Newsgroup Text Analysis – Word Embedding 2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Newsgroup Text Analysis\n",
    "\n",
    "This is a small exercise in text analysis using the NLTK library.\n",
    "\n",
    "Installing the NLTK corpora can be a little tricky for the uninitiated. After installing the library, you must use the downloader to download one or more text corpora. The downloader is part of the library. This utility has a GUI which tends to run fine on Windows and Linux. On Mac, it is a better bet to use the command-line version by calling download_shell(), e.g.\n",
    "\n",
    "```bash\n",
    "$ python\n",
    "Python 3.7.6 (default, Jan  8 2020, 13:42:34)\n",
    "[Clang 4.0.1 (tags/RELEASE_401/final)] :: Anaconda, Inc. on darwin\n",
    "Type \"help\", \"copyright\", \"credits\" or \"license\" for more information.\n",
    ">>> import nltk\n",
    ">>> nltk.download_shell()\n",
    "```\n",
    "\n",
    "For this exercise, it probably makes sense to download the 'popular' collection of corpora ('d popular'). At the very least, you'll need the stop words ('d stopwords'). You'll also need the [GloVe pre-trained word vectors](http://nlp.stanford.edu/data/glove.6B.zip) from Stanford (careful, it's and 822MB download!).\n",
    "\n",
    "References:\n",
    "* See [this link](https://github.com/tensorchiefs/dl_course_2018/blob/master/notebooks/13_sentiment_analysis_with_imdb_reviews_solution.ipynb) from a previous DL lecture\n",
    "* Also https://realpython.com/python-keras-text-classification/\n",
    "* ZHAW ML Lab [5_NaiveBayes_Newsgroup.py](https://moodle2.zhaw.ch/mod/folder/view.php?id=1382473)\n",
    "* A good introductory post on CNNs and text classification can be found here: http://www.wildml.com/2015/11/understanding-convolutional-neural-networks-for-nlp/\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from sklearn.datasets import fetch_20newsgroups\n",
    "from tensorflow.keras.preprocessing.text import Tokenizer\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras import layers\n",
    "from keras.utils.np_utils import to_categorical\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.tokenize import word_tokenize\n",
    "from nltk.stem import PorterStemmer\n",
    "from nltk.tokenize import RegexpTokenizer\n",
    "import nltk\n",
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read in some data\n",
    "categories = ['soc.religion.christian', 'comp.graphics', 'sci.med', 'comp.windows.x', 'sci.space', 'alt.atheism']\n",
    "\n",
    "newsgroup_train = fetch_20newsgroups(subset='train', categories=categories, shuffle=True, remove=('headers', 'footers', 'quotes'))\n",
    "newsgroup_test = fetch_20newsgroups(subset='test', categories=categories, remove=('headers', 'footers', 'quotes'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3443 documents\n",
      "6 categories\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"%d documents\" % len(newsgroup_train.filenames))\n",
    "print(\"%d categories\" % len(newsgroup_train.target_names))\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We have recently obtained a copy of color_xterm (from export.lcs.mit.edu) after\n",
      "seeing it mentioned in a previous article. On compilation, it reports the\n",
      "following undefined symbols:\n",
      "\t_get_wmShellWidgetClass\n",
      "\t_get_applicationShellWidgetClass\n",
      "\n",
      "\tbut still runs. When sending escape sequences to set the colour,\n",
      "any colour comes out as black text on a black background. Any Ideas?\n",
      "\n",
      "Has anyone used this program in colour successfully?\n",
      "\n",
      "Can anyone point me to any other colour terminal emulators?\n",
      "\n",
      "We are running OpenWindows 3 on Sun Sparcs running SunOS 4.1.3.\n",
      "\n",
      "\t\t\t\tadvaTHANKSnce  Ben Kelley.\n"
     ]
    }
   ],
   "source": [
    "# Print an example message\n",
    "print(newsgroup_train[\"data\"][1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'myself', 'theirs', 'because', 'they', 'herself', 'him', 'who', 'hasn', 'for', 'most', 'himself', 'through', 'into', 'here', 'more', \"you're\", \"doesn't\", \"you'd\", 'a', 'or', 'y', 'which', 'm', 'i', 'when', 'are', 'having', 'mightn', 'does', 'can', 'so', 'to', \"needn't\", 'didn', \"you'll\", 'being', 'yours', 'its', \"it's\", 'should', 'did', 'doing', 'my', 'while', 'don', 's', 'few', 'nor', 'over', 'there', 'own', \"didn't\", 'themselves', 'on', 'he', 'both', 'how', 'in', 'an', 'her', \"wasn't\", \"won't\", 'aren', 'whom', 'just', 'their', 'by', 'will', \"that'll\", 'am', 'weren', 'what', 'other', 'with', 'your', 'then', 'doesn', 'itself', 'same', \"mightn't\", 'too', 'be', \"isn't\", 'we', \"wouldn't\", 