BARI_class_lecture_14.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyPaKjHMaaHmolDbtUoAlTvG",
      "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/nousintasnia/Lecture-for-BARI/blob/main/BARI_class_lecture_14.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%tensorflow_version 2.x\n",
        "import tensorflow as tf\n",
        "device_name = tf.test.gpu_device_name()\n",
        "if device_name != '/device:GPU:0':\n",
        "  raise SystemError('GPU device not found')\n",
        "print('Found GPU at: {}'.format(device_name))\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pL9Yv0FzMbO-",
        "outputId": "6064dc01-3e86-4b42-84f4-e25def002e18"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found GPU at: /device:GPU:0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tGxys7NzLtZR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b4b15375-9827-4eb5-e4b6-0fb0d5c23cae"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time (s) to convolve 32x7x7x3 filter over random 100x100x100x3 images (batch x height x width x channel). Sum of ten runs.\n",
            "CPU (s):\n",
            "3.5704016680001587\n",
            "GPU (s):\n",
            "0.06338618900008441\n",
            "GPU speedup over CPU: 56x\n"
          ]
        }
      ],
      "source": [
        "%tensorflow_version 2.x\n",
        "import tensorflow as tf\n",
        "import timeit\n",
        "\n",
        "device_name = tf.test.gpu_device_name()\n",
        "if device_name != '/device:GPU:0':\n",
        "  print(\n",
        "      '\\n\\nThis error most likely means that this notebook is not '\n",
        "      'configured to use a GPU.  Change this in Notebook Settings via the '\n",
        "      'command palette (cmd/ctrl-shift-P) or the Edit menu.\\n\\n')\n",
        "  raise SystemError('GPU device not found')\n",
        "\n",
        "def cpu():\n",
        "  with tf.device('/cpu:0'):\n",
        "    random_image_cpu = tf.random.normal((100, 100, 100, 3))\n",
        "    net_cpu = tf.keras.layers.Conv2D(32, 7)(random_image_cpu)\n",
        "    return tf.math.reduce_sum(net_cpu)\n",
        "\n",
        "def gpu():\n",
        "  with tf.device('/device:GPU:0'):\n",
        "    random_image_gpu = tf.random.normal((100, 100, 100, 3))\n",
        "    net_gpu = tf.keras.layers.Conv2D(32, 7)(random_image_gpu)\n",
        "    return tf.math.reduce_sum(net_gpu)\n",
        "\n",
        "# We run each op once to warm up; see: https://stackoverflow.com/a/45067900\n",
        "cpu()\n",
        "gpu()\n",
        "\n",
        "# Run the op several times.\n",
        "print('Time (s) to convolve 32x7x7x3 filter over random 100x100x100x3 images '\n",
        "      '(batch x height x width x channel). Sum of ten runs.')\n",
        "print('CPU (s):')\n",
        "cpu_time = timeit.timeit('cpu()', number=10, setup=\"from __main__ import cpu\")\n",
        "print(cpu_time)\n",
        "print('GPU (s):')\n",
        "gpu_time = timeit.timeit('gpu()', number=10, setup=\"from __main__ import gpu\")\n",
        "print(gpu_time)\n",
        "print('GPU speedup over CPU: {}x'.format(int(cpu_time/gpu_time)))\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Install TF Hub for TF2\n",
        "!pip install 'tensorflow-hub == 0.5'"
      ],
      "metadata": {
        "id": "QjmotHj0L8Ke",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "41b6c747-54a7-4e36-bc38-6f24ade1b7d8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: tensorflow-hub==0.5 in /usr/local/lib/python3.7/dist-packages (0.5.0)\n",
            "Requirement already satisfied: protobuf>=3.4.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow-hub==0.5) (3.17.3)\n",
            "Requirement already satisfied: numpy>=1.12.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow-hub==0.5) (1.19.5)\n",
            "Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow-hub==0.5) (1.15.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from __future__ import absolute_import, division, print_function, unicode_literals\n",
        "\n",
        "\n",
        "import tensorflow as tf\n",
        "#tf.logging.set_verbosity(tf.logging.ERROR)\n",
        "#tf.enable_eager_execution()\n",
        "\n",
        "import tensorflow_hub as hub\n",
        "import os\n",
        "from tensorflow.keras.layers import Dense, Flatten, Conv2D\n",
        "from tensorflow.keras import Model\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras import layers\n",
        "#from keras import optimizers"
      ],
      "metadata": {
        "id": "dPods7NfSOax"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# verify TensorFlow version\n",
        "\n",
        "print(\"Version: \", tf.__version__)\n",
        "print(\"Eager mode: \", tf.executing_eagerly())\n",
        "print(\"Hub version: \", hub.__version__)\n",
        "print(\"GPU is\", \"available\" if tf.test.is_gpu_available() else \"NOT AVAILABLE\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fYhGN7DVUyCz",
        "outputId": "037f9352-7563-4d04-cd0a-d6b8f1003568"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Version:  2.7.0\n",
            "Eager mode:  True\n",
            "Hub version:  0.5.0\n",
            "GPU is available\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "zip_file = tf.keras.utils.get_file(origin='https://drive.google.com/drive/folders/archive.zip (Unzipped Files)/plantvillage/PlantVillage',fname='PlantVillage.zip', extract=True)"
      ],
      "metadata": {
        "id": "1vXvZFmUU5gK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_dir = os.path.join(os.path.dirname(zip_file), 'PlantVillage')\n",
        "train_dir = os.path.join(data_dir, 'train')\n",
        "validation_dir = os.path.join(data_dir, 'validation')"
      ],
      "metadata": {
        "id": "N6_RC3AGWyqU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import time\n",
        "import os\n",
        "from os.path import exists\n",
        "\n",
        "def count(dir, counter=0):\n",
        "    \"returns number of files in dir and subdirs\"\n",
        "    for pack in os.walk(dir):\n",
        "        for f in pack[2]:\n",
        "            counter += 1\n",
        "    return dir + \" : \" + str(counter) + \"files\"\n"
      ],
      "metadata": {
        "id": "HwFe6THeXBUO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('total images for training :', count(train_dir))\n",
        "print('total images for validation :', count(validation_dir))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FY1-PxstXDNr",
        "outputId": "d4abccf7-92c9-4cd2-bb3f-4bfc1e39064d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "total images for training : /root/.keras/datasets/PlantVillage/train : 0files\n",
            "total images for validation : /root/.keras/datasets/PlantVillage/validation : 0files\n"
          ]
        }
      ]
    }
  ]
}