cnn_w2v.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.7.2"},"colab":{"name":"cnn_w2v.ipynb","provenance":[],"collapsed_sections":[]}},"cells":[{"cell_type":"markdown","metadata":{"id":"3wL8kdZHRXjD"},"source":["# Emotion Classification in in texts using CNN\n"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"LUSGDlkESG4Q","executionInfo":{"status":"ok","timestamp":1623404289492,"user_tz":-330,"elapsed":23791,"user":{"displayName":"hdiryde linux","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgWENxDzH2dke1wSVAcfdheUY3NYNzgEXK8AFAw=s64","userId":"17839123472943995639"}},"outputId":"a8686d2d-9824-4882-c0dd-63c6e136a5d9"},"source":["# Mounting the Drive\n","from google.colab import drive\n","drive.mount('/content/drive')"],"execution_count":1,"outputs":[{"output_type":"stream","text":["Mounted at /content/drive\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"Yo4JFK45RXjJ","executionInfo":{"status":"ok","timestamp":1623404293000,"user_tz":-330,"elapsed":3513,"user":{"displayName":"hdiryde linux","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgWENxDzH2dke1wSVAcfdheUY3NYNzgEXK8AFAw=s64","userId":"17839123472943995639"}}},"source":["#Import Data\n","\n","import pandas as pd\n","import numpy as np\n","\n","# text preprocessing\n","from nltk.tokenize import word_tokenize\n","import re\n","\n","# plots and metrics\n","import matplotlib.pyplot as plt\n","from sklearn.metrics import accuracy_score, f1_score, confusion_matrix\n","\n","# preparing input to our model\n","from keras.preprocessing.text import Tokenizer\n","from keras.preprocessing.sequence import pad_sequences\n","from keras.utils.np_utils import to_categorical\n","\n","# keras layers\n","from keras.models import Sequential\n","from keras.layers import Embedding, Conv1D, GlobalMaxPooling1D, Dense"],"execution_count":2,"outputs":[]},{"cell_type":"code","metadata":{"id":"xi7CJkTWRXjL","executionInfo":{"status":"ok","timestamp":1623404293007,"user_tz":-330,"elapsed":10,"user":{"displayName":"hdiryde linux","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgWENxDzH2dke1wSVAcfdheUY3NYNzgEXK8AFAw=s64","userId":"17839123472943995639"}}},"source":["# Number of labels: joy, anger, fear, sadness, neutral\n","num_classes = 5\n","\n","# Number of dimensions for word embedding\n","embed_num_dims = 300\n","\n","# Max input length (max number of words) \n","max_seq_len = 500\n","\n","class_names = ['joy', 'fear', 'anger', 'sadness', 'neutral']"],"execution_count":3,"outputs":[]},{"cell_type":"code","metadata":{"id":"IXMP_fY-RXjL","executionInfo":{"status":"ok","timestamp":1623404293651,"user_tz":-330,"elapsed":652,"user":{"displayName":"hdiryde linux","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgWENxDzH2dke1wSVAcfdheUY3NYNzgEXK8AFAw=s64","userId":"17839123472943995639"}}},"source":["data_train = pd.read_csv('/content/drive/MyDrive/Capstone/data/data_train.csv', encoding='utf-8')\n","data_test = pd.read_csv('/content/drive/MyDrive/Capstone/data/data_test.csv', encoding='utf-8')\n","\n","X_train = data_train.Text\n","X_test = data_test.Text\n","\n","y_train = data_train.Emotion\n","y_test = data_test.Emotion\n","\n","data = data_train.append(data_test, ignore_index=True)"],"execution_count":4,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":337},"id":"kw5WF8ueRXjM","executionInfo":{"status":"ok","timestamp":1623404294095,"user_tz":-330,"elapsed":16,"user":{"displayName":"hdiryde linux","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgWENxDzH2dke1wSVAcfdheUY3NYNzgEXK8AFAw=s64","userId":"17839123472943995639"}},"outputId":"f74ebece-5ad0-4409-efe2-841d9e6ee0e4"},"source":["print(data.Emotion.value_counts())\n","data.head(6)"],"execution_count":5,"outputs":[{"output_type":"stream","text":["joy        2326\n","sadness    2317\n","anger      2259\n","neutral    2254\n","fear       2171\n","Name: Emotion, dtype: int64\n"],"name":"stdout"},{"output_type":"execute_result","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>Emotion</th>\n","      <th>Text</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>neutral</td>\n","      <td>There are tons of other paintings that I thin...