'above', 'is', 'as', 'from', 'it', 'his', 'down', 'no', 'if', \"hadn't\", \"shouldn't\", 'why', 'at', \"mustn't\", 'shouldn', 've', \"shan't\", 'ain', \"you've\", 'our', 'wouldn', 'has', 'each', 'below', 'until', \"couldn't\", 'wasn', 'needn', \"hasn't\", 'these', 'those', \"don't\", 'ma', 'once', 'll', 'where', 'any', 'this', \"should've\", 'some', 'isn', 'won', 'couldn', 'me', 'before', 'was', 'hadn', 'ourselves', 'that', 'only', 'she', 'mustn', 'd', 'than', 'after', 'not', 'up', 'you', 'ours', 'were', 'during', 'have', \"she's\", 'about', 'haven', \"haven't\", 'but', 'out', 'under', 'against', 'them', 'very', 'off', 'now', \"weren't\", 't', 'o', 'shan', 'between', 'such', \"aren't\", 'been', 'yourselves', 'the', 'had', 'again', 're', 'yourself', 'and', 'all', 'do', 'hers', 'of', 'further'}\n"
     ]
    }
   ],
   "source": [
    "# Set up standard stop words\n",
    "stop_words = set(stopwords.words('english')) \n",
    "print(stop_words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Put some additional words in the list\n",
    "stop_words.update(['find','would','be','one','say','made','think','subject','see','fnal','articl','feel','given','led',\n",
    "                  'view','way','either','however','yet','know','get','use','therefore','even','like','could'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " recently obtained copy color xterm export lcs mit edu seeing mentioned previous article compilation reports following undefined symbols wmshellwidgetclass applicationshellwidgetclass still runs sending escape sequences set colour colour comes black text black background ideas anyone used program colour successfully anyone point colour terminal emulators running openwindows sun sparcs running sunos advathanksnce ben kelley\n"
     ]
    }
   ],
   "source": [
    "# Pre-processing\n",
    "\n",
    "# Get rid of stop words e.g. with nltk (and stem)\n",
    "ps = PorterStemmer()\n",
    "tokenizer = RegexpTokenizer(r'\\w+') ## Std pattern is '\\w+|\\$[\\d\\.]+|\\S+'\n",
    "\n",
    "newsgroup_trainN = []\n",
    "\n",
    "\n",
    "for message in newsgroup_train.data:\n",
    "    \n",
    "    #r'[^A-Za-z0-9#]+'\n",
    "    \n",
    "    # Sub all the special characters (except _)\n",
    "    document = re.sub(r'\\W', ' ', message)\n",
    "    document = re.sub(r'_', ' ', document)\n",
    "    # Sub all single characters\n",
    "    document = re.sub(r'\\s+[a-zA-Z]\\s+', ' ', document)\n",
    "    # Sub single characters from the start\n",
    "    document = re.sub(r'\\^[a-zA-Z]\\s+', ' ', document) \n",
    "    # Sub digits (https://stackoverflow.com/questions/39582859/python-regex-remove-numbers-and-numbers-with-punctaution?rq=1)\n",
    "    document = re.sub(r'\\b\\d+(?:\\.\\d+)?\\s+', ' ', document)\n",
    "    \n",
    "    # Substituting multiple spaces with single space\n",
    "    document = re.sub(r'\\s+', ' ', document, flags=re.I)\n",
    "    # Convert to Lowercase\n",
    "    document = document.lower()\n",
    "    document = document.strip()\n",
    "    \n",
    "    # Original code\n",
    "    text_token = tokenizer.tokenize(document)\n",
    "    messageN = ''\n",
    "    \n",
    "    for word in text_token:\n",
    "        if word not in stop_words:\n",
    "            #messageN = messageN + ' ' + ps.stem(word)\n",
    "            # w/o stemming\n",
    "            messageN = messageN + ' ' + word\n",
    "            \n",
    "    newsgroup_trainN.append(messageN)\n",
    "\n",
    "newsgroup_train.data = newsgroup_trainN\n",
    "\n",
    "# Print the same message as above, now in its processed form\n",
    "print(newsgroup_train[\"data\"][1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7786\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['1024x768', '10th', '17th', '18084tm', '1960s', '1993', '1b', '1d',\n",
       "       '1g', '1mb', '1st', '20th', '24bit', '24th', '24x', '2a', '2d',\n",
       "       '2mb', '2nd', '320x200x256', '386bsd', '3d', '3do', '3ds', '3e',\n",
       "       '3rd', '4368', '4mb', '4th', '5th', '640x480', '64k', '6th', '7th',\n",
       "       'abandon', 'abandoned', 'abdominal', 'abilities', 'ability',\n",
       "       'able', 'aboard', 'abort', 'abortion', 'abraham', 'absence',\n",
       "       'absent', 'absolute', 'absolutely', 'absolutes', 'absorbed',\n",
       "       'absorption', 'abstinence', 'abstract', 'abstraction', 'abstracts',\n",
       "       'absurd', 'abuse', 'ac', 'acad3', 'academic', 'academy',\n",
       "       'acceleration', 'accelerator', 'accelerators', 'accept',\n",
       "       'acceptable', 'acceptance', 'accepted', 'accepting', 'accepts',\n",
       "       'access', 'accessible', 'accident', 'accidental', 'accidently',\n",
