Fine_tune_inception_resnet_v2.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Fine_tune_inception_resnet_v2.ipynb",
      "version": "0.3.2",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "[View in Colaboratory](https://colab.research.google.com/github/ZER-0-NE/ML_problems/blob/master/Fine_tune_inception_resnet_v2.ipynb)"
      ]
    },
    {
      "metadata": {
        "id": "JyIbBg2Xdiv3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "outputId": "0b37d45f-54d3-49d4-e0d0-7f1b3f500432"
      },
      "cell_type": "code",
      "source": [
        "from tensorflow.python.client import device_lib\n",
        "device_lib.list_local_devices()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[name: \"/device:CPU:0\"\n",
              " device_type: \"CPU\"\n",
              " memory_limit: 268435456\n",
              " locality {\n",
              " }\n",
              " incarnation: 6751157919643282574, name: \"/device:GPU:0\"\n",
              " device_type: \"GPU\"\n",
              " memory_limit: 11287966516\n",
              " locality {\n",
              "   bus_id: 1\n",
              "   links {\n",
              "   }\n",
              " }\n",
              " incarnation: 16574248453518870281\n",
              " physical_device_desc: \"device: 0, name: Tesla K80, pci bus id: 0000:00:04.0, compute capability: 3.7\"]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "metadata": {
        "id": "otiArwqIdqRL",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from google.colab import auth\n",
        "from googleapiclient.discovery import build\n",
        "import io , requests, os\n",
        "import sys\n",
        "auth.authenticate_user()\n",
        "from googleapiclient.discovery import build\n",
        "drive_service = build('drive', 'v3')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "_TTcpefUek_q",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 2278
        },
        "outputId": "267eec8f-4883-4bef-f69d-f8fdcf7c305c"
      },
      "cell_type": "code",
      "source": [
        "!apt-get install -y -qq software-properties-common python-software-properties module-init-tools\n",
        "!add-apt-repository -y ppa:alessandro-strada/ppa 2>&1 > /dev/null\n",
        "!apt-get update -qq 2>&1 > /dev/null\n",
        "!apt-get -y install -qq google-drive-ocamlfuse fuse"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Preconfiguring packages ...\n",
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            "Setting up python-apt-common (1.4.0~beta3build2) ...\n",
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            "Setting up iso-codes (3.75-1) ...\n",
            "Setting up distro-info-data (0.36ubuntu0.2) ...\n",
            "Setting up python-pycurl (7.43.0-2build2) ...\n",
            "Setting up lsb-release (9.20160110ubuntu5) ...\n",
            "Setting up libgirepository-1.0-1:amd64 (1.54.1-1) ...\n",
            "Setting up libkmod2:amd64 (24-1ubuntu2) ...\n",
            "Setting up gir1.2-glib-2.0:amd64 (1.54.1-1) ...\n",
            "Processing triggers for libc-bin (2.26-0ubuntu2.1) ...\n",
            "Setting up libapparmor1:amd64 (2.11.0-2ubuntu17.1) ...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Setting up unattended-upgrades (0.98ubuntu1.1) ...\n",
            "\n",
            "Creating config file /etc/apt/apt.conf.d/20auto-upgrades with new version\n",
            "\n",
            "Creating config file /etc/apt/apt.conf.d/50unattended-upgrades with new version\n",
            "invoke-rc.d: could not determine current runlevel\n",
            "invoke-rc.d: policy-rc.d denied execution of start.\n",
            "Setting up dirmngr (2.1.15-1ubuntu8) ...\n",
            "Setting up cron (3.0pl1-128ubuntu5) ...\n",
            "Adding group `crontab' (GID 102) ...\n",
            "Done.\n",
            "update-rc.d: warning: start and stop actions are no longer supported; falling back to defaults\n",
            "update-rc.d: warning: stop runlevel arguments (1) do not match cron Default-Stop values (none)\n",
            "invoke-rc.d: could not determine current runlevel\n",
            "invoke-rc.d: policy-rc.d denied execution of start.\n",
