Plant Pathology 2021 GPU training.ipynb

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   "source": [
    "# Plant Pathology 2021 - FGVC8\n",
    "<img src=\"https://www.researchgate.net/profile/Seung-Yeol-Lee/publication/282210822/figure/fig1/AS:502574476660736@1496834500292/Comparison-of-leaves-with-apple-blotch-disease-and-apple-blotch-like-symptom-A-H-apple.png\" width=\"400\" height=\"400\" />\n",
    "\n",
    "## Description\n",
    "Apples are one of the most important temperate fruit crops in the world. Foliar (leaf) diseases pose a major threat to the overall productivity and quality of apple orchards. The current process for disease diagnosis in apple orchards is based on manual scouting by humans, which is time-consuming and expensive.\n",
    "\n",
    "Although computer vision-based models have shown promise for plant disease identification, there are some limitations that need to be addressed. Large variations in visual symptoms of a single disease across different apple cultivars, or new varieties that originated under cultivation, are major challenges for computer vision-based disease identification. These variations arise from differences in natural and image capturing environments, for example, leaf color and leaf morphology, the age of infected tissues, non-uniform image background, and different light illumination during imaging etc.\n",
    "\n",
    "## About CVPR\n",
    "This competition is part of the Fine-Grained Visual Categorization FGVC8 workshop at the Computer Vision and Pattern Recognition Conference CVPR 2021. A panel will review the top submissions for the competition based on the description of the methods provided. From this, a subset may be invited to present their results at the workshop. Attending the workshop is not required to participate in the competition, however only teams that are attending the workshop will be considered to present their work."
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    {
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     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pip in /opt/conda/lib/python3.7/site-packages (21.0.1)\r\n"
     ]
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   ],
   "source": [
    "!/opt/conda/bin/python3.7 -m pip install --upgrade pip\n",
    "! pip install -q efficientnet"
   ]
  },
  {
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   "source": [
    "#-------------------\n",
    "# importing libraries\n",
    "#-------------------\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras import layers\n",
    "import efficientnet.tfkeras as efn\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import PIL\n",
    "import shutil\n",
    "import csv"
   ]
  },
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   "cell_type": "code",
   "execution_count": 3,
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of unique labels:  12\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>image</th>\n",
       "      <th>labels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>800113bb65efe69e.jpg</td>\n",
       "      <td>healthy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8002cb321f8bfcdf.jpg</td>\n",
       "      <td>scab frog_eye_leaf_spot complex</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>80070f7fb5e2ccaa.jpg</td>\n",
       "      <td>scab</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>80077517781fb94f.jpg</td>\n",
       "      <td>scab</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>800cbf0ff87721f8.jpg</td>\n",
       "      <td>complex</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "                  image                           labels\n",
       "0  800113bb65efe69e.jpg                          healthy\n",
       "1  8002cb321f8bfcdf.jpg  scab frog_eye_leaf_spot complex\n",
       "2  80070f7fb5e2ccaa.jpg                             scab\n",
       "3  80077517781fb94f.jpg                             scab\n",
       "4  800cbf0ff87721f8.jpg                          complex"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TRAIN_PATH = \"../input/plant-pathology-2021-fgvc8/train_images/\"\n",
    "train_df = pd.read_csv(\"../input/plant-pathology-2021-fgvc8/train.csv\")\n",
    "\n",
    "count_dict = train_df.labels.value_counts()\n",
    "classes = list(count_dict.index)\n",
    "classes_count = list(count_dict.values)\n",
    "print(\"Number of unique labels: \",len(classes))\n",