       "       'accompanied', 'accompanying', 'accomplish', 'accomplished',\n",
       "       'accordance', 'according', 'account', 'accountable', 'accounting',\n",
       "       'accounts', 'accumulate', 'accuracy', 'accurate', 'accurately',\n",
       "       'accuse', 'accused', 'ace', 'achieve', 'achieved', 'achieving',\n",
       "       'acid', 'acidophilus', 'acids', 'acknowledge', 'acknowledged',\n",
       "       'acm', 'acquire', 'acquired', 'acronym', 'across', 'acs', 'act',\n",
       "       'acting', 'action', 'actions', 'activated', 'active', 'actively',\n",
       "       'activities', 'activity', 'acts', 'actual', 'actually',\n",
       "       'acupuncture', 'acute', 'ad', 'adam', 'adams', 'adapt',\n",
       "       'adaptation', 'adapted', 'add', 'added', 'adding', 'addition',\n",
       "       'additional', 'additionally', 'additions', 'additive', 'additives',\n",
       "       'addr', 'address', 'addressed', 'addresses', 'addressing', 'adds',\n",
       "       'adequate', 'adequately', 'adjust', 'adjustment', 'admin',\n",
       "       'administered', 'administration', 'administrative',\n",
       "       'administrator', 'admission', 'admit', 'admitted', 'admittedly',\n",
       "       'adobe', 'adopt', 'adopted', 'ads', 'adult', 'adults', 'advance',\n",
       "       'advanced', 'advancement', 'advances', 'advantage', 'advantages',\n",
       "       'adverse', 'advertising', 'advice', 'advisory', 'advocacy',\n",
       "       'advocate', 'advocated', 'aero', 'aeronautics', 'aerospace', 'af',\n",
       "       'affairs', 'affect', 'affected', 'affecting', 'affects',\n",
       "       'affiliated', 'afflicted', 'afford', 'afraid', 'africa', 'african',\n",
       "       'afterlife', 'afternoon', 'afterward', 'afterwards', 'age',\n",
       "       'agencies', 'agency', 'agenda', 'agent', 'ages', 'aggressive',\n",
       "       'aggressively'], dtype='<U14')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## OPTIONAL: Baseline vocab\n",
    "#from sklearn.feature_extraction.text import CountVectorizer  \n",
    "#vectorizer = CountVectorizer(min_df=5, max_df=0.7)\n",
    "#X = vectorizer.fit_transform(newsgroup_train.data).toarray()\n",
    "#\n",
    "#print(len(vectorizer.get_feature_names())) #length of all tokens\n",
    "#np.array(vectorizer.get_feature_names()[0:200]) #the first 200 tokens\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alt.atheism: objective morality said well atheists islam religion atheism people god\n",
      "comp.graphics: format please looking program anyone file files image thanks graphics\n",
      "comp.windows.x: x11r5 display using application windows widget thanks motif server window\n",
      "sci.med: dsl shameful n3jxp chastity pitt geb banks gordon edu msg\n",
      "sci.space: also much lunar earth shuttle moon orbit launch nasa space\n",
      "soc.religion.christian: faith believe christ christian bible christians people church jesus god\n"
     ]
    }
   ],
   "source": [
    "## OPTIONAL: Run some metrics, determine most common words\n",
    "#from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "#from sklearn.naive_bayes import MultinomialNB\n",
    "#from sklearn import metrics\n",
    "#\n",
    "## Converting text to vectors\n",
    "#vectorizer2 = TfidfVectorizer()\n",
    "#vectors = vectorizer2.fit_transform(newsgroup_train.data)\n",
    "#vectors.shape\n",
    "#\n",
    "## Filtering text for more realistic training\n",
    "#vectors_test = vectorizer2.transform(newsgroup_test.data)\n",
    "#clf = MultinomialNB(alpha=.01)\n",
    "#clf.fit(vectors, newsgroup_train.target)\n",
    "#pred = clf.predict(vectors_test)\n",
    "#metrics.f1_score(newsgroup_test.target, pred, average='macro')\n",
    "#\n",
    "#feature_names = np.asarray(vectorizer2.get_feature_names())\n",
    "#for i, category in enumerate(newsgroup_train.target_names):\n",
    "#    top10 = np.argsort(clf.coef_[i])[-10:]\n",
    "#    print(\"%s: %s\" % (category, \" \".join(feature_names[top10])))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 35918 unique tokens.\n",
      "[11714, 272, 310, 1051, 161, 2834, 44, 3062, 9851, 2138]\n"
     ]
    }
   ],
   "source": [
    "# Tokenize words (tensorflow keras Tokenizer)\n",
    "tokenizer = Tokenizer()\n",
    "tokenizer.fit_on_texts(newsgroup_train.data)\n",
    "\n",
    "# Get size of vocabulary \n",
    "# 1 needs to be added because of resrver 0 index\n",
    "vocab_size = len(tokenizer.word_index) + 1\n",
    "print('Found %s unique tokens.' % vocab_size)\n",
    "\n",
    "# Transforms each text in texts to a sequence of integers\n",
    "X_train = tokenizer.texts_to_sequences(newsgroup_train.data)\n",
    "X_test = tokenizer.texts_to_sequences(newsgroup_test.data)\n",
