main-Finalized.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Show, Attend and Tell - Implementation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ## Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-02T13:06:54.264654Z",
     "start_time": "2020-08-02T13:06:54.261814Z"
    }
   },
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ['CUDA_LAUNCH_BLOCKING'] = \"0\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-03T09:36:16.785057Z",
     "start_time": "2020-08-03T09:36:16.778005Z"
    },
    "init_cell": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.container { width:100% !important; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.core.display import display, HTML\n",
    "display(HTML(\"<style>.container { width:100% !important; }</style>\"))\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import torch\n",
    "from torch import nn\n",
    "import json\n",
    "from torchvision import transforms\n",
    "#from modules.model import *\n",
    "from modules.custom_callbacks import *\n",
    "from fastai.callbacks import lr_finder, SaveModelCallback, EarlyStoppingCallback,ReduceLROnPlateauCallback\n",
    "#from fastai.callbacks.tensorboardX import *\n",
    "from torch.nn.utils.rnn import pack_padded_sequence \n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "from fastai.vision import learner, data\n",
    "from fastai.metrics import top_k_accuracy\n",
    "from fastai.text import *\n",
    "from fastai.vision import *\n",
    "from nltk.translate.bleu_score import corpus_bleu\n",
    "from nltk.translate.bleu_score import sentence_bleu\n",
    "from sklearn.utils import shuffle\n",
    "#import imageio\n",
    "from PIL import Image\n",
    "\n",
    "defaults.device = torch.device('cuda')\n",
    "#CUDA_VISIBLE_DEVICES=\"\"\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "\n",
    "#%load_ext tensorboard"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Image Databunch  Preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-03T09:36:17.372821Z",
     "start_time": "2020-08-03T09:36:16.786237Z"
    },
    "init_cell": true
   },
   "outputs": [],
   "source": [
    "# load caption data\n",
    "with open('caption_datasets/dataset_flickr8k.json','r') as f:\n",
    "    captions_data = json.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-03T09:36:17.509416Z",
     "start_time": "2020-08-03T09:36:17.373919Z"
    },
    "init_cell": true
   },
   "outputs": [],
   "source": [
    "# reformat Json to pd Dataframe\n",
    "image_meta = pd.DataFrame(captions_data['images'])\n",
    "image_meta['references'] = image_meta['sentences'].apply(lambda x: [i['tokens'] for i in x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-01T18:54:54.660385Z",
     "start_time": "2020-08-01T18:54:54.657239Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'tokens': ['a',\n",
       "  'black',\n",
       "  'dog',\n",
       "  'is',\n",
       "  'running',\n",
       "  'after',\n",
       "  'a',\n",
       "  'white',\n",
       "  'dog',\n",
       "  'in',\n",
       "  'the',\n",
       "  'snow'],\n",
       " 'raw': 'A black dog is running after a white dog in the snow .',\n",
       " 'imgid': 0,\n",
       " 'sentid': 0}"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "captions_data['images'][0]['sentences'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-23T13:10:32.215052Z",
     "start_time": "2020-04-23T13:10:32.122846Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sentids</th>\n",
       "      <th>imgid</th>\n",
       "      <th>sentences</th>\n",
       "      <th>split</th>\n",
       "      <th>filename</th>\n",
       "      <th>references</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[0, 1, 2, 3, 4]</td>\n",
       "      <td>0</td>\n",
       "      <td>[{'tokens': ['a', 'black', 'dog', 'is', 'runni...</td>\n",
       "      <td>train</td>\n",
       "      <td>2513260012_03d33305cf.jpg</td>\n",
       "      <td>[[a, black, dog, is, running, after, a, white,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[5, 6, 7, 8, 9]</td>\n",
       "      <td>1</td>\n",
       "      <td>[{'tokens': ['a', 'little', 'baby', 'plays', '...</td>\n",
       "      <td>train</td>\n",
       "      <td>2903617548_d3e38d7f88.jpg</td>\n",
       "      <td>[[a, little, baby, plays, croquet], [a, little...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[10, 11, 12, 13, 14]</td>\n",
       "      <td>2</td>\n",
       "      <td>[{'tokens': ['a', 'brown', 'dog', 'in', 'the',...</td>\n",
       "      <td>train</td>\n",
       "      <td>3338291921_fe7ae0c8f8.jpg</td>\n",
       "      <td>[[a, brown, dog, in, the, snow, has, something...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[15, 16, 17, 18, 19]</td>\n",
       "      <td>3</td>\n",
       "      <td>[{'tokens': ['a', 'brown', 'dog', 'is', 'runni...</td>\n",
       "      <td>train</td>\n",
       "      <td>488416045_1c6d903fe0.jpg</td>\n",
       "      <td>[[a, brown, dog, is, running, along, a, beach]...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[20, 21, 22, 23, 24]</td>\n",
       "      <td>4</td>\n",
       "      <td>[{'tokens': ['a', 'black', 'and', 'white', 'do...</td>\n",
       "      <td>train</td>\n",
       "      <td>2644326817_8f45080b87.jpg</td>\n",
       "      <td>[[a, black, and, white, dog, with, a, red, fri...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                sentids  imgid  \\\n",
       "0       [0, 1, 2, 3, 4]      0   \n",
       "1       [5, 6, 7, 8, 9]      1   \n",
       "2  [10, 11, 12, 13, 14]      2   \n",
       "3  [15, 16, 17, 18, 19]      3   \n",
       "4  [20, 21, 22, 23, 24]      4   \n",
       "\n",
       "                                           sentences  split  \\\n",
       "0  [{'tokens': ['a', 'black', 'dog', 'is', 'runni...  train   \n",
       "1  [{'tokens': ['a', 'little', 'baby', 'plays', '...  train   \n",
       "2  [{'tokens': ['a', 'brown', 'dog', 'in', 'the',...  train   \n",
       "3  [{'tokens': ['a', 'brown', 'dog', 'is', 'runni...  train   \n",
       "4  [{'tokens': ['a', 'black', 'and', 'white', 'do...  train   \n",
       "\n",
       "                    filename  \\\n",
       "0  2513260012_03d33305cf.jpg   \n",
       "1  2903617548_d3e38d7f88.jpg   \n",
       "2  3338291921_fe7ae0c8f8.jpg   \n",
       "3   488416045_1c6d903fe0.jpg   \n",
       "4  2644326817_8f45080b87.jpg   \n",
       "\n",
       "                                          references  \n",
       "0  [[a, black, dog, is, running, after, a, white,...  \n",
       "1  [[a, little, baby, plays, croquet], [a, little...  \n",
       "2  [[a, brown, dog, in, the, snow, has, something...  \n",
       "3  [[a, brown, dog, is, running, along, a, beach]...  \n",
       "4  [[a, black, and, white, dog, with, a, red, fri...  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_meta.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-03T09:36:18.739400Z",
     "start_time": "2020-08-03T09:36:17.510825Z"
    },
    "init_cell": true
   },
   "outputs": [],
   "source": [
    "# set working path\n",
    "path = Path() \n",
    "\n",
    "# load vocabulary dictionary\n",
    "vocab = json.load((path/\"flicker8k-dataset/Vocab_5_cap_per_img_2_min_word_freq.json\").open('rb'))\n",
    "\n",
    "def unnesting(df, explode_cols):\n",
    "    idx = df.index.repeat(df[explode_cols[0]].str.len())\n",
    "    df1 = pd.concat([\n",
    "        pd.DataFrame({x: np.concatenate(df[x].values)}) for x in explode_cols], axis=1)\n",
    "    df1.index = idx\n",
    "\n",
    "    return df1.join(df.drop(explode_cols, 1), how='left')\n",
    "\n",
    "# expand multi captions into rows (explode)\n",
    "metadata = unnesting(image_meta,['sentids','sentences'])\n",
    "metadata['labels'] =  metadata.sentences.apply(lambda x: x['raw'])\n",
    "metadata['tokens'] =  metadata.sentences.apply(lambda x: x['tokens'])\n",
    "metadata.reset_index(inplace = True)\n",
    "\n",
    "\n",
    "# attaching Image folder path to filename and add column with filepaths\n",
    "metadata['filename'] = metadata.filename.apply(lambda x: f'flicker8k-dataset/flickr8k_dataset/Flicker8k_Dataset/{x}')\n",
    "metadata.reset_index(inplace = True)\n",
    "\n",
    "# numericalize tokens and re format list to numeric array\n",
    "metadata['numericalized'] = metadata.tokens.apply(lambda x: [vocab['<start>']] + [vocab[i] if i in vocab.keys() else vocab['<unk>'] for i in x]+[vocab['<end>']])\n",
    "metadata['numericalized'] = metadata.numericalized.apply(lambda x: np.array(x))\n",
    "metadata['SeqLen'] = metadata.numericalized.apply(len)\n",
    "\n",
    "# numerricalise references \n",
    "def ref_numericalize(lst): return list(map(lambda x: [vocab[i] if i in vocab.keys() else vocab['<unk>'] for i in x],lst))\n",
    "\n",
    "# store corresaponding reference captions in numeric form\n",
    "metadata['numericalized_ref'] = metadata.references.apply(ref_numericalize)\n",
    "\n",
    "# shuffle \n",
    "metadata = shuffle(metadata)\n",
    "metadata.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-26T07:01:17.199426Z",
     "start_time": "2020-04-26T07:01:17.169693Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[1, 1504, 142, 28],\n",
       " [1, 1504, 4, 381, 167, 27, 9, 28],\n",
       " [1, 1504, 200, 167, 27, 9, 94, 73],\n",
       " [1, 1504, 1505, 142, 9, 28],\n",
       " [1, 1504, 1505, 54, 595, 167, 27, 9, 28]]"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metadata.numericalized_ref[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-03T09:36:18.764862Z",
     "start_time": "2020-08-03T09:36:18.740315Z"
    },
    "init_cell": true
   },
   "outputs": [],
   "source": [
    "# split data in train and valid\n",
    "train_idx = metadata.index[metadata.split == 'train']\n",
    "valid_idx = metadata.index[metadata.split == 'val']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-06T04:19:11.304029Z",
     "start_time": "2020-05-06T04:19:11.184621Z"
    }
   },
   "outputs": [],
   "source": [
    "# small batch of metadata for testing\n",
    "train_idx = train_idx[:100].to_list()\n",
    "valid_idx = valid_idx[:100].to_list()\n",
    "metadata = metadata[metadata.index.isin(train_idx+valid_idx)]\n",
    "metadata.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Custom Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-03T09:36:18.855238Z",
     "start_time": "2020-08-03T09:36:18.765607Z"
    },
    "init_cell": true
   },
   "outputs": [],
   "source": [
    "# define Dataset object that outputs img path, caption, indices of reference captions \n",
    "class ImageCaptionDataset(Dataset):\n",
    "    def __init__(self, data, split = 'train',transform=None):\n",
    "        data = data[data.split == split]\n",
    "        self.filenames = list(data['filename'])\n",
    "        self.captions  = list(data['numericalized'])\n",
    "        self.inds  = data.index.tolist()\n",
    "        self.transform = transform\n",
    "    \n",
    "    def __len__(self):\n",
    "        return len(self.filenames)\n",
    "    \n",
    "    def __getitem__(self, idx):\n",
    "        # Grayscale images in dataset have to be onverted as tensor shapes have to match except in dim=0\n",
    "        image = Image.open(self.filenames[idx]).convert('RGB')\n",
    "        caption = self.captions[idx]\n",
    "        ref_ind = self.inds[idx]\n",
    "\n",
    "        \n",
    "        if self.transform is not None:\n",
    "            image = self.transform(image)\n",
    "        \n",
    "        return (image, caption, ref_ind)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-03T09:36:18.966635Z",
     "start_time": "2020-08-03T09:36:18.856667Z"
    },
    "init_cell": true
   },
   "outputs": [],
   "source": [
    "# transformation resize img size to 350 px, tensoring, then normalize using image net stats\n",
    "trans = transforms.Compose([\n",
    "    transforms.Resize((350,350)),\n",
    "    transforms.ToTensor()\n",
    "    ,transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])\n",
    "\n",
    "inv_normalize = transforms.Normalize(\n",
    "    mean=[-0.5238/0.3159, -0.5003/0.3091, -0.4718/0.3216],\n",
    "    std=[1/0.3159, 1/0.3091, 1/0.3216]\n",
    ")\n",
    "\n",
    "denorm = transforms.Compose([\n",
    "    inv_normalize,\n",
    "    transforms.functional.to_pil_image\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-03T09:36:19.075652Z",
     "start_time": "2020-08-03T09:36:18.968640Z"
    },
    "init_cell": true
   },
   "outputs": [],
   "source": [
    "# create Dataset instense for both train set and valid\n",
    "train_dataset = ImageCaptionDataset(metadata,'train',trans)\n",
    "valid_dataset = ImageCaptionDataset(metadata,'val',trans)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-26T08:34:40.861222Z",
     "start_time": "2020-04-26T08:34:40.829439Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "577    [[1, 2, 45, 7, 3, 115, 69, 9, 270, 43, 1, 310,...\n",
       "Name: numericalized_ref, dtype: object"