    "\n",
    "\n",
    "label2id = dict(zip(range(train_df.labels.nunique()),train_df.labels.unique()))\n",
    "id2label = dict(zip(train_df.labels.unique(),range(train_df.labels.nunique())))\n",
    "#train_df[\"labels\"] = train_df[\"labels\"].map(id2label)\n",
    "#train_df.index = train_df[\"image\"]\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Which type of competition is this ? (Multi class or Multi label)\n",
    "### Many have doubt whether the follwing competition is multi-class or multi-label . Let me clarify this by giving more details.\n",
    "\n",
    "In the details of data it is mentioned as follows:\n",
    "> ## **Unhealthy leaves with too many diseases to classify visually will have the complex class, and may also have a subset of the diseases identified.**\n",
    "\n",
    "Let me show you some labels from `train.csv` file\n",
    "\n",
    "> **`8002cb321f8bfcdf.jpg     scab frog_eye_leaf_spot complex`**\n",
    "\n",
    "> **`801f78399a44e7af.jpg     complex`**\n",
    "\n",
    "> **`80769797ce42f658.jpg      scab frog_eye_leaf_spot`**\n",
    "\n",
    "You can see that the image with complex label may have some other diseases mentioned. But from data I got **number of unique labels as 12**. So I considered it as **`Multi-Class classification`** and proceded. "
   ]
  },
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    "tags": []
   },
   "source": [
    "# Data Visualization"
   ]
  },
  {
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   "outputs": [
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\n",
      "text/plain": [
       "<Figure size 2520x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(35,15))\n",
    "plt.bar(classes,classes_count)\n",
    "plt.title(\"Number of instances per class\",fontweight=\"bold\",fontsize=40)\n",
    "plt.xlabel(\"Classes\",fontsize = 30)\n",
    "plt.xticks(rotation=20,fontsize = 20,fontweight = \"bold\")\n",
    "plt.xticks(fontsize = 20,fontweight = \"bold\")\n",
    "plt.ylabel(\"Count\",fontsize=30)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.014253,
     "end_time": "2021-04-06T01:57:10.627244",
     "exception": false,
     "start_time": "2021-04-06T01:57:10.612991",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### It's a biased data\n",
    "\n",
    "Also the image size are varying as follows:\n",
    "## height = [1728,4032], width = [2592,5184]  \n",
    "\n",
    "### I don't have enough GPU time, so I am using Image size as = [128,128]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-06T01:57:10.660431Z",
     "iopub.status.busy": "2021-04-06T01:57:10.659754Z",
     "iopub.status.idle": "2021-04-06T01:57:10.663381Z",
     "shell.execute_reply": "2021-04-06T01:57:10.662865Z"
    },
    "papermill": {
     "duration": 0.021844,
     "end_time": "2021-04-06T01:57:10.663484",
     "exception": false,
     "start_time": "2021-04-06T01:57:10.641640",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time Taking\n"
     ]
    }
   ],
   "source": [
    "'''h_ls,w_ls = [],[]\n",
    "for img in os.listdir(TRAIN_PATH):\n",
    "    im = PIL.Image.open(os.path.join(TRAIN_PATH,img))\n",
    "    h_ls.append(im.height)\n",
    "    w_ls.append(im.width)\n",
    "\n",
    "\n",
    "plt.plot(h_ls,w_ls)\n",
    "plt.xlabel(\"Height of Image\")\n",
    "plt.ylabel(\"Width of Image\")\n",
    "plt.title(\"Distribution of Image size of training data\",fontweight = \"bold\")'''\n",
    "print(\"Time Taking\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.014445,
     "end_time": "2021-04-06T01:57:10.693476",
     "exception": false,
     "start_time": "2021-04-06T01:57:10.679031",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Data Pipeline\n",
    "\n",
    "### As the labels are given in csv file, data pipeline can be prepared by `tf.keras.preprocessing.image.ImageDataGenerator` and there by adding `.flow_from_dataframe` as attribute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-06T01:57:10.727287Z",
     "iopub.status.busy": "2021-04-06T01:57:10.726544Z",
     "iopub.status.idle": "2021-04-06T01:57:10.729925Z",
     "shell.execute_reply": "2021-04-06T01:57:10.730316Z"
    },
    "papermill": {
     "duration": 0.022334,
     "end_time": "2021-04-06T01:57:10.730443",
     "exception": false,
     "start_time": "2021-04-06T01:57:10.708109",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Different Data Pipeline\n"
     ]
    }
   ],
   "source": [