    "print (X_train[0][:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2000\n"
     ]
    }
   ],
   "source": [
    "# Padding\n",
    "#maxlen = max([len(i) for i in X_train])\n",
    "maxlen = 2000\n",
    "print(maxlen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3443, 2000)\n",
      "[  291    51    44  3724   372    46   146  1712 11722   811   202    80\n",
      "  1227  5819     1   204 14406  3181   381   340  6327  7657   196  1293\n",
      "   217    44    29 11723  6936    38  3182   604  2140   244  2392   745\n",
      "   138   228   444  1408   444  5443 14407  8606   440  2140  1408   444\n",
      "   348  1036]\n"
     ]
    }
   ],
   "source": [
    "X_train = pad_sequences(X_train, padding='post', maxlen=maxlen)\n",
    "X_test = pad_sequences(X_test, padding='post', maxlen=maxlen)\n",
    "\n",
    "print (X_train.shape)\n",
    "print (X_train[0][200:250])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of data tensor: (3443, 2000)\n",
      "Shape of label tensor: (3443, 6)\n"
     ]
    }
   ],
   "source": [
    "labels = to_categorical(np.array(newsgroup_train.target))\n",
    "\n",
    "print('Shape of data tensor:', X_train.shape)\n",
    "print('Shape of label tensor:', labels.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bag of Words (BOW) Approach\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding (Embedding)        (None, 2000, 50)          1795900   \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 100000)            0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 10)                1000010   \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 6)                 66        \n",
      "=================================================================\n",
      "Total params: 2,795,976\n",
      "Trainable params: 2,795,976\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "\n",
    "embedding_dim = 50\n",
    "\n",
    "# Feature extraction\n",
    "model.add(layers.Embedding(input_dim=vocab_size, \n",
    "                           output_dim=embedding_dim, \n",
    "                           input_length=maxlen))\n",
    "model.add(layers.Flatten())\n",
    "\n",
    "# Classification\n",
    "model.add(layers.Dense(10, activation='relu'))\n",
    "model.add(layers.Dense(6, activation='softmax'))\n",
    "\n",
    "model.compile(optimizer='adam',\n",
    "              loss='sparse_categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 3443 samples, validate on 2291 samples\n",
      "Epoch 1/10\n",
      "3443/3443 [==============================] - 16s 5ms/sample - loss: 1.8333 - accuracy: 0.1673 - val_loss: 1.7901 - val_accuracy: 0.1737\n",
      "Epoch 2/10\n",
      "3443/3443 [==============================] - 16s 5ms/sample - loss: 1.7896 - accuracy: 0.1647 - val_loss: 1.7895 - val_accuracy: 0.1737\n",
      "Epoch 3/10\n",
      "3443/3443 [==============================] - 16s 5ms/sample - loss: 1.7891 - accuracy: 0.1690 - val_loss: 1.7891 - val_accuracy: 0.1737\n",
      "Epoch 4/10\n",
      "3443/3443 [==============================] - 16s 5ms/sample - loss: 1.7889 - accuracy: 0.1690 - val_loss: 1.7890 - val_accuracy: 0.1737\n",
      "Epoch 5/10\n",
      "3443/3443 [==============================] - 16s 5ms/sample - loss: 1.7888 - accuracy: 0.1589 - val_loss: 1.7890 - val_accuracy: 0.1737\n",
      "Epoch 6/10\n",
      "3443/3443 [==============================] - 16s 5ms/sample - loss: 1.7887 - accuracy: 0.1743 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 7/10\n",
      "3443/3443 [==============================] - 16s 5ms/sample - loss: 1.7887 - accuracy: 0.1687 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 8/10\n",
      "3443/3443 [==============================] - 16s 5ms/sample - loss: 1.7887 - accuracy: 0.1653 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 9/10\n",
      "3443/3443 [==============================] - 16s 5ms/sample - loss: 1.7887 - accuracy: 0.1679 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 10/10\n",
      "3443/3443 [==============================] - 16s 5ms/sample - loss: 1.7888 - accuracy: 0.1647 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Training Accuracy: 0.1743\n",
      "Testing Accuracy:  0.1737\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(X_train, newsgroup_train.target,\n",
    "                    epochs=10,\n",
    "                    verbose=True,\n",
    "                    validation_data=(X_test, newsgroup_test.target),\n",
    "                    batch_size=10)\n",
    "loss, accuracy = model.evaluate(X_train, newsgroup_train.target, verbose=False)\n",
    "print(\"Training Accuracy: {:.4f}\".format(accuracy))\n",
