dataset_testing_nb.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.7.0\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import os\n",
    "import PIL\n",
    "import PIL.Image\n",
    "import tensorflow as tf\n",
    "import glob\n",
    "\n",
    "print(tf.__version__)\n",
    "\n",
    "batch_size = 32\n",
    "img_height = 180\n",
    "img_width = 180"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data_dir = 'dataset_lettuce'\n",
    "#\n",
    "# train_ds = tf.keras.utils.image_dataset_from_directory(\n",
    "#   data_dir,\n",
    "#   validation_split=0.2,\n",
    "#   subset=\"training\",\n",
    "#   seed=123,\n",
    "#   image_size=(img_height, img_width),\n",
    "#   batch_size=batch_size)\n",
    "#\n",
    "# data_dir = 'dataset_lettuce'\n",
    "# val_ds = tf.keras.utils.image_dataset_from_directory(\n",
    "#   data_dir,\n",
    "#   validation_split=0.2,\n",
    "#   subset=\"validation\",\n",
    "#   seed=123,\n",
    "#   image_size=(img_height, img_width),\n",
    "#   batch_size=batch_size)\n",
    "#\n",
    "# class_names = train_ds.class_names\n",
    "# print(class_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "directory:  dataset_lettuce\n",
      "Number of Images:  132\n"
     ]
    }
   ],
   "source": [
    "import pathlib\n",
    "data_dir = 'dataset_lettuce/'\n",
    "data_dir = pathlib.Path(data_dir)\n",
    "print('directory: ',data_dir)\n",
    "\n",
    "image_count = len(list(data_dir.glob('*/*.jpg')))\n",
    "print('Number of Images: ', image_count)\n",
    "list_ds = tf.data.Dataset.list_files(str(data_dir/'*/*'), shuffle=False)\n",
    "list_ds = list_ds.shuffle(image_count, reshuffle_each_iteration=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "for f in list_ds:\n",
    "  l = f.numpy()\n",
    "  print(l)\n",
    "  for i in range(len(l)):\n",
    "    print(l[i])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['bacterial' 'fungal_downy_mildew' 'fungal_powdery_mildew'\n",
      " 'fungal_septoria_blight' 'fungal_wilt_and_leaf_blight' 'healthy' 'viral']\n"
     ]
    }
   ],
   "source": [
    "class_names = np.array(sorted([item.name for item in data_dir.glob('*') if item.name != \"LICENSE.txt\"]))\n",
    "print(class_names)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "107\n",
      "26\n"
     ]
    }
   ],
   "source": [
    "val_size = int(image_count * 0.2)\n",
    "train_ds = list_ds.skip(val_size)\n",
    "val_ds = list_ds.take(val_size)\n",
    "\n",
    "print(tf.data.experimental.cardinality(train_ds).numpy())\n",
    "print(tf.data.experimental.cardinality(val_ds).numpy())"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "outputs": [],
   "source": [
    "def get_label(file_path):\n",
    "  # Convert the path to a list of path components\n",
    "  parts = tf.strings.split(file_path, os.path.sep)\n",
    "  # The second to last is the class-directory\n",
    "  one_hot = parts[-2] == class_names\n",
    "  # Integer encode the label\n",
    "  return tf.argmax(one_hot)\n",
    "\n",
    "def decode_img(img):\n",
    "  # Convert the compressed string to a 3D uint8 tensor\n",
    "  img = tf.io.decode_jpeg(img, channels=3)\n",
    "  # Resize the image to the desired size\n",
    "  return tf.image.resize(img, [img_height, img_width])\n",
    "\n",
    "def process_path(file_path):\n",
    "  label = get_label(file_path)\n",
    "  # Load the raw data from the file as a string\n",
    "  img = tf.io.read_file(file_path)\n",
    "  img = decode_img(img)\n",
    "  return img, label"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image shape:  (180, 180, 3)\n",
      "Label:  2\n"
     ]
    }
   ],
   "source": [
    "AUTOTUNE = tf.data.AUTOTUNE\n",
    "train_ds = train_ds.map(process_path, num_parallel_calls=AUTOTUNE)\n",
    "val_ds = val_ds.map(process_path, num_parallel_calls=AUTOTUNE)\n",
    "\n",
    "for image, label in train_ds.take(1):\n",
    "  print(\"Image shape: \", image.numpy().shape)\n",
    "  print(\"Label: \", label.numpy())"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "outputs": [],
   "source": [
    "def configure_for_performance(ds):\n",
    "  ds = ds.cache()\n",
    "  ds = ds.shuffle(buffer_size=1000)\n",
    "  ds = ds.batch(batch_size)\n",
    "  ds = ds.prefetch(buffer_size=AUTOTUNE)\n",
    "  return ds\n",
    "\n",
    "train_ds = configure_for_performance(train_ds)\n",
    "val_ds = configure_for_performance(val_ds)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'tensorflow.python.data.ops.dataset_ops.PrefetchDataset'>\n"
     ]
    },
    {
     "ename": "InvalidArgumentError",
     "evalue": "Unknown image file format. One of JPEG, PNG, GIF, BMP required.\n\t [[{{node DecodeJpeg}}]] [Op:IteratorGetNext]",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mInvalidArgumentError\u001B[0m                      Traceback (most recent call last)",
