CIFAR10-transfer-learning-tensornets.ipynb

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  {
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   "source": [
    "# Introduction\n",
    "\n",
    "This notebook presents how to train CIFAR-10 dataset on the pretrained VGG19 model. I borrow VGG19 included in tensornets package whose github repo is [here](https://github.com/taehoonlee/tensornets). Also, I will try with the different [implementation](https://github.com/machrisaa/tensorflow-vgg) of the same VGG19 architecture after done with tensornets package. \n",
    "\n",
    "The main purpose is to understand the concept of transfer learning and experience with the actual usage. I previously made an notebook to build classical CNN model to train CIFAR-10 dataset, so I can compare between the state of the art CNN model and the very basic CNN model. I am expecting VGG19 will outperform easily. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Package Dependency\n",
    "\n",
    "Before diving in, let's install required packages. If your system doesn't have these listed below, please run the code cell below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "!pip install h5py\n",
    "!pip install scipy\n",
    "\n",
    "# scikit-image package for transforming \n",
    "# size of CIFAR-10 image into the input size for VGG19 model.\n",
    "!pip install scikit-image\n",
    "\n",
    "# tqdm package for adding progress bar like UI\n",
    "!pip install tqdm\n",
    "\n",
    "# Cython package is required to install tensornets\n",
    "!pip install Cython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# this will install tensornets package automatically.\n",
    "# However, if you are on Windows and encountering Visual Studio Build Tool thing, please install it beforehand\n",
    "!pip install git+https://github.com/taehoonlee/tensornets.git"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "id": "W06VSAiWwNW2"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\h5py\\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n"
     ]
    }
   ],
   "source": [
    "from urllib.request import urlretrieve\n",
    "from os.path import isfile, isdir\n",
    "from tqdm import tqdm \n",
    "import tarfile\n",
    "import pickle\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import skimage\n",
    "import skimage.io\n",
    "import skimage.transform\n",
    "\n",
    "import tensorflow as tf\n",
    "import tensornets as nets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GPU configuration for Tensorflow\n",
    "\n",
    "If you have a very high-end GPU running on your machine, you can skip this section. I needed this configuration, and I have run the training with NVIDIA GTX 1080Ti"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "config = tf.ConfigProto(allow_soft_placement=True)\n",
    "\n",
    "# \"Best-fit with coalescing\" algorithm for memory allocation\n",
    "config.gpu_options.allocator_type = 'BFC'\n",
    "config.gpu_options.per_process_gpu_memory_fraction = 0.80"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CIFAR-10 Dataset Preparation\n",
    "\n",
    "This is the same step as described in my other github [repo](https://github.com/deep-diver/CIFAR10-img-classification-tensorflow). If you are more interested in this step, please read the detailed explanation from the link. Otherwise, you can skip to the next section.\n",
    "\n",
    "## Download CIFAR-10 Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 16122,
     "status": "ok",
     "timestamp": 1526728322562,
     "user": {
      "displayName": "",
      "photoUrl": "",
      "userId": ""
     },
     "user_tz": -540
    },
    "id": "yuLcGSrRwelh",
    "outputId": "012a8faf-9f47-4865-8279-f05213c7b358"
   },
   "outputs": [],
   "source": [
    "cifar10_dataset_folder_path = 'cifar-10-batches-py'\n",
    "\n",
    "class DownloadProgress(tqdm):\n",
    "    last_block = 0\n",
    "\n",
    "    def hook(self, block_num=1, block_size=1, total_size=None):\n",
    "        self.total = total_size\n",
    "        self.update((block_num - self.last_block) * block_size)\n",