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metadata.numericalized_ref.loc[metadata.index == ref]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-26T08:39:50.489640Z",
     "start_time": "2020-04-26T08:39:50.288455Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<start> a black and white dog stands on the grass with a baseball in his mouth <end>\n",
      "\n",
      "a black and white dog stands on the grass with a baseball in his mouth\n",
      "a closeup of a small black and white dog with a baseball in its mouth\n",
      "black and white dog holding a baseball in mouth\n",
      "brown and white dog with baseball in mouth field and building\n",
      "brown and white dog with a baseball in its mouth\n"
     ]
    },
    {
     "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": [
    "# check random sample\n",
    "idx = 500\n",
    "d = train_dataset\n",
    "img, cap,ref = d[idx]\n",
    "\n",
    "print(' '.join([dict(zip(vocab.values(),vocab.keys()))[x] for x in cap])+'\\n')\n",
    "\n",
    "\n",
    "for cap in metadata.numericalized_ref.loc[ref]:\n",
    "    print(' '.join([dict(zip(vocab.values(),vocab.keys()))[i] for i in cap]))\n",
    "#print(cap)\n",
    "plt.imshow(denorm(img));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Architecture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-17T12:49:45.159406Z",
     "start_time": "2020-04-17T12:49:45.129261Z"
    },
    "code_folding": [
     0
    ],
    "pixiedust": {
     "displayParams": {}
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing modules/models.py\n"
     ]
    }
   ],
   "source": [
    "#%%writefile modules/models.py\n",
    "\n",
    "# create a embedding layer\n",
    "def create_emb(embedding_array):\n",
    "    emb = nn.Embedding(len(word_map),embedding_dim)\n",
    "    emb.weight.data = torch.from_numpy(embedding_array)\n",
    "    return emb\n",
    "\n",
    "class Encoder(nn.Module):\n",
    "    def __init__(self,encode_img_size, fine_tune = False):\n",
    "        super(Encoder, self).__init__()\n",
    "        self.enc_imgsize = encode_img_size\n",
    "        resnet = models.resnet101(pretrained=True)\n",
    "        self.encoder = nn.Sequential(*list(resnet.children())[:-2]) # removing final Linear layer\n",
    "        self.adaptive_pool = nn.AdaptiveAvgPool2d((encode_img_size,encode_img_size))\n",
    "        self.fine_tune = fine_tune\n",
    "        self.fine_tune_h()\n",
    "        \n",
    "    def fine_tune_h(self):\n",
    "        \"\"\"\n",
    "        Allow or prevent the computation of gradients for convolutional blocks 2 through 4 of the encoder.\n",
    "        :param fine_tune: Allow?\n",
    "        \"\"\"\n",
    "        for p in self.encoder.parameters():\n",
    "            p.requires_grad = False\n",
    "        \n",
    "        # If fine-tuning, only fine-tune convolutional blocks 2 through 4\n",
    "        for c in list(self.encoder.children())[5:]:\n",
    "            for p in c.parameters():\n",
    "                p.requires_grad = self.fine_tune\n",
    "        \n",
    "    def forward(self,X):\n",
    "        out = self.encoder(X) # X is tensor of size (batch size, 3 (RGB), input height, width)\n",
    "        out = self.adaptive_pool(out) # output (batch_size, 2048, encoded_image_size, encoded_image_size)\n",
    "        out = out.permute(0, 2, 3, 1)\n",
    "        out = out.view(out.size(0), -1, out.size(3))\n",
    "        return out\n",
    "    \n",
    "class Decoder(nn.Module):\n",
    "    def __init__(self,attention_dim, embed_dim, decoder_dim, vocab_size, encoder_dim=2048, dropout=0.5, pretrained_embedding = None,teacher_forcing_ratio = 0):\n",
    "        super(Decoder, self).__init__()\n",
    "        self.attention_dim = attention_dim\n",
    "        self.embed_dim = embed_dim\n",
    "        self.decoder_dim = decoder_dim\n",
    "        self.encoder_dim = encoder_dim\n",
    "        self.vocab_size = vocab_size\n",
    "        self.teacher_forcing_ratio = teacher_forcing_ratio\n",
    "        self.dropout = nn.Dropout(dropout)\n",
    "        self.attention = Attention(encoder_dim, decoder_dim, attention_dim)\n",
    "        self.embedding = nn.Embedding(vocab_size,embed_dim)\n",
    "        self.lstm = nn.LSTM(decoder_dim + encoder_dim, decoder_dim,1, bias=True)\n",
    "        self.init_h = nn.Linear(encoder_dim, decoder_dim)  # linear layer to find initial hidden state of LSTMCell\n",
    "        self.init_c = nn.Linear(encoder_dim, decoder_dim)  # linear layer to find initial cell state of LSTMCell\n",
    "        self.f_beta = nn.Linear(decoder_dim, encoder_dim)  # gate\n",
    "        self.pretrained_embedding = pretrained_embedding\n",
    "        self.sigmoid = nn.Sigmoid()\n",
    "        self.fc = nn.Linear(decoder_dim, vocab_size)  # linear layer to find scores over vocabulary\n",
    "        self.emb2dec_dim = nn.Linear(embed_dim, decoder_dim) # embed dim to decoder dim\n",
    "        self.pretrained()\n",
    "        \n",
    "    def pretrained(self):\n",
    "        if self.pretrained_embedding is not None:\n",
    "            self.embedding.weight.data = torch.from_numpy(self.pretrained_embedding)\n",
    "            \n",
    "    def init_hidden_state(self, encoder_out):\n",
    "        \n",
    "        mean_encoder_out = encoder_out.mean(dim=1)\n",
    "        h = self.init_h(mean_encoder_out)  # (batch_size, decoder_dim)\n",
    "        c = self.init_c(mean_encoder_out)\n",
    "        return h.unsqueeze(0), c.unsqueeze(0)\n",
    "            \n",
    "    def forward(self,encoder_out, encoded_captions,decode_lengths,inds):\n",
    "        batch_size = encoder_out.size(0)\n",
    "        encoder_dim = encoder_out.size(-1)\n",
    "        vocab_size = self.vocab_size\n",
    "        num_pixels = encoder_out.size(1)\n",
    "        embeddings = self.embedding(encoded_captions)\n",
    "        \n",
    "        ## initililize hidden encoding\n",
    "        h, c = self.init_hidden_state(encoder_out)\n",
    "        dec_out = torch.zeros(1,batch_size,decoder_dim).to(device)\n",
    "        \n",
    "        \n",
    "        \n",
    "        decode_lengths = decode_lengths - 1\n",
    "        \n",
    "        max_len = max(decode_lengths).item()\n",
    "        # Create tensors to hold word predicion scores and alphas\n",
    "        predictions = torch.zeros(batch_size, max_len, vocab_size)\n",
    "        alphas = torch.zeros(batch_size, max_len, num_pixels)\n",
    "        \n",
    "        for t in range(max_len):\n",
    "            batch_size_t = sum([l.item() > t for l in decode_lengths])\n",
    "            attention_weighted_encoding, alpha = self.attention(encoder_out[:batch_size_t],\n",
    "                                                                h.squeeze(0)[:batch_size_t])\n",
    "            gate = self.sigmoid(self.f_beta(h.squeeze(0)[:batch_size_t]))  # gating scalar, (batch_size_t, encoder_dim)\n",
    "            attention_weighted_encoding = gate * attention_weighted_encoding\n",
    "            \n",
    "            # teacher forcing \n",
    "            use_teacher_forcing = True if random.random() < self.teacher_forcing_ratio else False\n",
    "            emb_ff = self.emb2dec_dim(embeddings[:batch_size_t, t, :])\n",
    "            inp_emb = emb_ff if (use_teacher_forcing or t==0) else dec_out.squeeze(0)[:batch_size_t]\n",
    "            \n",
    "            \n",
    "            dec_out,(h, c) = self.lstm(\n",
    "                torch.cat([inp_emb, attention_weighted_encoding], dim=1).unsqueeze(0),\n",
    "                (h[:,:batch_size_t], c[:,:batch_size_t]))  # (batch_size_t, decoder_dim)\n",
    "            preds = self.fc(self.dropout(dec_out.squeeze(0)))  # (batch_size_t, vocab_size)\n",
    "            predictions[:batch_size_t,t , :] = preds\n",
    "            alphas[:batch_size_t, t, :] = alpha\n",
    "        return F.log_softmax(predictions, dim=1),decode_lengths, alphas, inds\n",
    "        \n",
    "class Attention(nn.Module):\n",
    "    def __init__(self, encoder_dim, decoder_dim, attention_dim):\n",
    "        super(Attention, self).__init__()\n",
    "        self.enc_att = nn.Linear(encoder_dim,attention_dim)\n",
    "        self.dec_att = nn.Linear(decoder_dim,attention_dim)\n",
    "        self.att = nn.Linear(attention_dim,1)\n",
    "        self.relu = nn.ReLU()\n",
    "        self.softmax = nn.Softmax(dim=1)\n",
    "        \n",
    "    def forward(self,encoder_out, decoder_hidden):\n",
    "        encoder_att = self.enc_att(encoder_out)\n",
    "        decoder_att = self.dec_att(decoder_hidden)\n",
    "        att = self.att(self.relu(encoder_att + decoder_att.unsqueeze(1))).squeeze(2) #testing added batchnorm \n",
    "        alpha = self.softmax(att)\n",
    "        attention_weighted_encoding = (encoder_out*alpha.unsqueeze(2)).sum(dim=1)\n",
    "        \n",
    "        return attention_weighted_encoding, alpha\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DataLoaders  preparations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Custom collate function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "create a collate funtion with inputs bacth of imgs, caption pairs and sorts them by caption length in descending order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-03T09:36:19.133445Z",
     "start_time": "2020-08-03T09:36:19.076753Z"
    },
    "init_cell": true
   },
   "outputs": [],
   "source": [
    "\n",
    "from functools import partial\n",
    "def pad_collate_ImgCap(samples, pad_idx = 0, pad_first:bool=True, backwards:bool=False, transpose:bool=False, device = device):\n",
    "    \"Function that collect samples and adds padding. Flips token order if needed\"\n",
    "    images, captions, ref_ind = zip(*samples)\n",
    "    max_len_cap = max([len(c) for c in captions])\n",
    "    decode_lengths = torch.tensor([len(c) for c in captions])\n",
    "    res_cap = torch.zeros(len(samples), max_len_cap).long() + pad_idx\n",
    "    ref_ind = torch.tensor(ref_ind)\n",
    "    \n",
    "    if backwards: pad_first = not pad_first\n",
    "    for i,c in enumerate(captions):\n",
    "        if pad_first: \n",
    "            res_cap[i,-len(c):] = LongTensor(c)\n",
    "        else:         \n",
    "            res_cap[i,:len(c)] = LongTensor(c)\n",
    "    \n",
    "    if backwards:\n",
    "        cap = cap.flip(1)\n",
    "    if transpose:\n",
    "        res_cap.transpose_(0,1)\n",
    "    \n",
    "    images = torch.stack(images, 0, out=None)\n",
    "    \n",
    "    # Sort input data by decreasing lengths; why? apparent below\n",
    "    decode_lengths, sort_ind = decode_lengths.sort(dim=0, descending=True)\n",
    "    #set_trace()\n",
    "    \n",
    "    images = images[sort_ind].to(device)\n",
    "    res_cap = res_cap[sort_ind].to(device)\n",
    "    ref_ind = ref_ind[sort_ind].to(device)\n",
    "    decode_lengths = decode_lengths.to(device)\n",
    "    \n",
    "    \n",
    "    return (images, res_cap, decode_lengths,ref_ind), res_cap[:, 1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-03T09:36:19.249842Z",
     "start_time": "2020-08-03T09:36:19.134470Z"
    },
    "init_cell": true
   },
   "outputs": [],
   "source": [
    "imgcap_collate_func = partial(pad_collate_ImgCap, pad_idx=0, pad_first=False, transpose=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-03T09:36:19.332100Z",
     "start_time": "2020-08-03T09:36:19.251079Z"
    },
    "init_cell": true
   },
   "outputs": [],
   "source": [
    "bs=25"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create databunch with transformations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-03T09:36:19.432520Z",
     "start_time": "2020-08-03T09:36:19.333235Z"
    },
    "init_cell": true
   },
   "outputs": [],
   "source": [
    "### train and validation dataloaders\n",
    "train_sam = list(metadata.loc[metadata.split == 'train','numericalized'])\n",
    "val_sam = list(metadata.loc[metadata.split == 'val','numericalized'])\n",
    "\n",
    "### define Sampler for smapling bacthes for sorted pairs \n",
    "val_sampler = SortSampler(val_sam, key=lambda x: len(val_sam[x]))\n",
    "trn_sampler = SortishSampler(train_sam, key=lambda x: len(train_sam[x]), bs=bs)\n",
    "\n",
    "### define data loaders for loading inputs batches into network\n",
    "val_dl = DataLoader(dataset=valid_dataset, batch_size=bs, sampler=val_sampler, collate_fn=imgcap_collate_func,pin_memory=False)\n",
    "trn_dl = DataLoader(dataset=train_dataset, batch_size=bs, sampler=trn_sampler, collate_fn=imgcap_collate_func,pin_memory=False)\n",
    "\n",
    "\n",
    "# transformations \n",
    "tfms = get_transforms(flip_vert=False, max_lighting=0.1, max_zoom=1.05, max_warp=0.)\n",
    "\n",
    "# fastai databunch object \n",
    "dataBunch = DataBunch(train_dl=trn_dl, valid_dl=val_dl ,device=device,collate_fn=imgcap_collate_func)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-26T08:40:22.675739Z",