    "'''NUM_CLASSES =  len(classes)\n",
    "HEIGHT,WIDTH = 64,64\n",
    "CHANNELS = 3\n",
    "BATCH_SIZE = 32\n",
    "SEED = 143\n",
    "SPLIT = int(0.8*len(train_df))\n",
    "AUTO = tf.data.experimental.AUTOTUNE\n",
    "\n",
    "\n",
    "def process_img(image):\n",
    "    image = tf.image.decode_jpeg(image, channels=CHANNELS)\n",
    "    image = tf.image.convert_image_dtype(image, tf.float32) \n",
    "    image = tf.image.resize(image, [HEIGHT,WIDTH])\n",
    "    return image\n",
    "\n",
    "def process_label(filepath):\n",
    "    parts = tf.strings.split(filepath, '/')\n",
    "    filename = parts[-1]\n",
    "    label = train_df[train_df[\"image\"] == filename][\"labels\"][0]\n",
    "    label = tf.one_hot(label,depth = NUM_CLASSES,dtype = tf.int32)\n",
    "    return label\n",
    "\n",
    "def get_image_label(filepath : tf.Tensor):\n",
    "    image = tf.io.read_file(filepath)\n",
    "    image = process_img(image)\n",
    "    label = process_label(filepath)\n",
    "    return image,label\n",
    "\n",
    "\n",
    "def get_dataset(files_ds):\n",
    "    dataset = files_ds.map(lambda x: tf.py_function(func=get_image_label,\n",
    "                    inp=[x], Tout=(tf.float32,tf.int32)),\n",
    "                    num_parallel_calls=tf.data.AUTOTUNE,\n",
    "                    deterministic=False)\n",
    "    dataset = dataset.batch(BATCH_SIZE)\n",
    "    dataset = dataset.cache().prefetch(AUTO)\n",
    "    return dataset\n",
    "\n",
    "\n",
    "\n",
    "files_ls = tf.io.gfile.glob(TRAIN_PATH + '*.jpg')\n",
    "files_ds = tf.data.Dataset.list_files(files_ls)\n",
    "\n",
    "train_ds = files_ds.take(SPLIT)\n",
    "val_ds = files_ds.skip(SPLIT)\n",
    "\n",
    "train_ds =get_dataset(train_ds)\n",
    "val_ds =  get_dataset(val_ds)'''\n",
    "print(\"Different Data Pipeline\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-06T01:57:10.788817Z",
     "iopub.status.busy": "2021-04-06T01:57:10.786509Z",
     "iopub.status.idle": "2021-04-06T01:57:10.873206Z",
     "shell.execute_reply": "2021-04-06T01:57:10.872713Z"
    },
    "papermill": {
     "duration": 0.127978,
     "end_time": "2021-04-06T01:57:10.873322",
     "exception": false,
     "start_time": "2021-04-06T01:57:10.745344",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 14906 non-validated image filenames belonging to 12 classes.\n",
      "Found 3726 non-validated image filenames belonging to 12 classes.\n"
     ]
    }
   ],
   "source": [
    "NUM_CLASSES =  len(classes)\n",
    "HEIGHT,WIDTH = 128,128\n",
    "BATCH_SIZE = 32\n",
    "SEED = 143\n",
    "SPLIT = 0.2\n",
    "\n",
    "datagen = tf.keras.preprocessing.image.ImageDataGenerator(\n",
    "    rescale = 1/255.,\n",
    "    rotation_range=20,\n",
    "    width_shift_range=0.2,\n",
    "    height_shift_range=0.2,\n",
    "    horizontal_flip=True,\n",
    "    validation_split = SPLIT,\n",
    "    zoom_range = 0.2,\n",
    "    shear_range = 0.2,\n",
    "    vertical_flip = False\n",
    ")\n",
    "\n",
    "train_ds = datagen.flow_from_dataframe(\n",
    "    train_df,\n",
    "    directory = TRAIN_PATH,\n",
    "    x_col = \"image\",\n",
    "    y_col = \"labels\",\n",
    "    target_size = (HEIGHT,WIDTH),\n",
    "    class_mode='categorical',\n",
    "    batch_size = BATCH_SIZE,\n",
    "    subset = \"training\",\n",
    "    shuffle = True,\n",
    "    seed = SEED,\n",
    "    validate_filenames = False\n",
    ")\n",
    "\n",
    "val_ds = datagen.flow_from_dataframe(\n",
    "    train_df,\n",
    "    directory = TRAIN_PATH,\n",
    "    x_col = \"image\",\n",
    "    y_col = \"labels\",\n",
    "    target_size = (HEIGHT,WIDTH),\n",
    "    class_mode='categorical',\n",
    "    batch_size = BATCH_SIZE,\n",
    "    subset = \"validation\",\n",
    "    shuffle = True,\n",
    "    seed = SEED,\n",
    "    validate_filenames = False\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.015641,
     "end_time": "2021-04-06T01:57:10.905034",
     "exception": false,
     "start_time": "2021-04-06T01:57:10.889393",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-06T01:57:10.941750Z",
     "iopub.status.busy": "2021-04-06T01:57:10.941262Z",
     "iopub.status.idle": "2021-04-06T01:57:16.018325Z",
     "shell.execute_reply": "2021-04-06T01:57:16.017235Z"
    },
    "papermill": {
     "duration": 5.097721,
     "end_time": "2021-04-06T01:57:16.018468",