    "loss, accuracy = model.evaluate(X_test, newsgroup_test.target, verbose=False)\n",
    "print(\"Testing Accuracy:  {:.4f}\".format(accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot training & validation accuracy values\n",
    "plt.plot(history.history['accuracy'])\n",
    "plt.plot(history.history['val_accuracy'])\n",
    "plt.title('Model accuracy')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Test'], loc='upper left')\n",
    "plt.show()\n",
    "\n",
    "# Plot training & validation loss values\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('Model loss')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Test'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Word Embedding w/ pre-computed embedding space (GloVe)\n",
    "\n",
    "Now, we use Stanford's precomputed embedding space for our model.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We don't need the full GloVe vocabulary, only the words we really need\n",
    "# Helper function for creating the embedding matrix\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",
    "\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",
    "\n",
    "    return embedding_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the embedding matrix for our vocab\n",
    "embedding_dim = 100\n",
    "embedding_matrix = create_embedding_matrix('glove.6B.100d.txt', tokenizer.word_index, embedding_dim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7690294559830726"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check how many of our words are covered by the GloVe vocab\n",
    "nonzero_elements = np.count_nonzero(np.count_nonzero(embedding_matrix, axis=1))\n",
    "nonzero_elements / vocab_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding_1 (Embedding)      (None, 2000, 100)         3591800   \n",
      "_________________________________________________________________\n",
      "global_max_pooling1d_1 (Glob (None, 100)               0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 10)                1010      \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 6)                 66        \n",
      "=================================================================\n",
      "Total params: 3,592,876\n",
      "Trainable params: 1,076\n",
      "Non-trainable params: 3,591,800\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(layers.Embedding(input_dim=vocab_size, output_dim=embedding_dim, \n",
    "                           weights=[embedding_matrix], \n",
    "                           input_length=maxlen, \n",
    "                           trainable=False))\n",
    "model.add(layers.GlobalMaxPool1D())\n",
    "model.add(layers.Dense(10, activation='relu'))\n",
    "model.add(layers.Dense(6, activation='sigmoid'))\n",
    "model.compile(optimizer='adam',\n",
    "              loss='sparse_categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "model.summary()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 3443 samples, validate on 2291 samples\n",
      "Epoch 1/50\n",
      "3443/3443 [==============================] - 3s 978us/sample - loss: 1.7729 - accuracy: 0.2289 - val_loss: 1.7201 - val_accuracy: 0.2964\n",
      "Epoch 2/50\n",
      "3443/3443 [==============================] - 3s 875us/sample - loss: 1.6471 - accuracy: 0.3090 - val_loss: 1.5684 - val_accuracy: 0.3125\n",
      "Epoch 3/50\n",
      "3443/3443 [==============================] - 3s 874us/sample - loss: 1.4766 - accuracy: 0.3636 - val_loss: 1.4086 - val_accuracy: 0.3693\n",
      "Epoch 4/50\n",
      "3443/3443 [==============================] - 3s 876us/sample - loss: 1.3161 - accuracy: 0.4043 - val_loss: 1.2850 - val_accuracy: 0.4361\n",
      "Epoch 5/50\n",
      "3443/3443 [==============================] - 3s 874us/sample - loss: 1.2541 - accuracy: 0.4455 - val_loss: 1.2528 - val_accuracy: 0.4531\n",
      "Epoch 6/50\n",
      "3443/3443 [==============================] - 3s 878us/sample - loss: 1.2209 - accuracy: 0.4682 - val_loss: 1.2209 - val_accuracy: 0.4675\n",
      "Epoch 7/50\n",
      "3443/3443 [==============================] - 3s 875us/sample - loss: 1.1962 - accuracy: 0.4816 - val_loss: 1.2271 - val_accuracy: 0.4518\n",
      "Epoch 8/50\n",
      "3443/3443 [==============================] - 3s 876us/sample - loss: 1.1722 - accuracy: 0.4868 - val_loss: 1.1874 - val_accuracy: 0.4679\n",
      "Epoch 9/50\n",
      "3443/3443 [==============================] - 3s 875us/sample - loss: 1.1523 - accuracy: 0.4920 - val_loss: 1.1720 - val_accuracy: 0.4897\n",
      "Epoch 10/50\n",
      "3443/3443 [==============================] - 3s 875us/sample - loss: 1.1378 - accuracy: 0.4996 - val_loss: 1.1635 - val_accuracy: 0.4810\n",
      "Epoch 11/50\n",
