\n",
        "    layers.Conv2D(filters=64, kernel_size=(4, 4),activation='relu', input_shape=(32,32,3)),\n",
        "    layers.MaxPooling2D((2, 2)),\n",
        "\n",
        "    layers.Conv2D(filters=64, kernel_size=(4, 4), activation='relu'),\n",
        "    layers.MaxPooling2D((2, 2)),\n",
        "\n",
        "    layers.Conv2D(filters=32, kernel_size=(4, 4), activation='relu'),\n",
        "    layers.MaxPooling2D((2, 2)),\n",
        "\n",
        "    layers.Flatten(),\n",
        "    layers.Dense(64, activation='relu'),\n",
        "    layers.Dense(10, activation='softmax')\n",
        "])\n"
      ],
      "metadata": {
        "id": "-RqkJRJ3UMPS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "cnn.compile(optimizer='adam',\n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy'])\n"
      ],
      "metadata": {
        "id": "wppDynaiUw-q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cnn.fit(x_train, y_train, epochs=50)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "np9EDwSwV8nM",
        "outputId": "6567804c-ec2e-4e30-f294-ed3fc6609a14"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "1563/1563 [==============================] - 17s 5ms/step - loss: 1.6355 - accuracy: 0.3971\n",
            "Epoch 2/50\n",
            "1563/1563 [==============================] - 6s 4ms/step - loss: 1.3089 - accuracy: 0.5259\n",
            "Epoch 3/50\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 1.1767 - accuracy: 0.5767\n",
            "Epoch 4/50\n",
            "1563/1563 [==============================] - 7s 4ms/step - loss: 1.0853 - accuracy: 0.6157\n",
            "Epoch 5/50\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 1.0213 - accuracy: 0.6394\n",
            "Epoch 6/50\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 0.9735 - accuracy: 0.6571\n",
            "Epoch 7/50\n",
            "1563/1563 [==============================] - 7s 4ms/step - loss: 0.9385 - accuracy: 0.6720\n",
            "Epoch 8/50\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 0.9039 - accuracy: 0.6806\n",
            "Epoch 9/50\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 0.8768 - accuracy: 0.6949\n",
            "Epoch 10/50\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 0.8476 - accuracy: 0.7040\n",
            "Epoch 11/50\n",
            "1563/1563 [==============================] - 7s 4ms/step - loss: 0.8226 - accuracy: 0.7119\n",
            "Epoch 12/50\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 0.8037 - accuracy: 0.7186\n",
            "Epoch 13/50\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 0.7835 - accuracy: 0.7260\n",
            "Epoch 14/50\n",
            "1563/1563 [==============================] - 7s 4ms/step - loss: 0.7668 - accuracy: 0.7314\n",
            "Epoch 15/50\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 0.7519 - accuracy: 0.7378\n",
            "Epoch 16/50\n",
            "1563/1563 [==============================] - 7s 4ms/step - loss: 0.7277 - accuracy: 0.7449\n",
            "Epoch 17/50\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 0.7136 - accuracy: 0.7490\n",
            "Epoch 18/50\n",
            "1563/1563 [==============================] - 7s 4ms/step - loss: 0.7014 - accuracy: 0.7544\n",
            "Epoch 19/50\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 0.6788 - accuracy: 0.7631\n",
            "Epoch 20/50\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 0.6684 - accuracy: 0.7677\n",
            "Epoch 21/50\n",
            "1563/1563 [==============================] - 7s 4ms/step - loss: 0.6542 - accuracy: 0.7715\n",
            "Epoch 22/50\n",
            "1563/1563 [==============================] - 8s 5ms/step - loss: 0.6418 - accuracy: 0.7763\n",
            "Epoch 23/50\n",
            "1563/1563 [==============================] - 7s 4ms/step - loss: 0.6247 - accuracy: 0.7818\n",
            "Epoch 24/50\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 0.6141 - accuracy: 0.7854\n",
            "Epoch 25/50\n",
            "1563/1563 [==============================] - 7s 4ms/step - loss: 0.6036 - accuracy: 0.7865\n",
            "Epoch 26/50\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 0.5909 - accuracy: 0.7923\n",
            "Epoch 27/50\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 0.5784 - accuracy: 0.7971\n",
            "Epoch 28/50\n",
            "1563/1563 [==============================] - 7s 4ms/step - loss: 0.5737 - accuracy: 0.7982\n",
