grain_unearth-bilinear.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "executionInfo": {
     "elapsed": 2716,
     "status": "ok",
     "timestamp": 1624439925606,
     "user": {
      "displayName": "ramdhan wibawa",
      "photoUrl": "",
      "userId": "14519448541243845830"
     },
     "user_tz": -420
    },
    "id": "mW8mQghJfh6D"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import logging\n",
    "import sys\n",
    "import os\n",
    "from os import getenv\n",
    "from os.path import abspath, basename, split,dirname\n",
    "import tarfile\n",
    "from shutil import copyfile\n",
    "import random, glob\n",
    "from skimage.util import random_noise\n",
    "from skimage.transform import rotate\n",
    "import time\n",
    "import numpy as np\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import gmean\n",
    "import tensorflow_addons as tfa\n",
    "\n",
    "import tensorflow as tf\n",
    "import tensorflow_datasets as tfds\n",
    "#import efficientnet.keras as efn\n",
    "#from efficientnet.tfkeras import EfficientNetB2\n",
    "from tensorflow.keras.applications import EfficientNetB0,EfficientNetB3, EfficientNetB2, EfficientNetB7\n",
    "from tensorflow.keras.applications.resnet50 import ResNet50\n",
    "from tensorflow.keras.applications.resnet import ResNet101\n",
    "from tensorflow.keras.applications.vgg16 import VGG16\n",
    "from tensorflow.keras.applications.inception_v3 import InceptionV3\n",
    "from tensorflow.keras.applications.mobilenet import MobileNet\n",
    "from tensorflow.keras.applications.densenet import DenseNet121, DenseNet169, DenseNet201\n",
    "from tensorflow.keras import layers\n",
    "from tensorflow import keras\n",
    "#from tensorflow.keras.applications.efficientnet import preprocess_input\n",
    "from tensorflow.keras.applications.resnet50 import preprocess_input\n",
    "#from tensorflow.keras.applications.vgg16 import preprocess_input\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "import tensorflow.keras.backend as K\n",
    "from sklearn.metrics import accuracy_score\n",
    "from tensorflow.keras.callbacks import ModelCheckpoint"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "logger = logging.getLogger(__name__)\n",
    "logging.basicConfig(level=logging.INFO)\n",
    "ds_build_cmd = \"tfds build grains --manual_dir=\" + \"D:/Ramdhan/Unearth/visual-grain-analysis/data/public2\"\n",
    "logger.info(ds_build_cmd)\n",
    "os.system(ds_build_cmd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "input_2\n",
      "conv1_pad\n",
      "conv1_conv\n",
      "conv1_bn\n",
      "conv1_relu\n",
      "pool1_pad\n",
      "pool1_pool\n",
      "conv2_block1_1_conv\n",
      "conv2_block1_1_bn\n",
      "conv2_block1_1_relu\n",
      "conv2_block1_2_conv\n",
      "conv2_block1_2_bn\n",
      "conv2_block1_2_relu\n",
      "conv2_block1_0_conv\n",
      "conv2_block1_3_conv\n",
      "conv2_block1_0_bn\n",
      "conv2_block1_3_bn\n",
      "conv2_block1_add\n",
      "conv2_block1_out\n",
      "conv2_block2_1_conv\n",
      "conv2_block2_1_bn\n",
      "conv2_block2_1_relu\n",
      "conv2_block2_2_conv\n",
      "conv2_block2_2_bn\n",
      "conv2_block2_2_relu\n",
      "conv2_block2_3_conv\n",
      "conv2_block2_3_bn\n",
      "conv2_block2_add\n",
      "conv2_block2_out\n",
      "conv2_block3_1_conv\n",
      "conv2_block3_1_bn\n",
      "conv2_block3_1_relu\n",
      "conv2_block3_2_conv\n",
      "conv2_block3_2_bn\n",
      "conv2_block3_2_relu\n",
      "conv2_block3_3_conv\n",
      "conv2_block3_3_bn\n",
      "conv2_block3_add\n",
      "conv2_block3_out\n",
      "conv3_block1_1_conv\n",
      "conv3_block1_1_bn\n",
      "conv3_block1_1_relu\n",
      "conv3_block1_2_conv\n",
      "conv3_block1_2_bn\n",
      "conv3_block1_2_relu\n",
      "conv3_block1_0_conv\n",
      "conv3_block1_3_conv\n",
      "conv3_block1_0_bn\n",
      "conv3_block1_3_bn\n",
      "conv3_block1_add\n",
      "conv3_block1_out\n",
      "conv3_block2_1_conv\n",
      "conv3_block2_1_bn\n",
      "conv3_block2_1_relu\n",
      "conv3_block2_2_conv\n",
      "conv3_block2_2_bn\n",
      "conv3_block2_2_relu\n",
      "conv3_block2_3_conv\n",
      "conv3_block2_3_bn\n",
      "conv3_block2_add\n",
      "conv3_block2_out\n",
      "conv3_block3_1_conv\n",
      "conv3_block3_1_bn\n",
      "conv3_block3_1_relu\n",
      "conv3_block3_2_conv\n",
      "conv3_block3_2_bn\n",
      "conv3_block3_2_relu\n",
      "conv3_block3_3_conv\n",
      "conv3_block3_3_bn\n",
      "conv3_block3_add\n",
      "conv3_block3_out\n",
      "conv3_block4_1_conv\n",
      "conv3_block4_1_bn\n",
      "conv3_block4_1_relu\n",
      "conv3_block4_2_conv\n",
      "conv3_block4_2_bn\n",
      "conv3_block4_2_relu\n",
      "conv3_block4_3_conv\n",
      "conv3_block4_3_bn\n",
      "conv3_block4_add\n",
      "conv3_block4_out\n",
      "conv4_block1_1_conv\n",
      "conv4_block1_1_bn\n",
      "conv4_block1_1_relu\n",
      "conv4_block1_2_conv\n",
      "conv4_block1_2_bn\n",
      "conv4_block1_2_relu\n",
      "conv4_block1_0_conv\n",
      "conv4_block1_3_conv\n",
      "conv4_block1_0_bn\n",
      "conv4_block1_3_bn\n",
      "conv4_block1_add\n",
      "conv4_block1_out\n",
      "conv4_block2_1_conv\n",
      "conv4_block2_1_bn\n",
      "conv4_block2_1_relu\n",
      "conv4_block2_2_conv\n",
      "conv4_block2_2_bn\n",
      "conv4_block2_2_relu\n",
      "conv4_block2_3_conv\n",
      "conv4_block2_3_bn\n",
      "conv4_block2_add\n",
      "conv4_block2_out\n",
      "conv4_block3_1_conv\n",
      "conv4_block3_1_bn\n",
      "conv4_block3_1_relu\n",
      "conv4_block3_2_conv\n",
      "conv4_block3_2_bn\n",
      "conv4_block3_2_relu\n",
      "conv4_block3_3_conv\n",
      "conv4_block3_3_bn\n",
      "conv4_block3_add\n",
      "conv4_block3_out\n",
      "conv4_block4_1_conv\n",
      "conv4_block4_1_bn\n",
      "conv4_block4_1_relu\n",
      "conv4_block4_2_conv\n",
      "conv4_block4_2_bn\n",
      "conv4_block4_2_relu\n",
      "conv4_block4_3_conv\n",
      "conv4_block4_3_bn\n",
      "conv4_block4_add\n",
      "conv4_block4_out\n",
      "conv4_block5_1_conv\n",
      "conv4_block5_1_bn\n",
      "conv4_block5_1_relu\n",
      "conv4_block5_2_conv\n",
      "conv4_block5_2_bn\n",
      "conv4_block5_2_relu\n",
      "conv4_block5_3_conv\n",
      "conv4_block5_3_bn\n",
      "conv4_block5_add\n",
      "conv4_block5_out\n",
      "conv4_block6_1_conv\n",
      "conv4_block6_1_bn\n",
      "conv4_block6_1_relu\n",
      "conv4_block6_2_conv\n",
      "conv4_block6_2_bn\n",
      "conv4_block6_2_relu\n",
      "conv4_block6_3_conv\n",
      "conv4_block6_3_bn\n",
      "conv4_block6_add\n",
      "conv4_block6_out\n",
      "conv5_block1_1_conv\n",
      "conv5_block1_1_bn\n",
      "conv5_block1_1_relu\n",
      "conv5_block1_2_conv\n",
      "conv5_block1_2_bn\n",
      "conv5_block1_2_relu\n",
      "conv5_block1_0_conv\n",
      "conv5_block1_3_conv\n",
      "conv5_block1_0_bn\n",
      "conv5_block1_3_bn\n",
      "conv5_block1_add\n",
      "conv5_block1_out\n",
      "conv5_block2_1_conv\n",
      "conv5_block2_1_bn\n",
      "conv5_block2_1_relu\n",
      "conv5_block2_2_conv\n",
      "conv5_block2_2_bn\n",
      "conv5_block2_2_relu\n",
      "conv5_block2_3_conv\n",
