RNN/GRU_Classifier.ipynb

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  {
   "source": [
    "来源：[b站刘二大人-RNN高级篇](https://www.bilibili.com/video/BV1Y7411d7Ys?t=3&p=13)\n",
    "## model\n",
    "![model](https://ericpengshuai.github.io/pic/model.png)\n"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "### 准备数据\n",
    "1. 对于每个名字需要得到一个向量\n",
    "2. 通过ASCII对于每个名字的每个字符都得到一个`one-hot vector`\n",
    "![data](https://ericpengshuai.github.io/pic/data.png)\n",
    "\n",
    "3. 由于输入是矩阵所以需要`padding`\n"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch.utils.data import Dataset\n",
    "from torch.utils.data import DataLoader\n",
    "import gzip\n",
    "import csv\n",
    "\n",
    "class NameDataset(Dataset):\n",
    "    def __init__(self, is_train_set):\n",
    "        filename = '../dataset/names_train.csv.gz' if is_train_set else '../dataset/names_test.csv.gz'\n",
    "        with gzip.open(filename, 'rt') as f:    # r表示只读，从文件头开始 t表示文本模式\n",
    "            reader = csv.reader(f)\n",
    "            rows = list(reader)\n",
    "        self.names = [row[0] for row in rows]\n",
    "        self.len = len(self.names)\n",
    "        self.countries = [row[1] for row in rows]\n",
    "\n",
    "        self.country_list = list(sorted(set(self.countries)))\n",
    "        self.country_dict = self.getCountryDict()\n",
    "        self.country_num = len(self.country_list)\n",
    "\n",
    "    def __getitem__(self, index):       # 根据索引拿到的是 名字，国家的索引\n",
    "        return self.names[index], self.country_dict[self.countries[index]]\n",
    "\n",
    "    def __len__(self):\n",
    "        return self.len\n",
    "\n",
    "    def getCountryDict(self):\n",
    "        country_dict = dict()\n",
    "        for idx, country_name in enumerate(self.country_list, 0):\n",
    "            country_dict[country_name] = idx\n",
    "        return country_dict\n",
    "\n",
    "    def idx2country(self, index):\n",
    "        return self.country_list[index]\n",
    "\n",
    "    def getCountriesNum(self):\n",
    "        return self.country_num\n",
    "\n",
    "HIDDEN_SIZE = 100\n",
    "BATCH_SIZE = 256\n",
    "N_LAYER = 2\n",
    "N_EPOCHS = 50\n",
    "N_CHARS = 128   # 这个是为了构造嵌入层\n",
    "\n",
    "trainSet = NameDataset(is_train_set=True)\n",
    "trainLoader = DataLoader(trainSet, batch_size=BATCH_SIZE, shuffle=True)\n",
    "testSet = NameDataset(is_train_set=False)\n",
    "testLoader = DataLoader(testSet, batch_size=BATCH_SIZE, shuffle=False)\n",
    "\n",
    "N_COUNTRY = trainSet.getCountriesNum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "256 <class 'torch.Tensor'> torch.Size([256])\n"
     ]
    }
   ],
   "source": [
    "for idx, (names, countries) in enumerate(trainLoader):\n",
    "    print(names.__len__(), type(countries), countries.shape)\n",
    "    if idx == 0:\n",
    "        break"
   ]
  },
  {
   "source": [
    "### 构建模型\n",
    "#### GRU的维度\n",
    "1. 输入维度\n",
    "- 𝑖𝑛𝑝𝑢𝑡: (𝑠𝑒𝑞𝐿𝑒𝑛, 𝑏𝑎𝑡𝑐ℎ𝑆𝑖𝑧𝑒, ℎ𝑖𝑑𝑑𝑒𝑛𝑆𝑖𝑧𝑒) \n",
    "- hidden: (nLayers * nDirections, batchSize, hiddenSize)\n",
    "2. 输出维度\n",
    "- output: (seqLen, batchSize, hiddenSize * nDirections)\n",
    "- hidden: (nLayers * nDirections, batchSize, hiddenSize)\n",
    "3. 示意图  \n",
    "![model示意图](https://ericpengshuai.github.io/pic/model-show.png)\n",
    "\n",
    "#### GRU处理时可以使用`pack_padded_sequence`提高效率\n",
    "![pack_padded](https://ericpengshuai.github.io/pic/pack_padded_sequence.png)"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class RNNClassifier(torch.nn.Module):\n",
    "    def __init__(self, input_size, hidden_size, output_size, n_layers=1, bidirectional=True):\n",
    "        super(RNNClassifier, self).__init__() \n",
    "        self.hidden_size = hidden_size \n",
    "        self.n_layers = n_layers \n",
    "        self.n_directions = 2 if bidirectional else 1   # 使用双向的GRU\n",
    "        \n",