</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>sadness</td>\n","      <td>Yet the dog had grown old and less capable , a...</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>fear</td>\n","      <td>When I get into the tube or the train without ...</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>fear</td>\n","      <td>This last may be a source of considerable disq...</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>anger</td>\n","      <td>She disliked the intimacy he showed towards so...</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>sadness</td>\n","      <td>When my family heard that my Mother's cousin w...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   Emotion                                               Text\n","0  neutral   There are tons of other paintings that I thin...\n","1  sadness  Yet the dog had grown old and less capable , a...\n","2     fear  When I get into the tube or the train without ...\n","3     fear  This last may be a source of considerable disq...\n","4    anger  She disliked the intimacy he showed towards so...\n","5  sadness  When my family heard that my Mother's cousin w..."]},"metadata":{"tags":[]},"execution_count":5}]},{"cell_type":"code","metadata":{"id":"zo-iTYNZRXjN","executionInfo":{"status":"ok","timestamp":1623404294095,"user_tz":-330,"elapsed":13,"user":{"displayName":"hdiryde linux","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgWENxDzH2dke1wSVAcfdheUY3NYNzgEXK8AFAw=s64","userId":"17839123472943995639"}}},"source":["#To input the data to our NN Model we'll need some preprocessing:\n","\n","#Tokenize our texts and count unique tokens\n","#Padding: each input (sentence or text) has to be of the same lenght\n","#Labels have to be converted to integeres and categorized\n","\n","def clean_text(data):\n","    \n","    # remove hashtags and @usernames\n","    data = re.sub(r\"(#[\\d\\w\\.]+)\", '', data)\n","    data = re.sub(r\"(@[\\d\\w\\.]+)\", '', data)\n","    \n","    # tekenization using nltk\n","    data = word_tokenize(data)\n","    \n","    return data"],"execution_count":6,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"HwN1IlS7RXjO","executionInfo":{"status":"ok","timestamp":1623404298902,"user_tz":-330,"elapsed":4818,"user":{"displayName":"hdiryde linux","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgWENxDzH2dke1wSVAcfdheUY3NYNzgEXK8AFAw=s64","userId":"17839123472943995639"}},"outputId":"72c70c26-7ae3-4d09-ea4a-0b6970831d32"},"source":["#using the NLTK Downloader to obtain the resource:\n","import nltk\n","nltk.download('punkt')\n","\n","texts = [' '.join(clean_text(text)) for text in data.Text]\n","\n","texts_train = [' '.join(clean_text(text)) for text in X_train]\n","texts_test = [' '.join(clean_text(text)) for text in X_test]"],"execution_count":7,"outputs":[{"output_type":"stream","text":["[nltk_data] Downloading package punkt to /root/nltk_data...\n","[nltk_data]   Unzipping tokenizers/punkt.zip.