     "start_time": "2020-04-26T08:40:22.639429Z"
    }
   },
   "outputs": [],
   "source": [
    "dataBunch.valid_ds[0];"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-26T08:17:54.812867Z",
     "start_time": "2020-04-26T08:17:54.640511Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<start> a boy on a skateboard is at the top of a ramp <end>\n"
     ]
    },
    {
     "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": [
    "# visualize sample \n",
    "idx = 10000\n",
    "d = dataBunch.valid_ds\n",
    "img, cap,_ = d[-1]\n",
    "\n",
    "print(' '.join([dict(zip(vocab.values(),vocab.keys()))[x] for x in cap]))\n",
    "#print(cap)\n",
    "plt.imshow(denorm(img));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Intialization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### User inputs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-02T08:45:34.526312Z",
     "start_time": "2020-08-02T08:45:34.524201Z"
    }
   },
   "source": [
    "Set Model configuartion "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-03T09:36:20.029100Z",
     "start_time": "2020-08-03T09:36:19.433401Z"
    },
    "code_folding": [],
    "init_cell": true
   },
   "outputs": [],
   "source": [
    "# ****************** USER INPUTS *****************\n",
    "data_folder = 'flicker8k-dataset/'\n",
    "data_name = 'Vocab_5_cap_per_img_2_min_word_freq'\n",
    "wordemb_pkl_path = 'flicker8k-dataset/Fastext_embedd_wordMap.pkl' # path to word embedding Fasttext: 'Fastext_embedd_wordMap.pkl'\n",
    "\n",
    "trainWithEmb = True\n",
    "emb_dim = 300   # 300: pretrined words embedd GLove \n",
    "attention_dim = 512 # encoder_dim tranformed to attention_dim\n",
    "decoder_dim = 512  #  word_emb_dim tranformed to decoder_dim\n",
    "dropout = 0.5\n",
    "encoder_dim = 2048 #512 for resnet34 and 2048 for resnet 101 \n",
    "#cudnn.benchmark = True\n",
    "vocab_size = len(vocab)\n",
    "fine_tune_encoder = True\n",
    "criterion = nn.CrossEntropyLoss().to(device)\n",
    "\n",
    "# ****************** END OF USER INPUTS *****************\n",
    "\n",
    "# load word embeddings\n",
    "with open(wordemb_pkl_path,'rb') as f:\n",
    "    embedding = pickle.load(f)\n",
    "    embedding[4021:] = np.random.normal(embedding.mean(),embedding.std(),(4, 300))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-17T14:14:12.610304Z",
     "start_time": "2020-04-17T14:13:48.947870Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "x = range(embedding[:4021].shape[0])\n",
    "y = range(embedding[:4021].shape[1])\n",
    "\n",
    "\n",
    "hf = plt.figure(figsize=(15,20))\n",
    "ha = hf.add_subplot(111, projection='3d')\n",
    "\n",
    "X, Y = np.meshgrid(x, y)\n",
    "\n",
    "ha.scatter(X, Y, embedding[:4021])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Architectures"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model initialization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-03T09:36:20.805691Z",
     "start_time": "2020-08-03T09:36:20.030042Z"
    },
    "code_folding": [],
    "init_cell": true,
    "pixiedust": {
     "displayParams": {}
    }
   },
   "outputs": [],
   "source": [
    "from modules.model import *\n",
    "###########   Layer Initializations ##########\n",
    "# testing initiation\n",
    "enc = Encoder(14,fine_tune=False)\n",
    "dec = Decoder(attention_dim, \n",
    "              emb_dim, \n",
    "              decoder_dim, \n",
    "              vocab_size, \n",
    "              encoder_dim=encoder_dim, \n",
    "              dropout=0.5, \n",
    "              pretrained_embedding = embedding,teacher_forcing_ratio=1)\n",
    "\n",
    "\n",
    "# Models Ensemble \n",
    "class Ensemble(nn.Module):\n",
    "    def __init__(self,encoder, decoder):\n",
    "        super(Ensemble, self).__init__()\n",
    "        self.encoder = encoder\n",
    "        self.decoder = decoder\n",
    "    def forward(self,x1,caps,decode_lengths,inds): # you need flatten in between\n",
    "        imgs = self.encoder(x1) # here input x1 is Images: output (batch_size, encoded_image_size, encoded_image_size, 2048)\n",
    "        scores,decode_lengths,alphas, inds  = self.decoder(imgs, caps,decode_lengths,inds) #caps_sorted, decode_lengths, alphas, sort_ind\n",
    "        return scores,decode_lengths, alphas, inds\n",
    "\n",
    "# Testing\n",
    "criterion = nn.CrossEntropyLoss().to(device)\n",
    "enc = enc.to(device)\n",
    "dec = dec.to(device)\n",
    "arch = Ensemble(enc, dec).to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "code_folding": [
     0
    ]
   },
   "outputs": [],
   "source": [
    "def eval(img):\n",
    "    encoder_out = self.model.encoder(img)\n",
    "\n",
    "    num_pixels = encoder_out.size(1)\n",
    "    encoder_out = encoder_out.expand(3, num_pixels, encoder_dim)\n",
    "\n",
    "\n",
    "    # Tensor to store top k previous words at each step; now they're just 0\n",
    "    k_prev_words = torch.LongTensor([[0]] * k).to(device)  # (k, 1)\n",
    "\n",
    "    # Tensor to store top k sequences; now they're just <start>\n",
    "    seqs = k_prev_words  # (k, 1)\n",
    "\n",
    "    # Tensor to store top k sequences' scores; now they're just 0\n",
    "    top_k_scores = torch.zeros(k, 1).to(device)  # (k, 1)\n",
    "\n",
    "\n",
    "    #dec_inp = torch.zeros(h.size(1), requires_grad=False).long()\n",
    "    #dec_inp = dec_inp.to(self.device)\n",
    "\n",
    "    complete_seqs = list()\n",
    "    complete_seqs_scores = list()\n",
    "\n",
    "    step = 1\n",
    "\n",
    "    h,c = self.model.decoder.init_hidden_state(encoder_out)\n",
    "\n",
    "    a = True\n",
    "    while True:\n",
    "        k_prev_words = torch.LongTensor([[0]] * 3).to(device)\n",
    "        embeddings = self.model.decoder.embedding(k_prev_words).squeeze(1)\n",
    "        awe, _ = self.model.decoder.attention(encoder_out, h)\n",
    "        gate = self.model.decoder.sigmoid(self.model.decoder.f_beta(h))\n",
    "        awe = gate * awe\n",
    "        h, c = self.model.decoder.lstm(torch.cat([embeddings, awe], dim=1), (h, c))\n",
    "\n",
    "        scores = self.model.decoder.fc(h)  # (s, vocab_size)\n",
    "        scores = F.log_softmax(scores, dim=1)\n",
    "        top_k_scores = torch.zeros(k, 1).to(device)  # (k, 1)\n",
    "        scores = top_k_scores.expand_as(scores) + scores\n",
    "        if step == 1:\n",
    "            top_k_scores, top_k_words = scores[0].topk(k, 0, True, True)\n",
    "        else:\n",
    "            # Unroll and find top scores, and their unrolled indices\n",
    "            top_k_scores, top_k_words = scores.view(-1).topk(k, 0, True, True)  # (s)\n",
    "\n",
    "\n",
    "\n",
    "        # Convert unrolled indices to actual indices of scores\n",
    "        prev_word_inds = top_k_words / vocab_size  # (s)\n",
    "        next_word_inds = top_k_words % vocab_size  # (s)\n",
    "\n",
    "        seqs = torch.cat([seqs[prev_word_inds], next_word_inds.unsqueeze(1)], dim=1)\n",
    "\n",
    "\n",
    "        # If any sequence is not complete\n",
    "        incomplete_inds = [ind for ind, next_word in enumerate(next_word_inds) if\n",
    "                                       next_word != pad_idx]\n",
    "        complete_inds = list(set(range(len(next_word_inds))) - set(incomplete_inds))\n",
    "\n",
    "        # if at step any of seq completes we store complete_seqs and proceeds with\n",
    "        # remaining k - len(complete_seqs) incomplete seqs \n",
    "\n",
    "        if len(complete_inds) > 0:\n",
    "            complete_seqs.extend(seqs[complete_inds].tolist())\n",
    "            complete_seqs_scores.extend(top_k_scores[complete_inds])\n",
    "        k -= len(complete_inds)  # reduce beam length accordingly\n",
    "\n",
    "        if k == 0:\n",
    "            break\n",
    "\n",
    "        seqs = seqs[incomplete_inds]\n",
    "        h = h[prev_word_inds[incomplete_inds]]\n",
    "        c = c[prev_word_inds[incomplete_inds]]\n",
    "\n",
    "        encoder_out = encoder_out[prev_word_inds[incomplete_inds]]\n",
    "        top_k_scores = top_k_scores[incomplete_inds].unsqueeze(1)\n",
    "        k_prev_words = next_word_inds[incomplete_inds].unsqueeze(1)\n",
    "\n",
    "        # Break if things have been going on too long\n",
    "        if step > 50:\n",
    "            break\n",
    "        step += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-11T03:27:28.795304Z",
     "start_time": "2020-04-11T03:27:28.775530Z"
    }
   },
   "source": [
    "### Callback handler"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Defining Callbacks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-17T12:53:03.048676Z",
     "start_time": "2020-04-17T12:53:03.017684Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing custom_callbacks.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile custom_callbacks.py\n",
    "\n",
    "class TeacherForcingCallback(Callback):\n",
    "    def __init__(self, learn:Learner, decay_epochs=3):\n",
    "        super().__init__()\n",
    "        self.learn = learn\n",
    "        self.decay_iterations = decay_epochs * len(self.learn.data.train_ds) // self.learn.data.batch_size\n",
    "    \n",
    "    def on_batch_begin(self, iteration,**kwargs):\n",
    "        self.learn.model.decoder.teacher_forcing_ratio = (self.decay_iterations-iteration) * 1/self.decay_iterations if iteration < self.decay_iterations else 0\n",
    "        \n",
    "    def on_batch_end(self,**kwargs):\n",
    "        self.learn.model.decoder.teacher_forcing_ratio = 0.\n",
    "\n",
    "class GradientClipping(LearnerCallback):\n",
    "    \"Gradient clipping during training.\"\n",
    "    def __init__(self, learn:Learner, clip:float = 0.3):\n",
    "        super().__init__(learn)\n",
    "        self.clip = clip\n",
    "\n",
    "    def on_backward_end(self, **kwargs):\n",
    "        \"Clip the gradient before the optimizer step.\"\n",
    "        if self.clip: nn.utils.clip_grad_norm_(self.learn.model.parameters(), self.clip)\n",
    "\n",
    "        \n",
    "\n",
    "class BleuMetric(Callback):\n",
    "    def on_epoch_begin(self, **kwargs):\n",
    "        self.references = list()\n",
    "        self.candidates = list()\n",
    "        \n",
    "    def on_batch_end(self, last_output, last_target, **kwargs):\n",
    "        pred, decode_lengths,_,inds = last_output\n",
    "        references = metadata.references[metadata.index.isin(inds.tolist())]\n",
    "        _,pred_words = pred.max(dim=-1)\n",
    "        vocab_i2s = dict(zip(vocab.values(),vocab.keys()))\n",
    "        hypotheses = list()\n",
    "        for cap in pred_words: hypotheses.append([vocab_i2s[x] for x in cap.tolist() if x not in \n",
    "                               {vocab['<start>'], vocab['<end>'], vocab['<pad>']}])\n",
    "        self.references.extend(references)\n",
    "        self.candidates.extend(hypotheses)\n",
    "\n",
    "        \n",
    "    def on_epoch_end(self, last_metrics, **kwargs):\n",
    "        assert len(self.references) == len(self.candidates)\n",
    "        return add_metrics(last_metrics, corpus_bleu(self.references, self.candidates, weights=(0.5,0.5,0.5,0.5)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Learner create Object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-03T09:36:20.851017Z",
     "start_time": "2020-08-03T09:36:20.806522Z"
    },
    "init_cell": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#from modules.metrics import beam_search\n",
    "\n",
    "opt_fn = partial(optim.Adam, betas=(0.8, 0.99))\n",
    "\n",
    "\n",
    "# metrics functions\n",
    "loss_func = partial(loss_func, lamb = 1)\n",
    "#BleuMetric(metadata,vocab)\n",
    "#beam_fn = partial(beam_search,beam_size = 5)\n",
    "\n",
    "learn = Learner(dataBunch,arch,loss_func= loss_func,opt_func=opt_fn, metrics=[topK_accuracy,BleuMetric(metadata,vocab)],callback_fns=[ShowGraph]) #,TeacherForcingTurnOff,TeacherForcingCallback\n",
    "\n",
    "# split model into encoder and decoder layer groups\n",
    "split_list = [learn.model.encoder,learn.model.decoder]\n",
    "learn.split(split_list)\n",
    "len(learn.layer_groups)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-07T14:05:17.466875Z",
     "start_time": "2020-05-07T14:05:17.379554Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Module Name                                                                           Total Parameters  Trainable Parameters # Elements in Trainable Parametrs       \n",
      "Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)          1                 0                    0                                       \n",
      "BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)       2                 0                    0                                       \n",