    "        # 嵌入层（𝑠𝑒𝑞𝐿𝑒𝑛, 𝑏𝑎𝑡𝑐ℎ𝑆𝑖𝑧𝑒） --> (𝑠𝑒𝑞𝐿𝑒𝑛, 𝑏𝑎𝑡𝑐ℎ𝑆𝑖𝑧𝑒, hidden_size)\n",
    "        self.embedding = torch.nn.Embedding(input_size, hidden_size) \n",
    "        self.gru = torch.nn.GRU(hidden_size, hidden_size, n_layers, bidirectional=bidirectional) \n",
    "        self.fc = torch.nn.Linear(hidden_size * self.n_directions, output_size)\n",
    "\n",
    "    def _init_hidden(self, batch_size):\n",
    "        hidden = torch.zeros(self.n_layers * self.n_directions, batch_size, self.hidden_size) \n",
    "        return hidden\n",
    "\n",
    "    def forward(self, x_input, seq_lengths):\n",
    "        # x_input shape : B x S -> S x B\n",
    "        x_input = x_input.t()\n",
    "        batch_size = x_input.size(1)\n",
    "        hidden = self._init_hidden(batch_size) \n",
    "        embedding = self.embedding(x_input)\n",
    "\n",
    "        # pack them up\n",
    "        gru_input = torch.nn.utils.rnn.pack_padded_sequence(embedding, seq_lengths)\n",
    "        output, hidden = self.gru(gru_input, hidden) \n",
    "        if self.n_directions == 2:\n",
    "            hidden_cat = torch.cat([hidden[-1], hidden[-2]], dim=1)\n",
    "        else:\n",
    "            hidden_cat = hidden[-1]\n",
    "        fc_output = self.fc(hidden_cat) \n",
    "        return fc_output\n",
    "    "
   ]
  },
  {
   "source": [
    "### 数据转化成Tensor"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def name2list(name):\n",
    "    arr = [ord(c) for c in name]\n",
    "    return arr, len(arr)\n",
    "\n",
    "def make_tensors(names, countries):\n",
    "    sequences_and_lengths = [name2list(name) for name in names]\n",
    "    name_sequences = [s1[0] for s1 in sequences_and_lengths]\n",
    "    seq_lengths = torch.LongTensor([s1[1] for s1 in sequences_and_lengths])\n",
    "    countries = countries.long()\n",
    "\n",
    "    # make tensor of name, BatchSize * seqLen\n",
    "    # 他这里补零的方式先将所有的0 Tensor给初始化出来，然后在每行前面填充每个名字\n",
    "    seq_tensor = torch.zeros(len(name_sequences), seq_lengths.max()).long()\n",
    "    # print(\"seq_lengths.max:\", seq_lengths.max())\n",
    "    for idx, (seq, seq_len) in enumerate(zip(name_sequences, seq_lengths), 0):\n",
    "        seq_tensor[idx, :seq_len] = torch.LongTensor(seq)\n",
    "\n",
    "    # sort by length to use pack_padded_sequence\n",
    "    # 将名字长度降序排列，并且返回降序之后的长度在原tensor中的小标perm_idx\n",
    "    seq_lengths, perm_idx = seq_lengths.sort(dim=0, descending=True)\n",
    "    # 这个Tensor中的类似于列表中切片的方法神奇啊，直接返回下标对应的元素，相等于排序了\n",
    "    seq_tensor = seq_tensor[perm_idx]\n",
    "    countries = countries[perm_idx]\n",
    "\n",
    "    # 返回排序之后名字Tensor，排序之后的名字长度Tensor，排序之后的国家名字Tensor\n",
    "    return seq_tensor, seq_lengths, countries"
   ]
  },
  {
   "source": [
    "### 训练数据"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier = RNNClassifier(N_CHARS, HIDDEN_SIZE, N_COUNTRY, N_LAYER) \n",
    "\n",
    "criterion = torch.nn.CrossEntropyLoss() \n",
    "optimizer = torch.optim.Adam(classifier.parameters(), lr=0.001)\n",
    "\n",
    "import time\n",
    "import math\n",
    "\n",
    "def trainModel():\n",
    "    def time_since(since):\n",
    "        s = time.time() - since\n",
    "        m = math.floor(s / 60)\n",
    "        s -= m * 60\n",
    "        return '%dm %ds' % (m, s)\n",
    "\n",
    "    total_loss = 0 \n",
    "    for i, (names, countries) in enumerate(trainLoader, 1):\n",
    "        # print(type(names), type(countries))\n",
    "        # print(len(names), countries.shape)\n",
    "        inputs, seq_lengths, target = make_tensors(names, countries)\n",
    "        \n",
    "        output = classifier(inputs, seq_lengths) \n",
    "        # print(\"Shape:\", output.shape, target.shape)\n",
    "        # 注意输出和目标的维度：Shape: torch.Size([256, 18]) torch.Size([256])\n",
    "        loss = criterion(output, target) \n",