    "        self.last_block = block_num\n",
    "\n",
    "\"\"\" \n",
    "    check if the data (zip) file is already downloaded\n",
    "    if not, download it from \"https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\" and save as cifar-10-python.tar.gz\n",
    "\"\"\"\n",
    "if not isfile('cifar-10-python.tar.gz'):\n",
    "    with DownloadProgress(unit='B', unit_scale=True, miniters=1, desc='CIFAR-10 Dataset') as pbar:\n",
    "        urlretrieve(\n",
    "            'https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz',\n",
    "            'cifar-10-python.tar.gz',\n",
    "            pbar.hook)\n",
    "\n",
    "if not isdir(cifar10_dataset_folder_path):\n",
    "    with tarfile.open('cifar-10-python.tar.gz') as tar:\n",
    "        tar.extractall()\n",
    "        tar.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## load raw data and reshape the input images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "id": "9hAxLUckxBwR"
   },
   "outputs": [],
   "source": [
    "def load_cfar10_batch(cifar10_dataset_folder_path, batch_id):\n",
    "    with open(cifar10_dataset_folder_path + '/data_batch_' + str(batch_id), mode='rb') as file:\n",
    "        # note the encoding type is 'latin1'\n",
    "        batch = pickle.load(file, encoding='latin1')\n",
    "        \n",
    "    features = batch['data'].reshape((len(batch['data']), 3, 32, 32)).transpose(0, 2, 3, 1)\n",
    "    labels = batch['labels']\n",
    "        \n",
    "    return features, labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## one hot encoding method for label data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "id": "hipGIs1BxJiK"
   },
   "outputs": [],
   "source": [
    "def one_hot_encode(x):\n",
    "    \"\"\"\n",
    "        argument\n",
    "            - x: a list of labels\n",
    "        return\n",
    "            - one hot encoding matrix (number of labels, number of class)\n",
    "    \"\"\"\n",
    "    encoded = np.zeros((len(x), 10))\n",
    "    \n",
    "    for idx, val in enumerate(x):\n",
    "        encoded[idx][val] = 1\n",
    "    \n",
    "    return encoded"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## save the modified input(feature) and labels "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "id": "xz29Y5E8xJe6"
   },
   "outputs": [],
   "source": [
    "def _preprocess_and_save(one_hot_encode, features, labels, filename):\n",
    "    labels = one_hot_encode(labels)\n",
    "\n",
    "    pickle.dump((features, labels), open(filename, 'wb'))\n",
    "\n",
    "\n",
    "def preprocess_and_save_data(cifar10_dataset_folder_path, one_hot_encode):\n",
    "    n_batches = 5\n",
    "    valid_features = []\n",
    "    valid_labels = []\n",
    "\n",
    "    for batch_i in range(1, n_batches + 1):\n",
    "        features, labels = load_cfar10_batch(cifar10_dataset_folder_path, batch_i)\n",
    "        \n",
    "        # find index to be the point as validation data in the whole dataset of the batch (10%)\n",
    "        index_of_validation = int(len(features) * 0.1)\n",
    "\n",
    "        # preprocess the 90% of the whole dataset of the batch\n",
    "        # - normalize the features\n",
    "        # - one_hot_encode the lables\n",
    "        # - save in a new file named, \"preprocess_batch_\" + batch_number\n",
    "        # - each file for each batch\n",
    "        _preprocess_and_save(one_hot_encode,\n",
    "                             features[:-index_of_validation], labels[:-index_of_validation], \n",
    "                             'preprocess_batch_' + str(batch_i) + '.p')\n",
    "\n",
    "        # unlike the training dataset, validation dataset will be added through all batch dataset\n",
    "        # - take 10% of the whold dataset of the batch\n",
    "        # - add them into a list of\n",
    "        #   - valid_features\n",
    "        #   - valid_labels\n",
    "        valid_features.extend(features[-index_of_validation:])\n",
    "        valid_labels.extend(labels[-index_of_validation:])\n",
    "\n",
    "    # preprocess the all stacked validation dataset\n",