      "ReLU(inplace=True)                                                                    0                 0                    0                                       \n",
      "MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)            0                 0                    0                                       \n",
      "Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)                         1                 0                    0                                       \n",
      "BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)       2                 0                    0                                       \n",
      "Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)         1                 0                    0                                       \n",
      "BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)       2                 0                    0                                       \n",
      "Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)                        1                 0                    0                                       \n",
      "BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)      2                 0                    0                                       \n",
      "ReLU(inplace=True)                                                                    0                 0                    0                                       \n",
      "Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)                        1                 0                    0                                       \n",
      "BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)      2                 0                    0                                       \n",
      "Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)                        1                 0                    0                                       \n",
      "BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)       2                 0                    0                                       \n",
      "Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)         1                 0                    0                                       \n",
      "BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)       2                 0                    0                                       \n",
      "Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)                        1                 0                    0                                       \n",
      "BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)      2                 0                    0                                       \n",
      "ReLU(inplace=True)                                                                    0                 0                    0                                       \n",
      "Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)                        1                 0                    0                                       \n",
      "BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)       2                 0                    0                                       \n",
      "Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)         1                 0                    0                                       \n",
      "BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)       2                 0                    0                                       \n",
      "Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)                        1                 0                    0                                       \n",
      "BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)      2                 0                    0                                       \n",
      "ReLU(inplace=True)                                                                    0                 0                    0                                       \n",
      "Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)                       1                 0                    0                                       \n",
      "BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)      2                 0                    0                                       \n",
      "Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)       1                 0                    0                                       \n",
      "BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)      2                 0                    0                                       \n",
      "Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)                       1                 0                    0                                       \n",
      "BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)      2                 0                    0                                       \n",
      "ReLU(inplace=True)                                                                    0                 0                    0                                       \n",
      "Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)                       1                 0                    0                                       \n",
      "BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)      2                 0                    0                                       \n",
      "Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)                       1                 0                    0                                       \n",
      "BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)      2                 0                    0                                       \n",
      "Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)       1                 0                    0                                       \n",
      "BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)      2                 0                    0                                       \n",
      "Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)                       1                 0                    0                                       \n",
      "BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)      2                 0                    0                                       \n",
      "ReLU(inplace=True)                                                                    0                 0                    0                                       \n",
      "Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)                       1                 0                    0                                       \n",
      "BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)      2                 0                    0                                       \n",
      "Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)       1                 0                    0                                       \n",
      "BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)      2                 0                    0                                       \n",
      "Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)                       1                 0                    0                                       \n",
      "BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)      2                 0                    0                                       \n",
      "ReLU(inplace=True)                                                                    0                 0                    0                                       \n",
      "Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)                       1                 0                    0                                       \n",
      "BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)      2                 0                    0                                       \n",
      "Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)       1                 0                    0                                       \n",
      "BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)      2                 0                    0                                       \n",
      "Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)                       1                 0                    0                                       \n",
      "BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)      2                 0                    0                                       \n",
      "ReLU(inplace=True)                                                                    0                 0                    0                                       \n",
      "Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)                       1                 0                    0                                       \n",
      "BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)      2                 0                    0                                       \n",
      "Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)       1                 0                    0                                       \n",
      "BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)      2                 0                    0                                       \n",
      "Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)                      1                 0                    0                                       \n",
      "BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)     2                 0                    0                                       \n",
      "ReLU(inplace=True)                                                                    0                 0                    0                                       \n",
      "Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)                      1                 0                    0                                       \n",
      "BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)     2                 0                    0                                       \n",
      "Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)                      1                 0                    0                                       \n",
      "BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)      2                 0                    0                                       \n",
      "Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)       1                 0                    0                                       \n",
      "BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)      2                 0                    0                                       \n",
      "Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)                      1                 0                    0                                       \n",
      "BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)     2                 0                    0                                       \n",
      "ReLU(inplace=True)                                                                    0                 0                    0                                       \n",
      "Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)                      1                 0                    0                                       \n",
      "BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)      2                 0                    0                                       \n",
      "Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)       1                 0                    0                                       \n",
      "BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)      2                 0                    0                                       \n",
      "Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)                      1                 0                    0                                       \n",
      "BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)     2                 0                    0                                       \n",
      "ReLU(inplace=True)                                                                    0                 0                    0                                       \n",
      "Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)                      1                 0                    0                                       \n",
      "BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)      2                 0                    0                                       \n",
      "Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)       1                 0                    0                                       \n",
      "BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)      2                 0                    0                                       \n",