    "        optimizer.zero_grad() \n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        total_loss += loss.item() \n",
    "        if i % 10 == 0:\n",
    "            print(f'[{time_since(start)}] Epoch {epoch} ', end='')\n",
    "            print(f'[{i * len(inputs)}/{len(trainSet)}] ', end='')\n",
    "            print(f'loss={total_loss / (i * len(inputs))}') \n",
    "    return total_loss\n",
    "\n",
    "def testModel():\n",
    "    correct = 0\n",
    "    total = len(testSet)\n",
    "    print(\"evaluating trained model ... \")\n",
    "    with torch.no_grad():\n",
    "        for i, (names, countries) in enumerate(testLoader):\n",
    "            inputs, seq_lengths, target = make_tensors(names, countries)\n",
    "            output = classifier(inputs, seq_lengths)\n",
    "            # 注意这个keepdim的使用，为了直接和target计算loss\n",
    "            pred = output.max(dim=1, keepdim=True)[1]\n",
    "            # 注意这个view_as 和 eq\n",
    "            correct += pred.eq(target.view_as(pred)).sum().item()\n",
    "        \n",
    "        percent = '%.2f' % (100 * correct / total) \n",
    "        print(f'Test set: Accuracy {correct}/{total} {percent}%')\n",
    "\n",
    "    return correct / total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Training for 50 epochs...\n",
      "[0m 2s] Epoch 1 [2560/13374] loss=0.00895661092363298\n",
      "[0m 4s] Epoch 1 [5120/13374] loss=0.007739758561365306\n",
      "[0m 6s] Epoch 1 [7680/13374] loss=0.0069853457777450485\n",
      "[0m 8s] Epoch 1 [10240/13374] loss=0.006530495395418256\n",
      "[0m 11s] Epoch 1 [12800/13374] loss=0.006135637713596225\n",
      "evaluating trained model ... \n",
      "Test set: Accuracy 4535/6700 67.69%\n",
      "[0m 15s] Epoch 2 [2560/13374] loss=0.004228085093200207\n",
      "[0m 17s] Epoch 2 [5120/13374] loss=0.0041014277492649855\n",
      "[0m 19s] Epoch 2 [7680/13374] loss=0.004011582878107826\n",
      "[0m 22s] Epoch 2 [10240/13374] loss=0.0038964587613008915\n",
      "[0m 24s] Epoch 2 [12800/13374] loss=0.0038181920163333416\n",
      "evaluating trained model ... \n",
      "Test set: Accuracy 4979/6700 74.31%\n",
      "[0m 28s] Epoch 3 [2560/13374] loss=0.003288307460024953\n",
      "[0m 31s] Epoch 3 [5120/13374] loss=0.003178741259034723\n",
      "[0m 34s] Epoch 3 [7680/13374] loss=0.0031318385930111012\n",
      "[0m 36s] Epoch 3 [10240/13374] loss=0.003077746124472469\n",
      "[0m 39s] Epoch 3 [12800/13374] loss=0.00303727344609797\n",
      "evaluating trained model ... \n",
      "Test set: Accuracy 5231/6700 78.07%\n",
      "[0m 44s] Epoch 4 [2560/13374] loss=0.0026070217834785582\n",
      "[0m 47s] Epoch 4 [5120/13374] loss=0.0025748643092811108\n",
      "[0m 50s] Epoch 4 [7680/13374] loss=0.0025482608936727045\n",
      "[0m 52s] Epoch 4 [10240/13374] loss=0.0025563659612089394\n",
      "[0m 55s] Epoch 4 [12800/13374] loss=0.0025668759178370235\n",
      "evaluating trained model ... \n",
      "Test set: Accuracy 5387/6700 80.40%\n",
      "[1m 0s] Epoch 5 [2560/13374] loss=0.002344200084917247\n",
      "[1m 4s] Epoch 5 [5120/13374] loss=0.002308277500560507\n",
      "[1m 7s] Epoch 5 [7680/13374] loss=0.0022978237092805406\n",
      "[1m 10s] Epoch 5 [10240/13374] loss=0.0022821351216407494\n",
      "[1m 13s] Epoch 5 [12800/13374] loss=0.0022497599758207797\n",
      "evaluating trained model ... \n",
      "Test set: Accuracy 5424/6700 80.96%\n",
      "[1m 19s] Epoch 6 [2560/13374] loss=0.0020733892219141127\n",
      "[1m 22s] Epoch 6 [5120/13374] loss=0.002043392934137955\n",
      "[1m 25s] Epoch 6 [7680/13374] loss=0.0020092876511625946\n",
      "[1m 28s] Epoch 6 [10240/13374] loss=0.0020319709903560577\n",
      "[1m 31s] Epoch 6 [12800/13374] loss=0.002023737344425172\n",
      "evaluating trained model ... \n",
      "Test set: Accuracy 5519/6700 82.37%\n",
      "[1m 38s] Epoch 7 [2560/13374] loss=0.0019045063992962242\n",
      "[1m 41s] Epoch 7 [5120/13374] loss=0.0018604892888106407\n",
