RNN_decade_tfidf.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"RNN_decade_tfidf","provenance":[],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 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device#"],"metadata":{"id":"E3g1SKfOTKfP"}},{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"id":"JPgQj9-eKSHe","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1640520829984,"user_tz":-180,"elapsed":10647,"user":{"displayName":"Irmak Hatip","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10318308407840366918"}},"outputId":"89efd3ea-09b9-4fae-865f-887eb6934a9e"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"code","source":["%cd drive/MyDrive"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"YCSSK67mKTix","executionInfo":{"status":"ok","timestamp":1640520829985,"user_tz":-180,"elapsed":24,"user":{"displayName":"Irmak Hatip","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10318308407840366918"}},"outputId":"48281444-8c21-4112-e8e7-9c63d10483a5"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["/content/drive/MyDrive\n"]}]},{"cell_type":"code","source":["!pwd"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"vDLHPL6yKZ4f","executionInfo":{"status":"ok","timestamp":1640520829986,"user_tz":-180,"elapsed":16,"user":{"displayName":"Irmak Hatip","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10318308407840366918"}},"outputId":"e19be1c5-3a95-4441-e229-75779773f447"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["/content/drive/MyDrive\n"]}]},{"cell_type":"markdown","source":["#Import#"],"metadata":{"id":"kmxq_EyeTQkG"}},{"cell_type":"code","source":["from collections import Counter\n","import re\n","import numpy as np\n","import pandas as pd\n","from tqdm import tqdm_notebook\n","import tensorflow as tf\n","\n","import nltk\n","\n","from tensorflow.keras import regularizers, initializers, optimizers, callbacks\n","from tensorflow.keras.preprocessing.sequence import pad_sequences\n","from tensorflow.keras.preprocessing.text import Tokenizer\n","from keras.utils.np_utils import to_categorical\n","from tensorflow.keras.layers import *\n","from tensorflow.keras.models import Model\n","from sklearn.feature_extraction.text import TfidfVectorizer"],"metadata":{"id":"wJ5FT714My5-"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["#Define Constants#"],"metadata":{"id":"CbUsa7GdTbrP"}},{"cell_type":"code","source":["MAX_NB_WORDS = 100000    # max no. of words for tokenizer\n","MAX_SEQUENCE_LENGTH = 100 # max length of each entry (sentence), including padding\n","EMBEDDING_DIM = 100      # embedding dimensions for word vectors (word2vec/GloVe)\n","GLOVE_DIR = \"glove.6B.\"+str(EMBEDDING_DIM)+\"d.txt\""],"metadata":{"id":"G4KuqfpoM0g0"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["#Load Data#"],"metadata":{"id":"UN7AaqX8Tqby"}},{"cell_type":"code","source":["train = pd.read_csv('BALANCED.csv') #change to dataset here\n","y = train['period'].values\n","lyrics_train = train['lyrics']\n","lyrics_train = list(lyrics_train)\n","\n","labels = [60, 70, 80, 90, 0, 10]"],"metadata":{"id":"lFkg-ACiTj2g"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["print(len(lyrics_train))"],"metadata":{"id":"fY63OksTTkgt","executionInfo":{"status":"ok","timestamp":1640520872495,"user_tz":-180,"elapsed":12,"user":{"displayName":"Irmak Hatip","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10318308407840366918"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"344ee378-688d-4c99-d347-126339e5eb15"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["25078\n"]}]},{"cell_type":"code","source":["texts = [] \n","y_final = []\n","num = 0\n","\n","for line in tqdm_notebook(lyrics_train): \n","    if type(line) != float:\n","      texts.append(line)\n","      y_final.append(y[num])\n","    num += 1\n","\n","y = np.array(y_final)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":100,"referenced_widgets":["b7b42c8d0b3f4647a3a221607d066658","b73ccbad908849c3ac65a9bcc5472dcb","51d9ed4bf1d641f89c80ce2e381b5b6f","30f3f216c6594cf19242e3e4e94d766d","fc777bf80c3040609c6596aa2721fb99","4ba105101e904a1381fe9256ddb8e32e","e1b71b42d8884334b06c7a85d59527ba","0a91047319c24ccd849b04a1d360c24f","593ebfb231bc40c0aede19eff6bfe242","1f7553832b2f45759e22d83a394aa1a2","22d4dbfffc33423cbcd689c704f1be28"]},"id":"13zo9E6kTuAy","executionInfo":{"status":"ok","timestamp":1640520872875,"user_tz":-180,"elapsed":389,"user":{"displayName":"Irmak Hatip","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10318308407840366918"}},"outputId":"3859640f-a0ae-43aa-95f9-4c9d417fed57"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:5: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n","Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n","  \"\"\"\n"]},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"b7b42c8d0b3f4647a3a221607d066658","version_minor":0,"version_major":2},"text/plain":["  0%|          | 0/25078 [00:00<?, ?it/s]"]},"metadata":{}}]},{"cell_type":"code","source":["print('Sample data:', texts[753], y[753])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"AelTN4U5TwK_","executionInfo":{"status":"ok","timestamp":1640520872876,"user_tz":-180,"elapsed":7,"user":{"displayName":"Irmak Hatip","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10318308407840366918"}},"outputId":"6f1dbbbc-783b-40e9-fee2-9b275692e7dd"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Sample data: read news today oh boy lucky man made grade though news rather sad well laugh saw photograph blew mind car notice light changed crowd people stood stared seen face nobody really sure house lord 60.0\n"]}]},{"cell_type":"markdown","source":["#Tokenize#"],"metadata":{"id":"PKeoHssuT2R9"}},{"cell_type":"code","source":["for i, ex in enumerate(texts):\n","  if (type(ex) == float):\n","    print(i, ex)"],"metadata":{"id":"GRAlkJk4XtXR"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## TF-IDF"],"metadata":{"id":"WNlSHksO4j4Q"}},{"cell_type":"code","source":["vectorizer = TfidfVectorizer( max_features=MAX_NB_WORDS)\n","vectorizer.fit(texts)\n","\n","vector = vectorizer.transform(texts)\n","print(type(vector))"],"metadata":{"id":"Z1U-DV_FzTo_","executionInfo":{"status":"ok","timestamp":1640520879694,"user_tz":-180,"elapsed":6320,"user":{"displayName":"Irmak Hatip","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10318308407840366918"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"a668c999-5d5c-42d8-c1e9-efd00e80d1f4"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["<class 'scipy.sparse.csr.csr_matrix'>\n"]}]},{"cell_type":"markdown","source":["#Padding"],"metadata":{"id":"bGlatWYFT7go"}},{"cell_type":"code","source":["data = vector.toarray()\n","print('Shape of data tensor:', data.shape)\n","print('Shape of label tensor:', y.shape)"],"metadata":{"id":"LuJGW3o1T9Bo","executionInfo":{"status":"ok","timestamp":1640520884013,"user_tz":-180,"elapsed":4323,"user":{"displayName":"Irmak Hatip","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10318308407840366918"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"1bda8db5-b3e6-412a-e4ae-a7775b183d25"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Shape of data tensor: (25062, 38585)\n","Shape of label tensor: (25062,)\n"]}]},{"cell_type":"code","source":["indices = np.arange(data.shape[0])\n","np.random.shuffle(indices)\n","data = data[indices]\n","labels = y[indices]"],"metadata":{"id":"LWrUgqr7VXuM"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# convert to one hot encoding \n","one_hot_labels = []\n","for lab in labels:\n","  lab_arr = [0, 0, 0, 0, 0, 0] #60, 70, 80, 90, 0, 10\n","  if lab > 50:\n","    lab_arr[int((lab - 60) / 10)] = 1\n","  elif lab == 0:\n","    lab_arr[4] = 1\n","  else:\n","    lab_arr[5] = 1\n","\n","  one_hot_labels.append(lab_arr)\n","\n","one_hot_labels = np.array(one_hot_labels)\n","print(one_hot_labels[0])"],"metadata":{"id":"m3J9h3abdr4p","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1640426616321,"user_tz":-180,"elapsed":24,"user":{"displayName":"Irmak Hatip","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10318308407840366918"}},"outputId":"64192ab9-e804-403b-fbdf-6fb078f1eaf6"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["[1 0 0 0 0 0]\n"]}]},{"cell_type":"code","source":["print(type(data))"],"metadata":{"id":"e-vF-9TP8wAR","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1640426616322,"user_tz":-180,"elapsed":22,"user":{"displayName":"Irmak Hatip","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10318308407840366918"}},"outputId":"9035eeb5-4b46-40e7-ea0d-8ba4c1025cc0"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["<class 'numpy.ndarray'>\n"]}]},{"cell_type":"code","source":["print('Tokenized sentences: \\n', data[2])\n","print('Label: \\n', labels[2], one_hot_labels[2])"],"metadata":{"id":"cOCsVfSwVkgo","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1640426616322,"user_tz":-180,"elapsed":19,"user":{"displayName":"Irmak Hatip","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10318308407840366918"}},"outputId":"1ef4d448-d0d0-4686-fdca-d75164c765dd"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Tokenized sentences: \n"," [0. 0. 0. ... 0. 0. 0.]\n","Label: \n"," 60.0 [1 0 0 0 0 0]\n"]}]},{"cell_type":"code","source":["print(data.shape)\n","print(one_hot_labels.shape)"],"metadata":{"id":"h74ugOQ7VlcI","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1640426616322,"user_tz":-180,"elapsed":16,"user":{"displayName":"Irmak Hatip","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10318308407840366918"}},"outputId":"2772fb58-15df-4d25-ba5f-0a257e059d1d"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["(1000, 4360)\n","(1000, 6)\n"]}]},{"cell_type":"code","source":["# Converting our labels into numpy arrays\n","lyrics = np.array(data)\n","labels = np.array(one_hot_labels)"],"metadata":{"id":"Qy3aajVONA-7"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["print(lyrics[:20])\n","print(labels[:20])"],"metadata":{"id":"ZoKHQok3UVUl","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1640426616323,"user_tz":-180,"elapsed":13,"user":{"displayName":"Irmak Hatip","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10318308407840366918"}},"outputId":"778da46c-5b21-47a4-aec3-0856dd5e72b3"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["[[0. 0. 0. ... 0. 0. 0.]\n"," [0. 0. 0. ... 0. 0. 0.]\n"," [0. 0. 0. ... 0. 0. 0.]\n"," ...\n"," [0. 0. 0. ... 0. 0. 0.]\n"," [0. 0. 0. ... 0. 0. 0.]\n"," [0. 0. 0. ... 0. 0. 0.]]\n","[[1 0 0 0 0 0]\n"," [1 0 0 0 0 0]\n"," [1 0 0 0 0 0]\n"," [1 0 0 0 0 0]\n"," [1 0 0 0 0 0]\n"," [1 0 0 0 0 0]\n"," [1 0 0 0 0 0]\n"," [1 0 0 0 0 0]\n"," [1 0 0 0 0 0]\n"," [1 0 0 0 0 0]\n"," [1 0 0 0 0 0]\n"," [1 0 0 0 0 0]\n"," [1 0 0 0 0 0]\n"," [1 0 0 0 0 0]\n"," [1 0 0 0 0 0]\n"," [1 0 0 0 0 0]\n"," [1 0 0 0 0 0]\n"," [1 0 0 0 0 0]\n"," [1 0 0 0 0 0]\n"," [1 0 0 0 0 0]]\n"]}]},{"cell_type":"markdown","source":["#Partition the Data (train, val, test)#"],"metadata":{"id":"OOQFhP1rYOxv"}},{"cell_type":"code","source":["split_frac = 0.7 # 70% train, 30% test(val + test)\n","split_id = int(split_frac * len(lyrics))\n","train_lyrics, test_lyrics = lyrics[:split_id], lyrics[split_id:]\n","train_labels, test_labels = labels[:split_id], labels[split_id:]"],"metadata":{"id":"B18pZdJrYOCD"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["print(train_labels.shape)\n","print(train_lyrics.shape)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"b9yWALQ6x4c0","executionInfo":{"status":"ok","timestamp":1640426616324,"user_tz":-180,"elapsed":11,"user":{"displayName":"Irmak Hatip","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10318308407840366918"}},"outputId":"4d80e5e3-cb98-4d9a-9738-043e73c4007f"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["(700, 