      "3443/3443 [==============================] - 3s 875us/sample - loss: 1.1240 - accuracy: 0.4984 - val_loss: 1.1584 - val_accuracy: 0.4836\n",
      "Epoch 12/50\n",
      "3443/3443 [==============================] - 3s 873us/sample - loss: 1.1094 - accuracy: 0.5036 - val_loss: 1.1412 - val_accuracy: 0.5041\n",
      "Epoch 13/50\n",
      "3443/3443 [==============================] - 3s 874us/sample - loss: 1.1054 - accuracy: 0.5118 - val_loss: 1.1322 - val_accuracy: 0.5011\n",
      "Epoch 14/50\n",
      "3443/3443 [==============================] - 3s 876us/sample - loss: 1.0924 - accuracy: 0.5269 - val_loss: 1.1560 - val_accuracy: 0.4801\n",
      "Epoch 15/50\n",
      "3443/3443 [==============================] - 3s 880us/sample - loss: 1.0849 - accuracy: 0.5202 - val_loss: 1.1408 - val_accuracy: 0.4976\n",
      "Epoch 16/50\n",
      "3443/3443 [==============================] - 3s 877us/sample - loss: 1.0778 - accuracy: 0.5182 - val_loss: 1.1156 - val_accuracy: 0.5133\n",
      "Epoch 17/50\n",
      "3443/3443 [==============================] - 3s 874us/sample - loss: 1.0720 - accuracy: 0.5292 - val_loss: 1.1110 - val_accuracy: 0.5116\n",
      "Epoch 18/50\n",
      "3443/3443 [==============================] - 3s 873us/sample - loss: 1.0617 - accuracy: 0.5327 - val_loss: 1.1112 - val_accuracy: 0.5085\n",
      "Epoch 19/50\n",
      "3443/3443 [==============================] - 3s 874us/sample - loss: 1.0603 - accuracy: 0.5356 - val_loss: 1.1169 - val_accuracy: 0.5081\n",
      "Epoch 20/50\n",
      "3443/3443 [==============================] - 3s 874us/sample - loss: 1.0562 - accuracy: 0.5347 - val_loss: 1.1195 - val_accuracy: 0.5076\n",
      "Epoch 21/50\n",
      "3443/3443 [==============================] - 3s 873us/sample - loss: 1.0524 - accuracy: 0.5365 - val_loss: 1.1131 - val_accuracy: 0.5137\n",
      "Epoch 22/50\n",
      "3443/3443 [==============================] - 3s 875us/sample - loss: 1.0442 - accuracy: 0.5359 - val_loss: 1.1124 - val_accuracy: 0.5107\n",
      "Epoch 23/50\n",
      "3443/3443 [==============================] - 3s 873us/sample - loss: 1.0426 - accuracy: 0.5443 - val_loss: 1.0986 - val_accuracy: 0.5094\n",
      "Epoch 24/50\n",
      "3443/3443 [==============================] - 3s 874us/sample - loss: 1.0424 - accuracy: 0.5449 - val_loss: 1.1028 - val_accuracy: 0.5081\n",
      "Epoch 25/50\n",
      "3443/3443 [==============================] - 3s 874us/sample - loss: 1.0377 - accuracy: 0.5408 - val_loss: 1.0967 - val_accuracy: 0.5282\n",
      "Epoch 26/50\n",
      "3443/3443 [==============================] - 3s 877us/sample - loss: 1.0324 - accuracy: 0.5426 - val_loss: 1.0943 - val_accuracy: 0.5268\n",
      "Epoch 27/50\n",
      "3443/3443 [==============================] - 3s 874us/sample - loss: 1.0333 - accuracy: 0.5376 - val_loss: 1.0989 - val_accuracy: 0.5033\n",
      "Epoch 28/50\n",
      "3443/3443 [==============================] - 3s 874us/sample - loss: 1.0295 - accuracy: 0.5434 - val_loss: 1.0911 - val_accuracy: 0.5194\n",
      "Epoch 29/50\n",
      "3443/3443 [==============================] - 3s 875us/sample - loss: 1.0277 - accuracy: 0.5466 - val_loss: 1.1013 - val_accuracy: 0.5037\n",
      "Epoch 30/50\n",
      "3443/3443 [==============================] - 3s 875us/sample - loss: 1.0277 - accuracy: 0.5501 - val_loss: 1.0908 - val_accuracy: 0.5151\n",
      "Epoch 31/50\n",
      "3443/3443 [==============================] - 3s 874us/sample - loss: 1.0237 - accuracy: 0.5417 - val_loss: 1.0931 - val_accuracy: 0.5094\n",
      "Epoch 32/50\n",
      "3443/3443 [==============================] - 3s 874us/sample - loss: 1.0250 - accuracy: 0.5446 - val_loss: 1.0958 - val_accuracy: 0.5199\n",
      "Epoch 33/50\n",
      "3443/3443 [==============================] - 3s 875us/sample - loss: 1.0237 - accuracy: 0.5402 - val_loss: 1.0886 - val_accuracy: 0.5247\n",
      "Epoch 34/50\n",
      "3443/3443 [==============================] - 3s 874us/sample - loss: 1.0175 - accuracy: 0.5489 - val_loss: 1.0937 - val_accuracy: 0.5194\n",
      "Epoch 35/50\n",
      "3443/3443 [==============================] - 3s 875us/sample - loss: 1.0157 - accuracy: 0.5440 - val_loss: 1.1284 - val_accuracy: 0.5033\n",
      "Epoch 36/50\n",
      "3443/3443 [==============================] - 3s 879us/sample - loss: 1.0165 - accuracy: 0.5434 - val_loss: 1.0992 - val_accuracy: 0.5159\n",
      "Epoch 37/50\n",
      "3443/3443 [==============================] - 3s 875us/sample - loss: 1.0126 - accuracy: 0.5437 - val_loss: 1.1025 - val_accuracy: 0.5199\n",
      "Epoch 38/50\n",
      "3443/3443 [==============================] - 3s 874us/sample - loss: 1.0108 - accuracy: 0.5568 - val_loss: 1.0948 - val_accuracy: 0.5137\n",
      "Epoch 39/50\n",