            "Setting up libdbus-1-3:amd64 (1.10.22-1ubuntu1) ...\n",
            "Setting up kmod (24-1ubuntu2) ...\n",
            "Setting up libdbus-glib-1-2:amd64 (0.108-2) ...\n",
            "Setting up python3-gi (3.24.1-2build1) ...\n",
            "Setting up module-init-tools (24-1ubuntu2) ...\n",
            "Setting up python3-software-properties (0.96.24.17) ...\n",
            "Setting up dbus (1.10.22-1ubuntu1) ...\n",
            "Setting up python-apt (1.4.0~beta3build2) ...\n",
            "Setting up python3-dbus (1.2.4-1build3) ...\n",
            "Setting up python-software-properties (0.96.24.17) ...\n",
            "Setting up software-properties-common (0.96.24.17) ...\n",
            "Processing triggers for libc-bin (2.26-0ubuntu2.1) ...\n",
            "Processing triggers for dbus (1.10.22-1ubuntu1) ...\n",
            "gpg: keybox '/tmp/tmpna8ry86l/pubring.gpg' created\n",
            "gpg: /tmp/tmpna8ry86l/trustdb.gpg: trustdb created\n",
            "gpg: key AD5F235DF639B041: public key \"Launchpad PPA for Alessandro Strada\" imported\n",
            "gpg: Total number processed: 1\n",
            "gpg:               imported: 1\n",
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            "(Reading database ... 19706 files and directories currently installed.)\n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "SyTKQLEGfkeS",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from google.colab import auth\n",
        "auth.authenticate_user()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "xZ7bgrkzjls-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        },
        "outputId": "a0c9c1c5-aa52-49c3-c706-d884b08bb35a"
      },
      "cell_type": "code",
      "source": [
        "!pip install PyDrive"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting PyDrive\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/52/e0/0e64788e5dd58ce2d6934549676243dc69d982f198524be9b99e9c2a4fd5/PyDrive-1.3.1.tar.gz (987kB)\n",
            "\u001b[K    100% |████████████████████████████████| 993kB 6.9MB/s \n",
            "\u001b[?25hRequirement already satisfied: google-api-python-client>=1.2 in /usr/local/lib/python3.6/dist-packages (from PyDrive) (1.6.7)\n",
            "Requirement already satisfied: oauth2client>=4.0.0 in /usr/local/lib/python3.6/dist-packages (from PyDrive) (4.1.2)\n",
            "Requirement already satisfied: PyYAML>=3.0 in /usr/local/lib/python3.6/dist-packages (from PyDrive) (3.12)\n",
            "Requirement already satisfied: uritemplate<4dev,>=3.0.0 in /usr/local/lib/python3.6/dist-packages (from google-api-python-client>=1.2->PyDrive) (3.0.0)\n",
            "Requirement already satisfied: httplib2<1dev,>=0.9.2 in /usr/local/lib/python3.6/dist-packages (from google-api-python-client>=1.2->PyDrive) (0.11.3)\n",
            "Requirement already satisfied: six<2dev,>=1.6.1 in /usr/local/lib/python3.6/dist-packages (from google-api-python-client>=1.2->PyDrive) (1.11.0)\n",
            "Requirement already satisfied: pyasn1-modules>=0.0.5 in /usr/local/lib/python3.6/dist-packages (from oauth2client>=4.0.0->PyDrive) (0.2.1)\n",
            "Requirement already satisfied: pyasn1>=0.1.7 in /usr/local/lib/python3.6/dist-packages (from oauth2client>=4.0.0->PyDrive) (0.4.2)\n",
            "Requirement already satisfied: rsa>=3.1.4 in /usr/local/lib/python3.6/dist-packages (from oauth2client>=4.0.0->PyDrive) (3.4.2)\n",
            "Building wheels for collected packages: PyDrive\n",
            "  Running setup.py bdist_wheel for PyDrive ... \u001b[?25l-\b \b\\\b \bdone\n",
            "\u001b[?25h  Stored in directory: /content/.cache/pip/wheels/fa/d2/9a/d3b6b506c2da98289e5d417215ce34b696db856643bad779f4\n",
            "Successfully built PyDrive\n",
            "Installing collected packages: PyDrive\n",
            "Successfully installed PyDrive-1.3.1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "dMmbXcpjlywg",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from pydrive.auth import GoogleAuth\n",
        "from pydrive.drive import GoogleDrive\n",
        "from google.colab import auth\n",
        "from oauth2client.client import GoogleCredentials\n",