6)\n","(700, 4360)\n"]}]},{"cell_type":"code","source":["split_frac = 0.5 # 50% validation, 50% test\n","split_id = int(split_frac * len(test_lyrics))\n","val_lyrics, test_lyrics = test_lyrics[:split_id], test_lyrics[split_id:]\n","val_labels, test_labels = test_labels[:split_id], test_labels[split_id:]"],"metadata":{"id":"2JKHQ_NMNC5a"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["import torch\n","from torch.utils.data import TensorDataset, DataLoader\n","import torch.nn as nn\n","\n","train_data = TensorDataset(torch.from_numpy(train_lyrics), torch.from_numpy(train_labels))\n","val_data = TensorDataset(torch.from_numpy(val_lyrics), torch.from_numpy(val_labels))\n","test_data = TensorDataset(torch.from_numpy(test_lyrics), torch.from_numpy(test_labels))\n","\n","batch_size = 16 \n","hidden = 64\n","epochs = 30 \n","lr=0.0001\n","dropout=0.5\n","\n","train_loader = DataLoader(train_data, shuffle=True, batch_size=batch_size, drop_last=True)\n","val_loader = DataLoader(val_data, shuffle=True, batch_size=batch_size, drop_last=True)\n","test_loader = DataLoader(test_data, shuffle=True, batch_size=batch_size, drop_last=True)"],"metadata":{"id":"7FikYb4TNG_r"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# torch.cuda.is_available() checks and returns a Boolean True if a GPU is available, else it'll return False\n","is_cuda = torch.cuda.is_available()\n","\n","# If we have a GPU available, we'll set our device to GPU. We'll use this device variable later in our code.\n","if is_cuda:\n","    device = torch.device(\"cuda\")\n","else:\n","    device = torch.device(\"cpu\")"],"metadata":{"id":"RRssUNuPNI4G"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["class SentimentNet(nn.Module):\n","    def __init__(self,\n","                 weight_matrix=None,\n","                 vocab_size=None, \n","                 output_size=1,  \n","                 hidden_dim=512, \n","                 embedding_dim=400, \n","                 n_layers=1, \n","                 dropout_prob=0.5):\n","        super(SentimentNet, self).__init__()\n","        self.output_size = output_size\n","        self.n_layers = n_layers\n","        self.hidden_dim = hidden_dim\n","        # initialize the representation to pass to the LSTM\n","        self.embedding, embedding_dim = self.init_embedding(\n","            vocab_size, \n","            embedding_dim, \n","            weight_matrix)\n","        self.lstm = nn.LSTM(embedding_dim, hidden_dim, n_layers, \n","                            dropout=dropout_prob, batch_first=True)\n","        # dropout\n","        self.dropout = nn.Dropout(dropout_prob)\n","        # fully connected layer\n","        self.fc = nn.Linear(hidden_dim, output_size)\n","        self.softmax = nn.Softmax(dim = -1)\n","        \n","    def forward(self, x, hidden):\n","        # forward pass of the network\n","        batch_size = x.size(0)\n","        # transform input\n","        embeds = self.embedding(x)\n","        # run input embedding + hidden state through model\n","        lstm_out, hidden = self.lstm(embeds, hidden)\n","        # reshape\n","        lstm_out = lstm_out.contiguous().view(-1, self.hidden_dim)\n","        # dropout certain pct of connections\n","        out = self.dropout(lstm_out)\n","        # fully connected layer\n","        out = self.fc(out)\n","        # connect to 6 outputs (one_hot_encoding)\n","        # activation function\n","        out = self.softmax(out)\n","        out = out.view(batch_size, int(out.shape[0] / batch_size), out.shape[1])\n","        out = out[:,-1]\n","        # return the output and the hidden state\n","        return out, hidden, lstm_out\n","    \n","    def init_embedding(self, vocab_size, embedding_dim, weight_matrix):\n","        # initializes the embedding\n","        if weight_matrix is None:\n","            if vocab_size is None:\n","                raise ValueError('If no weight