    "    _preprocess_and_save(one_hot_encode,\n",
    "                         np.array(valid_features), np.array(valid_labels),\n",
    "                         'preprocess_validation.p')\n",
    "\n",
    "    # load the test dataset\n",
    "    with open(cifar10_dataset_folder_path + '/test_batch', mode='rb') as file:\n",
    "        batch = pickle.load(file, encoding='latin1')\n",
    "\n",
    "    # preprocess the testing data\n",
    "    test_features = batch['data'].reshape((len(batch['data']), 3, 32, 32)).transpose(0, 2, 3, 1)\n",
    "    test_labels = batch['labels']\n",
    "\n",
    "    # Preprocess and Save all testing data\n",
    "    _preprocess_and_save(one_hot_encode,\n",
    "                         np.array(test_features), np.array(test_labels),\n",
    "                         'preprocess_testing.p')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "id": "onL8b7jZxjGq"
   },
   "outputs": [],
   "source": [
    "preprocess_and_save_data(cifar10_dataset_folder_path, one_hot_encode)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Traning the model for CIFAR-10\n",
    "\n",
    "## Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "collapsed": true,
    "id": "qIGZ-8GexjBF"
   },
   "outputs": [],
   "source": [
    "valid_features, valid_labels = pickle.load(open('preprocess_validation.p', mode='rb'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define input (feature) and output (label)\n",
    "\n",
    "<img src=\"https://lihan.me/assets/images/vgg-hero-cover.jpg\"/>\n",
    "\n",
    "<p style=\"text-align:center;\">\n",
    "__VGG19 Architecture__\n",
    "</p>\n",
    "<p style=\"text-align:center;\">\n",
    "(borrowed from [vgg19-caltech101-classification](https://lihan.me/2018/01/vgg19-caltech101-classification/))\n",
    "</p>\n",
    "\n",
    "VGG models described in the [original paper](https://arxiv.org/pdf/1409.1556.pdf) are trained with images whose size is 224x224x3. This can not be changed, so the input image for the transfer learning task should have the same image size. For the output, CIFAR-10 contains 10 categorical image data which makes the output size 10 as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "id": "ZEr9SKaPxi1z"
   },
   "outputs": [],
   "source": [
    "x = tf.placeholder(tf.float32, shape=(None, 224, 224, 3), name='input_x')\n",
    "y = tf.placeholder(tf.float32, shape=(None, 10), name='output_y')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hyper-parameters\n",
    "\n",
    "- You don't need many epochs since this is transfer learning.\n",
    "- you can increase the batch size if you have a very high-end GPU running. 32 is the maximum value I could try with NVIDIA GTX 1080Ti"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "id": "qCivsIqkHGLn"
   },
   "outputs": [],
   "source": [
    "learning_rate = 0.00001\n",
    "epochs = 7\n",
    "batch_size = 32"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load VGG19 model, Define loss, train, and accuracy tensor/Operation\n",
    "\n",
    "The first step is to predefined VGG19 model. \n",
    "- if you want to run the model as it is, just pass the input placeholder.\n",
    "- if you want to train on your own image dataset, set is_training=True, and classes=# of class\n",
    "\n",
    "nets.VGG19 returns the final layer of the VGG19 which is softmax. If you know, tensorflow comes with tf.nn.softmax_cross_entropy_with_logits function, and this applies softmax and cross entropy together. However, nets.VGG19 returns the layer already applied with softmax, so we need only cross entropy. That can be achieved by tf.losses.softmax_cross_entropy function.\n",
    "\n",
    "In your choice, you can choose your favorite optimizer. I am going to use Adam optimizer since it is known to work moderate for most of deep learning problems."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "id": "XHpQqG9dxivw"
   },
   "outputs": [],
   "source": [
    "logits = nets.VGG19(x, is_training=True, classes=10)\n",
    "model = tf.identity(logits, name='logits')\n",
    "\n",
    "loss = tf.losses.softmax_cross_entropy(y, logits)\n",