        "\n",
        "# Authenticate and create the PyDrive client.\n",
        "# This only needs to be done once in a notebook.\n",
        "auth.authenticate_user()\n",
        "gauth = GoogleAuth()\n",
        "gauth.credentials = GoogleCredentials.get_application_default()\n",
        "drive = GoogleDrive(gauth)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "oxbSpvj5nDEN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "c7b337ee-0589-4742-f93b-aeaf3b117c90"
      },
      "cell_type": "code",
      "source": [
        "fileId = drive.CreateFile({'id': '1OhPBMbSOG3ejP26-peRmDPYX7WfF2ixN'}) #DRIVE_FILE_ID is file id example: 1iytA1n2z4go3uVCwE_vIKouTKyIDjEq\n",
        "print(fileId['title'])  # folder_data.zip\n",
        "fileId.GetContentFile('dataset_cfps.zip')  # Save Drive file as a local file"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "dataset_cfps.zip\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "JIdOwTZxrEA_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35972
        },
        "outputId": "5e842961-e891-48f0-b115-bc6a373f01fb"
      },
      "cell_type": "code",
      "source": [
        "!unzip dataset_cfps.zip -d ./"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  dataset_cfps.zip\r\n",
            "   creating: ./dataset_cfps/\r\n",
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      ]
    },
    {
      "metadata": {
        "id": "69pDwJFqrN6Q",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "68bcd22b-aaba-4e6b-bda3-d7b61a0c7760"
      },
      "cell_type": "code",
      "source": [
        "!ls\n"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "datalab  dataset_cfps  dataset_cfps.zip\r\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "9Fv2RC_Zrfe5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "75d5a282-4f43-4ada-dbc1-d4e7d444b6e3"
      },
      "cell_type": "code",
      "source": [
        "from keras import models\n",
        "from keras import layers\n",
        "from keras import optimizers\n",
        "from keras.applications import VGG16\n",
        "from keras.applications import InceptionResNetV2\n",
        "import sys\n",
        "import os\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "from keras import optimizers\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dropout, Flatten, Dense, Activation\n",
        "from keras.layers.convolutional import Convolution2D, MaxPooling2D\n",
        "from keras import callbacks\n",
        "from keras.models import load_model\n",
        "import matplotlib.pyplot as plt "
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "ssBWh8GHsS1y",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 13566
        },
        "outputId": "70d760fe-049a-4116-bc2f-a6fbc8598755"
      },
      "cell_type": "code",
      "source": [
        "train_data_path = 'dataset_cfps/train'\n",
        "validation_data_path = 'dataset_cfps/validation'\n",
        "\n",
        "#Parametres\n",
        "img_width, img_height = 150, 150\n",
        "\n",
        "# Loading inceptionResnetv2\n",
        "incep_res_v2 = InceptionResNetV2(include_top=False, weights='imagenet', input_shape=(img_width, img_height, 3))\n",
        "# Freezing the last 4 layeers\n",
        "for layer in incep_res_v2 .layers[1:]:\n",
        "    layer.trainable = False\n",
        " \n",
        "# Check the trainable status of the individual layers\n",
        "for layer in incep_res_v2 .layers:\n",
        "    print(layer, layer.trainable)\n",
        "\n",
        "# Create the model\n",
        "model = models.Sequential()\n",
        " \n",
        "# Add the convolutional base model\n",
        "model.add(incep_res_v2)\n",
        " \n",
        "# Add new layers\n",
        "model.add(layers.Flatten())\n",
        "model.add(layers.Dense(1024, activation='relu'))\n",
        "model.add(layers.Dropout(0.5))\n",
        "model.add(layers.Dense(12, activation='sigmoid'))\n",
        " \n",
        "# Show a summary of the model. Check the number of trainable parameters\n",
        "model.summary()\n",
        "model = load_model('incep_res_v2_new2.h5')\n",
        "train_datagen = ImageDataGenerator(\n",