      "Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)                      1                 0                    0                                       \n",
      "BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)     2                 0                    0                                       \n",
      "ReLU(inplace=True)                                                                    0                 0                    0                                       \n",
      "Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)                      1                 0                    0                                       \n",
      "BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)      2                 0                    0                                       \n",
      "Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)       1                 0                    0                                       \n",
      "BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)      2                 0                    0                                       \n",
      "Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)                      1                 0                    0                                       \n",
      "BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)     2                 0                    0                                       \n",
      "ReLU(inplace=True)                                                                    0                 0                    0                                       \n",
      "Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)                      1                 0                    0                                       \n",
      "BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)      2                 0                    0                                       \n",
      "Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)       1                 0                    0                                       \n",
      "BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)      2                 0                    0                                       \n",
      "Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)                      1                 0                    0                                       \n",
      "BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)     2                 0                    0                                       \n",
      "ReLU(inplace=True)                                                                    0                 0                    0                                       \n",
      "Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)                      1                 0                    0                                       \n",
      "BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)      2                 0                    0                                       \n",
      "Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)       1                 0                    0                                       \n",
      "BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)      2                 0                    0                                       \n",
      "Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)                      1                 0                    0                                       \n",
      "BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)     2                 0                    0                                       \n",
      "ReLU(inplace=True)                                                                    0                 0                    0                                       \n",
      "Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)                      1                 0                    0                                       \n",
      "BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)      2                 0                    0                                       \n",
      "Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)       1                 0                    0                                       \n",
      "BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)      2                 0                    0                                       \n",
      "Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)                      1                 0                    0                                       \n",
      "BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)     2                 0                    0                                       \n",
      "ReLU(inplace=True)                                                                    0                 0                    0                                       \n",
      "Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)                      1                 0                    0                                       \n",
      "BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)      2                 0                    0                                       \n",
      "Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)       1                 0                    0                                       \n",
      "BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)      2                 0                    0                                       \n",
      "Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)                      1                 0                    0                                       \n",
      "BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)     2                 0                    0                                       \n",
      "ReLU(inplace=True)                                                                    0                 0                    0                                       \n",
      "Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)                      1                 0                    0                                       \n",
      "BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)      2                 0                    0                                       \n",
      "Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)       1                 0                    0                                       \n",
      "BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)      2                 0                    0                                       \n",
      "Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)                      1                 0                    0                                       \n",
      "BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)     2                 0                    0                                       \n",
      "ReLU(inplace=True)                                                                    0                 0                    0                                       \n",
      "Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)                      1                 0                    0                                       \n",
      "BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)      2                 0                    0                                       \n",
      "Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)       1                 0                    0                                       \n",
      "BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)      2                 0                    0                                       \n",
      "Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)                      1                 0                    0                                       \n",
      "BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)     2                 0                    0                                       \n",
      "ReLU(inplace=True)                                                                    0                 0                    0                                       \n",
      "Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)                      1                 0                    0                                       \n",
      "BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)      2                 0                    0                                       \n",
      "Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)       1                 0                    0                                       \n",
      "BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)      2                 0                    0                                       \n",
      "Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)                      1                 0                    0                                       \n",
      "BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)     2                 0                    0                                       \n",
      "ReLU(inplace=True)                                                                    0                 0                    0                                       \n",
      "Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)                      1                 0                    0                                       \n",
      "BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)      2                 0                    0                                       \n",
      "Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)       1                 0                    0                                       \n",
      "BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)      2                 0                    0                                       \n",
      "Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)                      1                 0                    0                                       \n",
      "BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)     2                 0                    0                                       \n",
      "ReLU(inplace=True)                                                                    0                 0                    0                                       \n",
      "Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)                      1                 0                    0                                       \n",
      "BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)      2                 0                    0                                       \n",
      "Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)       1                 0                    0                                       \n",
      "BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)      2                 0                    0                                       \n",
      "Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)                      1                 0                    0                                       \n",
      "BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)     2                 0                    0                                       \n",
      "ReLU(inplace=True)                                                                    0                 0                    0                                       \n",
      "Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)                      1                 0                    0                                       \n",
      "BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)      2                 0                    0                                       \n",
      "Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)       1                 0                    0                                       \n",
      "BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)      2                 0                    0                                       \n",
      "Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)                      1                 0                    0                                       \n",
      "BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)     2                 0                    0                                       \n",
      "ReLU(inplace=True)                                                                    0                 0                    0                                       \n",
      "Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)                      1                 0                    0                                       \n",
      "BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)      2                 0                    0                                       \n",