matrix, need a vocab size')\n","            # if embedding is a size, initialize trainable\n","            return(nn.Embedding(vocab_size, embedding_dim),\n","                   embedding_dim)\n","        else:\n","            # otherwise use matrix as pretrained\n","            weights = torch.FloatTensor(weight_matrix)\n","            return(nn.Embedding.from_pretrained(weights),\n","                  weights.shape[1])\n","    \n","    def init_hidden(self, batch_size):\n","        # initializes the hidden state\n","        hidden = (torch.zeros(self.n_layers, batch_size, self.hidden_dim).to(device),\n","                  torch.zeros(self.n_layers, batch_size, self.hidden_dim).to(device))\n","        return hidden"],"metadata":{"id":"idkjoUlMqRm4"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["model_params = {'weight_matrix': None,\n","                'vocab_size': data.shape[1] + 1,\n","               'output_size': 6,\n","               'hidden_dim': hidden,\n","               'n_layers': 2,\n","               'embedding_dim': EMBEDDING_DIM,\n","               'dropout_prob': dropout}\n","model = SentimentNet(**model_params)\n","\n","model.to(device)\n","\n","criterion = nn.CrossEntropyLoss()\n","optimizer = torch.optim.Adam(model.parameters(), lr=lr)"],"metadata":{"id":"SBInTg7vNNz-"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["counter = 0\n","print_every = 10\n","clip = 5\n","valid_loss_min = np.Inf\n","\n","model.train()\n","\n","train_losses = []\n","val_losses = []\n","train_acc = []\n","val_acc = []\n","\n","for i in range(epochs):\n","    print(\"Epoch\")\n","    h = model.init_hidden(batch_size)\n","    train_correct = 0\n","    val_correct = 0\n","    calculated = 0\n","    train_loss = []\n","    val_loss = []\n","    train_count = 0\n","    val_count = 0\n","\n","    periods = [0, 0, 0, 0, 0, 0]\n","    periods_gt = [0, 0, 0, 0, 0, 0]\n","    \n","    for inputs, labels in train_loader:\n","        inputs = inputs.clone().detach().to(torch.int64)\n","        counter += 1\n","        h = tuple([e.data for e in h])\n","        inputs, labels = inputs.to(device), labels.to(device)\n","        model.zero_grad()\n","        output, h, lstm_out = model(inputs, h)\n","        loss = criterion(output.squeeze(), labels.float())\n","\n","        train_loss.append(loss.item())\n","\n","        for j, ex in enumerate(output):\n","          train_count += 1\n","          gt_label = torch.argmax(labels[j].float()) \n","          pred_label = torch.argmax(ex.squeeze())\n","\n","          periods[pred_label] += 1\n","          periods_gt[gt_label] += 1\n","\n","          if pred_label.eq(gt_label):\n","            train_correct += 1\n","\n","        loss.backward()\n","        nn.utils.clip_grad_norm_(model.parameters(), clip)\n","        optimizer.step()\n","        \n","    val_h = model.init_hidden(batch_size)\n","    model.eval()\n","\n","    for inp, lab in val_loader:\n","        inp = inp.clone().detach().to(torch.int64)\n","        val_h = tuple([each.data for each in val_h])\n","        inp, lab = inp.to(device), lab.to(device)\n","        out, val_h, _ = model(inp, val_h)\n","        loss_val = criterion(out.squeeze(), lab.float())\n","\n","        val_loss.append(loss_val.item())\n","\n","        for j, ex in enumerate(out):\n","          val_count += 1\n","          gt_label = torch.argmax(lab[j].float()) \n","          pred_label = torch.argmax(ex.squeeze())\n","\n","          if pred_label.eq(gt_label):\n","            val_correct += 1\n","        \n","    model.train()\n","\n","    print(\"Epoch: {}/{}\".format(i+1, epochs),\n","          \"Loss: {:.6f}\".format(np.mean(train_loss)),\n","          \"Val Loss: {:.6f}\".format(np.mean(val_loss)),\n","          \"Acc: {:.6f}\".format(train_correct/train_count),\n","          \"Val Acc: {:.6f}\".format(val_correct/val_count))\n","    print(train_correct)\n","    print(train_count)\n","    \n","    if np.mean(val_loss) <= valid_loss_min:\n","        torch.save(model.state_dict(), './state_dict_' + str(EMBEDDING_DIM) + '.pt')\n","        print('Validation loss decreased ({:.6f} --> {:.6f}).  