\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"g7sFdAZdRXjO","executionInfo":{"status":"ok","timestamp":1623404298903,"user_tz":-330,"elapsed":10,"user":{"displayName":"hdiryde linux","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgWENxDzH2dke1wSVAcfdheUY3NYNzgEXK8AFAw=s64","userId":"17839123472943995639"}},"outputId":"578610f8-f593-4283-d2ad-2d5b9c5fd63e"},"source":["print(texts_train[92])"],"execution_count":8,"outputs":[{"output_type":"stream","text":["a bit ? I 'm extremely annoyed that he did n't phone me when he promised me that he would ! He 's such a liar .\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"YkUQFDmaRXjO","executionInfo":{"status":"ok","timestamp":1623404299593,"user_tz":-330,"elapsed":694,"user":{"displayName":"hdiryde linux","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgWENxDzH2dke1wSVAcfdheUY3NYNzgEXK8AFAw=s64","userId":"17839123472943995639"}},"outputId":"58028412-cacc-4312-e61a-2a8f33685f1a"},"source":["tokenizer = Tokenizer()\n","tokenizer.fit_on_texts(texts)\n","\n","sequence_train = tokenizer.texts_to_sequences(texts_train)\n","sequence_test = tokenizer.texts_to_sequences(texts_test)\n","\n","index_of_words = tokenizer.word_index\n","\n","# vacab size is number of unique words + reserved 0 index for padding\n","vocab_size = len(index_of_words) + 1\n","\n","print('Number of unique words: {}'.format(len(index_of_words)))"],"execution_count":9,"outputs":[{"output_type":"stream","text":["Number of unique words: 12088\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"PlNah9WrRXjP","executionInfo":{"status":"ok","timestamp":1623404300074,"user_tz":-330,"elapsed":486,"user":{"displayName":"hdiryde linux","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgWENxDzH2dke1wSVAcfdheUY3NYNzgEXK8AFAw=s64","userId":"17839123472943995639"}},"outputId":"78d09fb2-9a56-4019-9f77-fb5beb508dac"},"source":["X_train_pad = pad_sequences(sequence_train, maxlen = max_seq_len )\n","X_test_pad = pad_sequences(sequence_test, maxlen = max_seq_len )\n","\n","X_train_pad"],"execution_count":10,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[    0,     0,     0, ...,   119,    51,   345],\n","       [    0,     0,     0, ...,    37,   277,   154],\n","       [    0,     0,     0, ...,    16,     2,  1210],\n","       ...,\n","       [    0,     0,     0, ...,   876,     4,   909],\n","       [    0,     0,     0, ...,     1,     6,   117],\n","       [    0,     0,     0, ..., 10259,   173,    13]], dtype=int32)"]},"metadata":{"tags":[]},"execution_count":10}]},{"cell_type":"code","metadata":{"id":"hXEVfX1WRXjQ","executionInfo":{"status":"ok","timestamp":1623404300079,"user_tz":-330,"elapsed":68,"user":{"displayName":"hdiryde linux","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgWENxDzH2dke1wSVAcfdheUY3NYNzgEXK8AFAw=s64","userId":"17839123472943995639"}}},"source":["#Categorizing the labels\n","\n","encoding = {\n","    'joy': 0,\n","    'fear': 1,\n","    'anger': 2,\n","    'sadness': 3,\n","    'neutral': 4\n","}\n","\n","# Integer labels\n","y_train = [encoding[x] for x in data_train.Emotion]\n","y_test = [encoding[x] for x in data_test.Emotion]"],"execution_count":11,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"0lAGGOg3RXjQ","executionInfo":{"status":"ok","timestamp":1623404300080,"user_tz":-330,"elapsed":67,"user":{"displayName":"hdiryde linux","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgWENxDzH2dke1wSVAcfdheUY3NYNzgEXK8AFAw=s64","userId":"17839123472943995639"}},"outputId":"169ffbe8-52ff-441c-e6e5-27f5ac8a709e"},"source":["y_train = to_categorical(y_train)\n","y_test = to_categorical(y_test)\n","\n","y_train"],"execution_count":12,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[0., 0., 0., 0., 1.],\n","       [0., 0., 0., 1., 0.],\n","       [0., 1., 0., 0., 0.],\n","       ...,\n","       [0., 0., 0., 1., 0.],\n","       [0., 1., 0., 0., 0.],\n","       [0., 0., 1., 0., 