     "exception": false,
     "start_time": "2021-04-06T01:57:10.920747",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://github.com/qubvel/efficientnet/releases/download/v0.0.1/efficientnet-b0_noisy-student_notop.h5\n",
      "16703488/16696600 [==============================] - 0s 0us/step\n"
     ]
    }
   ],
   "source": [
    "def create_model():\n",
    "    \n",
    "    pretrained = efn.EfficientNetB0(include_top=False, weights='noisy-student',input_shape=[HEIGHT,WIDTH, 3])\n",
    "            \n",
    "    x = pretrained.output\n",
    "    x = tf.keras.layers.GlobalAveragePooling2D() (x)\n",
    "    outputs = tf.keras.layers.Dense(NUM_CLASSES,activation=\"softmax\", dtype='float32')(x)\n",
    "        \n",
    "    model = tf.keras.Model(pretrained.input, outputs)\n",
    "    return model\n",
    "\n",
    "model = create_model()\n",
    "#model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.01765,
     "end_time": "2021-04-06T01:57:16.055243",
     "exception": false,
     "start_time": "2021-04-06T01:57:16.037593",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Compiling the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-06T01:57:16.096663Z",
     "iopub.status.busy": "2021-04-06T01:57:16.096092Z",
     "iopub.status.idle": "2021-04-06T01:57:16.223160Z",
     "shell.execute_reply": "2021-04-06T01:57:16.222658Z"
    },
    "papermill": {
     "duration": 0.150288,
     "end_time": "2021-04-06T01:57:16.223282",
     "exception": false,
     "start_time": "2021-04-06T01:57:16.072994",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import tensorflow_addons as tfa\n",
    "\n",
    "def compile_model(model, lr=0.0001):\n",
    "    \n",
    "    optimizer = tf.keras.optimizers.Adam(lr=lr)\n",
    "    \n",
    "    loss = tf.keras.losses.CategoricalCrossentropy()\n",
    "    \n",
    "    #tf.keras.metrics.CategoricalAccuracy(name='categorical_accuracy')\n",
    "    metrics = [\n",
    "    tfa.metrics.F1Score(num_classes = NUM_CLASSES,average = \"macro\",name = \"f1_score\")\n",
    "    ]\n",
    "\n",
    "    model.compile(optimizer=optimizer, loss=loss, metrics=metrics)\n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.017994,
     "end_time": "2021-04-06T01:57:16.259610",
     "exception": false,
     "start_time": "2021-04-06T01:57:16.241616",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Callbacks Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-06T01:57:16.302814Z",
     "iopub.status.busy": "2021-04-06T01:57:16.301237Z",
     "iopub.status.idle": "2021-04-06T01:57:16.306071Z",
     "shell.execute_reply": "2021-04-06T01:57:16.308480Z"
    },
    "papermill": {
     "duration": 0.031493,
     "end_time": "2021-04-06T01:57:16.308979",
     "exception": false,
     "start_time": "2021-04-06T01:57:16.277486",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "METRIC = \"val_f1_score\"\n",
    "\n",
    "def create_callbacks(metric = METRIC):\n",
    "    \n",
    "    cpk_path = './best_model.h5'\n",
    "    \n",
    "    checkpoint = tf.keras.callbacks.ModelCheckpoint(\n",
    "        filepath=cpk_path,\n",
    "        monitor= metric,\n",
    "        mode='max',\n",
    "        save_best_only=True,\n",
    "        verbose=1,\n",
    "    )\n",
    "\n",
    "    reducelr = tf.keras.callbacks.ReduceLROnPlateau(\n",
    "        monitor= metric,\n",
    "        mode='max',\n",
    "        factor=0.1,\n",
    "        patience=3,\n",
    "        verbose=0\n",
    "    )\n",
    "\n",
    "    earlystop = tf.keras.callbacks.EarlyStopping(\n",
    "        monitor= metric,\n",
    "        mode='max',\n",
    "        patience=10, \n",
    "        verbose=1\n",
    "    )\n",
    "    \n",
    "    callbacks = [checkpoint, reducelr, earlystop]         \n",
    "    \n",
    "    return callbacks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.034719,
     "end_time": "2021-04-06T01:57:16.376181",
     "exception": false,
     "start_time": "2021-04-06T01:57:16.341462",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-06T01:57:16.458358Z",
     "iopub.status.busy": "2021-04-06T01:57:16.457324Z",
     "iopub.status.idle": "2021-04-06T04:38:27.762743Z",
     "shell.execute_reply": "2021-04-06T04:38:27.762217Z"
    },
    "papermill": {
     "duration": 9671.352823,
     "end_time": "2021-04-06T04:38:27.762899",
     "exception": false,
     "start_time": "2021-04-06T01:57:16.410076",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/3\n",