        "      rescale=1./255,\n",
        "      rotation_range=20,\n",
        "      width_shift_range=0.2,\n",
        "      height_shift_range=0.2,\n",
        "      horizontal_flip=True,\n",
        "      fill_mode='nearest')"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
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            "<keras.layers.normalization.BatchNormalization object at 0x7fba0937ce48> False\n",
            "<keras.layers.core.Activation object at 0x7fba0953c7f0> False\n",
            "<keras.layers.core.Activation object at 0x7fba09268d30> False\n",
            "<keras.layers.merge.Concatenate object at 0x7fba092845f8> False\n",
            "<keras.layers.convolutional.Conv2D object at 0x7fba092c8ef0> False\n",
            "<keras.layers.core.Lambda object at 0x7fba09299eb8> False\n",
            "<keras.layers.core.Activation object at 0x7fba0923f470> False\n",
            "<keras.layers.convolutional.Conv2D object at 0x7fba0915bb70> False\n",
            "<keras.layers.normalization.BatchNormalization object at 0x7fba09174be0> False\n",
            "<keras.layers.core.Activation object at 0x7fba090c45f8> False\n",
            "<keras.layers.convolutional.Conv2D object at 0x7fba09143f60> False\n",
            "<keras.layers.normalization.BatchNormalization object at 0x7fba090a24e0> False\n",
            "<keras.layers.core.Activation object at 0x7fba08ff9908> False\n",
            "<keras.layers.convolutional.Conv2D object at 0x7fba091fdba8> False\n",
            "<keras.layers.convolutional.Conv2D object at 0x7fba08f9d4e0> False\n",
            "<keras.layers.normalization.BatchNormalization object at 0x7fba091ce898> False\n",
            "<keras.layers.normalization.BatchNormalization object at 0x7fba08fb1a20> False\n",
            "<keras.layers.core.Activation object at 0x7fba0915bb38> False\n",
            "<keras.layers.core.Activation object at 0x7fba08ee4550> False\n",
            "<keras.layers.merge.Concatenate object at 0x7fba08ea8940> False\n",
            "<keras.layers.convolutional.Conv2D object at 0x7fba08ec8a90> False\n",
            "<keras.layers.core.Lambda object at 0x7fba08f07eb8> False\n",
            "<keras.layers.core.Activation object at 0x7fba08e617b8> False\n",
            "<keras.layers.convolutional.Conv2D object at 0x7fba08dbf2e8> False\n",
            "<keras.layers.normalization.BatchNormalization object at 0x7fba08d5d9b0> False\n",
            "<keras.layers.core.Activation object at 0x7fba08ce9940> False\n",
            "<keras.layers.convolutional.Conv2D object at 0x7fba08d7a3c8> False\n",
            "<keras.layers.normalization.BatchNormalization object at 0x7fba08cbbba8> False\n",
            "<keras.layers.core.Activation object at 0x7fba08bd7f60> False\n",
            "<keras.layers.convolutional.Conv2D object at 0x7fba08e1ceb8> False\n",
            "<keras.layers.convolutional.Conv2D object at 0x7fba08c40978> False\n",
            "<keras.layers.normalization.BatchNormalization object at 0x7fba08deebe0> False\n",
            "<keras.layers.normalization.BatchNormalization object at 0x7fba08be5da0> False\n",
            "<keras.layers.core.Activation object at 0x7fba08e0d748> False\n",
            "<keras.layers.core.Activation object at 0x7fba08b82898> False\n",
            "<keras.layers.merge.Concatenate object at 0x7fba08b4ac88> False\n",
            "<keras.layers.convolutional.Conv2D object at 0x7fba08b27470> False\n",
            "<keras.layers.core.Lambda object at 0x7fba08b37160> False\n",
            "<keras.layers.core.Activation object at 0x7fba08b05b00> False\n",
            "<keras.layers.convolutional.Conv2D object at 0x7fba089e0588> False\n",
            "<keras.layers.normalization.BatchNormalization object at 0x7fba08a05dd8> False\n",
            "<keras.layers.core.Activation object at 0x7fba0898aeb8> False\n",
            "<keras.layers.convolutional.Conv2D object at 0x7fba08998898> False\n",
            "<keras.layers.normalization.BatchNormalization object at 0x7fba08957b70> False\n",
            "<keras.layers.core.Activation object at 0x7fba088818d0> False\n",
            "<keras.layers.convolutional.Conv2D object at 0x7fba08a6a278> False\n",