      "conv5_block2_3_bn\n",
      "conv5_block2_add\n",
      "conv5_block2_out\n",
      "conv5_block3_1_conv\n",
      "conv5_block3_1_bn\n",
      "conv5_block3_1_relu\n",
      "conv5_block3_2_conv\n",
      "conv5_block3_2_bn\n",
      "conv5_block3_2_relu\n",
      "conv5_block3_3_conv\n",
      "conv5_block3_3_bn\n",
      "conv5_block3_add\n",
      "conv5_block3_out\n"
     ]
    }
   ],
   "source": [
    "model1 = ResNet50(weights='imagenet', include_top=False, input_shape=(224, 224, 3))\n",
    "    \n",
    "for layer in model1.layers:\n",
    "    print(layer.name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "executionInfo": {
     "elapsed": 493,
     "status": "ok",
     "timestamp": 1624440223471,
     "user": {
      "displayName": "ramdhan wibawa",
      "photoUrl": "",
      "userId": "14519448541243845830"
     },
     "user_tz": -420
    },
    "id": "-GFNqiGNfh6F"
   },
   "outputs": [],
   "source": [
    "logger = logging.getLogger(__name__)\n",
    "logging.basicConfig(level=logging.INFO)\n",
    "IMG_SIZE = 224\n",
    "LBL = dict(zip(['B_BSMUT1', 'B_CLEV5B', 'B_DISTO', 'B_GRMEND', 'B_HDBARL',\n",
    "       'B_PICKLD', 'B_SKINED', 'B_SOUND', 'B_SPRTED', 'B_SPTMLD',\n",
    "       'O_GROAT', 'O_HDOATS', 'O_SEPAFF', 'O_SOUND', 'O_SPOTMA',\n",
    "       'WD_RADPODS', 'WD_RYEGRASS', 'WD_SPEARGRASS', 'WD_WILDOATS',\n",
    "       'W_DISTO', 'W_FLDFUN', 'W_INSDA2', 'W_PICKLE', 'W_SEVERE',\n",
    "       'W_SOUND', 'W_SPROUT', 'W_STAIND', 'W_WHITEG'], range(28)))\n",
    "\n",
    "cls_map = dict(zip(LBL.values(),LBL.keys()))\n",
    "\n",
    "model_version = '001'\n",
    "\n",
    "# Work around for a SageMaker path issue\n",
    "# (see https://github.com/aws/sagemaker-python-sdk/issues/648)\n",
    "# WARNING - removing this may cause the submission process to fail\n",
    "#if abspath(\"D:/Ramdhan/Unearth/visual-grain-analysis\") not in sys.path:\n",
    " #   sys.path.append(abspath(\"D:/Ramdhan/Unearth/visual-grain-analysis\"))\n",
    "\n",
    "def input_preprocess(image, label):\n",
    "    label = tf.one_hot(label, NUM_CLASSES)\n",
    "    return image, label\n",
    "\n",
    "def get_subfolders(directory):\n",
    "    subfolders = os.listdir(directory)\n",
    "    subfolders.sort()\n",
    "    return subfolders\n",
    "\n",
    "def save_class_list(class_list):\n",
    "    class_list.sort()\n",
    "    target=open(\"class_list.txt\",'w')\n",
    "    for c in class_list:\n",
    "        target.write(c)\n",
    "        target.write(\"\\n\")\n",
    "\n",
    "def get_num_files(directory):\n",
    "    if not os.path.exists(directory):\n",
    "        return 0\n",
    "    cnt = 0\n",
    "    for r, dirs, files in os.walk(directory):\n",
    "        for dr in dirs:\n",
    "            cnt += len(glob.glob(os.path.join(r, dr + \"/*\")))\n",
    "    return cnt\n",
    "\n",
    "def outer_product(x):\n",
    "    #Einstein Notation  [batch,1,1,depth] x [batch,1,1,depth] -> [batch,depth,depth]\n",
    "    phi_I = tf.einsum('ijkm,ijkn->imn',x[0],x[1])\n",
    "    \n",
    "    # Reshape from [batch_size,depth,depth] to [batch_size, depth*depth]\n",
    "    phi_I = tf.reshape(phi_I,[-1,x[0].shape[3]*x[1].shape[3]])\n",
    "    \n",
    "    # Divide by feature map size [sizexsize]\n",
    "    size1 = int(x[1].shape[1])\n",
    "    size2 = int(x[1].shape[2])\n",
    "    phi_I = tf.divide(phi_I, size1*size2)\n",
    "    \n",
    "    # Take signed square root of phi_I\n",
    "    y_ssqrt = tf.multiply(tf.sign(phi_I),tf.sqrt(tf.abs(phi_I)+1e-12))\n",
    "    \n",
    "    # Apply l2 normalization\n",
    "    z_l2 = tf.nn.l2_normalize(y_ssqrt, axis=1)\n",
    "    return z_l2\n",
    "\n",
    "def get_cosine_schedule_with_warmup(lr, num_warmup_steps, num_training_steps, num_cycles=0.5):\n",
    "    \"\"\"\n",
    "    Modified the get_cosine_schedule_with_warmup from huggingface for tensorflow\n",
    "    (https://huggingface.co/transformers/_modules/transformers/optimization.html#get_cosine_schedule_with_warmup)\n",
    "\n",
    "    Create a schedule with a learning rate that decreases following the\n",
    "    values of the cosine function between 0 and `pi * cycles` after a warmup\n",
    "    period during which it increases linearly between 0 and 1.\n",
    "    \"\"\"\n",
    "\n",
    "    def lrfn(epoch):\n",
    "        if epoch < num_warmup_steps:\n",
    "            return float(epoch) / float(max(1, num_warmup_steps)) * lr\n",
    "        progress = float(epoch - num_warmup_steps) / float(max(1, num_training_steps - num_warmup_steps))\n",
    "        return max(0.0, 0.5 * (1.0 + math.cos(math.pi * float(num_cycles) * 2.0 * progress))) * lr\n",
    "\n",
    "    return tf.keras.callbacks.LearningRateScheduler(lrfn, verbose=True)\n",
    "\n",
    "def color_jitter(x, strength=[0.4, 0.4, 0.4, 0.1]):\n",
    "    x = tf.image.random_brightness(x, max_delta=0.8 * strength[0])\n",
    "    x = tf.image.random_contrast(\n",
    "        x, lower=1 - 0.8 * strength[1], upper=1 + 0.8 * strength[1]\n",
    "    )\n",
    "    x = tf.image.random_saturation(\n",
    "        x, lower=1 - 0.8 * strength[2], upper=1 + 0.8 * strength[2]\n",
    "    )\n",
    "    x = tf.image.random_hue(x, max_delta=0.2 * strength[3])\n",
    "    # Affine transformations can disturb the natural range of\n",
    "    # RGB images, hence this is needed.\n",
    "    x = tf.clip_by_value(x, 0, 255)\n",
    "    return x\n",
    "\n",
    "\n",
    "def color_drop(x):\n",
    "    x = tf.image.rgb_to_grayscale(x)\n",
    "    x = tf.tile(x, [1, 1, 3])\n",
    "    return x\n",
    "\n",
    "def random_crop(img, random_crop_size):\n",
    "    # Note: image_data_format is 'channel_last'\n",
    "    assert img.shape[2] == 3\n",
    "    #img = tf.image.resize(img, (224, 224))\n",
    "    img = tf.image.random_crop(img, size=[random_crop_size, random_crop_size, 3])\n",
    "    img = tf.image.resize(img, (224, 224))\n",
    "    return img\n",
    "\n",
    "def noise(img):\n",
    "    img = random_noise(img, mode='speckle', mean=0, var=0.05, clip=True)\n",
    "    return img\n",
    "\n",
    "def saturation(img):\n",
    "    img = tf.image.random_hue(img, max_delta=0.2)\n",
    "    return img\n",
    "\n",
    "def resize_pad(img):\n",
    "    #img = tf.image.resize_with_pad(img, 224, 224,method='nearest')\n",
    "    img = tf.image.resize(img, (224, 224),method='nearest')\n",
    "    return img\n",
    "\n",
    "def random_rotation(image_array):\n",
    "    # pick a random degree of rotation between 25% on the left and 25% on the right\n",
    "    random_degree = random.uniform(-180, 180)\n",
    "    return rotate(image_array, random_degree, mode='edge')\n",
    "\n",
    "def defined_rotation(image_array, degree):\n",
    "    # pick a random degree of rotation between 25% on the left and 25% on the right\n",
    "    return rotate(image_array, degree, mode='edge')\n",
    "\n",
    "def color_jitter_generator(batches):\n",
    "    while True:\n",
    "        batch_x, batch_y = next(batches)\n",
    "        batch_crops = np.zeros((batch_x.shape[0], 224, 224, 3))\n",
    "        for i in range(batch_x.shape[0]):\n",
    "            batch_crops[i] = color_jitter(batch_x[i])\n",