    "train = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(loss)\n",
    "\n",
    "correct_pred = tf.equal(tf.argmax(model, 1), tf.argmax(y, 1))\n",
    "accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32), name='accuracy')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## print outputs of each layer\n",
    "\n",
    "- nets.VGG19 comes with handy methods, 'print_outputs', and 'get_outputs'. \n",
    "- With 'print_outputs' method, you can print out how this model is constructed. You could compare the actual implementation and the conceptual model. \n",
    "- With 'get_outputs', you can actually access each layer's output. In this way, you could modify the model in your own way like inserting new layer or removing the existing ones.\n",
    "- if you want to know the more compact to way to navigate the layers, you could g"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scope: vgg19\n",
      "conv1/1/conv/BiasAdd:0 (?, 224, 224, 64)\n",
      "conv1/1/Relu:0 (?, 224, 224, 64)\n",
      "conv1/2/conv/BiasAdd:0 (?, 224, 224, 64)\n",
      "conv1/2/Relu:0 (?, 224, 224, 64)\n",
      "conv1/pool/MaxPool:0 (?, 112, 112, 64)\n",
      "conv2/1/conv/BiasAdd:0 (?, 112, 112, 128)\n",
      "conv2/1/Relu:0 (?, 112, 112, 128)\n",
      "conv2/2/conv/BiasAdd:0 (?, 112, 112, 128)\n",
      "conv2/2/Relu:0 (?, 112, 112, 128)\n",
      "conv2/pool/MaxPool:0 (?, 56, 56, 128)\n",
      "conv3/1/conv/BiasAdd:0 (?, 56, 56, 256)\n",
      "conv3/1/Relu:0 (?, 56, 56, 256)\n",
      "conv3/2/conv/BiasAdd:0 (?, 56, 56, 256)\n",
      "conv3/2/Relu:0 (?, 56, 56, 256)\n",
      "conv3/3/conv/BiasAdd:0 (?, 56, 56, 256)\n",
      "conv3/3/Relu:0 (?, 56, 56, 256)\n",
      "conv3/4/conv/BiasAdd:0 (?, 56, 56, 256)\n",
      "conv3/4/Relu:0 (?, 56, 56, 256)\n",
      "conv3/pool/MaxPool:0 (?, 28, 28, 256)\n",
      "conv4/1/conv/BiasAdd:0 (?, 28, 28, 512)\n",
      "conv4/1/Relu:0 (?, 28, 28, 512)\n",
      "conv4/2/conv/BiasAdd:0 (?, 28, 28, 512)\n",
      "conv4/2/Relu:0 (?, 28, 28, 512)\n",
      "conv4/3/conv/BiasAdd:0 (?, 28, 28, 512)\n",
      "conv4/3/Relu:0 (?, 28, 28, 512)\n",
      "conv4/4/conv/BiasAdd:0 (?, 28, 28, 512)\n",
      "conv4/4/Relu:0 (?, 28, 28, 512)\n",
      "conv4/pool/MaxPool:0 (?, 14, 14, 512)\n",
      "conv5/1/conv/BiasAdd:0 (?, 14, 14, 512)\n",
      "conv5/1/Relu:0 (?, 14, 14, 512)\n",
      "conv5/2/conv/BiasAdd:0 (?, 14, 14, 512)\n",
      "conv5/2/Relu:0 (?, 14, 14, 512)\n",
      "conv5/3/conv/BiasAdd:0 (?, 14, 14, 512)\n",
      "conv5/3/Relu:0 (?, 14, 14, 512)\n",
      "conv5/4/conv/BiasAdd:0 (?, 14, 14, 512)\n",
      "conv5/4/Relu:0 (?, 14, 14, 512)\n",
      "conv5/pool/MaxPool:0 (?, 7, 7, 512)\n",
      "flatten/flatten/Reshape:0 (?, 25088)\n",
      "fc6/BiasAdd:0 (?, 4096)\n",
      "relu6:0 (?, 4096)\n",
      "drop6/dropout/mul:0 (?, 4096)\n",
      "fc7/BiasAdd:0 (?, 4096)\n",
      "relu7:0 (?, 4096)\n",
      "drop7/dropout/mul:0 (?, 4096)\n",
      "logits/BiasAdd:0 (?, 10)\n",
      "probs:0 (?, 10)\n"
     ]
    }
   ],
   "source": [
    "logits.print_outputs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scope: vgg19\n",
      "Total layers: 19\n",
      "Total weights: 114\n",
      "Total parameters: 418,833,630\n"
     ]
    }
   ],
   "source": [
    "logits.print_summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "id": "YjzH1tD5HafW"
   },
   "source": [
    "## Get Batches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "id": "fXXUD381xioX"
   },
   "outputs": [],
   "source": [
    "def batch_features_labels(features, labels, batch_size):\n",
    "    \"\"\"\n",
    "    Split features and labels into batches\n",
    "    \"\"\"\n",
    "    for start in range(0, len(features), batch_size):\n",
    "        end = min(start + batch_size, len(features))\n",
    "        yield features[start:end], labels[start:end]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The size of each CIFAR-10 image is 32x32, and VGG19 takes input image sizes 224x224 which is incompatible. Each CIFAR-10 image should be resized so that it can be fed into the VGG19 model. \n",
    "\n",
