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GloVe Vectors Embedding with TF 2.0 and Keras
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{
"cells": [
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "from tensorflow.keras.preprocessing.text import one_hot, Tokenizer\nfrom tensorflow.keras.preprocessing.sequence import pad_sequences\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Dense, Flatten, Embedding, Activation, Dropout, Conv1D, MaxPool1D, MaxPooling1D, GlobalMaxPooling1D\nfrom tensorflow.keras.optimizers import Adam\n\nimport numpy as np\nfrom numpy import array\nimport pandas as pd",
"execution_count": 2,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "from sklearn.model_selection import train_test_split",
"execution_count": 3,
"outputs": [
{
"output_type": "stream",
"text": "D:\\Anaconda\\lib\\importlib\\_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n return f(*args, **kwds)\nD:\\Anaconda\\lib\\importlib\\_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n return f(*args, **kwds)\nD:\\Anaconda\\lib\\importlib\\_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n return f(*args, **kwds)\nD:\\Anaconda\\lib\\importlib\\_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n return f(*args, **kwds)\n",
"name": "stderr"
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "import re",
"execution_count": 12,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "df16m = pd.read_csv('twitter_sentiment.csv', encoding='latin', header=None)",
"execution_count": 4,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "df0 = df16m[[5, 0]]",
"execution_count": 6,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "dfp = df0[df0[0] == 0].sample(2000)\ndfn = df0[df0[0] == 4].sample(2000)\ndf = dfp.append(dfn)",
"execution_count": 7,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "df.reset_index(drop=True, inplace=True)",
"execution_count": 8,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "df.columns = ['tweets', 'sentiment']",
"execution_count": 9,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "df.head()",
"execution_count": 10,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 10,
"data": {
"text/plain": " tweets sentiment\n0 @nkotbsplitsgirl no more shows for me I would... 0\n1 down one follower - ouch going to bed now - t... 0\n2 @16pebbles head to Chico When I get my vacati... 0\n3 i hate myself. i hate what i've become 0\n4 @NicLinkletter sorry about the adding thing my... 0",
"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>tweets</th>\n <th>sentiment</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>@nkotbsplitsgirl no more shows for me I would...</td>\n <td>0</td>\n </tr>\n <tr>\n <th>1</th>\n <td>down one follower - ouch going to bed now - t...</td>\n <td>0</td>\n </tr>\n <tr>\n <th>2</th>\n <td>@16pebbles head to Chico When I get my vacati...</td>\n <td>0</td>\n </tr>\n <tr>\n <th>3</th>\n <td>i hate myself. i hate what i've become</td>\n <td>0</td>\n </tr>\n <tr>\n <th>4</th>\n <td>@NicLinkletter sorry about the adding thing my...</td>\n <td>0</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "### PROCESSING AND CLEANING"
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "contractions = { \n\"ain't\": \"am not\",\n\"aren't\": \"are not\",\n\"can't\": \"cannot\",\n\"can't've\": \"cannot have\",\n\"'cause\": \"because\",\n\"could've\": \"could have\",\n\"couldn't\": \"could not\",\n\"couldn't've\": \"could not have\",\n\"didn't\": \"did not\",\n\"doesn't\": \"does not\",\n\"don't\": \"do not\",\n\"hadn't\": \"had not\",\n\"hadn't've\": \"had not have\",\n\"hasn't\": \"has not\",\n\"haven't\": \"have not\",\n\"he'd\": \"he had\",\n\"he'd've\": \"he would have\",\n\"he'll\": \"he will\",\n\"he'll've\": \"he will have\",\n\"he's\": \"he has\",\n\"how'd\": \"how did\",\n\"how'd'y\": \"how do you\",\n\"how'll\": \"how will\",\n\"how's\": \"how is\",\n\"I'd\": \"I had\",\n\"I'd've\": \"I would have\",\n\"I'll\": \"I will\",\n\"I'll've\": \"I will have\",\n\"I'm\": \"I am\",\n\"I've\": \"I have\",\n\"i'd\": \"i