0.]], dtype=float32)"]},"metadata":{"tags":[]},"execution_count":12}]},{"cell_type":"code","metadata":{"id":"fDBNuwzURXjR","executionInfo":{"status":"ok","timestamp":1623404300081,"user_tz":-330,"elapsed":44,"user":{"displayName":"hdiryde linux","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgWENxDzH2dke1wSVAcfdheUY3NYNzgEXK8AFAw=s64","userId":"17839123472943995639"}}},"source":["#Import pretrained word vectors\n","def create_embedding_matrix(filepath, word_index, embedding_dim):\n","    vocab_size = len(word_index) + 1  # Adding again 1 because of reserved 0 index\n","    embedding_matrix = np.zeros((vocab_size, embedding_dim))\n","    with open(filepath) as f:\n","        for line in f:\n","            word, *vector = line.split()\n","            if word in word_index:\n","                idx = word_index[word] \n","                embedding_matrix[idx] = np.array(\n","                    vector, dtype=np.float32)[:embedding_dim]\n","    return embedding_matrix"],"execution_count":13,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"8QgV-7ZoRXjR","executionInfo":{"status":"ok","timestamp":1623404345008,"user_tz":-330,"elapsed":44971,"user":{"displayName":"hdiryde linux","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgWENxDzH2dke1wSVAcfdheUY3NYNzgEXK8AFAw=s64","userId":"17839123472943995639"}},"outputId":"0b3d9d0c-1c04-4675-d5e0-7cde595b383d"},"source":["import urllib.request\n","import zipfile\n","import os\n","\n","fname = 'embeddings/wiki-news-300d-1M.vec'\n","\n","if not os.path.isfile(fname):\n","    print('Downloading word vectors...')\n","    urllib.request.urlretrieve('https://dl.fbaipublicfiles.com/fasttext/vectors-english/wiki-news-300d-1M.vec.zip',\n","                              'wiki-news-300d-1M.vec.zip')\n","    print('Unzipping...')\n","    with zipfile.ZipFile('wiki-news-300d-1M.vec.zip', 'r') as zip_ref:\n","        zip_ref.extractall('embeddings')\n","    print('done.')\n","    \n","    os.remove('wiki-news-300d-1M.vec.zip')"],"execution_count":14,"outputs":[{"output_type":"stream","text":["Downloading word vectors...\n","Unzipping...\n","done.\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"3Dhg57kaRXjS","executionInfo":{"status":"ok","timestamp":1623404365134,"user_tz":-330,"elapsed":20137,"user":{"displayName":"hdiryde linux","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgWENxDzH2dke1wSVAcfdheUY3NYNzgEXK8AFAw=s64","userId":"17839123472943995639"}},"outputId":"9855dc8b-49d4-42e7-fe2f-c236d785b416"},"source":["embedd_matrix = create_embedding_matrix(fname, index_of_words, embed_num_dims)\n","embedd_matrix.shape"],"execution_count":15,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(12089, 300)"]},"metadata":{"tags":[]},"execution_count":15}]},{"cell_type":"code","metadata":{"scrolled":true,"colab":{"base_uri":"https://localhost:8080/"},"id":"hMo9tus4RXjS","executionInfo":{"status":"ok","timestamp":1623404365135,"user_tz":-330,"elapsed":9,"user":{"displayName":"hdiryde linux","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgWENxDzH2dke1wSVAcfdheUY3NYNzgEXK8AFAw=s64","userId":"17839123472943995639"}},"outputId":"b83310ad-0e25-4f14-a67c-0af04060dece"},"source":["# Inspect unseen words\n","new_words = 0\n","\n","for word in index_of_words:\n","    entry = embedd_matrix[index_of_words[word]]\n","    if all(v == 0 for v in entry):\n","        new_words = new_words + 1\n","\n","print('Words found in wiki vocab: ' + str(len(index_of_words) - new_words))\n","print('New words found: ' + str(new_words))"],"execution_count":16,"outputs":[{"output_type":"stream","text":["Words found in wiki vocab: 11442\n","New words found: 646\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"gybrZJfIRXjT"},"source":["## 3. Create CNN