            "<keras.layers.convolutional.Conv2D object at 0x7fba08865cc0> False\n",
            "<keras.layers.normalization.BatchNormalization object at 0x7fba08a1af28> False\n",
            "<keras.layers.normalization.BatchNormalization object at 0x7fba08841b38> False\n",
            "<keras.layers.core.Activation object at 0x7fba08a28ef0> False\n",
            "<keras.layers.core.Activation object at 0x7fba0875def0> False\n",
            "<keras.layers.merge.Concatenate object at 0x7fba0876b780> False\n",
            "<keras.layers.convolutional.Conv2D object at 0x7fba087c6908> False\n",
            "<keras.layers.core.Lambda object at 0x7fba0879abe0> False\n",
            "<keras.layers.convolutional.Conv2D object at 0x7fba0872bf28> False\n",
            "<keras.layers.normalization.BatchNormalization object at 0x7fba08708588> False\n",
            "<keras.layers.core.Activation object at 0x7fba0865cba8> False\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "inception_resnet_v2 (Model)  (None, 3, 3, 1536)        54336736  \n",
            "_________________________________________________________________\n",
            "flatten_4 (Flatten)          (None, 13824)             0         \n",
            "_________________________________________________________________\n",
            "dense_7 (Dense)              (None, 1024)              14156800  \n",
            "_________________________________________________________________\n",
            "dropout_4 (Dropout)          (None, 1024)              0         \n",
            "_________________________________________________________________\n",
            "dense_8 (Dense)              (None, 12)                12300     \n",
            "=================================================================\n",
            "Total params: 68,505,836\n",
            "Trainable params: 14,169,100\n",
            "Non-trainable params: 54,336,736\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "9h1cWItxsnqo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 2461
        },
        "outputId": "2882186d-79ab-4649-eb8a-46b4ac3e9efe"
      },
      "cell_type": "code",
      "source": [
        "validation_datagen = ImageDataGenerator(rescale=1./255)\n",
        " \n",
        "# Change the batchsize according to your system RAM\n",
        "train_batchsize = 512\n",
        "val_batchsize = 512\n",
        " \n",
        "train_generator = train_datagen.flow_from_directory(\n",
        "        train_data_path,\n",
        "        target_size=(img_width, img_height),\n",
        "        batch_size=train_batchsize,\n",
        "        class_mode='categorical')\n",
        " \n",
        "validation_generator = validation_datagen.flow_from_directory(\n",
        "        validation_data_path,\n",
        "        target_size=(img_width, img_height),\n",
        "        batch_size=val_batchsize,\n",
        "        class_mode='categorical',\n",
        "        shuffle=False)\n",
        "\n",
        "# Compile the model\n",
        "model.compile(loss='categorical_crossentropy',\n",
        "              optimizer=optimizers.RMSprop(lr=1e-4,clipvalue=0.5),\n",
        "              metrics=['acc'])\n",
        "\n",
        "#class_weights = {0: 4, 1: 1, 2: 1, 3: 1, 4: 1, 5: 1, 6: 4, 7: 2, 8: 4, 9: 2, 10: 6, 11: 1}\n",
        "# Train the model\n",
        "history = model.fit_generator(\n",
        "      train_generator,\n",
        "      steps_per_epoch=train_generator.samples/train_generator.batch_size ,\n",
        "      epochs=50,\n",
        "      validation_data=validation_generator,\n",
        "      validation_steps=validation_generator.samples/validation_generator.batch_size,\n",
        "      verbose=1)\n",
        " \n",
        "# Save the model\n",
        "model.save('incep_res_v2_new3.h5')\n",
        "\n",
        "# loss and accuracy curves.\n",
        "acc = history.history['acc']\n",
        "val_acc = history.history['val_acc']\n",
        "loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        " \n",
        "epochs = range(len(acc))\n",
        " \n",
        "plt.plot(epochs, acc, 'b', label='Training acc')\n",