had\",\n\"i'd've\": \"i would have\",\n\"i'll\": \"i will\",\n\"i'll've\": \"i will have\",\n\"i'm\": \"i am\",\n\"i've\": \"i have\",\n\"isn't\": \"is not\",\n\"it'd\": \"it had\",\n\"it'd've\": \"it would have\",\n\"it'll\": \"it will\",\n\"it'll've\": \"it will have\",\n\"it's\": \"it is\",\n\"let's\": \"let us\",\n\"ma'am\": \"madam\",\n\"mayn't\": \"may not\",\n\"might've\": \"might have\",\n\"mightn't\": \"might not\",\n\"mightn't've\": \"might not have\",\n\"must've\": \"must have\",\n\"mustn't\": \"must not\",\n\"mustn't've\": \"must not have\",\n\"needn't\": \"need not\",\n\"needn't've\": \"need not have\",\n\"o'clock\": \"of the clock\",\n\"oughtn't\": \"ought not\",\n\"oughtn't've\": \"ought not have\",\n\"shan't\": \"shall not\",\n\"sha'n't\": \"shall not\",\n\"shan't've\": \"shall not have\",\n\"she'd\": \"she would\",\n\"she'd've\": \"she would have\",\n\"she'll\": \"she will\",\n\"she'll've\": \"she will have\",\n\"she's\": \"she is\",\n\"should've\": \"should have\",\n\"shouldn't\": \"should not\",\n\"shouldn't've\": \"should not have\",\n\"so've\": \"so have\",\n\"so's\": \"so is\",\n\"that'd\": \"that had\",\n\"that'd've\": \"that would have\",\n\"that's\": \"that is\",\n\"there'd\": \"there would\",\n\"there'd've\": \"there would have\",\n\"there's\": \"there is\",\n\"they'd\": \"they would\",\n\"they'd've\": \"they would have\",\n\"they'll\": \"they will\",\n\"they'll've\": \"they will have\",\n\"they're\": \"they are\",\n\"they've\": \"they have\",\n\"to've\": \"to have\",\n\"wasn't\": \"was not\",\n\"we'd\": \"we would\",\n\"we'd've\": \"we would have\",\n\"we'll\": \"we will\",\n\"we'll've\": \"we will have\",\n\"we're\": \"we are\",\n\"we've\": \"we have\",\n\"weren't\": \"were not\",\n\"what'll\": \"what will\",\n\"what'll've\": \"what will have\",\n\"what're\": \"what are\",\n\"what's\": \"what is\",\n\"what've\": \"what have\",\n\"when's\": \"when is\",\n\"when've\": \"when have\",\n\"where'd\": \"where did\",\n\"where's\": \"where is\",\n\"where've\": \"where have\",\n\"who'll\": \"who will\",\n\"who'll've\": \"who will have\",\n\"who's\": \"who is\",\n\"who've\": \"who have\",\n\"why's\": \"why is\",\n\"why've\": \"why have\",\n\"will've\": \"will have\",\n\"won't\": \"will not\",\n\"won't've\": \"will not have\",\n\"would've\": \"would have\",\n\"wouldn't\": \"would not\",\n\"wouldn't've\": \"would not have\",\n\"y'all\": \"you all\",\n\"y'all'd\": \"you all would\",\n\"y'all'd've\": \"you all would have\",\n\"y'all're\": \"you all are\",\n\"y'all've\": \"you all have\",\n\"you'd\": \"you would\",\n\"you'd've\": \"you would have\",\n\"you'll\": \"you will\",\n\"you'll've\": \"you will have\",\n\"you're\": \"you are\",\n\"you've\": \"you have\",\n\" u \": \" you \",\n\" u\": \" you\",\n\" ur \": \" your \",\n\" ur\": \" your\",\n\" n \": \"and\"\n}",
"execution_count": 11,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "%%time\n\n# join all the tweets into single text\ntext = ' '.join(df['tweets'])\n# tokenize\ntext = text.split()\n#count the frequency of words\nfreq_comm = pd.Series(text).value_counts()\n# get the rare word count\nrare = freq_comm[freq_comm.values == 1]",
"execution_count": 17,
"outputs": [
{
"output_type": "stream",
"text": "Wall time: 50.9 ms\n",
"name": "stdout"
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "def get_clean_text(x):\n if type(x) is str:\n # lowercase\n x = x.lower()\n \n # contraction to expansion\n for key in contractions:\n value = contractions[key]\n x = x.replace(key, value)\n \n # remove email\n x = re.sub(r'([a-zA-Z0-9+._-]+@[a-zA-Z0-9+._-]+\\.[a-zA-Z0-9+._-]+)', '', x)\n \n # remove url\n x = re.sub(r'(http|ftp|https)://([\\w_-]+(?:(?:\\.[\\w_-]+)+))([\\w.,@?^=%&:/~+#-]*[\\w@?^=%&/~+#-])?', '', x)\n \n # remove RT\n x = re.sub('RT', '', x)\n \n # remove special characters and numerical values\n x = re.sub('[^A-Z a-z]+', '', x)\n \n # remove rare words\n x = ' '.join([t for t in x.split() if t not in rare])\n \n return x\n else:\n return x",