      "\u001B[1;32m~\\AppData\\Local\\Temp/ipykernel_8952/2180219581.py\u001B[0m in \u001B[0;36m<module>\u001B[1;34m\u001B[0m\n\u001B[0;32m      2\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m      3\u001B[0m \u001B[0mprint\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mtype\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mtrain_ds\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----> 4\u001B[1;33m \u001B[0mimage_batch\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mlabel_batch\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mnext\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0miter\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mtrain_ds\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      5\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m      6\u001B[0m \u001B[0mplt\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mfigure\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mfigsize\u001B[0m\u001B[1;33m=\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;36m10\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;36m10\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~\\anaconda3\\envs\\lettuce-v2\\lib\\site-packages\\tensorflow\\python\\data\\ops\\iterator_ops.py\u001B[0m in \u001B[0;36m__next__\u001B[1;34m(self)\u001B[0m\n\u001B[0;32m    798\u001B[0m   \u001B[1;32mdef\u001B[0m \u001B[0m__next__\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mself\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    799\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--> 800\u001B[1;33m       \u001B[1;32mreturn\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m_next_internal\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    801\u001B[0m     \u001B[1;32mexcept\u001B[0m \u001B[0merrors\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mOutOfRangeError\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    802\u001B[0m       \u001B[1;32mraise\u001B[0m \u001B[0mStopIteration\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32m~\\anaconda3\\envs\\lettuce-v2\\lib\\site-packages\\tensorflow\\python\\data\\ops\\iterator_ops.py\u001B[0m in \u001B[0;36m_next_internal\u001B[1;34m(self)\u001B[0m\n\u001B[0;32m    781\u001B[0m     \u001B[1;31m# to communicate that there is no more data to iterate over.\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    782\u001B[0m     \u001B[1;32mwith\u001B[0m \u001B[0mcontext\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mexecution_mode\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mcontext\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mSYNC\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--> 783\u001B[1;33m       ret = gen_dataset_ops.iterator_get_next(\n\u001B[0m\u001B[0;32m    784\u001B[0m           \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m_iterator_resource\u001B[0m\u001B[1;33m,\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    785\u001B[0m           \u001B[0moutput_types\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m_flat_output_types\u001B[0m\u001B[1;33m,\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32m~\\anaconda3\\envs\\lettuce-v2\\lib\\site-packages\\tensorflow\\python\\ops\\gen_dataset_ops.py\u001B[0m in \u001B[0;36miterator_get_next\u001B[1;34m(iterator, output_types, output_shapes, name)\u001B[0m\n\u001B[0;32m   2842\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   2843\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-> 2844\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   2845\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   2846\u001B[0m       \u001B[1;32mpass\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32m~\\anaconda3\\envs\\lettuce-v2\\lib\\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   7105\u001B[0m \u001B[1;32mdef\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[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m   7106\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[1;32m-> 7107\u001B[1;33m   \u001B[1;32mraise\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[1;32mfrom\u001B[0m \u001B[1;32mNone\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[0m\u001B[0;32m   7108\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m   7109\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;31mInvalidArgumentError\u001B[0m: Unknown image file format. One of JPEG, PNG, GIF, BMP required.\n\t [[{{node DecodeJpeg}}]] [Op:IteratorGetNext]"
     ]
    }
   ],
   "source": [
    "import matplotlib as plt\n",
    "\n",
    "print(type(train_ds))\n",
    "image_batch, label_batch = next(iter(train_ds))\n",
    "\n",
    "plt.figure(figsize=(10, 10))\n",
    "for i in range(9):\n",
    "  ax = plt.subplot(3, 3, i + 1)\n",
    "  plt.imshow(image_batch[i].numpy().astype(\"uint8\"))\n",
    "  label = label_batch[i]\n",
    "  plt.title(class_names[label])\n",
    "  plt.axis(\"off\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "num_classes = 5\n",
    "\n",
    "model = tf.keras.Sequential([\n",
    "  tf.keras.layers.Rescaling(1./255),\n",
    "  tf.keras.layers.Conv2D(32, 3, activation='relu'),\n",
    "  tf.keras.layers.MaxPooling2D(),\n",
    "  tf.keras.layers.Conv2D(32, 3, activation='relu'),\n",
    "  tf.keras.layers.MaxPooling2D(),\n",
    "  tf.keras.layers.Conv2D(32, 3, activation='relu'),\n",
    "  tf.keras.layers.MaxPooling2D(),\n",
    "  tf.keras.layers.Flatten(),\n",
    "  tf.keras.layers.Dense(128, activation='relu'),\n",
    "  tf.keras.layers.Dense(num_classes)\n",
    "])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "model.compile(\n",
    "  optimizer='adam',\n",
    "  loss=tf.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
    "  metrics=['accuracy'])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "model.fit(\n",
    "  train_ds,\n",
    "  validation_data=val_ds,\n",
    "  epochs=3\n",
    ")\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
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