(happy,sad)TensorflowKerasCNN.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "private_outputs": true,
      "provenance": [],
      "gpuType": "T4",
      "authorship_tag": "ABX9TyPQy17Ebui+lXYxjcSMw/lv",
      "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/ExoticCoder16/ExoticCoder16-.github.io/blob/main/(happy%2Csad)TensorflowKerasCNN.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 1. Install Dependencies and Setup"
      ],
      "metadata": {
        "id": "BqkGU4b5na_c"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install tensorflow opencv-python matplotlib"
      ],
      "metadata": {
        "id": "3_Ih0XSKoIA1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip list"
      ],
      "metadata": {
        "id": "lc8EY9L3c1G5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "import os"
      ],
      "metadata": {
        "id": "9SKlFAKkc9K3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Avoid OOM errors by setting GPU Memory Consumption Growth\n",
        "gpus = tf.config.experimental.list_physical_devices('GPU')\n",
        "for gpu in gpus:\n",
        "    tf.config.experimental.set_memory_growth(gpu, True)"
      ],
      "metadata": {
        "id": "F4CHE76OdDkq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tf.config.list_physical_devices('GPU')"
      ],
      "metadata": {
        "id": "rBd8_glOdIS6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 2. Remove dodgy images"
      ],
      "metadata": {
        "id": "s_TNLdxWnhS3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import cv2\n",
        "import imghdr"
      ],
      "metadata": {
        "id": "q3pwtBGddMD8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "\n",
        "drive.mount('/content/gdrive')"
      ],
      "metadata": {
        "id": "Vwi8noqrdRtk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_dir = '/content/gdrive/My Drive/data_set_TensorflowKerasCNN'"
      ],
      "metadata": {
        "id": "IcLJyh5kejJ2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "image_exts = ['jpeg','jpg', 'bmp', 'png']"
      ],
      "metadata": {
        "id": "ZBuHb6kOe5sF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for image_class in os.listdir(data_dir):\n",
        "    for image in os.listdir(os.path.join(data_dir, image_class)):\n",
        "        image_path = os.path.join(data_dir, image_class, image)\n",
        "        try:\n",
        "            img = cv2.imread(image_path)\n",
        "            tip = imghdr.what(image_path)\n",
        "            if tip not in image_exts:\n",
        "                print('Image not in ext list {}'.format(image_path))\n",
        "                os.remove(image_path)\n",
        "        except Exception as e:\n",
        "            print('Issue with image {}'.format(image_path))\n",
        "            # os.remove(image_path)"
      ],
      "metadata": {
        "id": "8rLWd1VVe-gt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 3. Load Data"
      ],
      "metadata": {
        "id": "0vUvOQ0nnmrT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from matplotlib import pyplot as plt"
      ],
      "metadata": {
        "id": "GBehhkcVfduH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = tf.keras.utils.image_dataset_from_directory('/content/gdrive/My Drive/data_set_TensorflowKerasCNN')"
      ],
      "metadata": {
        "id": "n5u0rgJ5fg82"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_iterator = data.as_numpy_iterator()"
      ],
      "metadata": {
        "id": "utEqafcyf4TS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "batch = data_iterator.next()"
      ],
      "metadata": {
        "id": "fXFsoKhgf82c"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(ncols=4, figsize=(20,20))\n",
        "for idx, img in enumerate(batch[0][:4]):\n",
        "    ax[idx].imshow(img.astype(int))\n",
        "    ax[idx].title.set_text(batch[1][idx])"
      ],
      "metadata": {
        "id": "OgP5oNubf_vD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 4. Scale Data"
      ],
      "metadata": {
        "id": "AFkFS0TgoDCS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = data.map(lambda x,y: (x/255, y))"
      ],
      "metadata": {
        "id": "gNTsu8TngnHi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.as_numpy_iterator().next()"
      ],
      "metadata": {
        "id": "gVtw6f3Vg20x"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 5. Split Data"
      ],
      "metadata": {
        "id": "22BjaQW5oNCy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_size = int(len(data)*.7)\n",
        "val_size = int(len(data)*.2)\n",
        "test_size = int(len(data)*.1)"
      ],
      "metadata": {
        "id": "ESMsKKRFg-Ny"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_size"
      ],
      "metadata": {
        "id": "tgKEcaRkhCTQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train = data.take(train_size)\n",
        "val = data.skip(train_size).take(val_size)\n",
        "test = data.skip(train_size+val_size).take(test_size)"
      ],
      "metadata": {
        "id": "5Q6S5LpMhFQH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 6. Build Deep Learning Model"
      ],
      "metadata": {
        "id": "8JEFnkUBoUE1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train"
      ],
      "metadata": {
        "id": "rvCXvWoOhJ8j"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dense, Flatten, Dropout"