"execution_count": 16,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "df['tweets'] = df['tweets'].apply(lambda x: get_clean_text(x))",
"execution_count": 18,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "df.head()",
"execution_count": 19,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 19,
"data": {
"text/plain": " tweets sentiment\n0 nkotbsplitsgirl no more shows for me i would l... 0\n1 down one follower ouch going to bed now tmr sh... 0\n2 pebbles head to chico when i get my vacation t... 0\n3 i hate myself i hate what i have become 0\n4 niclinkletter sorry about the thing my phone w... 0",
"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>tweets</th>\n <th>sentiment</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>nkotbsplitsgirl no more shows for me i would l...</td>\n <td>0</td>\n </tr>\n <tr>\n <th>1</th>\n <td>down one follower ouch going to bed now tmr sh...</td>\n <td>0</td>\n </tr>\n <tr>\n <th>2</th>\n <td>pebbles head to chico when i get my vacation t...</td>\n <td>0</td>\n </tr>\n <tr>\n <th>3</th>\n <td>i hate myself i hate what i have become</td>\n <td>0</td>\n </tr>\n <tr>\n <th>4</th>\n <td>niclinkletter sorry about the thing my phone w...</td>\n <td>0</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "text = df['tweets'].tolist()",
"execution_count": 20,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "y = df['sentiment']",
"execution_count": 21,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "token = Tokenizer()\ntoken.fit_on_texts(text)",
"execution_count": 23,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "vocab_size = len(token.word_index) + 1",
"execution_count": 24,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "vocab_size",
"execution_count": 25,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 25,
"data": {
"text/plain": "6720"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "### ENCODE TEXT TO SEQUENCES"
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "encoded_text = token.texts_to_sequences(text)",
"execution_count": 28,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "print(encoded_text[:3])",
"execution_count": 29,
"outputs": [
{
"output_type": "stream",
"text": "[[2240, 38, 75, 548, 12, 18, 1, 91, 34, 2, 24, 3, 22, 10, 143, 59, 1, 66], [144, 51, 1066, 651, 49, 2, 128, 27, 1067, 125, 21, 109, 549, 717, 201, 7, 362, 2241], [2242, 262, 2, 1572, 92, 1, 35, 7, 504, 47, 1, 30, 21, 408, 8, 16, 1572, 138, 9, 1, 13, 337, 55, 15]]\n",
"name": "stdout"
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "max_len = 120\nX = pad_sequences(encoded_text, maxlen=max_len, padding='post')",
"execution_count": 39,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "print(X[:2])",
"execution_count": 40,
"outputs": [
{
"output_type": "stream",
"text": "[[2240 38 75 548 12 18 1 91 34 2 24 3 22 10\n 143 59 1 66 0 0 0 0 0 0 0 0 0 0\n 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n 0 0 0 0 0 0 0 0]\n [ 144 51 1066 651 49 2 128 27 1067 125 21 109 549 717\n 201 7 362 2241 0 0 0 0 0 0 0 0 0 0\n 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n 0 0 0 0 0 0 0 0]]\n",
"name": "stdout"
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "X.shape",
"execution_count": 41,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 41,
"data": {
"text/plain": "(4000, 120)"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "### `GloVe` VECTORS"
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "glove_vec = dict()",
"execution_count": 33,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "%%time\nfile = open('glove.twitter.27B/glove.twitter.27B.200d.txt', encoding='utf-8')\n\nfor line in file:\n values = line.split()\n \n word = values[0]\n vectors = np.asarray(values[1:])\n \n glove_vec[word] = vectors\n \nfile.close()",
"execution_count": 34,
"outputs": [
{
"output_type": "stream",
"text": "Wall time: 2min 23s\n",
"name": "stdout"
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "len(glove_vec.keys())",
"execution_count": 35,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 35,
"data": {