      "3443/3443 [==============================] - 3s 875us/sample - loss: 1.0089 - accuracy: 0.5518 - val_loss: 1.1024 - val_accuracy: 0.5028\n",
      "Epoch 40/50\n",
      "3443/3443 [==============================] - 3s 876us/sample - loss: 1.0094 - accuracy: 0.5472 - val_loss: 1.1074 - val_accuracy: 0.5089\n",
      "Epoch 41/50\n",
      "3443/3443 [==============================] - 3s 875us/sample - loss: 1.0107 - accuracy: 0.5443 - val_loss: 1.1067 - val_accuracy: 0.5107\n",
      "Epoch 42/50\n",
      "3443/3443 [==============================] - 3s 875us/sample - loss: 1.0062 - accuracy: 0.5507 - val_loss: 1.1006 - val_accuracy: 0.5094\n",
      "Epoch 43/50\n",
      "3443/3443 [==============================] - 3s 874us/sample - loss: 1.0111 - accuracy: 0.5443 - val_loss: 1.0908 - val_accuracy: 0.5255\n",
      "Epoch 44/50\n",
      "3443/3443 [==============================] - 3s 874us/sample - loss: 1.0075 - accuracy: 0.5492 - val_loss: 1.0958 - val_accuracy: 0.5142\n",
      "Epoch 45/50\n",
      "3443/3443 [==============================] - 3s 873us/sample - loss: 1.0020 - accuracy: 0.5457 - val_loss: 1.0903 - val_accuracy: 0.5142\n",
      "Epoch 46/50\n",
      "3443/3443 [==============================] - 3s 879us/sample - loss: 1.0040 - accuracy: 0.5553 - val_loss: 1.1032 - val_accuracy: 0.5007\n",
      "Epoch 47/50\n",
      "3443/3443 [==============================] - 3s 873us/sample - loss: 1.0035 - accuracy: 0.5530 - val_loss: 1.0934 - val_accuracy: 0.5072\n",
      "Epoch 48/50\n",
      "3443/3443 [==============================] - 3s 877us/sample - loss: 1.0023 - accuracy: 0.5518 - val_loss: 1.0993 - val_accuracy: 0.5050\n",
      "Epoch 49/50\n",
      "3443/3443 [==============================] - 3s 874us/sample - loss: 1.0013 - accuracy: 0.5428 - val_loss: 1.0968 - val_accuracy: 0.5072\n",
      "Epoch 50/50\n",
      "3443/3443 [==============================] - 3s 873us/sample - loss: 1.0013 - accuracy: 0.5521 - val_loss: 1.0942 - val_accuracy: 0.5085\n",
      "Training Accuracy: 0.5440\n",
      "Testing Accuracy:  0.5085\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(X_train, newsgroup_train.target,\n",
    "                    epochs=50,\n",
    "                    verbose=True,\n",
    "                    validation_data=(X_test, newsgroup_test.target),\n",
    "                    batch_size=10)\n",
    "loss, accuracy = model.evaluate(X_train, newsgroup_train.target, verbose=False)\n",
    "print(\"Training Accuracy: {:.4f}\".format(accuracy))\n",
    "loss, accuracy = model.evaluate(X_test, newsgroup_test.target, verbose=False)\n",
    "print(\"Testing Accuracy:  {:.4f}\".format(accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot training & validation accuracy values\n",
    "plt.plot(history.history['accuracy'])\n",
    "plt.plot(history.history['val_accuracy'])\n",
    "plt.title('Model accuracy')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Test'], loc='upper left')\n",
    "plt.show()\n",
    "\n",
    "# Plot training & validation loss values\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('Model loss')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Test'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Same model with training the word embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding_2 (Embedding)      (None, 2000, 100)         3591800   \n",
      "_________________________________________________________________\n",
      "global_max_pooling1d_2 (Glob (None, 100)               0         \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 10)                1010      \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 6)                 66        \n",
      "=================================================================\n",
      "Total params: 3,592,876\n",
      "Trainable params: 3,592,876\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(layers.Embedding(input_dim=vocab_size, output_dim=embedding_dim, \n",
    "                           weights=[embedding_matrix], \n",
    "                           input_length=maxlen, \n",
    "                           trainable=True))\n",
    "model.add(layers.GlobalMaxPool1D())\n",
    "model.add(layers.Dense(10, activation='relu'))\n",
    "model.add(layers.Dense(6, activation='sigmoid'))\n",
    "model.compile(optimizer='adam',\n",
    "              loss='sparse_categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 3443 samples, validate on 2291 samples\n",
      "Epoch 1/50\n",
      "3443/3443 [==============================] - 18s 5ms/sample - loss: 1.7692 - accuracy: 0.2094 - val_loss: 1.7205 - val_accuracy: 0.2850\n",
      "Epoch 2/50\n",