      ],
      "metadata": {
        "id": "PFn1zs1uhYax"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()"
      ],
      "metadata": {
        "id": "WYCzVdrXhd7O"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.add(Conv2D(16, (3,3), 1, activation='relu', input_shape=(256,256,3)))\n",
        "model.add(MaxPooling2D())\n",
        "model.add(Conv2D(32, (3,3), 1, activation='relu'))\n",
        "model.add(MaxPooling2D())\n",
        "model.add(Conv2D(16, (3,3), 1, activation='relu'))\n",
        "model.add(MaxPooling2D())\n",
        "model.add(Flatten())\n",
        "model.add(Dense(256, activation='relu'))\n",
        "model.add(Dense(1, activation='sigmoid'))"
      ],
      "metadata": {
        "id": "-1AaT9aShlsb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile('adam', loss=tf.losses.BinaryCrossentropy(), metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "4yOX7fK_h5eH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "id": "hkSxhVr0h9NP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 7. Train"
      ],
      "metadata": {
        "id": "x-k9uwXZoaHG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "logdir='logs'"
      ],
      "metadata": {
        "id": "eBvOnx2diFap"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=logdir)"
      ],
      "metadata": {
        "id": "l3hS-WS3iGrU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "hist = model.fit(train, epochs=20, validation_data=val, callbacks=[tensorboard_callback])"
      ],
      "metadata": {
        "id": "y8Rml3ohiJsn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 8. Plot Performance"
      ],
      "metadata": {
        "id": "STOgB81Hofci"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig = plt.figure()\n",
        "plt.plot(hist.history['loss'], color='teal', label='loss')\n",
        "plt.plot(hist.history['val_loss'], color='orange', label='val_loss')\n",
        "fig.suptitle('Loss', fontsize=20)\n",
        "plt.legend(loc=\"upper left\")\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "IvVMqIMpjuIO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fig = plt.figure()\n",
        "plt.plot(hist.history['accuracy'], color='teal', label='accuracy')\n",
        "plt.plot(hist.history['val_accuracy'], color='orange', label='val_accuracy')\n",
        "fig.suptitle('Accuracy', fontsize=20)\n",
        "plt.legend(loc=\"upper left\")\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "DeePOMObjzGL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 9. Evaluate"
      ],
      "metadata": {
        "id": "KOjGypi-mubm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.metrics import Precision, Recall, BinaryAccuracy"
      ],
      "metadata": {
        "id": "QF6sRrx-mjf5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pre = Precision()\n",
        "re = Recall()\n",
        "acc = BinaryAccuracy()"
      ],
      "metadata": {
        "id": "As5o0vaEmoz6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for batch in test.as_numpy_iterator():\n",
        "    X, y = batch\n",
        "    yhat = model.predict(X)\n",
        "    pre.update_state(y, yhat)\n",
        "    re.update_state(y, yhat)\n",
        "    acc.update_state(y, yhat)"
      ],
      "metadata": {
        "id": "Gsa1CSBBor17"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(pre.result(), re.result(), acc.result())"
      ],
      "metadata": {
        "id": "C0XsCIHQr902"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 10. Test"
      ],
      "metadata": {
        "id": "pTFRawC0t43w"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import cv2"
      ],
      "metadata": {
        "id": "5o_y85y1t1k9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "img = cv2.imread('/content/gdrive/My Drive/154006829.jpg')\n",
        "plt.imshow(img)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "zrr2_TDtt9DL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "resize = tf.image.resize(img, (256,256))\n",
        "plt.imshow(resize.numpy().astype(int))\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "s5lSwb5huIMQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "yhat = model.predict(np.expand_dims(resize/255, 0))"
      ],
      "metadata": {
        "id": "hz2ajOZ-uRlA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "yhat"
      ],
      "metadata": {
        "id": "u5E_UNFZuTVp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "if yhat > 0.5:\n",
        "    print(f'Predicted class is Sad')\n",
        "else:\n",
        "    print(f'Predicted class is Happy')"
      ],
      "metadata": {
        "id": "eKZ3hNBFuX_D"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 11. Save the Model"
      ],
      "metadata": {
        "id": "lkmDvM2Jv4tO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.models import load_model"
      ],
      "metadata": {
        "id": "-fPzis9bv7Jw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.save(os.path.join('/content/models','imageclassifier.h5'))"
      ],
      "metadata": {
        "id": "5mWWtJlEv_lc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "new_model = load_model('/content/models/imageclassifier.h5')"
      ],
      "metadata": {
        "id": "nhyUqFBeweZj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "new_model.predict(np.expand_dims(resize/255, 0))"
      ],
      "metadata": {
        "id": "6oUtiV76xrrk"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}