Saving model.'.format(valid_loss_min,np.mean(val_loss)))\n","        valid_loss_min = np.mean(val_loss)\n","\n","    train_losses.append(np.mean(train_loss))\n","    val_losses.append(np.mean(val_loss))\n","    train_acc.append(train_correct/train_count)\n","    val_acc.append(val_correct/val_count)\n","    print(periods)\n","    print(periods_gt)"],"metadata":{"id":"YaF4M1-fNOTp","colab":{"base_uri":"https://localhost:8080/","height":868},"outputId":"ced067a4-3ec7-46f6-b9e2-6bf30f956653","executionInfo":{"status":"error","timestamp":1640428137343,"user_tz":-180,"elapsed":172279,"user":{"displayName":"Irmak Hatip","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10318308407840366918"}}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch\n","Epoch: 1/30 Loss: 1.703726 Val Loss: 1.613644 Acc: 0.984012 Val Acc: 1.000000\n","677\n","688\n","Validation loss decreased (inf --> 1.613644).  Saving model.\n","[677, 6, 1, 4, 0, 0]\n","[688, 0, 0, 0, 0, 0]\n","Epoch\n","Epoch: 2/30 Loss: 1.455494 Val Loss: 1.223298 Acc: 1.000000 Val Acc: 1.000000\n","688\n","688\n","Validation loss decreased (1.613644 --> 1.223298).  Saving model.\n","[688, 0, 0, 0, 0, 0]\n","[688, 0, 0, 0, 0, 0]\n","Epoch\n","Epoch: 3/30 Loss: 1.162593 Val Loss: 1.092557 Acc: 1.000000 Val Acc: 1.000000\n","688\n","688\n","Validation loss decreased (1.223298 --> 1.092557).  Saving model.\n","[688, 0, 0, 0, 0, 0]\n","[688, 0, 0, 0, 0, 0]\n","Epoch\n","Epoch: 4/30 Loss: 1.090637 Val Loss: 1.067114 Acc: 1.000000 Val Acc: 1.000000\n","688\n","688\n","Validation loss decreased (1.092557 --> 1.067114).  Saving model.\n","[688, 0, 0, 0, 0, 0]\n","[688, 0, 0, 0, 0, 0]\n","Epoch\n"]},{"output_type":"error","ename":"KeyboardInterrupt","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)","\u001b[0;32m<ipython-input-31-8d3a59c903b9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     31\u001b[0m         \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     32\u001b[0m         \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzero_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 33\u001b[0;31m         \u001b[0moutput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mh\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlstm_out\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mh\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     34\u001b[0m         \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcriterion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqueeze\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     35\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m   1100\u001b[0m         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001b[1;32m   1101\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1102\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1103\u001b[0m         \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1104\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m<ipython-input-25-5eb2eef876f5>\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, x, hidden)\u001b[0m\n\u001b[1;32m     38\u001b[0m         \u001b[0membeds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0membedding\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     39\u001b[0m         \u001b[0;31m# run input embedding + hidden state through model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 40\u001b[0;31m         \u001b[0mlstm_out\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhidden\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlstm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0membeds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhidden\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     