        "plt.plot(epochs, val_acc, 'r', label='Validation acc')\n",
        "plt.title('Training and validation accuracy')\n",
        "plt.legend()\n",
        " \n",
        "plt.figure()\n",
        " \n",
        "plt.plot(epochs, loss, 'b', label='Training loss')\n",
        "plt.plot(epochs, val_loss, 'r', label='Validation loss')\n",
        "plt.title('Training and validation loss')\n",
        "plt.legend()\n",
        " \n",
        "plt.show()\n"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 1774 images belonging to 12 classes.\n",
            "Found 313 images belonging to 12 classes.\n",
            "Epoch 1/50\n",
            "4/3 [==================================] - 70s 17s/step - loss: 1.2861 - acc: 0.6390 - val_loss: 7.2090 - val_acc: 0.2173\n",
            "Epoch 2/50\n",
            "4/3 [==================================] - 15s 4s/step - loss: 0.8657 - acc: 0.7347 - val_loss: 7.2938 - val_acc: 0.2620\n",
            "Epoch 3/50\n",
            "4/3 [==================================] - 18s 5s/step - loss: 0.8723 - acc: 0.7312 - val_loss: 7.6496 - val_acc: 0.2204\n",
            "Epoch 4/50\n",
            "4/3 [==================================] - 17s 4s/step - loss: 0.7909 - acc: 0.7501 - val_loss: 7.9039 - val_acc: 0.2364\n",
            "Epoch 5/50\n",
            "4/3 [==================================] - 19s 5s/step - loss: 0.8294 - acc: 0.7360 - val_loss: 7.6462 - val_acc: 0.2620\n",
            "Epoch 6/50\n",
            "4/3 [==================================] - 17s 4s/step - loss: 0.7583 - acc: 0.7591 - val_loss: 6.9371 - val_acc: 0.2524\n",
            "Epoch 7/50\n",
            "4/3 [==================================] - 19s 5s/step - loss: 0.7093 - acc: 0.7638 - val_loss: 7.2927 - val_acc: 0.2652\n",
            "Epoch 8/50\n",
            "4/3 [==================================] - 18s 5s/step - loss: 0.7505 - acc: 0.7666 - val_loss: 7.8283 - val_acc: 0.2460\n",
            "Epoch 9/50\n",
            "4/3 [==================================] - 17s 4s/step - loss: 0.8329 - acc: 0.7391 - val_loss: 7.7084 - val_acc: 0.2268\n",
            "Epoch 10/50\n",
            "4/3 [==================================] - 19s 5s/step - loss: 0.8496 - acc: 0.7339 - val_loss: 7.1537 - val_acc: 0.2780\n",
            "Epoch 11/50\n",
            "4/3 [==================================] - 18s 5s/step - loss: 0.7411 - acc: 0.7674 - val_loss: 7.0284 - val_acc: 0.2748\n",
            "Epoch 12/50\n",
            "4/3 [==================================] - 18s 5s/step - loss: 0.7916 - acc: 0.7608 - val_loss: 6.9543 - val_acc: 0.2524\n",
            "Epoch 13/50\n",
            "4/3 [==================================] - 17s 4s/step - loss: 0.7703 - acc: 0.7630 - val_loss: 6.9231 - val_acc: 0.2588\n",
            "Epoch 14/50\n",
            "4/3 [==================================] - 19s 5s/step - loss: 0.7457 - acc: 0.7659 - val_loss: 7.4390 - val_acc: 0.2460\n",
            "Epoch 15/50\n",
            "4/3 [==================================] - 17s 4s/step - loss: 0.7780 - acc: 0.7469 - val_loss: 7.0841 - val_acc: 0.2716\n",
            "Epoch 16/50\n",
            "4/3 [==================================] - 18s 5s/step - loss: 0.7017 - acc: 0.7787 - val_loss: 7.7469 - val_acc: 0.2780\n",
            "Epoch 17/50\n",
            "2/3 [================>.............] - ETA: 4s - loss: 0.7451 - acc: 0.7532 "
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "4/3 [==================================] - 19s 5s/step - loss: 0.8146 - acc: 0.7472 - val_loss: 8.1747 - val_acc: 0.2204\n",
            "Epoch 18/50\n",
            "4/3 [==================================] - 18s 5s/step - loss: 0.7603 - acc: 0.7533 - val_loss: 7.5063 - val_acc: 0.2684\n",
            "Epoch 19/50\n",
            "4/3 [==================================] - 18s 5s/step - loss: 0.7533 - acc: 0.7640 - val_loss: 7.2712 - val_acc: 0.2620\n",
            "Epoch 20/50\n",
            "4/3 [==================================] - 18s 5s/step - loss: 0.7381 - acc: 0.7625 - val_loss: 7.2429 - val_acc: 0.2652\n",
            "Epoch 21/50\n",
            "4/3 [==================================] - 18s 5s/step - loss: 0.7083 - acc: 0.7749 - val_loss: 7.9171 - val_acc: 0.2428\n",