      "[1m 45s] Epoch 7 [7680/13374] loss=0.001881595782469958\n",
      "[1m 48s] Epoch 7 [10240/13374] loss=0.0018579594121547415\n",
      "[1m 51s] Epoch 7 [12800/13374] loss=0.0018408470205031335\n",
      "evaluating trained model ... \n",
      "Test set: Accuracy 5583/6700 83.33%\n",
      "[1m 57s] Epoch 8 [2560/13374] loss=0.00168605629587546\n",
      "[2m 0s] Epoch 8 [5120/13374] loss=0.0016667626681737603\n",
      "[2m 3s] Epoch 8 [7680/13374] loss=0.0016320539793620507\n",
      "[2m 6s] Epoch 8 [10240/13374] loss=0.0016648953460389748\n",
      "[2m 10s] Epoch 8 [12800/13374] loss=0.0016562619665637613\n",
      "evaluating trained model ... \n",
      "Test set: Accuracy 5571/6700 83.15%\n",
      "[2m 16s] Epoch 9 [2560/13374] loss=0.001556115981657058\n",
      "[2m 19s] Epoch 9 [5120/13374] loss=0.0015433134627528488\n",
      "[2m 21s] Epoch 9 [7680/13374] loss=0.001529551208174477\n",
      "[2m 24s] Epoch 9 [10240/13374] loss=0.001517663276172243\n",
      "[2m 27s] Epoch 9 [12800/13374] loss=0.0015023158234544099\n",
      "evaluating trained model ... \n",
      "Test set: Accuracy 5612/6700 83.76%\n",
      "[2m 34s] Epoch 10 [2560/13374] loss=0.0013113723602145911\n",
      "[2m 37s] Epoch 10 [5120/13374] loss=0.0013705759251024575\n",
      "[2m 40s] Epoch 10 [7680/13374] loss=0.001341037976089865\n",
      "[2m 43s] Epoch 10 [10240/13374] loss=0.001369979081209749\n",
      "[2m 46s] Epoch 10 [12800/13374] loss=0.001372250176500529\n",
      "evaluating trained model ... \n",
      "Test set: Accuracy 5614/6700 83.79%\n",
      "[2m 52s] Epoch 11 [2560/13374] loss=0.0012813179870136081\n",
      "[2m 55s] Epoch 11 [5120/13374] loss=0.001294858515029773\n",
      "[2m 58s] Epoch 11 [7680/13374] loss=0.0012564665288664401\n",
      "[3m 1s] Epoch 11 [10240/13374] loss=0.0012502123907324858\n",
      "[3m 3s] Epoch 11 [12800/13374] loss=0.0012431384657975286\n",
      "evaluating trained model ... \n",
      "Test set: Accuracy 5632/6700 84.06%\n",
      "[3m 8s] Epoch 12 [2560/13374] loss=0.0010137596924323588\n",
      "[3m 11s] Epoch 12 [5120/13374] loss=0.0010476751369424165\n",
      "[3m 14s] Epoch 12 [7680/13374] loss=0.0010755926293010514\n",
      "[3m 17s] Epoch 12 [10240/13374] loss=0.0010838138798135333\n",
      "[3m 20s] Epoch 12 [12800/13374] loss=0.0011182099615689367\n",
      "evaluating trained model ... \n",
      "Test set: Accuracy 5650/6700 84.33%\n",
      "[3m 25s] Epoch 13 [2560/13374] loss=0.0010238180169835688\n",
      "[3m 28s] Epoch 13 [5120/13374] loss=0.0010145066829863937\n",
      "[3m 30s] Epoch 13 [7680/13374] loss=0.0010349769009432444\n",
      "[3m 33s] Epoch 13 [10240/13374] loss=0.0010367659415351227\n",
      "[3m 36s] Epoch 13 [12800/13374] loss=0.001017363256542012\n",
      "evaluating trained model ... \n",
      "Test set: Accuracy 5673/6700 84.67%\n",
      "[3m 41s] Epoch 14 [2560/13374] loss=0.0008797223796136677\n",
      "[3m 44s] Epoch 14 [5120/13374] loss=0.000864122121129185\n",
      "[3m 47s] Epoch 14 [7680/13374] loss=0.0008748298646726956\n",
      "[3m 50s] Epoch 14 [10240/13374] loss=0.0008839857837301679\n",
      "[3m 52s] Epoch 14 [12800/13374] loss=0.0008900446689222008\n",
      "evaluating trained model ... \n",
      "Test set: Accuracy 5655/6700 84.40%\n",
      "[3m 57s] Epoch 15 [2560/13374] loss=0.0007388620171695948\n",
      "[4m 0s] Epoch 15 [5120/13374] loss=0.0007413410494336858\n",
      "[4m 3s] Epoch 15 [7680/13374] loss=0.0007524877495598048\n",
      "[4m 6s] Epoch 15 [10240/13374] loss=0.0007662840318516829\n",
      "[4m 9s] Epoch 15 [12800/13374] loss=0.0007863063889089972\n",
      "evaluating trained model ... \n",
      "Test set: Accuracy 5619/6700 83.87%\n",
      "[4m 14s] Epoch 16 [2560/13374] loss=0.0006768183899112046\n",
      "[4m 16s] Epoch 16 [5120/13374] loss=0.0006984732201090083\n",