41\u001b[0m         \u001b[0;31m# reshape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     42\u001b[0m         \u001b[0mlstm_out\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlstm_out\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontiguous\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhidden_dim\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m   1100\u001b[0m         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001b[1;32m   1101\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1102\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1103\u001b[0m         \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1104\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/nn/modules/rnn.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input, hx)\u001b[0m\n\u001b[1;32m    690\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mbatch_sizes\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    691\u001b[0m             result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n\u001b[0;32m--> 692\u001b[0;31m                               self.dropout, self.training, self.bidirectional, self.batch_first)\n\u001b[0m\u001b[1;32m    693\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    694\u001b[0m             result = _VF.lstm(input, batch_sizes, hx, self._flat_weights, self.bias,\n","\u001b[0;31mKeyboardInterrupt\u001b[0m: "]}]},{"cell_type":"code","source":[" # Loading the best model\n","model.load_state_dict(torch.load('./state_dict_' + str(EMBEDDING_DIM) + '.pt'))\n","\n","test_losses = []\n","periods = [0, 0, 0, 0, 0, 0]\n","gt_periods = [0, 0, 0, 0, 0, 0]\n","num_correct = 0\n","h = model.init_hidden(batch_size)\n","\n","model.eval()\n","for inputs, labels in test_loader:\n","    inputs = inputs.clone().detach().to(torch.int64)\n","    h = tuple([each.data for each in h])\n","    inputs, labels = inputs.to(device), labels.to(device)\n","    output, h, _ = model(inputs, h)\n","    test_loss = criterion(output.squeeze(), labels.float())\n","    test_losses.append(test_loss.item())\n","\n","    for i, ex in enumerate(output):\n","      gt_label = torch.argmax(labels[i].float()) \n","      pred_label = torch.argmax(ex.squeeze())\n","\n","      periods[pred_label] += 1\n","      gt_periods[gt_label] += 1\n","\n","      if pred_label.eq(gt_label):\n","        num_correct += 1\n","\n","print(\"Test loss: {:.3f}\".format(np.mean(test_losses)))\n","test_acc = num_correct/len(test_loader.dataset)\n","print(\"Test accuracy: {:.3f}\".format(test_acc))\n","print(num_correct)\n","print(len(test_loader.dataset))\n","print(periods)\n","print(gt_periods)"],"metadata":{"id":"Am3NGxfsNSYc"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["import matplotlib.pyplot as plt\n","%matplotlib inline \n","\n","\n","plt.figure(figsize=(10,5))\n","plt.title(\"Training and Validation Loss for Embeddigns = \" + str(EMBEDDING_DIM))\n","plt.plot(val_losses,label=\"val\")\n","plt.plot(train_losses,label=\"train\")\n","plt.xlabel(\"epochs\")\n","plt.ylabel(\"Loss\")\n","plt.legend()\n","plt.savefig(\"tfidf_loss_\" + str(epochs)+\"_\"+ str(batch_size) + \"_\" + str(lr)+ \"_\" + str(EMBEDDING_DIM) + \"_\" + str(hidden) + \".jpg\")\n","plt.show()"],"metadata":{"id":"KVmTjU5RgHcG"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["plt.figure(figsize=(10,5))\n","plt.title(\"Training and Validation Accuracy for Embeddigns = \" + str(EMBEDDING_DIM))\n","plt.plot(val_acc,label=\"val\")\n","plt.plot(train_acc,label=\"train\")\n","plt.xlabel(\"epoch\")\n","plt.ylabel(\"Accuracy\")\n","plt.legend()\n","plt.savefig(\"tfidf_acc_\" + str(epochs)+\"_\"+ str(batch_size) + \"_\" + str(lr)+ \"_\" + str(EMBEDDING_DIM) + \"_\" + str(hidden) + \".jpg\")\n","plt.show()"],"metadata":{"id":"5FgOaW0mUpri"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[""],"metadata":{"id":"vdjEIN_2YoAi"},"execution_count":null,"outputs":[]}]}