    "        yield (batch_crops, batch_y)\n",
    "\n",
    "def rotation_generator(batches):\n",
    "    while True:\n",
    "        batch_x, batch_y = next(batches)\n",
    "        batch_crops = np.zeros((batch_x.shape[0], 224, 224, 3))\n",
    "        for i in range(batch_x.shape[0]):\n",
    "            #batch_crops[i] = resize_pad(batch_x[i])\n",
    "            batch_crops[i] = random_rotation(batch_x[i])\n",
    "        yield (batch_crops, batch_y)\n",
    "\n",
    "def saturation_generator(batches):\n",
    "    while True:\n",
    "        batch_x, batch_y = next(batches)\n",
    "        batch_crops = np.zeros((batch_x.shape[0], 224, 224, 3))\n",
    "        for i in range(batch_x.shape[0]):\n",
    "            batch_crops[i] = saturation(batch_x[i])\n",
    "        yield (batch_crops, batch_y)\n",
    "\n",
    "def crop_generator(batches, crop_length):\n",
    "    \"\"\"Take as input a Keras ImageGen (Iterator) and generate random\n",
    "    crops from the image batches generated by the original iterator.\n",
    "    \"\"\"\n",
    "    while True:\n",
    "        batch_x, batch_y = next(batches)\n",
    "        batch_crops = np.zeros((batch_x.shape[0], 224, 224, 3))\n",
    "        for i in range(batch_x.shape[0]):\n",
    "            batch_crops[i] = random_crop(batch_x[i], crop_length)\n",
    "        yield (batch_crops, batch_y)\n",
    "        \n",
    "def noise_generator(batches):\n",
    "    while True:\n",
    "        batch_x, batch_y = next(batches)\n",
    "        batch_crops = np.zeros((batch_x.shape[0], 224, 224, 3))\n",
    "        for i in range(batch_x.shape[0]):\n",
    "            batch_crops[i] = noise(batch_x[i])\n",
    "        yield (batch_crops, batch_y)\n",
    "\n",
    "def train(args):\n",
    "    global NUM_CLASSES\n",
    "    \"\"\"Train\n",
    "    \"\"\"\n",
    "    logger.info(\"calling training function\")\n",
    "\n",
    "    ### Build the tensorflow dataset from downloaded files\n",
    "    \n",
    "    train_datagen =  ImageDataGenerator(preprocessing_function=preprocess_input,\n",
    "                                       rotation_range=360,\n",
    "                                      #shear_range=args.shear,\n",
    "                                      #zoom_range=2,\n",
    "                                      #horizontal_flip=True,\n",
    "                                      #vertical_flip=True,\n",
    "                                      #fill_mode=\"nearest\",\n",
    "                                      #width_shift_range=args.shear,\n",
    "                                      #height_shift_range=args.shear,\n",
    "                                     # brightness_range = [0,0.1],\n",
    "                                      #channel_shift_range = 0.5,\n",
    "                                      #rescale=1.0 / 255\n",
    "                                       )\n",
    "      \n",
    "\n",
    "    val_datagen = ImageDataGenerator(preprocessing_function=preprocess_input)\n",
    "    \n",
    "    TRAIN_DIR = os.path.join(args['data_dir'],  'train')\n",
    "    VAL_DIR = os.path.join(args['data_dir'], 'val')\n",
    "    \n",
    "    size = (IMG_SIZE, IMG_SIZE)\n",
    "    batch_size = 32\n",
    "\n",
    "    train_generator = train_datagen.flow_from_directory(TRAIN_DIR, target_size=size, batch_size=batch_size)\n",
    "    samples = train_generator.samples\n",
    "    #train_crops = color_jitter_generator(train_generator)\n",
    "\n",
    "    validation_generator = val_datagen.flow_from_directory(VAL_DIR, target_size=size, batch_size=batch_size)\n",
    "    \n",
    "    #ds_build_cmd = \"tfds build grains --manual_dir=\" + args['data_dir']\n",
    "    #logger.info(ds_build_cmd)\n",
    "    #os.system(ds_build_cmd)\n",
    "\n",
    "    logger.info(\"Training Model\")\n",
    "    #strategy = tf.distribute.MirroredStrategy()\n",
    "    \n",
    "    #dataset_name = \"grains\"\n",
    "    #(ds_train, ds_test), ds_info = tfds.load(dataset_name, split=[\"train\", \"val\"], with_info=True, as_supervised=True)\n",
    "    class_list = get_subfolders(TRAIN_DIR)\n",
    "    NUM_CLASSES = len(class_list)\n",
    "    save_class_list(class_list)\n",
    "\n",
    "    num_train_images = get_num_files(TRAIN_DIR)\n",
    "    num_val_images = get_num_files(VAL_DIR)\n",
    "\n",
    "    logger.info(\"number of classes is \" + str(NUM_CLASSES))\n",
    "\n",
    "    #with strategy.scope():\n",
    "    #model = build_model(num_classes=NUM_CLASSES)\n",
    "    model = build_model_bilinear(num_classes=NUM_CLASSES)\n",
    "    filename = 'best.h5'\n",
    "    #reduce_lr = tf.keras.callbacks.ReduceLROnPlateau(monitor='loss', factor=0.5, patience=50, min_lr=0.0001)\n",
    "    checkpoint = ModelCheckpoint(filename, monitor='val_acc_grain', verbose=1, save_best_only=True, mode='max')\n",
    "    stop = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=40, mode= 'min', min_delta=0.00)\n",
    "    lr_schedule= get_cosine_schedule_with_warmup(lr=0.001, num_warmup_steps=4, num_training_steps=10)\n",
    "\n",
    "    # This comes from an environment variable so we can set it to 1 for our\n",
    "    # development pipeline. Feel free to set this to any value.\n",
    "    weights = dict(zip([0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27], [1,1,1,1,1,1,1,8.94,1,1,1,1,1,8.94,1,1,1,1,1,1,1,1,1,1,8.94,1,1,1]))\n",
    "    \n",
    "    history = model.fit(train_generator, epochs=10, validation_data=validation_generator, shuffle=True,\n",
    "                        callbacks=[stop,checkpoint,lr_schedule],#steps_per_epoch=samples/batch_size,\n",
    "              verbose=1, #class_weight=weights \n",
    "                        #, workers=0, steps_per_epoch=num_train_images // batch_size, \n",
    "        #, validation_steps=num_val_images // batch_size,shuffle=True\n",
    "                        #,callbacks=callbacks_list\n",
    "                       )\n",
    "    #save_model(model,args['model_dir'])\n",
    "    #save_model(model,filename)\n",
    "    \n",
    "    return history\n",
    "\n",
    "def acc_grain(y_true, y_pred):\n",
    "    y_true_class = K.argmax(y_true, axis=-1)\n",
    "    y_pred_class = K.argmax(y_pred, axis=-1)\n",
    "    sample_weight = np.hstack((np.where(y_true_class.numpy().reshape(-1,1)==7,True,False),np.where(y_true_class.numpy().reshape(-1,1)==24,True,False),np.where(y_true_class.numpy().reshape(-1,1)==13,True,False)))\n",
    "    sample_weight = np.any(sample_weight,axis=1)\n",
    "    sample_weight = np.where(sample_weight,8.94,1.0)\n",
    "    return K.constant(accuracy_score(y_true_class,y_pred_class,sample_weight=sample_weight))\n",
    "\n",
    "# Add on new FC layers with dropout for fine tuning\n",
    "def build_model_bilinear(num_classes):\n",
    "    \n",
    "    input_tensor = layers.Input(shape=(IMG_SIZE, IMG_SIZE, 3))\n",
    "    \n",
    "    model1 = ResNet50(weights='imagenet', include_top=False, input_tensor=input_tensor,input_shape=(IMG_SIZE, IMG_SIZE, 3))\n",
    "    model2 = ResNet50(weights='imagenet', include_top=False, input_tensor=input_tensor,input_shape=(IMG_SIZE, IMG_SIZE, 3))\n",
    "    \n",
    "    for layer in model1.layers[:]:\n",
    "        layer.trainable = False\n",