      "[4m 19s] Epoch 16 [7680/13374] loss=0.0007195420776649068\n",
      "[4m 22s] Epoch 16 [10240/13374] loss=0.0007080970521201379\n",
      "[4m 25s] Epoch 16 [12800/13374] loss=0.0007314662274438888\n",
      "evaluating trained model ... \n",
      "Test set: Accuracy 5648/6700 84.30%\n",
      "[4m 30s] Epoch 17 [2560/13374] loss=0.0006218034512130543\n",
      "[4m 33s] Epoch 17 [5120/13374] loss=0.0006253211147850379\n",
      "[4m 35s] Epoch 17 [7680/13374] loss=0.000631057593272999\n",
      "[4m 38s] Epoch 17 [10240/13374] loss=0.000633853611361701\n",
      "[4m 41s] Epoch 17 [12800/13374] loss=0.0006443013972602785\n",
      "evaluating trained model ... \n",
      "Test set: Accuracy 5649/6700 84.31%\n",
      "[4m 46s] Epoch 18 [2560/13374] loss=0.0005009277927456423\n",
      "[4m 49s] Epoch 18 [5120/13374] loss=0.0005194071869482286\n",
      "[4m 51s] Epoch 18 [7680/13374] loss=0.0005148581762720521\n",
      "[4m 54s] Epoch 18 [10240/13374] loss=0.0005477635473653209\n",
      "[4m 57s] Epoch 18 [12800/13374] loss=0.0005537319683935493\n",
      "evaluating trained model ... \n",
      "Test set: Accuracy 5647/6700 84.28%\n",
      "[5m 2s] Epoch 19 [2560/13374] loss=0.0004887953400611877\n",
      "[5m 4s] Epoch 19 [5120/13374] loss=0.0005000366800231859\n",
      "[5m 7s] Epoch 19 [7680/13374] loss=0.0005150090990355238\n",
      "[5m 10s] Epoch 19 [10240/13374] loss=0.0005093677063996438\n",
      "[5m 13s] Epoch 19 [12800/13374] loss=0.000505349209997803\n",
      "evaluating trained model ... \n",
      "Test set: Accuracy 5655/6700 84.40%\n",
      "[5m 18s] Epoch 20 [2560/13374] loss=0.00041363554773852227\n",
      "[5m 21s] Epoch 20 [5120/13374] loss=0.0004201160132652149\n",
      "[5m 23s] Epoch 20 [7680/13374] loss=0.0004332240399283667\n",
      "[5m 26s] Epoch 20 [10240/13374] loss=0.00043309144966769965\n",
      "[5m 29s] Epoch 20 [12800/13374] loss=0.0004413255309918895\n",
      "evaluating trained model ... \n",
      "Test set: Accuracy 5614/6700 83.79%\n",
      "[5m 34s] Epoch 21 [2560/13374] loss=0.00036581208987627176\n",
      "[5m 37s] Epoch 21 [5120/13374] loss=0.0003885535872541368\n",
      "[5m 40s] Epoch 21 [7680/13374] loss=0.0003868770310267185\n",
      "[5m 43s] Epoch 21 [10240/13374] loss=0.00040823663730407136\n",
      "[5m 45s] Epoch 21 [12800/13374] loss=0.0004077313735615462\n",
      "evaluating trained model ... \n",
      "Test set: Accuracy 5619/6700 83.87%\n",
      "[5m 50s] Epoch 22 [2560/13374] loss=0.00033817943767644465\n",
      "[5m 53s] Epoch 22 [5120/13374] loss=0.0003411393860005774\n",
      "[5m 56s] Epoch 22 [7680/13374] loss=0.00034937215192864336\n",
      "[5m 58s] Epoch 22 [10240/13374] loss=0.00035912294915760865\n",
      "[6m 1s] Epoch 22 [12800/13374] loss=0.00037851146480534227\n",
      "evaluating trained model ... \n",
      "Test set: Accuracy 5630/6700 84.03%\n",
      "[6m 6s] Epoch 23 [2560/13374] loss=0.00029115875076968223\n",
      "[6m 9s] Epoch 23 [5120/13374] loss=0.00032092933397507293\n",
      "[6m 12s] Epoch 23 [7680/13374] loss=0.0003246793222691243\n",
      "[6m 14s] Epoch 23 [10240/13374] loss=0.0003298963238194119\n",
      "[6m 17s] Epoch 23 [12800/13374] loss=0.0003415495011722669\n",
      "evaluating trained model ... \n",
      "Test set: Accuracy 5606/6700 83.67%\n",
      "[6m 22s] Epoch 24 [2560/13374] loss=0.0003220970553229563\n",
      "[6m 25s] Epoch 24 [5120/13374] loss=0.0003115159459412098\n",
      "[6m 27s] Epoch 24 [7680/13374] loss=0.0003127490291565967\n",
      "[6m 30s] Epoch 24 [10240/13374] loss=0.00031900887879601216\n",
      "[6m 33s] Epoch 24 [12800/13374] loss=0.0003240739009925164\n",
      "evaluating trained model ... \n",
      "Test set: Accuracy 5596/6700 83.52%\n",
      "[6m 38s] Epoch 25 [2560/13374] loss=0.00032832466822583226\n",
      "[6m 40s] Epoch 25 [5120/13374] loss=0.000289515417534858\n",
      "[6m 43s] Epoch 25 [7680/13374] loss=0.00030385288749433433\n",