    "skimage.transform.resize function does the trick. It takes parameters..\n",
    "- takes a numpy matrix representation of an image\n",
    "- image size to be transformed\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "collapsed": true,
    "id": "X0ub-PEPHF7x"
   },
   "outputs": [],
   "source": [
    "def load_preprocess_training_batch(batch_id, batch_size):\n",
    "    \"\"\"\n",
    "    Load the Preprocessed Training data and return them in batches of <batch_size> or less\n",
    "    \"\"\"\n",
    "    filename = 'preprocess_batch_' + str(batch_id) + '.p'\n",
    "    features, labels = pickle.load(open(filename, mode='rb'))\n",
    "    \n",
    "    tmpFeatures = []\n",
    "    \n",
    "    for feature in features:\n",
    "        tmpFeature = skimage.transform.resize(feature, (224, 224), mode='constant')\n",
    "        tmpFeatures.append(tmpFeature)\n",
    "\n",
    "    # Return the training data in batches of size <batch_size> or less\n",
    "    return batch_features_labels(tmpFeatures, labels, batch_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get inputs for validation\n",
    "\n",
    "The same process from load_preprocess_training_batch is applied."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "collapsed": true,
    "id": "D3M7KvDKHGDD"
   },
   "outputs": [],
   "source": [
    "tmpValidFeatures = []\n",
    "\n",
    "for feature in valid_features:\n",
    "    tmpValidFeature = skimage.transform.resize(feature, (224, 224), mode='constant')\n",
    "    tmpValidFeatures.append(tmpValidFeature)\n",
    "    \n",
    "tmpValidFeatures = np.array(tmpValidFeatures)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "5,000 images in a batch is huge to be trained. If you have a very high-end GPU card, you can try all images at once, but I will go with batch validation, then calculate the mean of them. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 553,
     "status": "ok",
     "timestamp": 1526729611931,
     "user": {
      "displayName": "",
      "photoUrl": "",
      "userId": ""
     },
     "user_tz": -540
    },
    "id": "uf4tGgBHJUq0",
    "outputId": "eb99c4e7-d188-43a3-d797-424563b65625"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5000, 224, 224, 3)\n"
     ]
    }
   ],
   "source": [
    "print(tmpValidFeatures.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 262
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 651,
     "status": "error",
     "timestamp": 1526729647966,
     "user": {
      "displayName": "",
      "photoUrl": "",
      "userId": ""
     },
     "user_tz": -540
    },
    "id": "jclFs6j0HGJV",
    "outputId": "3c7fce73-dc72-44a3-eec1-8923ac9fc0ab",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training...\n",
      "global_variables_initializer ... done ...\n",
      "model.pretrained ... done ... \n",
      "starting training ... \n",
      "Epoch  1, CIFAR-10 Batch 1:  Validation Accuracy: 0.510000\n",
      "Epoch  1, CIFAR-10 Batch 2:  Validation Accuracy: 0.719000\n",
      "Epoch  1, CIFAR-10 Batch 3:  Validation Accuracy: 0.770200\n",
      "Epoch  1, CIFAR-10 Batch 4:  Validation Accuracy: 0.814000\n",
      "Epoch  1, CIFAR-10 Batch 5:  Validation Accuracy: 0.832000\n",
      "Epoch  2, CIFAR-10 Batch 1:  Validation Accuracy: 0.841600\n",
      "Epoch  2, CIFAR-10 Batch 2:  Validation Accuracy: 0.850000\n",
      "Epoch  2, CIFAR-10 Batch 3:  Validation Accuracy: 0.868000\n",
      "Epoch  2, CIFAR-10 Batch 4:  Validation Accuracy: 0.856600\n",
      "Epoch  2, CIFAR-10 Batch 5:  Validation Accuracy: 0.857400\n",
      "Epoch  3, CIFAR-10 Batch 1:  Validation Accuracy: 0.867600\n",
      "Epoch  3, CIFAR-10 Batch 2:  Validation Accuracy: 0.874400\n",
      "Epoch  3, CIFAR-10 Batch 3:  Validation Accuracy: 0.887600\n",
      "Epoch  3, CIFAR-10 Batch 4:  Validation Accuracy: 0.867200\n",
      "Epoch  3, CIFAR-10 Batch 5:  Validation Accuracy: 0.888400\n",
      "Epoch  4, CIFAR-10 Batch 1:  Validation Accuracy: 0.865400\n",
      "Epoch  4, CIFAR-10 Batch 2:  Validation Accuracy: 0.901200\n",
      "Epoch  4, CIFAR-10 Batch 3:  Validation Accuracy: 0.863200\n",