    "    for layer in model2.layers[:-1]:\n",
    "        layer.trainable = False\n",
    "        \n",
    "    for i, layer in enumerate(model1.layers):\n",
    "        layer._name = 'model1_' + layer.name\n",
    "\n",
    "    last_layer1 = model1.get_layer('model1_conv5_block3_out')\n",
    "    last_output1 = last_layer1.output\n",
    "\n",
    "    for i, layer in enumerate(model2.layers):\n",
    "        layer._name = 'model2_' + layer.name\n",
    "\n",
    "    last_layer2 = model2.get_layer('model2_conv5_block3_out')\n",
    "    #last_layer2 = model2.get_layer('model2_top_conv')\n",
    "    last_output2 = last_layer2.output\n",
    "    \n",
    "    \n",
    "    model1_ = tf.keras.Model(inputs=model1.input, outputs=last_output1)\n",
    "    model2_ = tf.keras.Model(inputs=model2.input, outputs=last_output2)\n",
    "    \n",
    "    #optimizer = tf.keras.optimizers.Adam(learning_rate=1e-5, decay=1e-3)\n",
    "   \n",
    "    \n",
    "    model1_.compile(optimizer='adam',loss=\"categorical_crossentropy\", metrics=[acc_grain],run_eagerly=True)\n",
    "    model2_.compile(optimizer='adam',loss=\"categorical_crossentropy\", metrics=[acc_grain],run_eagerly=True)\n",
    "    \n",
    "    d1=model1_.output\n",
    "    d2=model2_.output\n",
    "\n",
    "    bilinear = layers.Lambda(outer_product, name='outer_product1')([d1,d2])\n",
    "    \n",
    "    predictions= layers.Dense(num_classes, activation='softmax', name='pred')(bilinear)\n",
    "    model = tf.keras.Model(inputs=model1.input, outputs=predictions)\n",
    "    \n",
    "    model.compile(optimizer='adam', loss=\"categorical_crossentropy\", metrics=[acc_grain],run_eagerly=True)\n",
    "    \n",
    "    return model\n",
    "    \n",
    "def save_model(model, model_dir):\n",
    "    \"\"\"Save model to a binary file.\n",
    "\n",
    "    This function must write the model to disk in a format that can\n",
    "    be loaded from the model_fn.\n",
    "\n",
    "    WARNING - modifying this function may cause the submission process to fail.\n",
    "    \"\"\"\n",
    "    #sm_model_dir = os.path.join(model_dir, model_version)\n",
    "    sm_model_dir = model_dir\n",
    "    if (not os.path.isdir(sm_model_dir)):\n",
    "        os.makedirs(sm_model_dir)\n",
    "        \n",
    "    logger.info(f\" model dir is {model_dir}\")\n",
    "    model.save(sm_model_dir)\n",
    "    \n",
    "    modelPath = os.path.join(sm_model_dir, 'output')\n",
    "    if (not os.path.isdir(modelPath)):\n",
    "        os.makedirs(modelPath)\n",
    "    #if (not os.path.isdir(sm_model_dir + '/code')):\n",
    "        #os.makedirs(sm_model_dir + '/code')\n",
    "\n",
    "    # Move inference.py so it gets picked up in the archive\n",
    "    #copyfile('/content/sample_data/inference.py', sm_model_dir + '/code/inference.py')\n",
    "    #copyfile('/content/sample_data/inference-requirements.txt', sm_model_dir+ '/code/requirements.txt')\n",
    "\n",
    "    with tarfile.open(os.path.join(modelPath, 'model.tar.gz'), mode='x:gz') as archive:\n",
    "        archive.add(sm_model_dir, recursive=True)\n",
    "\n",
    "def model_fn(model_dir):\n",
    "    \"\"\"Load model from binary file.\n",
    "\n",
    "    This function loads the model from disk. It is called by SageMaker.\n",
    "\n",
    "    WARNING - modifying this function may case the submission process to fail.\n",
    "    \"\"\"\n",
    "    #model_filepath = os.path.join(model_dir,  model_version)\n",
    "    #logger.info(\"loading model from \" + model_filepath)\n",
    "    model = keras.models.load_model('best.h5',custom_objects={'acc_grain':acc_grain},compile=False)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.test.is_gpu_available()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/device:GPU:0'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.test.gpu_device_name() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 459
    },
    "executionInfo": {
     "elapsed": 38441,
     "status": "error",
     "timestamp": 1624440265326,
     "user": {
      "displayName": "ramdhan wibawa",
      "photoUrl": "",
      "userId": "14519448541243845830"
     },
     "user_tz": -420
    },
    "id": "RLmWtwmRfh6P",
    "outputId": "e5e2ae19-620c-4d31-f907-12bb2132639e"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:__main__:calling training function\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 19182 images belonging to 28 classes.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:__main__:Training Model\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 7648 images belonging to 28 classes.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:__main__:number of classes is 28\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 00001: LearningRateScheduler reducing learning rate to 0.0.\n",
      "Epoch 1/10\n"
     ]
    },
    {
     "ename": "UnknownError",
     "evalue": "Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above. [Op:Conv2D]",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mUnknownError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-9-f940733bccd2>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     13\u001b[0m            'data_dir' : \"D:/Ramdhan/Unearth/visual-grain-analysis/data/public/\"}\n\u001b[0;32m     14\u001b[0m     \u001b[0mstart\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 15\u001b[1;33m     \u001b[0mhistory\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtrain\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     16\u001b[0m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'duration run'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mstart\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;36m60\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;36m60\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m' hours'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-6-2d5f0082dd7b>\u001b[0m in \u001b[0;36mtrain\u001b[1;34m(args)\u001b[0m\n\u001b[0;32m    255\u001b[0m     history = model.fit(train_generator, epochs=10, validation_data=validation_generator, shuffle=True,\n\u001b[0;32m    256\u001b[0m                         \u001b[0mcallbacks\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mstop\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mcheckpoint\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlr_schedule\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;31m#steps_per_epoch=samples/batch_size,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 257\u001b[1;33m               \u001b[0mverbose\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;31m#class_weight=weights\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    258\u001b[0m                         \u001b[1;31m#, workers=0, steps_per_epoch=num_train_images // batch_size,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    259\u001b[0m         \u001b[1;31m#, validation_steps=num_val_images // batch_size,shuffle=True\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python36\\site-packages\\tensorflow\\python\\keras\\engine\\training.py\u001b[0m in \u001b[0;36m_method_wrapper\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    106\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0m_method_wrapper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    107\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_in_multi_worker_mode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m  \u001b[1;31m# pylint: disable=protected-access\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 108\u001b[1;33m       \u001b[1;32mreturn\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    109\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    110\u001b[0m     \u001b[1;31m# Running inside `run_distribute_coordinator` already.