      "Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)       1                 0                    0                                       \n",
      "BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)      2                 0                    0                                       \n",
      "Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)                      1                 0                    0                                       \n",
      "BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)     2                 0                    0                                       \n",
      "ReLU(inplace=True)                                                                    0                 0                    0                                       \n",
      "Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)                      1                 0                    0                                       \n",
      "BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)      2                 0                    0                                       \n",
      "Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)       1                 0                    0                                       \n",
      "BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)      2                 0                    0                                       \n",
      "Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)                      1                 0                    0                                       \n",
      "BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)     2                 0                    0                                       \n",
      "ReLU(inplace=True)                                                                    0                 0                    0                                       \n",
      "Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)                      1                 0                    0                                       \n",
      "BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)      2                 0                    0                                       \n",
      "Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)       1                 0                    0                                       \n",
      "BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)      2                 0                    0                                       \n",
      "Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)                      1                 0                    0                                       \n",
      "BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)     2                 0                    0                                       \n",
      "ReLU(inplace=True)                                                                    0                 0                    0                                       \n",
      "Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)                      1                 0                    0                                       \n",
      "BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)      2                 0                    0                                       \n",
      "Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)       1                 0                    0                                       \n",
      "BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)      2                 0                    0                                       \n",
      "Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)                      1                 0                    0                                       \n",
      "BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)     2                 0                    0                                       \n",
      "ReLU(inplace=True)                                                                    0                 0                    0                                       \n",
      "Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)                      1                 0                    0                                       \n",
      "BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)      2                 0                    0                                       \n",
      "Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)       1                 0                    0                                       \n",
      "BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)      2                 0                    0                                       \n",
      "Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)                      1                 0                    0                                       \n",
      "BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)     2                 0                    0                                       \n",
      "ReLU(inplace=True)                                                                    0                 0                    0                                       \n",
      "Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)                      1                 0                    0                                       \n",
      "BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)      2                 0                    0                                       \n",
      "Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)       1                 0                    0                                       \n",
      "BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)      2                 0                    0                                       \n",
      "Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)                      1                 0                    0                                       \n",
      "BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)     2                 0                    0                                       \n",
      "ReLU(inplace=True)                                                                    0                 0                    0                                       \n",
      "Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)                      1                 0                    0                                       \n",
      "BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)      2                 0                    0                                       \n",
      "Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)       1                 0                    0                                       \n",
      "BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)      2                 0                    0                                       \n",
      "Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)                      1                 0                    0                                       \n",
      "BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)     2                 0                    0                                       \n",
      "ReLU(inplace=True)                                                                    0                 0                    0                                       \n",
      "Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)                      1                 0                    0                                       \n",
      "BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)      2                 0                    0                                       \n",
      "Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)       1                 0                    0                                       \n",
      "BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)      2                 0                    0                                       \n",
      "Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)                      1                 0                    0                                       \n",
      "BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)     2                 0                    0                                       \n",
      "ReLU(inplace=True)                                                                    0                 0                    0                                       \n",
      "Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)                      1                 0                    0                                       \n",
      "BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)      2                 0                    0                                       \n",
      "Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)       1                 0                    0                                       \n",
      "BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)      2                 0                    0                                       \n",
      "Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)                      1                 0                    0                                       \n",
      "BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)     2                 0                    0                                       \n",
      "ReLU(inplace=True)                                                                    0                 0                    0                                       \n",
      "Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)                     1                 0                    0                                       \n",
      "BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)     2                 0                    0                                       \n",
      "Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)                      1                 0                    0                                       \n",
      "BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)      2                 0                    0                                       \n",
      "Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)       1                 0                    0                                       \n",
      "BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)      2                 0                    0                                       \n",
      "Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)                      1                 0                    0                                       \n",
      "BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)     2                 0                    0                                       \n",
      "ReLU(inplace=True)                                                                    0                 0                    0                                       \n",
      "Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)                      1                 0                    0                                       \n",
      "BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)      2                 0                    0                                       \n",
      "Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)       1                 0                    0                                       \n",
      "BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)      2                 0                    0                                       \n",
      "Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)                      1                 0                    0                                       \n",
      "BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)     2                 0                    0                                       \n",
      "ReLU(inplace=True)                                                                    0                 0                    0                                       \n",
      "AdaptiveAvgPool2d(output_size=(14, 14))                                               0                 0                    0                                       \n",
      "Dropout(p=0.5, inplace=False)                                                         0                 0                    0                                       \n",
      "Linear(in_features=2048, out_features=512, bias=True)                                 2                 2                    1,049,088                               \n",
      "Linear(in_features=512, out_features=512, bias=True)                                  2                 2                    262,656                                 \n",
      "Linear(in_features=512, out_features=1, bias=True)                                    2                 2                    513                                     \n",