Pipeline"]},{"cell_type":"code","metadata":{"id":"g7EuXCxWRXjT","executionInfo":{"status":"ok","timestamp":1623404365135,"user_tz":-330,"elapsed":5,"user":{"displayName":"hdiryde linux","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgWENxDzH2dke1wSVAcfdheUY3NYNzgEXK8AFAw=s64","userId":"17839123472943995639"}}},"source":["# Embedding layer \n","embedd_layer = Embedding(vocab_size,\n","                         embed_num_dims,\n","                         input_length = max_seq_len,\n","                         weights = [embedd_matrix],\n","                         trainable=False)"],"execution_count":17,"outputs":[]},{"cell_type":"code","metadata":{"id":"fwufXkNpRXjU","executionInfo":{"status":"ok","timestamp":1623404365779,"user_tz":-330,"elapsed":648,"user":{"displayName":"hdiryde linux","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgWENxDzH2dke1wSVAcfdheUY3NYNzgEXK8AFAw=s64","userId":"17839123472943995639"}}},"source":["# Convolution\n","kernel_size = 3\n","filters = 256\n","\n","model = Sequential()\n","model.add(embedd_layer)\n","model.add(Conv1D(filters, kernel_size, activation='relu'))\n","model.add(GlobalMaxPooling1D())\n","model.add(Dense(256, activation='relu'))\n","model.add(Dense(num_classes, activation='softmax'))"],"execution_count":18,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"EWzyxnTaRXjU","executionInfo":{"status":"ok","timestamp":1623404365780,"user_tz":-330,"elapsed":7,"user":{"displayName":"hdiryde linux","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgWENxDzH2dke1wSVAcfdheUY3NYNzgEXK8AFAw=s64","userId":"17839123472943995639"}},"outputId":"d50188f1-7dff-4049-c9ee-9a2a5fc8a30c"},"source":["model.compile(loss = 'categorical_crossentropy', optimizer = 'adam', metrics = ['accuracy'])\n","model.summary()"],"execution_count":19,"outputs":[{"output_type":"stream","text":["Model: \"sequential\"\n","_________________________________________________________________\n","Layer (type)                 Output Shape              Param #   \n","=================================================================\n","embedding (Embedding)        (None, 500, 300)          3626700   \n","_________________________________________________________________\n","conv1d (Conv1D)              (None, 498, 256)          230656    \n","_________________________________________________________________\n","global_max_pooling1d (Global (None, 256)               0         \n","_________________________________________________________________\n","dense (Dense)                (None, 256)               65792     \n","_________________________________________________________________\n","dense_1 (Dense)              (None, 5)                 1285      \n","=================================================================\n","Total params: 3,924,433\n","Trainable params: 297,733\n","Non-trainable params: 3,626,700\n","_________________________________________________________________\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"EqIynlMnRXjU"},"source":["## 3. Train the Model"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"-oKOsrG6RXjV","executionInfo":{"status":"ok","timestamp":1623408089783,"user_tz":-330,"elapsed":1801591,"user":{"displayName":"hdiryde linux","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgWENxDzH2dke1wSVAcfdheUY3NYNzgEXK8AFAw=s64","userId":"17839123472943995639"}},"outputId":"284aa540-a05a-439b-e2d6-58ca27e6b88a"},"source":["batch_size = 256\n","epochs = 20\n","\n","hist = model.fit(X_train_pad, y_train, \n","                 batch_size=batch_size,\n","                 epochs=epochs,\n","                 validation_data=(X_test_pad,y_test))"],"execution_count":30,"outputs":[{"output_type":"stream","text":["Epoch 