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python36\\site-packages\\tensorflow\\python\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[0;32m   1096\u001b[0m                 batch_size=batch_size):\n\u001b[0;32m   1097\u001b[0m               \u001b[0mcallbacks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mon_train_batch_begin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1098\u001b[1;33m               \u001b[0mtmp_logs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtrain_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1099\u001b[0m               \u001b[1;32mif\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshould_sync\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1100\u001b[0m                 \u001b[0mcontext\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masync_wait\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python36\\site-packages\\tensorflow\\python\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mtrain_function\u001b[1;34m(iterator)\u001b[0m\n\u001b[0;32m    804\u001b[0m       \u001b[1;32mdef\u001b[0m \u001b[0mtrain_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    805\u001b[0m         \u001b[1;34m\"\"\"Runs a training execution with one step.\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 806\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mstep_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0miterator\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    807\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    808\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python36\\site-packages\\tensorflow\\python\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mstep_function\u001b[1;34m(model, iterator)\u001b[0m\n\u001b[0;32m    794\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    795\u001b[0m       \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnext\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 796\u001b[1;33m       \u001b[0moutputs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdistribute_strategy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrun_step\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    797\u001b[0m       outputs = reduce_per_replica(\n\u001b[0;32m    798\u001b[0m           outputs, self.distribute_strategy, reduction='first')\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python36\\site-packages\\tensorflow\\python\\distribute\\distribute_lib.py\u001b[0m in \u001b[0;36mrun\u001b[1;34m(***failed resolving arguments***)\u001b[0m\n\u001b[0;32m   1209\u001b[0m       fn = autograph.tf_convert(\n\u001b[0;32m   1210\u001b[0m           fn, autograph_ctx.control_status_ctx(), convert_by_default=False)\n\u001b[1;32m-> 1211\u001b[1;33m       \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_extended\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcall_for_each_replica\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1212\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1213\u001b[0m   \u001b[1;31m# TODO(b/151224785): Remove deprecated alias.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python36\\site-packages\\tensorflow\\python\\distribute\\distribute_lib.py\u001b[0m in \u001b[0;36mcall_for_each_replica\u001b[1;34m(self, fn, args, kwargs)\u001b[0m\n\u001b[0;32m   2583\u001b[0m       \u001b[0mkwargs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2584\u001b[0m     \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_container_strategy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscope\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2585\u001b[1;33m       \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_call_for_each_replica\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2586\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2587\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0m_call_for_each_replica\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python36\\site-packages\\tensorflow\\python\\distribute\\distribute_lib.py\u001b[0m in \u001b[0;36m_call_for_each_replica\u001b[1;34m(self, fn, args, kwargs)\u001b[0m\n\u001b[0;32m   2943\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_container_strategy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2944\u001b[0m         replica_id_in_sync_group=constant_op.constant(0, dtypes.int32)):\n\u001b[1;32m-> 2945\u001b[1;33m       \u001b[1;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2946\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2947\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0m_reduce_to\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreduce_op\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdestinations\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexperimental_hints\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python36\\site-packages\\tensorflow\\python\\autograph\\impl\\api.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    273\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    274\u001b[0m     \u001b[1;32mwith\u001b[0m \u001b[0mag_ctx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mControlStatusCtx\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstatus\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mag_ctx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mStatus\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mUNSPECIFIED\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 275\u001b[1;33m       \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    276\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    277\u001b[0m   \u001b[1;32mif\u001b[0m \u001b[0minspect\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0misfunction\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0minspect\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mismethod\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python36\\site-packages\\tensorflow\\python\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mrun_step\u001b[1;34m(data)\u001b[0m\n\u001b[0;32m    787\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    788\u001b[0m       \u001b[1;32mdef\u001b[0m \u001b[0mrun_step\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 789\u001b[1;33m         \u001b[0moutputs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtrain_step\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    790\u001b[0m         \u001b[1;31m# Ensure counter is updated only if `train_step` succeeds.