      "Epoch  4, CIFAR-10 Batch 4:  Validation Accuracy: 0.885600\n",
      "Epoch  4, CIFAR-10 Batch 5:  Validation Accuracy: 0.891400\n",
      "Epoch  5, CIFAR-10 Batch 1:  Validation Accuracy: 0.874400\n",
      "Epoch  5, CIFAR-10 Batch 2:  Validation Accuracy: 0.901200\n",
      "Epoch  5, CIFAR-10 Batch 3:  Validation Accuracy: 0.896000\n",
      "Epoch  5, CIFAR-10 Batch 4:  Validation Accuracy: 0.886400\n",
      "Epoch  5, CIFAR-10 Batch 5:  Validation Accuracy: 0.910000\n",
      "Epoch  6, CIFAR-10 Batch 1:  Validation Accuracy: 0.899800\n",
      "Epoch  6, CIFAR-10 Batch 2:  Validation Accuracy: 0.901000\n",
      "Epoch  6, CIFAR-10 Batch 3:  Validation Accuracy: 0.899600\n",
      "Epoch  6, CIFAR-10 Batch 4:  Validation Accuracy: 0.913600\n",
      "Epoch  6, CIFAR-10 Batch 5:  Validation Accuracy: 0.903600\n",
      "Epoch  7, CIFAR-10 Batch 1:  Validation Accuracy: 0.911600\n",
      "Epoch  7, CIFAR-10 Batch 2:  Validation Accuracy: 0.916200\n",
      "Epoch  7, CIFAR-10 Batch 3:  Validation Accuracy: 0.914400\n",
      "Epoch  7, CIFAR-10 Batch 4:  Validation Accuracy: 0.908200\n",
      "Epoch  7, CIFAR-10 Batch 5:  Validation Accuracy: 0.915200\n"
     ]
    }
   ],
   "source": [
    "save_model_path = './image_classification'\n",
    "\n",
    "print('Training...')\n",
    "with tf.Session() as sess:    \n",
    "    # Initializing the variables\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "    print('global_variables_initializer ... done ...')\n",
    "    sess.run(logits.pretrained())\n",
    "    print('model.pretrained ... done ... ')    \n",
    "    \n",
    "    # Training cycle\n",
    "    print('starting training ... ')\n",
    "    for epoch in range(epochs):\n",
    "        # Loop over all batches\n",
    "        n_batches = 5\n",
    "        for batch_i in range(1, n_batches + 1):\n",
    "            for batch_features, batch_labels in load_preprocess_training_batch(batch_i, batch_size):\n",
    "                sess.run(train, {x: batch_features, y: batch_labels})\n",
    "                \n",
    "            print('Epoch {:>2}, CIFAR-10 Batch {}:  '.format(epoch + 1, batch_i), end='')\n",
    "            \n",
    "            # calculate the mean accuracy over all validation dataset\n",
    "            valid_acc = 0\n",
    "            for batch_valid_features, batch_valid_labels in batch_features_labels(tmpValidFeatures, valid_labels, batch_size):\n",
    "                valid_acc += sess.run(accuracy, {x:batch_valid_features, y:batch_valid_labels})\n",
    "            \n",
    "            tmp_num = tmpValidFeatures.shape[0]/batch_size\n",
    "            print('Validation Accuracy: {:.6f}'.format(valid_acc/tmp_num))\n",
    "            \n",
    "    # Save Model\n",
    "    saver = tf.train.Saver()\n",
    "    save_path = saver.save(sess, save_model_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Testing the model over random sample images\n",
    "\n",
    "## mapping label index to label name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "id": "cw8naOjVxB2c"
   },
   "outputs": [],
   "source": [
    "def load_label_names():\n",
    "    return ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## display prediction results (match or not?)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "id": "elh_ZDAyHGGg"
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelBinarizer\n",
    "\n",
    "def display_image_predictions(features, labels, predictions):\n",
    "    n_classes = 10\n",
    "    label_names = load_label_names()\n",
    "    label_binarizer = LabelBinarizer()\n",
    "    label_binarizer.fit(range(n_classes))\n",
    "    label_ids = label_binarizer.inverse_transform(np.array(labels))\n",
    "\n",
    "    fig, axs = plt.subplots(10, 2, figsize=(12,24))\n",
    "\n",
    "    margin = 0.05\n",
    "    ind = np.arange(n_classes)\n",
    "    width = (1. - 2. * margin) / n_classes    \n",
    "    \n",
    "    for image_i, (feature, label_id, prediction) in enumerate(zip(features, label_ids, predictions)):\n",
    "        correct_name = label_names[label_id]\n",