            "Epoch 29/50\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 0.5559 - accuracy: 0.8038\n",
            "Epoch 30/50\n",
            "1563/1563 [==============================] - 7s 4ms/step - loss: 0.5501 - accuracy: 0.8062\n",
            "Epoch 31/50\n",
            "1563/1563 [==============================] - 8s 5ms/step - loss: 0.5379 - accuracy: 0.8118\n",
            "Epoch 32/50\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 0.5276 - accuracy: 0.8134\n",
            "Epoch 33/50\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 0.5208 - accuracy: 0.8165\n",
            "Epoch 34/50\n",
            "1563/1563 [==============================] - 8s 5ms/step - loss: 0.5145 - accuracy: 0.8197\n",
            "Epoch 35/50\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 0.5051 - accuracy: 0.8223\n",
            "Epoch 36/50\n",
            "1563/1563 [==============================] - 8s 5ms/step - loss: 0.4976 - accuracy: 0.8258\n",
            "Epoch 37/50\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 0.4911 - accuracy: 0.8266\n",
            "Epoch 38/50\n",
            "1563/1563 [==============================] - 8s 5ms/step - loss: 0.4850 - accuracy: 0.8285\n",
            "Epoch 39/50\n",
            "1563/1563 [==============================] - 8s 5ms/step - loss: 0.4731 - accuracy: 0.8330\n",
            "Epoch 40/50\n",
            "1563/1563 [==============================] - 7s 4ms/step - loss: 0.4688 - accuracy: 0.8338\n",
            "Epoch 41/50\n",
            "1563/1563 [==============================] - 8s 5ms/step - loss: 0.4627 - accuracy: 0.8368\n",
            "Epoch 42/50\n",
            "1563/1563 [==============================] - 8s 5ms/step - loss: 0.4543 - accuracy: 0.8392\n",
            "Epoch 43/50\n",
            "1563/1563 [==============================] - 7s 4ms/step - loss: 0.4479 - accuracy: 0.8426\n",
            "Epoch 44/50\n",
            "1563/1563 [==============================] - 8s 5ms/step - loss: 0.4410 - accuracy: 0.8433\n",
            "Epoch 45/50\n",
            "1563/1563 [==============================] - 7s 4ms/step - loss: 0.4340 - accuracy: 0.8453\n",
            "Epoch 46/50\n",
            "1563/1563 [==============================] - 8s 5ms/step - loss: 0.4307 - accuracy: 0.8457\n",
            "Epoch 47/50\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 0.4256 - accuracy: 0.8502\n",
            "Epoch 48/50\n",
            "1563/1563 [==============================] - 7s 5ms/step - loss: 0.4190 - accuracy: 0.8522\n",
            "Epoch 49/50\n",
            "1563/1563 [==============================] - 13s 8ms/step - loss: 0.4127 - accuracy: 0.8542\n",
            "Epoch 50/50\n",
            "1563/1563 [==============================] - 13s 8ms/step - loss: 0.4047 - accuracy: 0.8553\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f007f37e920>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## RESHAPING TEST DATA"
      ],
      "metadata": {
        "id": "o75LjtDRYOQ3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_test=y_test.reshape(-1)"
      ],
      "metadata": {
        "id": "fwNwTYlWX7sg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_test=x_test/255.0"
      ],
      "metadata": {
        "id": "PWttGzWPYD3r"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## predicting output"
      ],
      "metadata": {
        "id": "7aVXBPqQVQ7D"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "predict=cnn.predict(x_test)"
      ],
      "metadata": {
        "id": "pJ3JPgpTYQtJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f628e57d-6a8f-49e2-ae4a-6b2036176db0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 2ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_classes = [np.argmax(element) for element in predict]"
      ],
      "metadata": {
        "id": "D8zpElmrRNVt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cnn.evaluate(x_test,y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BltJ9A1zRSwp",
        "outputId": "da01d2bb-766e-42f8-bfeb-2c6513284e57"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 3ms/step - loss: 1.2833 - accuracy: 0.6754\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1.2832614183425903, 0.6754000186920166]"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "crYKUURtVa9Y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "6e3yAckvVg45"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}