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    791\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcontrol_dependencies\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_minimum_control_deps\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python36\\site-packages\\tensorflow\\python\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mtrain_step\u001b[1;34m(self, data)\u001b[0m\n\u001b[0;32m    745\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    746\u001b[0m     \u001b[1;32mwith\u001b[0m \u001b[0mbackprop\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mGradientTape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mtape\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 747\u001b[1;33m       \u001b[0my_pred\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtraining\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    748\u001b[0m       loss = self.compiled_loss(\n\u001b[0;32m    749\u001b[0m           y, y_pred, sample_weight, regularization_losses=self.losses)\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python36\\site-packages\\tensorflow\\python\\keras\\engine\\base_layer.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    983\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    984\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0menable_auto_cast_variables\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_compute_dtype_object\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 985\u001b[1;33m           \u001b[0moutputs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcall_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    986\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    987\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_activity_regularizer\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python36\\site-packages\\tensorflow\\python\\keras\\engine\\functional.py\u001b[0m in \u001b[0;36mcall\u001b[1;34m(self, inputs, training, mask)\u001b[0m\n\u001b[0;32m    384\u001b[0m     \"\"\"\n\u001b[0;32m    385\u001b[0m     return self._run_internal_graph(\n\u001b[1;32m--> 386\u001b[1;33m         inputs, training=training, mask=mask)\n\u001b[0m\u001b[0;32m    387\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    388\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0mcompute_output_shape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput_shape\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python36\\site-packages\\tensorflow\\python\\keras\\engine\\functional.py\u001b[0m in \u001b[0;36m_run_internal_graph\u001b[1;34m(self, inputs, training, mask)\u001b[0m\n\u001b[0;32m    506\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    507\u001b[0m         \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnode\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmap_arguments\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtensor_dict\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 508\u001b[1;33m         \u001b[0moutputs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnode\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlayer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    509\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    510\u001b[0m         \u001b[1;31m# Update tensor_dict.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python36\\site-packages\\tensorflow\\python\\keras\\engine\\base_layer.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    983\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    984\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0menable_auto_cast_variables\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_compute_dtype_object\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 985\u001b[1;33m           \u001b[0moutputs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcall_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    986\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    987\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_activity_regularizer\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python36\\site-packages\\tensorflow\\python\\keras\\layers\\convolutional.py\u001b[0m in \u001b[0;36mcall\u001b[1;34m(self, inputs)\u001b[0m\n\u001b[0;32m    245\u001b[0m       \u001b[0minputs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0marray_ops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpad\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_compute_causal_padding\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    246\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 247\u001b[1;33m     \u001b[0moutputs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_convolution_op\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkernel\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    248\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    249\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0muse_bias\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python36\\site-packages\\tensorflow\\python\\util\\dispatch.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    199\u001b[0m     \u001b[1;34m\"\"\"Call target, and fall back on dispatchers if there is a TypeError.\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    200\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 201\u001b[1;33m       \u001b[1;32mreturn\u001b[0m \u001b[0mtarget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    202\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mTypeError\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    203\u001b[0m       \u001b[1;31m# Note: convert_to_eager_tensor currently raises a ValueError, not a\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python36\\site-packages\\tensorflow\\python\\ops\\nn_ops.py\u001b[0m in \u001b[0;36mconvolution_v2\u001b[1;34m(input, filters, strides, padding, data_format, dilations, name)\u001b[0m\n\u001b[0;32m   1016\u001b[0m       \u001b[0mdata_format\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdata_format\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1017\u001b[0m       \u001b[0mdilations\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdilations\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1018\u001b[1;33m       name=name)\n\u001b[0m\u001b[0;32m   1019\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1020\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python36\\site-packages\\tensorflow\\python\\ops\\nn_ops.py\u001b[0m in \u001b[0;36mconvolution_internal\u001b[1;34m(input, filters, strides, padding, data_format, dilations, name, call_from_convolution, num_spatial_dims)\u001b[0m\n\u001b[0;32m   1146\u001b[0m           \u001b[0mdata_format\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdata_format\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1147\u001b[0m           \u001b[0mdilations\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdilations\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1148\u001b[1;33m           name=name)\n\u001b[0m\u001b[0;32m   1149\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1150\u001b[0m       \u001b[1;32mif\u001b[0m \u001b[0mchannel_index\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python36\\site-packages\\tensorflow\\python\\ops\\nn_ops.py\u001b[0m in \u001b[0;36m_conv2d_expanded_batch\u001b[1;34m(input, filters, strides, padding, data_format, dilations, name)\u001b[0m\n\u001b[0;32m   2590\u001b[0m         \u001b[0mdata_format\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdata_format\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2591\u001b[0m         \u001b[0mdilations\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdilations\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2592\u001b[1;33m         name=name)\n\u001b[0m\u001b[0;32m   2593\u001b[0m   return squeeze_batch_dims(\n\u001b[0;32m   2594\u001b[0m       \u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python36\\site-packages\\tensorflow\\python\\ops\\gen_nn_ops.py\u001b[0m in \u001b[0;36mconv2d\u001b[1;34m(input, filter, strides, padding, use_cudnn_on_gpu, explicit_paddings, data_format, dilations, name)\u001b[0m\n\u001b[0;32m    936\u001b[0m       \u001b[1;32mreturn\u001b[0m \u001b[0m_result\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    937\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0m_core\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 938\u001b[1;33m       \u001b[0m_ops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mraise_from_not_ok_status\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    939\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0m_core\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_FallbackException\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    940\u001b[0m       \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python36\\site-packages\\tensorflow\\python\\framework\\ops.py\u001b[0m in \u001b[0;36mraise_from_not_ok_status\u001b[1;34m(e, name)\u001b[0m\n\u001b[0;32m   6841\u001b[0m   \u001b[0mmessage\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmessage\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;34m\" name: \"\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mname\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32melse\u001b[0m \u001b[1;34m\"\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   6842\u001b[0m   \u001b[1;31m# pylint: disable=protected-access\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 6843\u001b[1;33m   \u001b[0msix\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mraise_from\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcore\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_status_to_exception\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcode\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmessage\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   6844\u001b[0m   \u001b[1;31m# pylint: enable=protected-access\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   6845\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\object_detection_gpu\\lib\\site-packages\\six.py\u001b[0m in \u001b[0;36mraise_from\u001b[1;34m(value, from_value)\u001b[0m\n",
      "\u001b[1;31mUnknownError\u001b[0m: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above. [Op:Conv2D]"
     ]
    }
   ],
   "source": [
    "if __name__ == \"__main__\":\n",
    "    \"\"\"Training Main\n",
    "\n",
    "    The main function is called by both Unearthed's SageMaker pipeline and the\n",
    "    Unearthed CLI's \"unearthed train\" command.\n",
    "    \n",
    "    WARNING - modifying this function may cause the submission process to fail.\n",
    "\n",
    "    The main function must call preprocess, arrange th\n",
    "    \"\"\"\n",
    "    \n",
    "    args = {'model_dir': \"D:/Ramdhan/Unearth/visual-grain-analysis/models/\",\n",
    "           'data_dir' : \"D:/Ramdhan/Unearth/visual-grain-analysis/data/public/\"}\n",
    "    start = time.time()\n",
    "    history = train(args)\n",
    "    print('duration run',(time.time()-start)/60/60,' hours')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2160x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "# Plot training & validation iou_score values\n",
    "plt.figure(figsize=(30, 20))\n",
    "plt.subplot(121)\n",
    "plt.plot(history.history['acc_grain'])\n",
    "plt.plot(history.history['val_acc_grain'])\n",
    "plt.title('Model acc_score')\n",
    "plt.ylabel('acc_score')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Test'], loc='upper left')\n",
    "\n",
    "# Plot training & validation loss values\n",
    "plt.subplot(122)\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('Model loss')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Test'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 224, 224, 3)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img_preprocessed.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x24a240bf470>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(random_rotation(img_preprocessed[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "class TTA_ModelWrapper():\n",
    "    \"\"\"A simple TTA wrapper for keras computer vision models.\n",
    "    Args:\n",
    "        model (keras model): A fitted keras model with a predict method.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, model):\n",
    "        self.model = model\n",
    "\n",
    "    def predict(self, X):\n",
    "        \"\"\"Wraps the predict method of the provided model.\n",
    "        Augments the testdata with horizontal and vertical flips and\n",
    "        averages the results.\n",
    "        Args:\n",
    "            X (numpy array of dim 4): The data to get predictions for.\n",
    "        \"\"\"\n",
    "        pred = []\n",
    "        angle = [0,45,90,135,180,225, 270,315, 360]\n",
    "        #p0 = self.model.predict(X)\n",
    "        #pred.append(p0.ravel())\n",
    "        for i in range(9):\n",
    "            #p = self.model.predict(np.expand_dims(random_rotation(X[0]),axis=0)) \n",
    "            p = self.model.predict(np.expand_dims(defined_rotation(X[0],angle[i]),axis=0))\n",
    "            pred.append(p.ravel())\n",
    "        #return np.expand_dims(np.mean(np.array(pred),axis=0),axis=0) \n",
    "        return np.expand_dims(gmean(np.array(pred),axis=0),axis=0) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "model1 = TTA_ModelWrapper(model)\n",
    "pred = model1.predict(img_preprocessed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 28)"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x24a1148f780>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(np.flipud(img_preprocessed)[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 1, 224, 1, 224, 3)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def expand(x):\n",
    "    return np.expand_dims(np.expand_dims(x, axis=0), axis=3)\n",
    "\n",
    "expand(img_preprocessed[:, :, :, :]).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from edafa import ClassPredictor\n",
    "\n",
    "class myPredictor(ClassPredictor):\n",
    "    def __init__(self,model,*args,**kwargs):\n",
    "        super().__init__(*args,**kwargs)\n",
    "        self.model = model\n",
    "\n",
    "    def predict_patches(self,patches):\n",
    "        return self.model.predict(patches)\n",
    "\n",
    "conf = '{\"augs\":[\"NO\",\\\n",
    "                \"ROT90\",\"ROT180\",\"ROT270\"],\\\n",
    "        \"mean\":\"GEO\"}'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 350
    },
    "executionInfo": {
     "elapsed": 2298,
     "status": "error",
     "timestamp": 1624373965288,
     "user": {
      "displayName": "ramdhan wibawa",
      "photoUrl": "",
      "userId": "14519448541243845830"
     },
     "user_tz": -420
    },
    "id": "UejZgfeXZ5C7",