      "[6m 46s] Epoch 25 [10240/13374] loss=0.0003134246286208509\n",
      "[6m 48s] Epoch 25 [12800/13374] loss=0.00032076418545329944\n",
      "evaluating trained model ... \n",
      "Test set: Accuracy 5608/6700 83.70%\n",
      "[6m 53s] Epoch 26 [2560/13374] loss=0.000252859694592189\n",
      "[6m 56s] Epoch 26 [5120/13374] loss=0.00025265586155001076\n",
      "[6m 59s] Epoch 26 [7680/13374] loss=0.0002662323347370451\n",
      "[7m 1s] Epoch 26 [10240/13374] loss=0.0002735664558713324\n",
      "[7m 4s] Epoch 26 [12800/13374] loss=0.00028566357417730614\n",
      "evaluating trained model ... \n",
      "Test set: Accuracy 5627/6700 83.99%\n",
      "[7m 9s] Epoch 27 [2560/13374] loss=0.000273244189156685\n",
      "[7m 12s] Epoch 27 [5120/13374] loss=0.00028045807484886607\n",
      "[7m 14s] Epoch 27 [7680/13374] loss=0.00027934127477540946\n",
      "[7m 17s] Epoch 27 [10240/13374] loss=0.0002742609187407652\n",
      "[7m 20s] Epoch 27 [12800/13374] loss=0.0002803219758789055\n",
      "evaluating trained model ... \n",
      "Test set: Accuracy 5621/6700 83.90%\n",
      "[7m 25s] Epoch 28 [2560/13374] loss=0.00020860043296124786\n",
      "[7m 27s] Epoch 28 [5120/13374] loss=0.0002035222016274929\n",
      "[7m 30s] Epoch 28 [7680/13374] loss=0.00022799459014398357\n",
      "[7m 33s] Epoch 28 [10240/13374] loss=0.0002521845108276466\n",
      "[7m 35s] Epoch 28 [12800/13374] loss=0.0002671138572623022\n",
      "evaluating trained model ... \n",
      "Test set: Accuracy 5617/6700 83.84%\n",
      "[7m 40s] Epoch 29 [2560/13374] loss=0.00019375939373276197\n",
      "[7m 43s] Epoch 29 [5120/13374] loss=0.00021515088410524187\n",
      "[7m 46s] Epoch 29 [7680/13374] loss=0.0002393476711101054\n",
      "[7m 48s] Epoch 29 [10240/13374] loss=0.00024992488997668263\n",
      "[7m 51s] Epoch 29 [12800/13374] loss=0.0002558952286199201\n",
      "evaluating trained model ... \n",
      "Test set: Accuracy 5618/6700 83.85%\n",
      "[7m 56s] Epoch 30 [2560/13374] loss=0.0002484855969669297\n",
      "[7m 58s] Epoch 30 [5120/13374] loss=0.0002544465023675002\n",
      "[8m 1s] Epoch 30 [7680/13374] loss=0.00023781391306935498\n",
      "[8m 4s] Epoch 30 [10240/13374] loss=0.00024378192392759956\n",
      "[8m 6s] Epoch 30 [12800/13374] loss=0.0002490653036511503\n",
      "evaluating trained model ... \n",
      "Test set: Accuracy 5622/6700 83.91%\n",
      "[8m 11s] Epoch 31 [2560/13374] loss=0.0001993803700315766\n",
      "[8m 14s] Epoch 31 [5120/13374] loss=0.00022291590430540965\n",
      "[8m 16s] Epoch 31 [7680/13374] loss=0.0002239866026987632\n",
      "[8m 19s] Epoch 31 [10240/13374] loss=0.00022923230062588118\n",
      "[8m 22s] Epoch 31 [12800/13374] loss=0.00023651976807741447\n",
      "evaluating trained model ... \n",
      "Test set: Accuracy 5608/6700 83.70%\n",
      "[8m 26s] Epoch 32 [2560/13374] loss=0.00018573534616734834\n",
      "[8m 29s] Epoch 32 [5120/13374] loss=0.00020701281318906694\n",
      "[8m 32s] Epoch 32 [7680/13374] loss=0.000205532392525735\n",
      "[8m 34s] Epoch 32 [10240/13374] loss=0.00022832357135484926\n",
      "[8m 37s] Epoch 32 [12800/13374] loss=0.00023525819240603597\n",
      "evaluating trained model ... \n",
      "Test set: Accuracy 5581/6700 83.30%\n",
      "[8m 42s] Epoch 33 [2560/13374] loss=0.00022191096504684538\n",
      "[8m 44s] Epoch 33 [5120/13374] loss=0.00021584527894447092\n",
      "[8m 47s] Epoch 33 [7680/13374] loss=0.0002196749078090458\n",
      "[8m 50s] Epoch 33 [10240/13374] loss=0.00023347617225226712\n",
      "[8m 52s] Epoch 33 [12800/13374] loss=0.00023487696671509184\n",
      "evaluating trained model ... \n",
      "Test set: Accuracy 5613/6700 83.78%\n",
      "[8m 57s] Epoch 34 [2560/13374] loss=0.00020252853501006028\n",
      "[9m 0s] Epoch 34 [5120/13374] loss=0.00019658725832414348\n",
      "[9m 2s] Epoch 34 [7680/13374] loss=0.00020717701724303576\n",