1/20\n","31/31 [==============================] - 92s 3s/step - loss: 0.2075 - accuracy: 0.9551 - val_loss: 0.7290 - val_accuracy: 0.7566\n","Epoch 2/20\n","31/31 [==============================] - 92s 3s/step - loss: 0.1512 - accuracy: 0.9726 - val_loss: 0.7476 - val_accuracy: 0.7569\n","Epoch 3/20\n","31/31 [==============================] - 93s 3s/step - loss: 0.1065 - accuracy: 0.9825 - val_loss: 0.7787 - val_accuracy: 0.7533\n","Epoch 4/20\n","31/31 [==============================] - 91s 3s/step - loss: 0.0798 - accuracy: 0.9884 - val_loss: 0.7937 - val_accuracy: 0.7510\n","Epoch 5/20\n","31/31 [==============================] - 92s 3s/step - loss: 0.0600 - accuracy: 0.9913 - val_loss: 0.8235 - val_accuracy: 0.7554\n","Epoch 6/20\n","31/31 [==============================] - 91s 3s/step - loss: 0.0482 - accuracy: 0.9940 - val_loss: 0.8422 - val_accuracy: 0.7510\n","Epoch 7/20\n","31/31 [==============================] - 92s 3s/step - loss: 0.0453 - accuracy: 0.9929 - val_loss: 0.8797 - val_accuracy: 0.7513\n","Epoch 8/20\n","31/31 [==============================] - 92s 3s/step - loss: 0.0379 - accuracy: 0.9933 - val_loss: 0.8933 - val_accuracy: 0.7483\n","Epoch 9/20\n","31/31 [==============================] - 91s 3s/step - loss: 0.0340 - accuracy: 0.9932 - val_loss: 0.9223 - val_accuracy: 0.7521\n","Epoch 10/20\n","31/31 [==============================] - 91s 3s/step - loss: 0.0317 - accuracy: 0.9933 - val_loss: 0.9258 - val_accuracy: 0.7551\n","Epoch 11/20\n","31/31 [==============================] - 90s 3s/step - loss: 0.0298 - accuracy: 0.9941 - val_loss: 0.9444 - val_accuracy: 0.7495\n","Epoch 12/20\n","31/31 [==============================] - 91s 3s/step - loss: 0.0310 - accuracy: 0.9934 - val_loss: 0.9622 - val_accuracy: 0.7524\n","Epoch 13/20\n","31/31 [==============================] - 91s 3s/step - loss: 0.0296 - accuracy: 0.9929 - val_loss: 0.9754 - val_accuracy: 0.7569\n","Epoch 14/20\n","31/31 [==============================] - 91s 3s/step - loss: 0.0292 - accuracy: 0.9947 - val_loss: 0.9946 - val_accuracy: 0.7477\n","Epoch 15/20\n","31/31 [==============================] - 92s 3s/step - loss: 0.0301 - accuracy: 0.9938 - val_loss: 1.0088 - val_accuracy: 0.7486\n","Epoch 16/20\n","31/31 [==============================] - 87s 3s/step - loss: 0.0307 - accuracy: 0.9936 - val_loss: 1.0192 - val_accuracy: 0.7536\n","Epoch 17/20\n","31/31 [==============================] - 85s 3s/step - loss: 0.0322 - accuracy: 0.9932 - val_loss: 1.0480 - val_accuracy: 0.7451\n","Epoch 18/20\n","31/31 [==============================] - 85s 3s/step - loss: 0.0318 - accuracy: 0.9938 - val_loss: 1.0547 - val_accuracy: 0.7489\n","Epoch 19/20\n","31/31 [==============================] - 87s 3s/step - loss: 0.0276 - accuracy: 0.9940 - val_loss: 1.0368 - val_accuracy: 0.7518\n","Epoch 20/20\n","31/31 [==============================] - 85s 3s/step - loss: 0.0281 - accuracy: 0.9933 - val_loss: 1.0955 - val_accuracy: 0.7498\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":573},"id":"CTM72y_WRXjV","executionInfo":{"status":"ok","timestamp":1623404945365,"user_tz":-330,"elapsed":1779,"user":{"displayName":"hdiryde linux","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgWENxDzH2dke1wSVAcfdheUY3NYNzgEXK8AFAw=s64","userId":"17839123472943995639"}},"outputId":"0451e47e-8944-4098-e3a9-3f52112aa551"},"source":["# Accuracy plot\n","plt.plot(hist.history['accuracy'])\n","plt.plot(hist.history['val_accuracy'])\n","plt.title('model accuracy')\n","plt.ylabel('accuracy')\n","plt.xlabel('epoch')\n","plt.legend(['train', 'validation'], loc='upper left')\n","plt.show()\n","\n","# Loss plot\n","plt.plot(hist.history['loss'])\n","plt.plot(hist.history['val_loss'])\n","plt.title('model