      "ReLU()                                                                                0                 0                    0                                       \n",
      "Softmax(dim=1)                                                                        0                 0                    0                                       \n",
      "Embedding(4025, 300)                                                                  1                 1                    1,207,500                               \n",
      "LSTMCell(2348, 512)                                                                   4                 4                    5,861,376                               \n",
      "Linear(in_features=2048, out_features=512, bias=True)                                 2                 2                    1,049,088                               \n",
      "Linear(in_features=2048, out_features=512, bias=True)                                 2                 2                    1,049,088                               \n",
      "Linear(in_features=512, out_features=2048, bias=True)                                 2                 2                    1,050,624                               \n",
      "Sigmoid()                                                                             0                 0                    0                                       \n",
      "Linear(in_features=512, out_features=4025, bias=True)                                 2                 2                    2,064,825                               \n",
      "Total number of parameters elements 13,594,758\n"
     ]
    }
   ],
   "source": [
    "def summary_trainable(learner):\n",
    "    result = []\n",
    "    total_params_element = 0\n",
    "    def check_trainable(module):\n",
    "        nonlocal total_params_element\n",
    "        if len(list(module.children())) == 0:\n",
    "            num_param = 0\n",
    "            num_trainable_param = 0\n",
    "            num_param_numel = 0\n",
    "            for parameter in module.parameters():\n",
    "                num_param += 1\n",
    "                if parameter.requires_grad:\n",
    "                    num_param_numel += parameter.numel()\n",
    "                    total_params_element += parameter.numel()\n",
    "                    num_trainable_param += 1\n",
    "\n",
    "            result.append({'module': module, 'num_param': num_param , 'num_trainable_param' : num_trainable_param, 'num_param_numel': num_param_numel})\n",
    "    learner.model.apply(check_trainable)\n",
    "\n",
    "    print(\"{: <85} {: <17} {: <20} {: <40}\".format('Module Name', 'Total Parameters', 'Trainable Parameters', '# Elements in Trainable Parametrs'))\n",
    "    for row in result:\n",
    "        print(\"{: <85} {: <17} {: <20} {: <40,}\".format(row['module'].__str__(), row['num_param'], row['num_trainable_param'], row['num_param_numel']))\n",
    "    print('Total number of parameters elements {:,}'.format(total_params_element))\n",
    "\n",
    "\n",
    "\n",
    "# uncomment below to print summary of trainable layers \n",
    "#learn.freeze()\n",
    "summary_trainable(learn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stage 1: with encoder part freezed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-01T16:00:50.406415Z",
     "start_time": "2020-08-01T15:59:05.117515Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "        <style>\n",
       "            /* Turns off some styling */\n",
       "            progress {\n",
       "                /* gets rid of default border in Firefox and Opera. */\n",
       "                border: none;\n",
       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "                background-size: auto;\n",
       "            }\n",
       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "                background: #F44336;\n",
       "            }\n",
       "        </style>\n",
       "      <progress value='0' class='' max='1', style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      0.00% [0/1 00:00<00:00]\n",
       "    </div>\n",
       "    \n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>topK_accuracy</th>\n",
       "      <th>bleu_metric</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table><p>\n",
       "\n",
       "    <div>\n",
       "        <style>\n",
       "            /* Turns off some styling */\n",
       "            progress {\n",
       "                /* gets rid of default border in Firefox and Opera. */\n",
       "                border: none;\n",
       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "                background-size: auto;\n",
       "            }\n",
       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "                background: #F44336;\n",
       "            }\n",
       "        </style>\n",
       "      <progress value='99' class='' max='1000', style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      9.90% [99/1000 01:42<15:36 9.2395]\n",
       "    </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\n",
      "Min numerical gradient: 9.77E-04\n",
      "Min loss divided by 10: 1.23E-02\n"
     ]
    },
    {
     "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": [
    "learn.freeze()\n",
    "learn.lr_find(end_lr = 1)\n",
    "learn.recorder.plot(suggestion=True)#;\n",
    "#find_appropriate_lr(learn,plot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-01T18:54:48.372193Z",
     "start_time": "2020-08-01T16:06:47.937087Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>topK_accuracy</th>\n",
       "      <th>bleu_metric</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>4.649515</td>\n",
       "      <td>4.511709</td>\n",
       "      <td>58.052895</td>\n",
       "      <td>0.106774</td>\n",
       "      <td>18:29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>4.234053</td>\n",
       "      <td>4.231682</td>\n",
       "      <td>62.291264</td>\n",
       "      <td>0.125098</td>\n",
       "      <td>17:41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>4.048578</td>\n",
       "      <td>4.089489</td>\n",
       "      <td>64.173981</td>\n",
       "      <td>0.136820</td>\n",
       "      <td>17:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3.918362</td>\n",
       "      <td>4.001822</td>\n",
       "      <td>65.538071</td>\n",
       "      <td>0.142155</td>\n",
       "      <td>17:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>3.820599</td>\n",
       "      <td>3.946904</td>\n",
       "      <td>66.606972</td>\n",
       "      <td>0.147784</td>\n",
       "      <td>16:14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>3.676066</td>\n",
       "      <td>3.904321</td>\n",
       "      <td>67.152397</td>\n",
       "      <td>0.140314</td>\n",
       "      <td>16:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>3.632400</td>\n",
       "      <td>3.884929</td>\n",
       "      <td>67.566093</td>\n",
       "      <td>0.145791</td>\n",
       "      <td>16:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>3.533431</td>\n",
       "      <td>3.860997</td>\n",
       "      <td>68.075752</td>\n",
       "      <td>0.154064</td>\n",
       "      <td>16:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>3.480697</td>\n",
       "      <td>3.852596</td>\n",
       "      <td>68.334770</td>\n",
       "      <td>0.151733</td>\n",
       "      <td>16:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>3.406797</td>\n",
       "      <td>3.853946</td>\n",
       "      <td>68.293274</td>\n",
       "      <td>0.150269</td>\n",
       "      <td>16:08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD4CAYAAADiry33AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAfAUlEQVR4nO3deXxU9b3/8dcnyYSQDZIQ1iAJi6DsGBEBl0qr4n4tVby111r743e1rVtvW6S3V+1yq3302lv7q3qttSsXa1GrtYpWK2IVoUHZd2QLawhJSMie+f7+mCEJISQTmJMJJ+/n4zGPmfme7znzPd/A+5z5nmXMOYeIiPhXXKwbICIi3lLQi4j4nIJeRMTnFPQiIj6noBcR8bkELxbaKyPTjRg21ItFi4j40ooVKw4557K9WLYnQd934GAKCgq8WLSIiC+Z2U6vlu3J0I1OzRcR6To0Ri8i4nPe7NGjXXoRka7CkzF6EZGOqKuro7CwkOrq6lg3xXNJSUnk5OQQCAQ67TM9CXqN0YtIRxQWFpKWlkZubi5mFuvmeMY5R3FxMYWFheTl5XXa52qMXkRirrq6mqysLF+HPICZkZWV1enfXDwaoxcR6Ri/h/wxsVhPb/bolfQiIl2GzroRkW6vtLSUJ554osPzXXXVVZSWlnrQoujSGL2IdHsnC/qGhoY253vttdfo3bu3V82KGm/OuvFioSIiHpk7dy7btm1jwoQJBAIBUlNTGTBgACtXrmT9+vXccMMN7N69m+rqau655x7mzJkDQG5uLgUFBVRUVDBz5kymT5/OBx98wKBBg3j55Zfp2bNnjNcsxJvz6JX0InKKHv7zOtbvPRLVZZ47MJ0Hrx190umPPPIIa9euZeXKlSxevJirr76atWvXNp4C+eyzz5KZmUlVVRXnn38+n/3sZ8nKyjpuGVu2bGHBggX84he/4KabbuKFF17g1ltvjep6nCrt0YuItDB58uTjznN//PHHeemllwDYvXs3W7ZsOSHo8/LymDBhAgDnnXceO3bs6LT2tkdXxopIl9LWnndnSUlJaXy9ePFi3nrrLZYuXUpycjKXXnppq+fB9+jRo/F1fHw8VVVVndLWSHh090rt04vImSMtLY3y8vJWp5WVlZGRkUFycjIbN27kww8/7OTWnT7t0YtIt5eVlcW0adMYM2YMPXv2pF+/fo3TrrzySp566inGjRvHyJEjmTJlSgxbemrMi73vnLPHuMLNa6O+XBHxpw0bNnDOOefEuhmdprX1NbMVzrl8Lz5P59GLiPicboEgIuJzuqmZiIjP6V43IiI+p6EbERGfiyjozew+M1tnZmvNbIGZJXndMBERiY52g97MBgF3A/nOuTFAPDC7rXm0Qy8ifpeamgrA3r17mTVrVqt1Lr30UgoKCjqzWa2KdOgmAehpZglAMrC3zdpKehHpJgYOHMjChQtj3Yw2tRv0zrk9wI+BXcA+oMw592bLemY2x8wKzKygqhv8kruI+Mu3vvWt4+5J/9BDD/Hwww8zY8YMJk2axNixY3n55ZdPmG/Hjh2MGTMGgKqqKmbPns24ceO4+eabu8z9btq9BYKZZQDXA3lAKfBHM7vVOff75vWcc08DTwP0HzZa+/Qicmpenwv710R3mf3HwsxH2qwye/Zs7r33Xu666y4Ann/+eRYtWsR9991Heno6hw4dYsqUKVx33XUn/d3XJ598kuTkZFavXs3q1auZNGlSdNfjFEVyr5tPA9udc0UAZvYiMBX4/clm0OmVInKmmThxIgcPHmTv3r0UFRWRkZHBgAEDuO+++1iyZAlxcXHs2bOHAwcO0L9//1aXsWTJEu6++24Axo0bx7hx4zpzFU4qkqDfBUwxs2SgCpgBtH10QTkvIqeqnT1vL82aNYuFCxeyf/9+Zs+ezfz58ykqKmLFihUEAgFyc3NbvUVxcyfb24+lSMbolwELgY+ANeF5nm5znqg0TUSkc82ePZvnnnuOhQsXMmvWLMrKyujbty+BQIB33nmHnTt3tjn/xRdfzPz58wFYu3Ytq1ev7oxmtyui2xQ75x4EHvS4LSIiMTV69GjKy8sZNGgQAwYM4POf/zzXXnst+fn5TJgwgVGjRrU5/5133sntt9/OuHHjmDBhApMnT+6klrfNk9sUZ+ed64q2r4/6ckXEn3SbYt2mWEREToNuaiYi4nO6qZmIdAnd5bemY7Geuh+9iMRcUlISxcXFvg975xzFxcUkJXXufSE9+XFwn/+tRCTKcnJyKCwspKioKNZN8VxSUhI5OTmd+pmeBH1QSS8iHRAIBMjLy4t1M3zLm6Eb5byISJfhSdBrj15EpOtQ0IuI+JyGbkREfE579CIiPufZefTBoMJeRKQr8OxeNzX1Qa8WLSIiHeBZ0FfVNXi1aBER6QDPgr5aQS8i0iUo6EVEfE5DNyIiPufhHr0OxoqIdAXenXWjPXoRkS5BQzciIj7nWdBX1iroRUS6As+Cvry63qtFi4hIB3gW9GVVdV4tWkREOsCToDcU9CIiXYUnQR8fZwp6EZEuwsOgr/Vi0SIi0kGeBX1ppfboRUS6Ak+CPhAfx97SKi8WLSIiHeRJ0PdIiGPX4Upq6nUuvYhIrHkU9PEEHewsrvRi8SIi0gHeBH0gtNitByu8WLyIiHSAZ0M3ANsU9CIiMedJ0MeZMah3T7YVKehFRGLNs1sgDM1OYVvRUa8WLyIiEfIs6NN7Blizp0w/KSgiEmOeBf2kszIAWLW71KuPEBGRCHgW9FeN7Q/A5gPlXn2EiIhEwLOg75+eREpiPFt05o2ISExFFPRm1tvMFprZRjPbYGYXRjAPk/MyWbR2P3UN+qFwEZFYiXSP/qfAIufcKGA8sCGSmb5w4RAOltfwxrr9p9o+ERE5Te0GvZmlAxcDvwRwztU65yI6wnrJ2X0ZnNmT3y7deXqtFBGRUxbJHv1QoAj4lZl9bGbPmFlKy0pmNsfMCsysoKioCAjdrvjWC4awfPthNu4/Et2Wi4hIRCIJ+gRgEvCkc24icBSY27KSc+5p51y+cy4/Ozu7sfym/MEkJsTxyOsbo9VmERHpgEiCvhAodM4tC79fSCj4I5KRksjN+YNZvKmIv208cCptFBGR09Bu0Dvn9gO7zWxkuGgGsL4jHzLvqnMAeOa97R1tn4iInKZIz7r5GjDfzFYDE4D/7MiH9EyM52uXDeeDbcX8fcuhjrZRREROQ0RB75xbGR5/H+ecu8E5V9LRD5pz8VAG9Eri1l8uwznX8ZaKiMgp8ezK2JbSkgJcO34gAAuW7+6sjxUR6fY6LegB7p4xAoB5L62hXlfLioh0ik4N+tQeCdx24RAA/riisDM/WkSk2+rUoAf4j2tHA/DAi2tYuq24sz9eRKTb6fSgj48zHpg5CoBHF+kiKhERr3V60AP830uG8cDMUazcXcrra/bFogkiIt1GTIIe4MZJOQDcOf8jdh+ujFUzRER8L2ZBn53Wg+9ccy4ATyzeGqtmiIj4XsyCHuCO6XlMG57FguW79eMkIiIeiWnQA0wfHrrT5c/+pr16EREvxDzob5+WC8Djb2/REI6IiAdiHvRJgXheumsqAD9atImyyroYt0hExF9iHvQAE8/K4LvXhy6kGv/dN6mua4hxi0RE/KNLBD3AF6YMaXx989MfxrAlIiL+0mWC3sxY9eDlAKzaXUpFTX2MWyQi4g9dJugBevUMcNelwwAY8+AbGq8XEYmCLhX0AN+4YmTj6/HffVN79iIip6nLBb2ZseORqxvfj3nwDYJB/SKViMip6nJBf8yahy5vfD103msxbImIyJmtywZ9WlKg8eAswP3Pr4xha0REzlxdNughdHB2yTc+BcCLH+0hd+5fNIwjItJBXTroAc7KSubHnxvf+H7ovNfInfsXGhT4IiIR6fJBDzDrvBzeuv+S48qGzXuNi3/0Ds4p8EVE2nJGBD3A8L6p7HjkalY3O0i763AleQ+8xlfmf8SRap1zLyLSmjMm6I9JTwqw4P9M4YtTcxvL/rJmH+MeepPcuX9h3d4yAO3pi4iEmReBmJ+f7woKCqK+3Jaq6xq48YkPWL/vSKvT775sOPdfPrLVaSIiXYmZrXDO5Xuy7DM56JtzzvHgK+v47dKdJ0xrfgGWiEhXpKA/Be9vPcTnn1nW+P5bV47itqlDSE5MiGGrRERa52XQn3Fj9BFZuYBpBxfw2nWQSiUAjy7ayLn/8QZ/33KIzzz2Lss+KWb34coYN1RExHv+3L3d+CpsfJVzgbVJUJI0mPeO5rAmmMfPf7WO/cG8xnveXzSiD7+744LYtldExEP+DPrZ86GiCPathL0rydi3kmv2fsx1R5Y2Vtke7Mdal8fqT4ay7v0iRp93EWXBZAB6JQdi1XIRkajz7Rh9a2rLDhI4uAoLbwBqd39M4tE9jdOPhX+/kVOYeMGnqMwajUvqRe/kxBi2WkS6Ax2M9dCcJxdRvfsjxth2xsaFHjl2qHH6jnD4X3PFTOr6jefZbemkZvQhf0gm33t1PTdMHMQNEwaSEO/Pwx0i0jkU9B5zzvHRrhLKq+tZvKmIlz9YzZi4HYy17YyJ285Y287guKLG+juDfVnj8lgbzGONy2NNMI8jpAJw56XD+NaVo2K1KiJyhlLQx9CKnSV89skP6E15aI8/wvC/7cbrWVVsfOMKhb6ItE9BH2M/f2crWw9W8NhN4zGzpgmVhxsP+Lq9K9m38UMGugONkwtdHwpdNkcTs5k87lzS+uRA2gBI69/0nJgSgzUSka5GQX8GOXhgH/f99DeMte2cE7eL/naYvpTQ30roabUn1K8gmeSsQbi0AeyoSWNI7jASeg08fmOQ2h8CSTFYGxHpLAr6M0xD0LGz+ChDs1PZcqCcz/xkCeBIo4q+VkI/K6Ef4WcrCZeV0o/Q6x524g+iu54ZkNofC28AjgSySMocRGLvQc02CP0gQWcIiZyJukTQm1k8UADscc5d01bd7h70rXl00UaeXLztuLJxOb1YXVjWoqajF0cbNwL9rIS+jRuF0sYNQ19KCVjDCZ9TQi9Ss3MINP9WkNIHknq1/khMhebDUSISE10l6O8H8oF0BX10Hamu48+r9nJz/mCGf/v1iOYxgmRSHg7+0tC3gmYbhJyEMkamVEDFQeIItrGguJNvBJJ6n3xaj/Twc5o2FCJR4GXQR3RlrJnlAFcDPwDu96Ih3Vl6UoDPXzAEaLrTZkPQMWzeawC8cOdUDh+tZeXuEt7ZWMT6fUe4fkIOD183hg+2HeLO+R+duNA6oAriCJLOUdKtksVfmUhcbRlUn/zhqsqw4m1NZbUVbTfe4ppCv82NRDoEksOPns2eex5fFu/Pi7VFYimiPXozWwj8EEgD/q21PXozmwPMATjrrLPO27nzxNsFizcago69pVXsLa3i/NxMFm8+yJd+3fY3quXzZpCd1gMzwzlH/vffovho08HicTm9+MnNEyitOMod//MOozMdv75lJCWHi7DqMrID1W1uMCLeULQUFzjJRqBn+xuJiKYlQUISxMWfSleLeCamQzdmdg1wlXPuLjO7lJMEfXMauukayqvrSE5M4GhtPeMeevOE6VkpiceFe0c9fN1oiitqKK+p51fv7wBgfE4v/mniIA6U13DPjBGM+c6rpFJFulXyxlfy6Ukt1FVCXVUrz6dYdiriEkKBH58Yek7oEX5u8b5xeiR1ejR77tFGnR6hz7f40AZHQ19C7IP+h8AXgHogCUgHXnTO3XqyeRT0XU9dQ5CEOKOovIY5v1vByt2lJ9T54tRc7v30CP75F8tO+qtdp+OcAem8fs9F0V2oc1BffZINQosNQ21lqG59DTTUhJ7rq8OP2qZprdapafaohmAUf6P4WOA3D//j3ie0UhaFOo2P+NA3qbiE0NBZY3mgqW58oMU8x8rim9VNaGUZLeu3Um6mjR1d5GBsuCGXoj16X3hi8VaeeW87t0/N5eKzsxk/uHer9arrGkgKhIY5PimqYHBmMs8t38V3Xl53Qt1F917Elf/93gnlP509gXueWwlAn9REyqvrqakPEog3Nn9/JvVBR32DIykQul/QsYvSjtbUkxSIJz7OeHdzEZv3l/Pli/KOu2it5GgtG/YdYerwPid8bjDomP30hwzrm8LcK89h3p/WMO+qcxjUu2cHe6sVwWB4Q9B843CyjUUrdVwDBBsgWN/03FjW/H19K2WR1KkPtfFkdY6VN9Qf/z6WLA6w0LPFhTcAzcuObRCsjenN33dkmS2cEIstCk7IzdOdDva1fyjo5czinOOtDQcZ0CuJMYN6sXRbMbf84sOI5r1hwkD+tHJvhz/zy9Pz+M3SHYwd1Is9pVUcOFLTar1t/3kV8XHagzxBMNi0UWioa7ZxqGtWdmxD0Wx6Y90Wj8b6zaY337AEw9OdAxcEws/H3jeWuZPXafV9G/VPKAu/bvUbRYuy9r51nDC9vfmPf283/7ZrBH2kFPTSmo92lXDjEx8A8KVpeTz7/vaYtWXdw1eQ0iOBqtoGeiaeeGC2pr6BPSVVDM1OPe3PKquq48NPirn83H7H30JDpJkuM3QTKQW9RGJfWRVbDlQwdVgWa/aUkZGcyOtr9/Pooo0AvD/3Mh57czP7yqp45rZ8CkuquPwnS0hLSmDMwF4s/aSY9775KZIC8fzp4z384LUNxy3/s5Ny+K+bxlNT38D7Ww9x0YhsRjS7TuGSs7N5d3MRLb1011T+KbxBau6FO6dy3pAMAN7dXMRtzy5n2bwZVNU20CMQR219kMEZyZhBRU09c19Yw+3Tcpn1VNMP3ozP6cXz/3ohPRJ01o8cT0EvEiXr9pZx9eN/j3UzeG7OFKYMzfJs+U8s3sr8D3fx569NJzMlkRU7S1iyuYiEOOPmyYNxDvql6/5JXYmCXiTKPimqYOZP3+P2aXm8u7mI2ecP5o11+9lfVs0nh44C8NsvTeZfnl1+Wp+TlZLI7dNyuWBoFp9rtmcPMDQ7hefmTGHz/gqmDM0kIT6OozX1BJ1j3ktrGTsonRsn5dAntcdx8y3ZXMTI/mn0S0+itj7I37cWcdGIbKrrGrjiJ0vYW1YdUdvmzhzFv14y7LTWT6JHQS/SSZxzbDpQzqj+6ceV19YH+enbm9lbWs304X246Ow+9E0L7RGXVtYSdJCZkkhNfQM9EuKpqm0gKRDX6ph87ty/dKhNL901lZ3Fldz7h5XHlfdJTeRQxalfBwGw8XtXNp5VdTr2l1WTlZpIID6OJxdvY3xOr1bPhJKTU9CL+Mz8ZTv59ktrPVn2tOFZ5PVJ4bvXjaG2IcjkH7zF5aP78+PPjQdCG7OHXlnHb5aGrl7/96vP4bJRfVlVWMqFQ/vQv1cSlbX1vLFuPweP1LBs+2H+tvEg/371OdwxPY+tBytYsbOEwpIqpo/oQ5/UHnz6sXfbbdfWH8wkIT6OwpJK3t5wkMl5mewsruTKMf096YdjG90zhYJexIfqGoLsPlxJXp8U1uwp48FX1nHd+IHcPi0P5xxmxpvr9jPndysA+N4NY6hvCJI/JJMPPynmB69t4OufOZs7Lsqjrt5RFwySmZxIXASnjgaDjqHheynF2rXjB/KzWya2Wcc5x8sr93Lt+IGtnhpb3xAkPs4oqqjh3udW8r0bxjDjv0Ibnx/eOJZbJp8FwKGKGtbtPUK8Gfm5GSQF4qmtD+JwMd8oKOhFurFg0EUU3qfiNx/s4MFXTrz47VRs+v6VbNhXTsnRWs7un0ZdfZCM5ES2HCw/7syjk1n6wGUM6NV0Mduitfu5cGgW6T0TyHugaaN00Yg+9EiI560NoV9zG9U/jY37y0+7/V4cIK+uayAQH3fS6zaO1tQTH2ckBeIV9CLSOQ5V1PDr93dwwdBMLhqRTU19A4nxTccadhVXkpQY13h8oqO+/+p6Dh+t5bGbJwDwzsaD3P7rfzROH5/Ti1Un/EbDqXv8loncveDjiOu/8tVpDO+bSnJiAs45nKPdjWxReQ0ZyQEOVdSys/go+49UMz6nNyt2lvD1P65iSFYyO4ub7sk0emDoIPvVYwcw5YdvA/Dnr05n3ODeCnoR8aejNfX84R+7+e6r69ust3zeDJZsOcS//XEVAL/64vmNG4nl82bwv8t3kdcnhesnDGLT/nLy+qSQmBDHvrIq/rr+AP3Sk5gxqi8J8aFbbTzy+kaGZCXz6XP6ceBINdf8rPXTbv/5grP4cFsxtQ1BUhITKKms5WB5DSP7pbHpwOl/kzhm56PXKOhFxN8KSyqZ/ug7AEwdlsVXPjWc84Zk4BytXr0cbd9cuIrnCwqjusz8IRkU7Cxps86NEwfx4sd7FPQi0j38Y8dhzspMjtnFXMeGa55aso2LR2Szraii8YZ8zSUmhK+EzuzJn+6aRmZK4nGn0gaDoYPjkR7g/WDbIaYNz1bQi4j4mZcHY+O8WKiIiHQdCnoREZ9T0IuI+JyCXkTE5xT0IiI+p6AXEfE5Bb2IiM8p6EVEfE5BLyLicwp6ERGfU9CLiPicgl5ExOcU9CIiPqegFxHxOQW9iIjPKehFRHxOQS8i4nMKehERn1PQi4j4nIJeRMTnFPQiIj6noBcR8TkFvYiIzynoRUR8TkEvIuJzCnoREZ9T0IuI+Fy7QW9mg83sHTPbYGbrzOyezmiYiIhER0IEdeqBrzvnPjKzNGCFmf3VObfe47aJiEgUtLtH75zb55z7KPy6HNgADPK6YSIiEh0dGqM3s1xgIrCslWlzzKzAzAqKioqi0zoRETltEQe9maUCLwD3OueOtJzunHvaOZfvnMvPzs6OZhtFROQ0RBT0ZhYgFPLznXMvetskERGJpkjOujHgl8AG59xj3jdJRESiKZI9+mnAF4DLzGxl+HGVx+0SEZEoaff0Sufc3wHrhLaIiIgHdGWsiIjPKehFRHxOQS8i4nMKehERn1PQi4j4nIJeRMTnFPQiIj6noBcR8TkFvYiIzynoRUR8TkEvIuJzCnoREZ9T0IuI+JyCXkTE5xT0IiI+p6AXEfE5Bb2IiM8p6EVEfE5BLyLicwp6ERGfU9CLiPicgl5ExOcU9CIiPqegFxHxOQW9iIjPKehFRHxOQS8i4nMKehERn1PQi4j4nIJeRMTnFPQiIj6noBcR8TkFvYiIzynoRUR8TkEvIuJzCnoREZ9T0IuI+JyCXkTE5xT0IiI+F1HQm9mVZrbJzLaa2VyvGyUiItHTbtCbWTzwc2AmcC5wi5md63XDREQkOiLZo58MbHXOfeKcqwWeA673tlkiIhItCRHUGQTsbva+ELigZSUzmwPMCb+tMbO1p988X+gDHIp1I7oA9UMT9UUT9UWTkV4tOJKgt1bK3AkFzj0NPA1gZgXOufzTbJsvqC9C1A9N1BdN1BdNzKzAq2VHMnRTCAxu9j4H2OtNc0REJNoiCfp/ACPMLM/MEoHZwCveNktERKKl3aEb51y9mX0VeAOIB551zq1rZ7ano9E4n1BfhKgfmqgvmqgvmnjWF+bcCcPtIiLiI7oyVkTE5xT0IiI+F9Wg7w63SjCzwWb2jpltMLN1ZnZPuDzTzP5qZlvCzxnN5nkg3CebzOyKZuXnmdma8LTHzay1U1m7NDOLN7OPzezV8Pvu2g+9zWyhmW0M/9u4sBv3xX3h/xtrzWyBmSV1p74ws2fN7GDza4miuf5m1sPM/hAuX2Zmue02yjkXlQehA7XbgKFAIrAKODday+8qD2AAMCn8Og3YTOjWED8C5obL5wKPhl+fG+6LHkBeuI/iw9OWAxcSulbhdWBmrNfvFPrjfuB/gVfD77trP/wG+HL4dSLQuzv2BaELLLcDPcPvnwe+2J36ArgYmASsbVYWtfUH7gKeCr+eDfyh3TZFceUuBN5o9v4B4IFYd3on/FFfBj4DbAIGhMsGAJta6wdCZy9dGK6zsVn5LcD/xHp9OrjuOcDbwGXNgr479kN6ONysRXl37ItjV9JnEjqr71Xg8u7WF0Bui6CP2vofqxN+nUDoymJrqz3RHLpp7VYJg6K4/C4n/JVpIrAM6Oec2wcQfu4brnayfhkUft2y/Ezy38A3gWCzsu7YD0OBIuBX4WGsZ8wshW7YF865PcCPgV3APqDMOfcm3bAvWojm+jfO45yrB8qArLY+PJpBH9GtEvzCzFKBF4B7nXNH2qraSplro/yMYGbXAAedcysinaWVsjO+H8ISCH1Vf9I5NxE4Sujr+cn4ti/CY8/XExqGGAikmNmtbc3SSpkv+iJCp7L+He6baAZ9t7lVgpkFCIX8fOfci+HiA2Y2IDx9AHAwXH6yfikMv25ZfqaYBlxnZjsI3dH0MjP7Pd2vHyC0DoXOuWXh9wsJBX937ItPA9udc0XOuTrgRWAq3bMvmovm+jfOY2YJQC/gcFsfHs2g7xa3Sggf+f4lsME591izSa8At4Vf30Zo7P5Y+ezwkfI8YASwPPz1rdzMpoSX+S/N5unynHMPOOdynHO5hP7Wf3PO3Uo36wcA59x+YLeZHbv74AxgPd2wLwgN2Uwxs+TwOswANtA9+6K5aK5/82XNIvR/r+1vO1E+AHEVobNQtgHfjvUBEY8Oskwn9DVpNbAy/LiK0BjZ28CW8HNms3m+He6TTTQ7cwDIB9aGp/0/2jmg0lUfwKU0HYztlv0ATAAKwv8u/gRkdOO+eBjYGF6P3xE6o6Tb9AWwgNDxiTpCe993RHP9gSTgj8BWQmfmDG2vTboFgoiIz+nKWBERn1PQi4j4nIJeRMTnFPQiIj6noBcR8TkFvYiIzynoRUR87v8DbZtONwsb4EEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Better model found at epoch 0 with bleu_metric value: 0.10677446421266548.\n",
      "Better model found at epoch 1 with bleu_metric value: 0.12509844069650222.\n",
      "Better model found at epoch 2 with bleu_metric value: 0.13682008850365326.\n",
      "Better model found at epoch 3 with bleu_metric value: 0.14215501531102712.\n",
      "Better model found at epoch 4 with bleu_metric value: 0.14778400014690365.\n",
      "Better model found at epoch 7 with bleu_metric value: 0.15406443613883752.\n"
     ]
    }
   ],
   "source": [
    "# writer = SummaryWriter(comment='stage1')\n",
    "# tb_callback = partial(TensorBoardFastAI, writer, \n",
    "#                       track_weight=True, track_grad=True, \n",
    "#                       metric_names=['val loss','accuracy','bleu_metric']) #beam_search_bleu4\n",
    "                      \n",
    "                      \n",
    "#tb_callback(learn)\n",
    "\n",
    "\n",
    "#TeacherForcingCallback(learn,decay_epochs=5),\n",
    "#ReduceLROnPlateauCallback(learn, monitor='bleu_metric',patience=1,factor=0.1,min_lr=1e-10),\n",
    "\n",
    "### train for 10 epochs\n",
    "learn.freeze()\n",
    "learn.fit(10,4e-4,callbacks = [SaveModelCallback(learn, monitor='bleu_metric',name='bestmodel_stage1'),\n",
    "                                         GradientClipping(learn = learn, clip=5.)])\n",
    "                                         #TeacherForcingCallback(learn)])\n",
    "                    #,tb_callback(learn)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stage-2: Unfreeze encoder part as well"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-06T09:44:55.815152Z",
     "start_time": "2020-05-06T09:44:04.662236Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "        <style>\n",
       "            /* Turns off some styling */\n",
       "            progress {\n",
       "                /* gets rid of default border in Firefox and Opera. */\n",
       "                border: none;\n",
       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "                background-size: auto;\n",
       "            }\n",
       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "                background: #F44336;\n",
       "            }\n",
       "        </style>\n",
       "      <progress value='0' class='' max='1', style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      0.00% [0/1 00:00<00:00]\n",
       "    </div>\n",
       "    \n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>topK_accuracy</th>\n",
       "      <th>bleu_metric</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table><p>\n",
       "\n",
       "    <div>\n",
       "        <style>\n",
       "            /* Turns off some styling */\n",
       "            progress {\n",
       "                /* gets rid of default border in Firefox and Opera. */\n",
       "                border: none;\n",
       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "                background-size: auto;\n",
       "            }\n",
       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "                background: #F44336;\n",
       "            }\n",
       "        </style>\n",
       "      <progress value='99' class='' max='5000', style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      1.98% [99/5000 00:48<40:20 3.4133]\n",
       "    </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\n",
      "Min numerical gradient: 5.25E-09\n",
      "Min loss divided by 10: 3.98E-09\n"
     ]
    },
    {
     "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": [
    "learn.data.batch_size = 6\n",
    "learn.unfreeze()\n",
    "learn.load('bestmodel_stage1');\n",
    "learn.lr_find(start_lr=1e-11,end_lr = 1e-03)\n",
    "learn.recorder.plot(suggestion=True)#;\n",
    "\n",
    "#find_appropriate_lr(learn,plot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-02T18:09:53.772511Z",
     "start_time": "2020-08-02T14:01:56.278137Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>topK_accuracy</th>\n",
       "      <th>bleu_metric</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>3.547406</td>\n",
       "      <td>3.914244</td>\n",
       "      <td>67.741348</td>\n",
       "      <td>0.134781</td>\n",
       "      <td>40:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>3.717416</td>\n",
       "      <td>3.972998</td>\n",
       "      <td>66.951462</td>\n",
       "      <td>0.142118</td>\n",
       "      <td>42:23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3.721014</td>\n",
       "      <td>3.950798</td>\n",
       "      <td>67.553833</td>\n",
       "      <td>0.150034</td>\n",
       "      <td>42:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3.566937</td>\n",
       "      <td>3.928402</td>\n",
       "      <td>68.072418</td>\n",
       "      <td>0.155043</td>\n",
       "      <td>41:56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>3.473794</td>\n",
       "      <td>3.910442</td>\n",
       "      <td>68.245857</td>\n",
       "      <td>0.163102</td>\n",
       "      <td>40:16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>3.350647</td>\n",
       "      <td>3.915221</td>\n",
       "      <td>68.383591</td>\n",
       "      <td>0.161378</td>\n",
       "      <td>39:18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Better model found at epoch 0 with bleu_metric value: 0.13478130291573615.\n",
      "Better model found at epoch 1 with bleu_metric value: 0.14211778815646411.\n",
      "Better model found at epoch 2 with bleu_metric value: 0.15003388065100642.\n",
      "Better model found at epoch 3 with bleu_metric value: 0.15504291002860376.\n",
      "Better model found at epoch 4 with bleu_metric value: 0.1631019687892537.\n"
     ]
    }
   ],
   "source": [
    "learn.data.batch_size = 6\n",
    "\n",
    "# writer = SummaryWriter(comment='stage2')\n",
    "# tb_callback = partial(TensorBoardFastAI, writer, \n",
    "#                       track_weight=True, track_grad=True, \n",
    "#                       metric_names=['val loss','accuracy','bleu_metric'])\n",
    "                      \n",
    "                      \n",
    "#tb_callback(learn)\n",
    "\n",
    "\n",
    "learn.unfreeze()\n",
    "learn.load('bestmodel_stage1');\n",
    "learn.fit_one_cycle(6,1e-4,\n",
    "                    callbacks = [SaveModelCallback(learn, monitor='bleu_metric',name='bestmodel_stage2'),\n",
    "                                 GradientClipping(learn = learn, clip=5.)])\n",
    "#                                  TeacherForcingCallback(learn,decay_epochs=5000),\n",
    "#                                  tb_callback(learn)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-02T18:10:03.842713Z",
     "start_time": "2020-08-02T18:10:03.331164Z"
    }
   },
   "outputs": [],
   "source": [
    "learn.save('last_epoch_stage2');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-02T18:10:13.554401Z",
     "start_time": "2020-08-02T18:10:13.403581Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shutdown scheduled for Sun 2020-08-02 23:50:13 IST, use 'shutdown -c' to cancel.\r\n"
     ]
    }
   ],
   "source": [
    "!shutdown -h 10"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Initialization Cell",
  "kernelspec": {
   "display_name": "CapGen",
   "language": "python",
   "name": "capgen"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {
    "height": "868.889px",
    "left": "1653.99px",
    "top": "641.465px",
    "width": "165px"
   },
   "toc_section_display": true,
   "toc_window_display": false
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}