      "[9m 5s] Epoch 34 [10240/13374] loss=0.00022548529905179747\n",
      "[9m 8s] Epoch 34 [12800/13374] loss=0.00023297513151192107\n",
      "evaluating trained model ... \n",
      "Test set: Accuracy 5615/6700 83.81%\n",
      "[9m 12s] Epoch 35 [2560/13374] loss=0.00017661504680290818\n",
      "[9m 15s] Epoch 35 [5120/13374] loss=0.00019079839548794553\n",
      "[9m 18s] Epoch 35 [7680/13374] loss=0.00020124359677235285\n",
      "[9m 20s] Epoch 35 [10240/13374] loss=0.00020924364071106538\n",
      "[9m 23s] Epoch 35 [12800/13374] loss=0.0002183733184938319\n",
      "evaluating trained model ... \n",
      "Test set: Accuracy 5636/6700 84.12%\n",
      "[9m 28s] Epoch 36 [2560/13374] loss=0.00019383612161618657\n",
      "[9m 30s] Epoch 36 [5120/13374] loss=0.00019368599023437128\n",
      "[9m 33s] Epoch 36 [7680/13374] loss=0.0002011219073513833\n",
      "[9m 35s] Epoch 36 [10240/13374] loss=0.000202027325394738\n",
      "[9m 38s] Epoch 36 [12800/13374] loss=0.00020715603735879995\n",
      "evaluating trained model ... \n",
      "Test set: Accuracy 5624/6700 83.94%\n",
      "[9m 43s] Epoch 37 [2560/13374] loss=0.00018045943870674818\n",
      "[9m 45s] Epoch 37 [5120/13374] loss=0.00017510554898763074\n",
      "[9m 48s] Epoch 37 [7680/13374] loss=0.00018748440731239195\n",
      "[9m 51s] Epoch 37 [10240/13374] loss=0.00020091392107133288\n",
      "[9m 53s] Epoch 37 [12800/13374] loss=0.00021142929152119904\n",
      "evaluating trained model ... \n",
      "Test set: Accuracy 5626/6700 83.97%\n",
      "[9m 58s] Epoch 38 [2560/13374] loss=0.0001583891484187916\n",
      "[10m 1s] Epoch 38 [5120/13374] loss=0.0001925244134326931\n",
      "[10m 3s] Epoch 38 [7680/13374] loss=0.00019614530756371096\n",
      "[10m 6s] Epoch 38 [10240/13374] loss=0.00020651041631936096\n",
      "[10m 9s] Epoch 38 [12800/13374] loss=0.00020928369252942502\n",
      "evaluating trained model ... \n",
      "Test set: Accuracy 5600/6700 83.58%\n",
      "[10m 13s] Epoch 39 [2560/13374] loss=0.00017197362758452072\n",
      "[10m 16s] Epoch 39 [5120/13374] loss=0.00018969650118378923\n",
      "[10m 19s] Epoch 39 [7680/13374] loss=0.00018348861922277138\n",
      "[10m 21s] Epoch 39 [10240/13374] loss=0.00019708280287886737\n",
      "[10m 24s] Epoch 39 [12800/13374] loss=0.00021043422471848317\n",
      "evaluating trained model ... \n",
      "Test set: Accuracy 5615/6700 83.81%\n",
      "[10m 29s] Epoch 40 [2560/13374] loss=0.0001739911902404856\n",
      "[10m 31s] Epoch 40 [5120/13374] loss=0.00017092167108785362\n",
      "[10m 34s] Epoch 40 [7680/13374] loss=0.00018156692628205443\n",
      "[10m 37s] Epoch 40 [10240/13374] loss=0.00019046553170483095\n",
      "[10m 39s] Epoch 40 [12800/13374] loss=0.00020132457808358595\n",
      "evaluating trained model ... \n",
      "Test set: Accuracy 5600/6700 83.58%\n",
      "[10m 44s] Epoch 41 [2560/13374] loss=0.00015454681779374367\n",
      "[10m 47s] Epoch 41 [5120/13374] loss=0.0001604782370122848\n",
      "[10m 49s] Epoch 41 [7680/13374] loss=0.00018331942992517726\n",
      "[10m 52s] Epoch 41 [10240/13374] loss=0.00018242328260384966\n",
      "[10m 55s] Epoch 41 [12800/13374] loss=0.00019288321054773406\n",
      "evaluating trained model ... \n",
      "Test set: Accuracy 5633/6700 84.07%\n",
      "[10m 59s] Epoch 42 [2560/13374] loss=0.00013136914567439816\n",
      "[11m 2s] Epoch 42 [5120/13374] loss=0.00014578105437976774\n",
      "[11m 5s] Epoch 42 [7680/13374] loss=0.00016088678094092757\n",
      "[11m 7s] Epoch 42 [10240/13374] loss=0.00017861415199149633\n",
      "[11m 10s] Epoch 42 [12800/13374] loss=0.0001904372668650467\n",
      "evaluating trained model ... \n",
      "Test set: Accuracy 5600/6700 83.58%\n",
      "[11m 14s] Epoch 43 [2560/13374] loss=0.00018125459318980576\n",
      "[11m 17s] Epoch 43 [5120/13374] loss=0.000173538410308538\n",