loss')\n","plt.ylabel('loss')\n","plt.xlabel('epoch')\n","plt.legend(['train', 'validation'], loc='upper left')\n","plt.show()"],"execution_count":21,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"KfLEkgtVRXjV"},"source":["## 4. Evaluation"]},{"cell_type":"code","metadata":{"id":"vc9VZ-NHRXjV","executionInfo":{"status":"ok","timestamp":1623404965063,"user_tz":-330,"elapsed":19712,"user":{"displayName":"hdiryde linux","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgWENxDzH2dke1wSVAcfdheUY3NYNzgEXK8AFAw=s64","userId":"17839123472943995639"}}},"source":["predictions = model.predict(X_test_pad)\n","predictions = np.argmax(predictions, axis=1)\n","predictions = [class_names[pred] for pred in predictions]"],"execution_count":22,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"d1L8jV01RXjW","executionInfo":{"status":"ok","timestamp":1623404965068,"user_tz":-330,"elapsed":14,"user":{"displayName":"hdiryde linux","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgWENxDzH2dke1wSVAcfdheUY3NYNzgEXK8AFAw=s64","userId":"17839123472943995639"}},"outputId":"ed3a61ee-3d5b-4ec4-e3ca-0f4d25474a7a"},"source":["print(\"Accuracy: {:.2f}%\".format(accuracy_score(data_test.Emotion, predictions) * 100))\n","print(\"\\nF1 Score: {:.2f}\".format(f1_score(data_test.Emotion, predictions, average='micro') * 100))"],"execution_count":23,"outputs":[{"output_type":"stream","text":["Accuracy: 75.15%\n","\n","F1 Score: 75.15\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"mTm0cVvlRXjW"},"source":["#### Plotting confusion Matrix:"]},{"cell_type":"code","metadata":{"id":"q7QYa1LTRXjW","executionInfo":{"status":"ok","timestamp":1623404965069,"user_tz":-330,"elapsed":10,"user":{"displayName":"hdiryde linux","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgWENxDzH2dke1wSVAcfdheUY3NYNzgEXK8AFAw=s64","userId":"17839123472943995639"}}},"source":["def plot_confusion_matrix(y_true, y_pred, classes,\n","                          normalize=False,\n","                          title=None,\n","                          cmap=plt.cm.Blues):\n","    '''\n","    This function prints and plots the confusion matrix.\n","    Normalization can be applied by setting `normalize=True`.\n","    '''\n","    if not title:\n","        if normalize:\n","            title = 'Normalized confusion matrix'\n","        else:\n","            title = 'Confusion matrix, without normalization'\n","\n","    # Compute confusion matrix\n","    cm = confusion_matrix(y_true, y_pred)\n","\n","    if normalize:\n","        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n","\n","    fig, ax = plt.subplots()\n","    \n","    # Set size\n","    fig.set_size_inches(12.5, 7.5)\n","    im = ax.imshow(cm, interpolation='nearest', cmap=cmap)\n","    ax.figure.colorbar(im, ax=ax)\n","    ax.grid(False)\n","    \n","    # We want to show all ticks...\n","    ax.set(xticks=np.arange(cm.shape[1]),\n","           yticks=np.arange(cm.shape[0]),\n","           # ... and label them with the respective list entries\n","           xticklabels=classes, yticklabels=classes,\n","           title=title,\n","           ylabel='True label',\n","           xlabel='Predicted label')\n","\n","    # Rotate the tick labels and set their alignment.\n","    plt.setp(ax.get_xticklabels(), rotation=45, ha=\"right\",\n","             rotation_mode=\"anchor\")\n","\n","    # Loop over data dimensions and create text annotations.\n","    fmt = '.2f' if normalize else 'd'\n","    thresh = cm.max() / 2.