"text/plain": "1193514"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "glove_vec.get('people').shape",
"execution_count": 37,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 37,
"data": {
"text/plain": "(200,)"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "### GET THE GLOVE VECTORS FOR TWITTER DATA WE PREPARED"
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "word_vec_matrix = np.zeros((vocab_size, 200))\n\nfor word, index in token.word_index.items():\n vector = glove_vec.get(word)\n \n if vector is not None:\n word_vec_matrix[index] = vector\n \n else:\n print(word)",
"execution_count": 38,
"outputs": [
{
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"cell_type": "markdown",
"source": "### TF and Keras Model Building"
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"source": "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state = 42, test_size = 0.2, stratify = y)",
"execution_count": 42,
"outputs": []
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"source": "vec_size = 200\n\nmodel = Sequential()\nmodel.add(Embedding(vocab_size, vec_size, input_length = max_len, weights = [word_vec_matrix], trainable = False))\n\nmodel.add(Conv1D(64, 8, activation='relu'))\nmodel.add(MaxPooling1D(2))\nmodel.add(Dropout(0.5))\n\nmodel.add(Dense(32, activation='relu'))\nmodel.add(Dropout(0.5))\n\nmodel.add(Dense(16, activation='relu'))\nmodel.add(GlobalMaxPooling1D())\n\nmodel.add(Dense(1, activation='sigmoid'))\n\nmodel.compile(optimizer=Adam(learning_rate = 0.0001), loss='binary_crossentropy', metrics=['accuracy'])",
"execution_count": 45,
"outputs": []
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"cell_type": "code",
"source": "%%time\nmodel.fit(X_train, y_train, epochs=30, validation_data= (X_test, y_test))",
"execution_count": 46,
"outputs": [
{
"output_type": "stream",
"text": "Epoch 1/30\n100/100 [==============================] - 5s 46ms/step - loss: 0.5860 - accuracy: 0.2403 - val_loss: -1.6214 - val_accuracy: 0.0000e+00\nEpoch 2/30\n100/100 [==============================] - 4s 44ms/step - loss: -3.5822 - accuracy: 0.0088 - val_loss: -5.6338 - val_accuracy: 0.0000e+00\nEpoch 3/30\n100/100 [==============================] - 4s 44ms/step - loss: -11.0900 - accuracy: 0.0000e+00 - val_loss: -13.7076 - val_accuracy: 0.0000e+00\nEpoch 4/30\n100/100 [==============================] - 4s 43ms/step - loss: -26.4359 - accuracy: 0.0000e+00 - val_loss: -30.2744 - val_accuracy: 0.0000e+00\nEpoch 5/30\n100/100 [==============================] - 4s 43ms/step - loss: -57.8003 - accuracy: 0.0000e+00 - val_loss: -61.6745 - val_accuracy: 0.0000e+00\nEpoch 6/30\n100/100 [==============================] - 5s 49ms/step - loss: -117.7616 - accuracy: 0.0000e+00 - val_loss: -120.6311 - val_accuracy: 0.0000e+00\nEpoch 7/30\n100/100 [==============================] - 4s 44ms/step - loss: -219.5031 - accuracy: 0.0000e+00 - val_loss: -219.9777 - val_accuracy: 0.0000e+00\nEpoch 8/30\n100/100 [==============================] - 4s 45ms/step - loss: -379.3741 - accuracy: 0.0000e+00 - val_loss: -377.2446 - val_accuracy: 0.0000e+00\nEpoch 9/30\n100/100 [==============================] - 4s 45ms/step - loss: -630.0646 - accuracy: 0.0000e+00 - val_loss: -620.6599 - val_accuracy: 0.0000e+00\nEpoch 10/30\n100/100 [==============================] - 5s 48ms/step - loss: -1008.0973 - accuracy: 0.0000e+00 - val_loss: -980.7618 - val_accuracy: 0.0000e+00\nEpoch 11/30\n100/100 [==============================] - 5s 50ms/step - loss: -1546.3839 - accuracy: 0.0000e+00 - val_loss: -1468.6462 - val_accuracy: 0.0000e+00\nEpoch 12/30\n100/100 [==============================] - 5s 46ms/step - loss: -2251.2024 - accuracy: 0.0000e+00 - val_loss: -2113.5232 - val_accuracy: 0.0000e+00\nEpoch 13/30\n100/100 [==============================] - 5s 48ms/step - loss: -3098.2000 - accuracy: 0.0000e+00 - val_loss: -2912.8125 - val_accuracy: 0.0000e+00\nEpoch 14/30\n100/100 [==============================] - 5s 48ms/step - loss: -4199.6289 - accuracy: 0.0000e+00 - val_loss: -3934.8538 - val_accuracy: 0.0000e+00\nEpoch 15/30\n100/100 [==============================] - 5s 47ms/step - loss: -5660.9058 - accuracy: 0.0000e+00 - val_loss: -5192.2158 - val_accuracy: 0.0000e+00\nEpoch 16/30\n100/100 [==============================] - 5s 46ms/step - loss: -7259.1255 - accuracy: 0.0000e+00 - val_loss: -6690.9146 - val_accuracy: 0.0000e+00\nEpoch 17/30\n100/100 [==============================] - 4s 44ms/step - loss: -9247.7588 - accuracy: 0.0000e+00 - val_loss: -8481.9756 - val_accuracy: 0.0000e+00\nEpoch 18/30\n100/100 [==============================] - 5s 45ms/step - loss: -11485.6260 - accuracy: 0.0000e+00 - val_loss: -10564.0967 - val_accuracy: 0.0000e+00\nEpoch 19/30\n100/100 [==============================] - 5s 47ms/step - loss: -14165.1709 - accuracy: 0.0000e+00 - val_loss: -12912.4561 - val_accuracy: 0.0000e+00\nEpoch 20/30\n100/100 [==============================] - 5s 47ms/step - loss: -17262.2266 - accuracy: 0.0000e+00 - val_loss: -15667.5723 - val_accuracy: 0.0000e+00\nEpoch 21/30\n100/100 [==============================] - 4s 44ms/step - loss: -20634.9551 - accuracy: 0.0000e+00 - val_loss: -18701.4492 - val_accuracy: 0.0000e+00\nEpoch 22/30\n100/100 [==============================] - 4s 45ms/step - loss: -24605.5859 - accuracy: 0.0000e+00 - val_loss: -22182.5371 - val_accuracy: 0.0000e+00\nEpoch 23/30\n100/100 [==============================] - 4s 45ms/step - loss: -28890.0684 - accuracy: 0.0000e+00 - val_loss: -26029.6816 - val_accuracy: 0.0000e+00\nEpoch 24/30\n100/100 [==============================] - 4s 45ms/step - loss: -34252.6719 - accuracy: 0.0000e+00 - val_loss: -30299.3008 - val_accuracy: 0.0000e+00\nEpoch 25/30\n100/100 [==============================] - 4s 43ms/step - loss: -39558.5078 - accuracy: 0.0000e+00 - val_loss: -35083.2461 - val_accuracy: 0.0000e+00\nEpoch 26/30\n100/100 [==============================] - 5s 46ms/step - loss: -45825.0703 - accuracy: 0.0000e+00 - val_loss: -40421.0508 - val_accuracy: 0.0000e+00\nEpoch 27/30\n100/100 [==============================] - 5s 48ms/step - loss: -52869.0859 - accuracy: 0.0000e+00 - val_loss: -46173.0000 - val_accuracy: 0.0000e+00\nEpoch 28/30\n100/100 [==============================] - 5s 51ms/step - loss: -59024.1602 - accuracy: 0.0000e+00 - val_loss: -52391.3594 - val_accuracy: 0.0000e+00\nEpoch 29/30\n100/100 [==============================] - 5s 46ms/step - loss: -67355.5547 - accuracy: 0.0000e+00 - val_loss: -59283.5117 - val_accuracy: 0.0000e+00\nEpoch 30/30\n100/100 [==============================] - 5s 46ms/step - loss: -75173.7266 - accuracy: 0.0000e+00 - val_loss: -66673.7578 - val_accuracy: 0.0000e+00\nWall time: 2min 22s\n",
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"output_type": "execute_result",
"execution_count": 46,
"data": {
"text/plain": "<tensorflow.python.keras.callbacks.History at 0x1e03159f3c8>"
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"cell_type": "code",
"source": "def get_encode(x):\n x = get_clean_text(x)\n x = token.texts_to_sequences(x)\n x = pad_sequences(x, maxlen=max_len, padding='post')\n \n return x",
"execution_count": 47,
"outputs": []
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"metadata": {
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"cell_type": "code",
"source": "get_encode(['Hello world!'])",
"execution_count": 48,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 48,
"data": {
"text/plain": "array([[501, 267, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0]])"
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{
"metadata": {
"trusted": true
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"cell_type": "code",
"source": "model.predict_classes(get_encode(['Thank for sharing this tutorial.']))",
"execution_count": 55,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 55,
"data": {
"text/plain": "array([[1]])"
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"execution_count": null,
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"description": "GloVe Vectors Embedding with TF 2.0 and Keras",
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