            "Epoch 22/50\n",
            "4/3 [==================================] - 18s 4s/step - loss: 0.7007 - acc: 0.7809 - val_loss: 6.9089 - val_acc: 0.2684\n",
            "Epoch 23/50\n",
            "4/3 [==================================] - 19s 5s/step - loss: 0.7426 - acc: 0.7672 - val_loss: 8.0018 - val_acc: 0.2620\n",
            "Epoch 24/50\n",
            "4/3 [==================================] - 18s 5s/step - loss: 0.7367 - acc: 0.7754 - val_loss: 6.9268 - val_acc: 0.2652\n",
            "Epoch 25/50\n",
            "4/3 [==================================] - 18s 5s/step - loss: 0.6489 - acc: 0.7831 - val_loss: 7.3404 - val_acc: 0.2332\n",
            "Epoch 26/50\n",
            "4/3 [==================================] - 18s 5s/step - loss: 0.7441 - acc: 0.7609 - val_loss: 7.5386 - val_acc: 0.2588\n",
            "Epoch 27/50\n",
            "4/3 [==================================] - 18s 5s/step - loss: 0.6748 - acc: 0.7956 - val_loss: 7.7573 - val_acc: 0.2524\n",
            "Epoch 28/50\n",
            "4/3 [==================================] - 18s 5s/step - loss: 0.7447 - acc: 0.7487 - val_loss: 8.0564 - val_acc: 0.2460\n",
            "Epoch 29/50\n",
            "4/3 [==================================] - 17s 4s/step - loss: 0.6973 - acc: 0.7775 - val_loss: 7.4396 - val_acc: 0.2556\n",
            "Epoch 30/50\n",
            "4/3 [==================================] - 19s 5s/step - loss: 0.6682 - acc: 0.7891 - val_loss: 7.9120 - val_acc: 0.2652\n",
            "Epoch 31/50\n",
            "4/3 [==================================] - 18s 5s/step - loss: 0.7330 - acc: 0.7692 - val_loss: 6.9254 - val_acc: 0.2812\n",
            "Epoch 32/50\n",
            "4/3 [==================================] - 18s 5s/step - loss: 0.6893 - acc: 0.7830 - val_loss: 7.6427 - val_acc: 0.2620\n",
            "Epoch 33/50\n",
            "4/3 [==================================] - 18s 5s/step - loss: 0.7208 - acc: 0.7787 - val_loss: 7.1132 - val_acc: 0.2556\n",
            "Epoch 34/50\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "4/3 [==================================] - 17s 4s/step - loss: 0.6492 - acc: 0.7799 - val_loss: 8.3028 - val_acc: 0.2332\n",
            "Epoch 35/50\n",
            "4/3 [==================================] - 19s 5s/step - loss: 0.7674 - acc: 0.7556 - val_loss: 7.1914 - val_acc: 0.2652\n",
            "Epoch 36/50\n",
            "4/3 [==================================] - 18s 5s/step - loss: 0.6988 - acc: 0.7803 - val_loss: 6.3041 - val_acc: 0.2875\n",
            "Epoch 37/50\n",
            "4/3 [==================================] - 18s 5s/step - loss: 0.6452 - acc: 0.7850 - val_loss: 7.1154 - val_acc: 0.2812\n",
            "Epoch 38/50\n",
            "4/3 [==================================] - 18s 5s/step - loss: 0.6271 - acc: 0.7939 - val_loss: 7.9468 - val_acc: 0.2524\n",
            "Epoch 39/50\n",
            "4/3 [==================================] - 18s 5s/step - loss: 0.6943 - acc: 0.7793 - val_loss: 7.1120 - val_acc: 0.2620\n",
            "Epoch 40/50\n",
            "4/3 [==================================] - 17s 4s/step - loss: 0.5991 - acc: 0.8234 - val_loss: 6.9533 - val_acc: 0.2907\n",
            "Epoch 41/50\n",
            "4/3 [==================================] - 18s 5s/step - loss: 0.6503 - acc: 0.7828 - val_loss: 6.8077 - val_acc: 0.2812\n",
            "Epoch 42/50\n",
            "4/3 [==================================] - 18s 5s/step - loss: 0.7162 - acc: 0.7858 - val_loss: 7.6513 - val_acc: 0.2684\n",
            "Epoch 43/50\n",
            "4/3 [==================================] - 18s 5s/step - loss: 0.6461 - acc: 0.7909 - val_loss: 7.5939 - val_acc: 0.2556\n",
            "Epoch 44/50\n",
            "4/3 [==================================] - 19s 5s/step - loss: 0.6374 - acc: 0.8008 - val_loss: 8.2958 - val_acc: 0.2396\n",
            "Epoch 45/50\n",
            "4/3 [==================================] - 18s 5s/step - loss: 0.7077 - acc: 0.7871 - val_loss: 7.2952 - val_acc: 0.2652\n",
            "Epoch 46/50\n",