    "        pred_name = label_names[np.argmax(prediction)]\n",
    "        \n",
    "        is_match = 'False'        \n",
    "        \n",
    "        if np.argmax(prediction) == label_id:\n",
    "            is_match = 'True'\n",
    "            \n",
    "        predictions_array = []\n",
    "        pred_names = []\n",
    "        \n",
    "        for index, pred_value in enumerate(prediction):\n",
    "            tmp_pred_name = label_names[index]\n",
    "            predictions_array.append({tmp_pred_name : pred_value})\n",
    "            pred_names.append(tmp_pred_name)\n",
    "        \n",
    "        print('[{}] ground truth: {}, predicted result: {} | {}'.format(image_i, correct_name, pred_name, is_match))\n",
    "        print('\\t- {}\\n'.format(predictions_array))\n",
    "        \n",
    "#         print('image_i: ', image_i)\n",
    "#         print('axs: ', axs, ', axs len: ', len(axs))\n",
    "        axs[image_i][0].imshow(feature)\n",
    "        axs[image_i][0].set_title(pred_name)\n",
    "        axs[image_i][0].set_axis_off()\n",
    "        \n",
    "        axs[image_i][1].barh(ind + margin, prediction, width)\n",
    "        axs[image_i][1].set_yticks(ind + margin)\n",
    "        axs[image_i][1].set_yticklabels(pred_names)\n",
    "        \n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load dataset for testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_features, test_labels = pickle.load(open('preprocess_testing.p', mode='rb'))\n",
    "tmpFeatures = []\n",
    "\n",
    "for feature in test_features:\n",
    "    tmpFeature = skimage.transform.resize(feature, (224, 224), mode='constant')\n",
    "    tmpFeatures.append(tmpFeature)\n",
    "\n",
    "tmpFeatures = np.asarray(tmpFeatures)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "id": "yT3TlbjrHGA8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./image_classification\n",
      "Testing Accuracy: 0.917296974522293\n",
      "\n",
      "[0] ground truth: dog, predicted result: dog | True\n",
      "\t- [{'airplane': 0.085336745}, {'automobile': 0.085336745}, {'bird': 0.085336745}, {'cat': 0.085336745}, {'deer': 0.085336745}, {'dog': 0.23196931}, {'frog': 0.085336745}, {'horse': 0.085336745}, {'ship': 0.085336745}, {'truck': 0.085336745}]\n",
      "\n",
      "[1] ground truth: dog, predicted result: dog | True\n",
      "\t- [{'airplane': 0.085336745}, {'automobile': 0.085336745}, {'bird': 0.085336745}, {'cat': 0.085336745}, {'deer': 0.085336745}, {'dog': 0.23196931}, {'frog': 0.085336745}, {'horse': 0.085336745}, {'ship': 0.085336745}, {'truck': 0.085336745}]\n",
      "\n",
      "[2] ground truth: deer, predicted result: deer | True\n",
      "\t- [{'airplane': 0.085336745}, {'automobile': 0.085336745}, {'bird': 0.08533677}, {'cat': 0.085336745}, {'deer': 0.23196924}, {'dog': 0.085336745}, {'frog': 0.085336745}, {'horse': 0.085336745}, {'ship': 0.085336745}, {'truck': 0.085336745}]\n",
      "\n",
      "[3] ground truth: bird, predicted result: bird | True\n",
      "\t- [{'airplane': 0.085336745}, {'automobile': 0.085336745}, {'bird': 0.23196931}, {'cat': 0.085336745}, {'deer': 0.085336745}, {'dog': 0.085336745}, {'frog': 0.085336745}, {'horse': 0.085336745}, {'ship': 0.085336745}, {'truck': 0.085336745}]\n",
      "\n",
      "[4] ground truth: airplane, predicted result: ship | False\n",
      "\t- [{'airplane': 0.08535101}, {'automobile': 0.08533857}, {'bird': 0.08533857}, {'cat': 0.08533857}, {'deer': 0.08533858}, {'dog': 0.08533857}, {'frog': 0.08533857}, {'horse': 0.08533857}, {'ship': 0.23194045}, {'truck': 0.08533857}]\n",
      "\n",
      "[5] ground truth: airplane, predicted result: airplane | True\n",
      "\t- [{'airplane': 0.23195149}, {'automobile': 0.08534539}, {'bird': 0.08533788}, {'cat': 0.08533788}, {'deer': 0.08533788}, {'dog': 0.085337885}, {'frog': 0.08533788}, {'horse': 0.08533788}, {'ship': 0.08533788}, {'truck': 0.08533804}]\n",
      "\n",
      "[6] ground truth: deer, predicted result: deer | True\n",