\n","    for i in range(cm.shape[0]):\n","        for j in range(cm.shape[1]):\n","            ax.text(j, i, format(cm[i, j], fmt),\n","                    ha=\"center\", va=\"center\",\n","                    color=\"white\" if cm[i, j] > thresh else \"black\")\n","    fig.tight_layout()\n","    return ax"],"execution_count":24,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":583},"id":"el-Fx2PzRXjW","executionInfo":{"status":"ok","timestamp":1623404967031,"user_tz":-330,"elapsed":1971,"user":{"displayName":"hdiryde linux","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgWENxDzH2dke1wSVAcfdheUY3NYNzgEXK8AFAw=s64","userId":"17839123472943995639"}},"outputId":"90de4ede-5f55-4923-f410-5726bbb25b6b"},"source":["print(\"\\nF1 Score: {:.2f}\".format(f1_score(data_test.Emotion, predictions, average='micro') * 100))\n","\n","# Plot normalized confusion matrix\n","plot_confusion_matrix(data_test.Emotion, predictions, classes=class_names, normalize=True, title='Normalized confusion matrix')\n","plt.show()"],"execution_count":25,"outputs":[{"output_type":"stream","text":["\n","F1 Score: 75.15\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 900x540 with 2 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"Dgl9ZpL2RXjX"},"source":["#### Let's try other inputs:"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"5HMZLxX3RXjX","executionInfo":{"status":"ok","timestamp":1623404967033,"user_tz":-330,"elapsed":19,"user":{"displayName":"hdiryde linux","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgWENxDzH2dke1wSVAcfdheUY3NYNzgEXK8AFAw=s64","userId":"17839123472943995639"}},"outputId":"4388b0ef-ddfa-4ae9-e3bd-606f1ca86168"},"source":["print('Message: {}\\nPredicted: {}'.format(X_test[4], predictions[4]))"],"execution_count":26,"outputs":[{"output_type":"stream","text":["Message: My boyfriend didn't turn up after promising that he was coming.\n","Predicted: sadness\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xTEJ9ccFRXjX","executionInfo":{"status":"ok","timestamp":1623411630307,"user_tz":-330,"elapsed":464,"user":{"displayName":"hdiryde linux","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgWENxDzH2dke1wSVAcfdheUY3NYNzgEXK8AFAw=s64","userId":"17839123472943995639"}},"outputId":"24f7312a-1646-4d97-ad8f-17d2254ef1a3"},"source":["import time\n","\n","message = ['that was really good']\n","\n","seq = tokenizer.texts_to_sequences(message)\n","padded = pad_sequences(seq, maxlen=max_seq_len)\n","\n","start_time = time.time()\n","pred = model.predict(padded)\n","\n","print('Message: ' + str(message))\n","print('predicted: {} ({:.2f} seconds)'.format(class_names[np.argmax(pred)], (time.time() - start_time)))"],"execution_count":34,"outputs":[{"output_type":"stream","text":["Message: ['that was really good']\n","predicted: joy (0.07 seconds)\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"P507pt7xRXjX"},"source":["# Done\n","Save the model for later use  "]},{"cell_type":"code","metadata":{"id":"GVLYKRlWRXjY","executionInfo":{"status":"ok","timestamp":1623404967037,"user_tz":-330,"elapsed":15,"user":{"displayName":"hdiryde linux","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgWENxDzH2dke1wSVAcfdheUY3NYNzgEXK8AFAw=s64","userId":"17839123472943995639"}}},"source":["# creates a HDF5 file 'my_model.h5'\n","model.save('/content/drive/MyDrive/Capstone/models/cnn_w2v.h5')"],"execution_count":28,"outputs":[]},{"cell_type":"code","metadata":{"id":"5oxOl4n1RXjY","executionInfo":{"status":"ok","timestamp":1623404967038,"user_tz":-330,"elapsed":15,"user":{"displayName":"hdiryde linux","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GgWENxDzH2dke1wSVAcfdheUY3NYNzgEXK8AFAw=s64","userId":"17839123472943995639"}}},"source":["from keras.models import load_model\n","predictor = load_model('/content/drive/MyDrive/Capstone/models/cnn_w2v.h5')"],"execution_count":29,"outputs":[]}]}