            "4/3 [==================================] - 18s 5s/step - loss: 0.6281 - acc: 0.7940 - val_loss: 8.0428 - val_acc: 0.2588\n",
            "Epoch 47/50\n",
            "4/3 [==================================] - 18s 5s/step - loss: 0.6544 - acc: 0.7884 - val_loss: 7.1709 - val_acc: 0.2812\n",
            "Epoch 48/50\n",
            "4/3 [==================================] - 18s 5s/step - loss: 0.6419 - acc: 0.7991 - val_loss: 7.4483 - val_acc: 0.2556\n",
            "Epoch 49/50\n",
            "4/3 [==================================] - 18s 4s/step - loss: 0.6582 - acc: 0.7902 - val_loss: 6.9123 - val_acc: 0.2780\n",
            "Epoch 50/50\n",
            "4/3 [==================================] - 18s 5s/step - loss: 0.6428 - acc: 0.8048 - val_loss: 6.7012 - val_acc: 0.2652\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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RNvr3t/PRRxYeeSSQZs2cfPJJeq4f+Of75BMLDzwQSLdudmbPPjcjun//QNas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\nYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fba668e4320>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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xHN3ePdha33xhJyWdDnuTZuSMeIKMmd9xfv9hUtZuJG3OwoA/iNlatEYxmdDt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\nloiIyCb/B+nQ+YkR4VHIAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fba1e9bfbe0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "Ov3kaFClu_H0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "outputId": "41a0b661-c609-4561-86ef-8a5ea543ad56"
      },
      "cell_type": "code",
      "source": [
        "# memory footprint support libraries/code\n",
        "!ln -sf /opt/bin/nvidia-smi /usr/bin/nvidia-smi\n",
        "!pip install gputil\n",
        "!pip install psutil\n",
        "!pip install humanize\n",
        "import psutil\n",
        "import humanize\n",
        "import os\n",
        "import GPUtil as GPU\n",
        "GPUs = GPU.getGPUs()\n",
        "# XXX: only one GPU on Colab and isn’t guaranteed\n",
        "gpu = GPUs[0]\n",
        "def printm():\n",
        " process = psutil.Process(os.getpid())\n",
        " print(\"Gen RAM Free: \" + humanize.naturalsize( psutil.virtual_memory().available ), \" I Proc size: \" + humanize.naturalsize( process.memory_info().rss))\n",
        " print(\"GPU RAM Free: {0:.0f}MB | Used: {1:.0f}MB | Util {2:3.0f}% | Total {3:.0f}MB\".format(gpu.memoryFree, gpu.memoryUsed, gpu.memoryUtil*100, gpu.memoryTotal))\n",
        "printm()\n"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting gputil\n",
            "  Downloading https://files.pythonhosted.org/packages/45/99/837428d26b47ebd6b66d6e1b180e98ec4a557767a93a81a02ea9d6242611/GPUtil-1.3.0.tar.gz\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from gputil) (1.14.3)\n",
            "Building wheels for collected packages: gputil\n",
            "  Running setup.py bdist_wheel for gputil ... \u001b[?25l-\b \bdone\n",
            "\u001b[?25h  Stored in directory: /content/.cache/pip/wheels/17/0f/04/b79c006972335e35472c0b835ed52bfc0815258d409f560108\n",
            "Successfully built gputil\n",
            "Installing collected packages: gputil\n",
            "Successfully installed gputil-1.3.0\n",
            "Requirement already satisfied: psutil in /usr/local/lib/python3.6/dist-packages (5.4.5)\n",
            "Collecting humanize\n",
            "  Downloading https://files.pythonhosted.org/packages/8c/e0/e512e4ac6d091fc990bbe13f9e0378f34cf6eecd1c6c268c9e598dcf5bb9/humanize-0.5.1.tar.gz\n",
            "Building wheels for collected packages: humanize\n",
            "  Running setup.py bdist_wheel for humanize ... \u001b[?25l-\b \bdone\n",
            "\u001b[?25h  Stored in directory: /content/.cache/pip/wheels/69/86/6c/f8b8593bc273ec4b0c653d3827f7482bb2001a2781a73b7f44\n",
            "Successfully built humanize\n",
            "Installing collected packages: humanize\n",
            "Successfully installed humanize-0.5.1\n",
            "Gen RAM Free: 4.8 GB  I Proc size: 8.3 GB\n",
            "GPU RAM Free: 503MB | Used: 10936MB | Util  96% | Total 11439MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "bJc1jsT8woOm",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}