    "outputId": "fbfcc699-a9ac-4b16-ccbb-0a8c4a25222e"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:__main__:loading the model\n",
      "INFO:__main__:Reading the test set\n",
      "INFO:__main__:predictions have shape of (7648, 9)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score:0.9764857885325867\n",
      "Duration Run 1.097132101588779  hours\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.preprocessing import image\n",
    "import pandas as pd\n",
    "import zipfile\n",
    "import csv\n",
    "from tta_wrapper import tta_segmentation\n",
    "\n",
    "start = time.time()\n",
    "\n",
    "def load_class_list(class_list_file):\n",
    "    class_list = []\n",
    "    with open(class_list_file, 'r') as csvfile:\n",
    "        file_reader = csv.reader(csvfile)\n",
    "        for row in file_reader:\n",
    "            class_list.append(row[0])\n",
    "    class_list.sort()\n",
    "    return class_list\n",
    "\n",
    "class_list = load_class_list('class_list.txt')\n",
    "\n",
    "IMG_SIZE=224\n",
    "LBL = dict(zip(class_list, range(28)))\n",
    "\n",
    "cls_map = dict(zip(LBL.values(),LBL.keys()))\n",
    "\n",
    "def init_dir(pth):\n",
    "    if os.path.exists(pth):\n",
    "        shutil.rmtree(pth)\n",
    "    os.mkdir(pth)\n",
    "\n",
    "logger.info(\"loading the model\")\n",
    "model = model_fn(args['model_dir'])\n",
    "logger.info(\"Reading the test set\")\n",
    "\n",
    "#model1 = tta_classification(model, h_flip=False, rotation=(0, 360), merge='mean')\n",
    "model = TTA_ModelWrapper(model)\n",
    "#model1 = myPredictor(model,conf)\n",
    "\n",
    "res = []\n",
    "for fn in glob.iglob(args['data_dir'] + 'val/**/*.png', recursive=True):\n",
    "    file_name = os.path.basename(fn)\n",
    "    path = os.path.abspath(fn)\n",
    "    folder = os.path.split(os.path.dirname(path))[1]\n",
    "    if len(file_name.split(\"-\")) > 2:  # ignore master image with may grains, raw image names are in guid format\n",
    "        im = image.load_img(path, target_size=(IMG_SIZE, IMG_SIZE))\n",
    "        img_array = image.img_to_array(im)\n",
    "        img_batch = np.expand_dims(img_array, axis=0)\n",
    "        img_preprocessed = preprocess_input(img_batch)\n",
    "        pred = model.predict(img_preprocessed)\n",
    "        top3 = (-pred[0]).argsort()[:3]\n",
    "        res.append({'file_name': file_name, 'path': path, 'cls': folder, 'prediction':top3[0],  'proba_1':pred[0][top3[0]], 'prediction2':top3[1], 'proba_2':pred[0][top3[1]],  'prediction3':top3[2], 'proba_3':pred[0][top3[2]]})\n",
    "\n",
    "df = pd.DataFrame(res)\n",
    "df['prediction'] = df.prediction.map(cls_map)\n",
    "df['prediction2'] = df.prediction2.map(cls_map)\n",
    "df['prediction3'] = df.prediction3.map(cls_map)\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "def w(x):\n",
    "    if \"_SOUND\" in x:\n",
    "        return 8.94 # weighting  Healthy grains more important than defective grains\n",
    "    return 1\n",
    "\n",
    "def scoring_fn(df):\n",
    "    \"\"\"\n",
    "    Weighted Accuracy Metric 90% Healthy grains, 10% unhealthy grains\n",
    "    \"\"\"\n",
    "    df['weight'] = df.cls.apply(w)\n",
    "    return accuracy_score(df.cls,df.prediction,sample_weight=df.weight)\n",
    "\n",
    "logger.info(f\"predictions have shape of {df.shape}\")\n",
    "\n",
    "score = scoring_fn(df)\n",
    "print (\"Accuracy Score:\" + str(score))\n",
    "# write to the output location\n",
    "\n",
    "print('Duration Run',(time.time()-start)/3600,' hours')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "0.974128288220523 0.9762641016371597 0.9761019905618932 0.9764857885325867"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               precision    recall  f1-score   support\n",
      "\n",
      "     B_BSMUT1       0.98      0.90      0.94       105\n",
      "     B_CLEV5B       0.95      0.41      0.57       100\n",
      "      B_DISTO       0.93      0.43      0.59       100\n",
      "     B_GRMEND       0.98      0.51      0.67        88\n",
      "     B_HDBARL       0.67      0.33      0.44        30\n",
      "     B_PICKLD       0.96      0.47      0.63        92\n",
      "     B_SKINED       1.00      0.20      0.34        88\n",
      "      B_SOUND       0.81      1.00      0.89      1653\n",
      "     B_SPRTED       1.00      0.87      0.93        93\n",
      "     B_SPTMLD       0.90      0.23      0.37       112\n",
      "      O_GROAT       1.00      0.78      0.88        90\n",
      "     O_HDOATS       0.76      0.26      0.39        99\n",
      "     O_SEPAFF       0.94      0.72      0.81       109\n",
      "      O_SOUND       0.92      1.00      0.96      1988\n",
      "     O_SPOTMA       0.83      0.29      0.43       103\n",
      "   WD_RADPODS       0.99      0.99      0.99        94\n",
      "  WD_RYEGRASS       0.96      0.94      0.95        87\n",
      "WD_SPEARGRASS       0.99      0.95      0.97        94\n",
      "  WD_WILDOATS       1.00      0.99      0.99        92\n",
      "      W_DISTO       0.87      0.66      0.75       102\n",
      "     W_FLDFUN       0.74      0.16      0.27       105\n",
      "     W_INSDA2       0.89      0.60      0.72        97\n",
      "     W_PICKLE       0.96      0.88      0.92       104\n",
      "     W_SEVERE       0.97      0.93      0.95       101\n",
      "      W_SOUND       0.83      1.00      0.90      1611\n",
      "     W_SPROUT       0.97      0.33      0.50       108\n",
      "     W_STAIND       0.75      0.43      0.55        92\n",
      "     W_WHITEG       0.93      0.47      0.62       111\n",
      "\n",
      "     accuracy                           0.87      7648\n",
      "    macro avg       0.91      0.63      0.71      7648\n",
      " weighted avg       0.88      0.87      0.85      7648\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(df['cls'], df['prediction']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import confusion_matrix\n",
    "ticks=np.linspace(0, 27,num=28)\n",
    "plt.figure(figsize=(15,15))\n",
    "plt.imshow(confusion_matrix(df['cls'], df['prediction'],normalize='true'), interpolation='none')\n",
    "plt.colorbar()\n",
    "plt.xticks(ticks,fontsize=6)\n",
    "plt.yticks(ticks,fontsize=6)\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "WZXP_9Kzfh6Q"
   },
   "outputs": [],
   "source": [
    "from keras_preprocessing import image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "s0bgHcc9rskI"
   },
   "outputs": [],
   "source": [
    "NUM_CLASSES=28\n",
    "model = build_model(num_classes=NUM_CLASSES)\n",
    "model.fit?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "3Fcx6Qvnfh6Q",
    "outputId": "fc7bceb9-5ffb-46bc-ffaf-7e1cf07f05c0"
   },
   "outputs": [],
   "source": [
    "image.random_rotation(90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "saWJd9X3fh6R"
   },
   "outputs": [],
   "source": [
    " rotation_range=args.rotation,\n",
    "      shear_range=args.shear,\n",
    "      zoom_range=args.zoom,\n",
    "      horizontal_flip=args.h_flip,\n",
    "      vertical_flip=args.v_flip"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "grain_unearth.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}