      "\t- [{'airplane': 0.085336745}, {'automobile': 0.085336745}, {'bird': 0.085336745}, {'cat': 0.085336745}, {'deer': 0.23196931}, {'dog': 0.085336745}, {'frog': 0.085336745}, {'horse': 0.085336745}, {'ship': 0.085336745}, {'truck': 0.085336745}]\n",
      "\n",
      "[7] ground truth: truck, predicted result: truck | True\n",
      "\t- [{'airplane': 0.085336745}, {'automobile': 0.085336745}, {'bird': 0.085336745}, {'cat': 0.085336745}, {'deer': 0.085336745}, {'dog': 0.085336745}, {'frog': 0.085336745}, {'horse': 0.085336745}, {'ship': 0.085336745}, {'truck': 0.23196931}]\n",
      "\n",
      "[8] ground truth: automobile, predicted result: automobile | True\n",
      "\t- [{'airplane': 0.08533675}, {'automobile': 0.23196934}, {'bird': 0.08533675}, {'cat': 0.08533675}, {'deer': 0.08533675}, {'dog': 0.08533675}, {'frog': 0.08533675}, {'horse': 0.08533675}, {'ship': 0.08533675}, {'truck': 0.08533675}]\n",
      "\n",
      "[9] ground truth: truck, predicted result: truck | True\n",
      "\t- [{'airplane': 0.08539996}, {'automobile': 0.08534937}, {'bird': 0.085349366}, {'cat': 0.08534981}, {'deer': 0.085349366}, {'dog': 0.08534937}, {'frog': 0.08534939}, {'horse': 0.08534948}, {'ship': 0.085384354}, {'truck': 0.2317695}]\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x1728 with 20 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 1719,
       "width": 724
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "\n",
    "import random\n",
    "\n",
    "save_model_path = './image_classification'\n",
    "batch_size = 64\n",
    "n_samples = 10\n",
    "top_n_predictions = 5\n",
    "\n",
    "def test_model(tmpFeatures):\n",
    "    loaded_graph = tf.Graph()\n",
    "    \n",
    "    with tf.Session(graph=loaded_graph) as sess:\n",
    "        loader = tf.train.import_meta_graph(save_model_path + '.meta')\n",
    "        loader.restore(sess, save_model_path)\n",
    "        \n",
    "        # Get accuracy in batches for memory limitations\n",
    "        test_batch_acc_total = 0\n",
    "        test_batch_count = 0\n",
    "        \n",
    "        loaded_x = loaded_graph.get_tensor_by_name('input_x:0')\n",
    "        loaded_y = loaded_graph.get_tensor_by_name('output_y:0')\n",
    "        loaded_logits = loaded_graph.get_tensor_by_name('logits:0')\n",
    "        loaded_acc = loaded_graph.get_tensor_by_name('accuracy:0')\n",
    "        \n",
    "        for train_feature_batch, train_label_batch in batch_features_labels(tmpFeatures, test_labels, batch_size):\n",
    "            test_batch_acc_total += sess.run(\n",
    "                loaded_acc,\n",
    "                feed_dict={loaded_x: train_feature_batch, loaded_y: train_label_batch})\n",
    "            test_batch_count += 1\n",
    "\n",
    "        print('Testing Accuracy: {}\\n'.format(test_batch_acc_total/test_batch_count))\n",
    "\n",
    "        # Print Random Samples\n",
    "        random_test_features, random_test_labels = tuple(zip(*random.sample(list(zip(test_features, test_labels)), n_samples)))\n",
    "        \n",
    "        tmpTestFeatures = []\n",
    "    \n",
    "        for feature in random_test_features:\n",
    "            tmpFeature = skimage.transform.resize(feature, (224, 224), mode='constant')\n",
    "            tmpTestFeatures.append(tmpFeature)\n",
    "           \n",
    "        random_test_predictions = sess.run(\n",
    "            tf.nn.softmax(loaded_logits),\n",
    "            feed_dict={loaded_x: tmpTestFeatures, loaded_y: random_test_labels})\n",
    "        \n",
    "        display_image_predictions(random_test_features, random_test_labels, random_test_predictions)\n",
    "\n",
    "test_model(tmpFeatures)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "collapsed": true,
    "id": "dFqUGLUixBkz"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "default_view": {},
   "name": "안녕하세요, Colaboratory입니다의 사본",
   "provenance": [
    {
     "file_id": "/v2/external/notebooks/welcome.ipynb",
     "timestamp": 1526729659664
    }
   ],
   "version": "0.3.2",
   "views": {}
  },
  "kernelspec": {
   "display_name": "(Python) tensorflow",
   "language": "python",
   "name": "tensorflow"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}