      "[11m 20s] Epoch 43 [7680/13374] loss=0.00018883308706184229\n",
      "[11m 22s] Epoch 43 [10240/13374] loss=0.000189640519056411\n",
      "[11m 25s] Epoch 43 [12800/13374] loss=0.0001959858251211699\n",
      "evaluating trained model ... \n",
      "Test set: Accuracy 5634/6700 84.09%\n",
      "[11m 30s] Epoch 44 [2560/13374] loss=0.00016199836172745564\n",
      "[11m 32s] Epoch 44 [5120/13374] loss=0.0001620839237148175\n",
      "[11m 35s] Epoch 44 [7680/13374] loss=0.00015935149543414202\n",
      "[11m 38s] Epoch 44 [10240/13374] loss=0.00017535904535179725\n",
      "[11m 40s] Epoch 44 [12800/13374] loss=0.00018964152128319255\n",
      "evaluating trained model ... \n",
      "Test set: Accuracy 5599/6700 83.57%\n",
      "[11m 45s] Epoch 45 [2560/13374] loss=0.0001598291244590655\n",
      "[11m 48s] Epoch 45 [5120/13374] loss=0.0001583139928698074\n",
      "[11m 50s] Epoch 45 [7680/13374] loss=0.00016637611042824574\n",
      "[11m 53s] Epoch 45 [10240/13374] loss=0.00018084142811858328\n",
      "[11m 55s] Epoch 45 [12800/13374] loss=0.00018861363743781113\n",
      "evaluating trained model ... \n",
      "Test set: Accuracy 5632/6700 84.06%\n",
      "[12m 0s] Epoch 46 [2560/13374] loss=0.0001689109230937902\n",
      "[12m 3s] Epoch 46 [5120/13374] loss=0.00016760054932092318\n",
      "[12m 6s] Epoch 46 [7680/13374] loss=0.00018200817236599203\n",
      "[12m 8s] Epoch 46 [10240/13374] loss=0.00019016875266970602\n",
      "[12m 11s] Epoch 46 [12800/13374] loss=0.00019272740377346054\n",
      "evaluating trained model ... \n",
      "Test set: Accuracy 5630/6700 84.03%\n",
      "[12m 16s] Epoch 47 [2560/13374] loss=0.00015406873790198007\n",
      "[12m 18s] Epoch 47 [5120/13374] loss=0.0001796539825591026\n",
      "[12m 21s] Epoch 47 [7680/13374] loss=0.00018698882607471508\n",
      "[12m 24s] Epoch 47 [10240/13374] loss=0.0001901168188851443\n",
      "[12m 26s] Epoch 47 [12800/13374] loss=0.0001986714308441151\n",
      "evaluating trained model ... \n",
      "Test set: Accuracy 5608/6700 83.70%\n",
      "[12m 31s] Epoch 48 [2560/13374] loss=0.00016132625169120728\n",
      "[12m 33s] Epoch 48 [5120/13374] loss=0.0001697970084933331\n",
      "[12m 36s] Epoch 48 [7680/13374] loss=0.00016833750778459943\n",
      "[12m 39s] Epoch 48 [10240/13374] loss=0.00017330244572804076\n",
      "[12m 41s] Epoch 48 [12800/13374] loss=0.0001808109722333029\n",
      "evaluating trained model ... \n",
      "Test set: Accuracy 5633/6700 84.07%\n",
      "[12m 46s] Epoch 49 [2560/13374] loss=0.00016357196727767587\n",
      "[12m 49s] Epoch 49 [5120/13374] loss=0.0001682748734310735\n",
      "[12m 51s] Epoch 49 [7680/13374] loss=0.00017566338913942067\n",
      "[12m 54s] Epoch 49 [10240/13374] loss=0.0001776946208337904\n",
      "[12m 57s] Epoch 49 [12800/13374] loss=0.00018831568930181676\n",
      "evaluating trained model ... \n",
      "Test set: Accuracy 5627/6700 83.99%\n",
      "[13m 2s] Epoch 50 [2560/13374] loss=0.00016892087151063607\n",
      "[13m 5s] Epoch 50 [5120/13374] loss=0.00015529338124906645\n",
      "[13m 7s] Epoch 50 [7680/13374] loss=0.00017500294488854707\n",
      "[13m 11s] Epoch 50 [10240/13374] loss=0.00017692927776806754\n",
      "[13m 14s] Epoch 50 [12800/13374] loss=0.00018558732335804962\n",
      "evaluating trained model ... \n",
      "Test set: Accuracy 5613/6700 83.78%\n"
     ]
    }
   ],
   "source": [
    "N_EPOCHS = 50\n",
    "start = time.time() \n",
    "print(\"Training for %d epochs...\" % N_EPOCHS) \n",
    "acc_list = [] \n",
    "for epoch in range(1, N_EPOCHS + 1):\n",
    "    # Train cycle\n",
    "    trainModel() \n",
    "    acc = testModel() \n",
    "    acc_list.append(acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "epoch = np.arange(1, len(acc_list) + 1)\n",
    "acc_list = np.array(acc_list)\n",
    "plt.plot(epoch, acc_list)\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  }
 ]
}