      "466/466 [==============================] - 3338s 7s/step - loss: 1.7135 - f1_score: 0.1966 - val_loss: 1.0207 - val_f1_score: 0.3347\n",
      "\n",
      "Epoch 00001: val_f1_score improved from -inf to 0.33473, saving model to ./best_model.h5\n",
      "Epoch 2/3\n",
      "466/466 [==============================] - 3148s 7s/step - loss: 0.7599 - f1_score: 0.3903 - val_loss: 0.5862 - val_f1_score: 0.4190\n",
      "\n",
      "Epoch 00002: val_f1_score improved from 0.33473 to 0.41896, saving model to ./best_model.h5\n",
      "Epoch 3/3\n",
      "466/466 [==============================] - 3176s 7s/step - loss: 0.6331 - f1_score: 0.4298 - val_loss: 0.5213 - val_f1_score: 0.4407\n",
      "\n",
      "Epoch 00003: val_f1_score improved from 0.41896 to 0.44073, saving model to ./best_model.h5\n"
     ]
    }
   ],
   "source": [
    "EPOCHS= 3\n",
    "VERBOSE =1\n",
    "\n",
    "tf.keras.backend.clear_session()\n",
    "\n",
    "with tf.device('/device:GPU:0'):\n",
    "    \n",
    "    model = create_model()\n",
    "    model = compile_model(model, lr=0.0001)\n",
    "   \n",
    "    callbacks = create_callbacks()\n",
    "    \n",
    "    history = model.fit(\n",
    "                        train_ds,\n",
    "                        epochs=EPOCHS,\n",
    "                        callbacks=callbacks,\n",
    "                        validation_data = val_ds,\n",
    "                        verbose=VERBOSE\n",
    "                       )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.372842,
     "end_time": "2021-04-06T04:38:28.509573",
     "exception": false,
     "start_time": "2021-04-06T04:38:28.136731",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# History plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-06T04:38:29.267796Z",
     "iopub.status.busy": "2021-04-06T04:38:29.267029Z",
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     "shell.execute_reply": "2021-04-06T04:38:29.287917Z"
    },
    "papermill": {
     "duration": 0.406685,
     "end_time": "2021-04-06T04:38:29.288477",
     "exception": false,
     "start_time": "2021-04-06T04:38:28.881792",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'categorical_accuracy'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-12-0c9cd65ddd47>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0macc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhistory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'categorical_accuracy'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mval_acc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhistory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'val_categorical_accuracy'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhistory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'loss'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mval_loss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhistory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'val_loss'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mepochs_range\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'val_loss'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'categorical_accuracy'"
     ]
    }
   ],
   "source": [
    "acc = history.history['categorical_accuracy']\n",
    "val_acc = history.history['val_categorical_accuracy']\n",
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "epochs_range = range(len(history.history['val_loss']))\n",
    "plt.figure(figsize=(8, 8))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(epochs_range, acc, label='Training Categorical Accuracy')\n",
    "plt.plot(epochs_range, val_acc, label='Validation Categorical Accuracy')\n",
    "plt.legend(loc='lower right')\n",
    "plt.title('Training and Validation Categorical Accuracy')\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(epochs_range, loss, label='Training Loss')\n",
    "plt.plot(epochs_range, val_loss, label='Validation Loss')\n",
    "plt.legend(loc='upper right')\n",
    "plt.title('Training and Validation Loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.611848,
     "end_time": "2021-04-06T04:38:30.470348",
     "exception": false,
     "start_time": "2021-04-06T04:38:29.858500",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### If you have any doubts or suggestions feel free to contact me.\n",
    "## Happy coding❤ "
   ]
  }
 ],
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