      "3443/3443 [==============================] - 18s 5ms/sample - loss: 1.5952 - accuracy: 0.3607 - val_loss: 1.4662 - val_accuracy: 0.4470\n",
      "Epoch 3/50\n",
      "3443/3443 [==============================] - 17s 5ms/sample - loss: 1.1746 - accuracy: 0.5440 - val_loss: 1.1474 - val_accuracy: 0.5687\n",
      "Epoch 4/50\n",
      "3443/3443 [==============================] - 18s 5ms/sample - loss: 0.8418 - accuracy: 0.7020 - val_loss: 1.0211 - val_accuracy: 0.6019\n",
      "Epoch 5/50\n",
      "3443/3443 [==============================] - 17s 5ms/sample - loss: 0.6183 - accuracy: 0.7979 - val_loss: 0.8897 - val_accuracy: 0.6735\n",
      "Epoch 6/50\n",
      "3443/3443 [==============================] - 18s 5ms/sample - loss: 0.4472 - accuracy: 0.8763 - val_loss: 0.8303 - val_accuracy: 0.6993\n",
      "Epoch 7/50\n",
      "3443/3443 [==============================] - 17s 5ms/sample - loss: 0.3271 - accuracy: 0.9123 - val_loss: 0.8004 - val_accuracy: 0.7154\n",
      "Epoch 8/50\n",
      "3443/3443 [==============================] - 17s 5ms/sample - loss: 0.2386 - accuracy: 0.9396 - val_loss: 0.7695 - val_accuracy: 0.7241\n",
      "Epoch 9/50\n",
      "3443/3443 [==============================] - 17s 5ms/sample - loss: 0.1736 - accuracy: 0.9590 - val_loss: 0.7677 - val_accuracy: 0.7228\n",
      "Epoch 10/50\n",
      "3443/3443 [==============================] - 17s 5ms/sample - loss: 0.1313 - accuracy: 0.9675 - val_loss: 0.7768 - val_accuracy: 0.7307\n",
      "Epoch 11/50\n",
      "3443/3443 [==============================] - 17s 5ms/sample - loss: 0.1023 - accuracy: 0.9750 - val_loss: 0.7833 - val_accuracy: 0.7333\n",
      "Epoch 12/50\n",
      "3443/3443 [==============================] - 18s 5ms/sample - loss: 0.0817 - accuracy: 0.9800 - val_loss: 0.7762 - val_accuracy: 0.7372\n",
      "Epoch 13/50\n",
      "3443/3443 [==============================] - 18s 5ms/sample - loss: 0.0686 - accuracy: 0.9817 - val_loss: 0.7849 - val_accuracy: 0.7394\n",
      "Epoch 14/50\n",
      "3443/3443 [==============================] - 17s 5ms/sample - loss: 0.0594 - accuracy: 0.9817 - val_loss: 0.7934 - val_accuracy: 0.7385\n",
      "Epoch 15/50\n",
      "3443/3443 [==============================] - 17s 5ms/sample - loss: 0.0532 - accuracy: 0.9829 - val_loss: 0.8038 - val_accuracy: 0.7420\n",
      "Epoch 16/50\n",
      "3443/3443 [==============================] - 17s 5ms/sample - loss: 0.0491 - accuracy: 0.9826 - val_loss: 0.8111 - val_accuracy: 0.7455\n",
      "Epoch 17/50\n",
      "3443/3443 [==============================] - 17s 5ms/sample - loss: 0.0463 - accuracy: 0.9826 - val_loss: 0.8225 - val_accuracy: 0.7399\n",
      "Epoch 18/50\n",
      "3443/3443 [==============================] - 17s 5ms/sample - loss: 0.0440 - accuracy: 0.9829 - val_loss: 0.8341 - val_accuracy: 0.7433\n",
      "Epoch 19/50\n",
      "3443/3443 [==============================] - 18s 5ms/sample - loss: 0.0424 - accuracy: 0.9829 - val_loss: 0.8446 - val_accuracy: 0.7438\n",
      "Epoch 20/50\n",
      "3443/3443 [==============================] - 18s 5ms/sample - loss: 0.0415 - accuracy: 0.9829 - val_loss: 0.8532 - val_accuracy: 0.7438\n",
      "Epoch 21/50\n",
      "3443/3443 [==============================] - 18s 5ms/sample - loss: 0.0404 - accuracy: 0.9829 - val_loss: 0.8715 - val_accuracy: 0.7464\n",
      "Epoch 22/50\n",
      "3443/3443 [==============================] - 18s 5ms/sample - loss: 0.0399 - accuracy: 0.9826 - val_loss: 0.8738 - val_accuracy: 0.7477\n",
      "Epoch 23/50\n",
      "3443/3443 [==============================] - 17s 5ms/sample - loss: 0.0391 - accuracy: 0.9826 - val_loss: 0.8872 - val_accuracy: 0.7521\n",
      "Epoch 24/50\n",
      "3443/3443 [==============================] - 17s 5ms/sample - loss: 0.0388 - accuracy: 0.9832 - val_loss: 0.8972 - val_accuracy: 0.7508\n",
      "Epoch 25/50\n",
      "3443/3443 [==============================] - 18s 5ms/sample - loss: 0.0382 - accuracy: 0.9832 - val_loss: 0.9128 - val_accuracy: 0.7508\n",
      "Epoch 26/50\n",
      "3443/3443 [==============================] - 18s 5ms/sample - loss: 0.0379 - accuracy: 0.9829 - val_loss: 0.9254 - val_accuracy: 0.7521\n",
      "Epoch 27/50\n",
      "3443/3443 [==============================] - 18s 5ms/sample - loss: 0.0374 - accuracy: 0.9840 - val_loss: 0.9302 - val_accuracy: 0.7442\n",
      "Epoch 28/50\n",
      "3443/3443 [==============================] - 18s 5ms/sample - loss: 0.0374 - accuracy: 0.9840 - val_loss: 0.9432 - val_accuracy: 0.7477\n",
      "Epoch 29/50\n",
      "3443/3443 [==============================] - 17s 5ms/sample - loss: 0.0366 - accuracy: 0.9840 - val_loss: 0.9770 - val_accuracy: 0.7516\n",
      "Epoch 30/50\n",
      "3443/3443 [==============================] - 17s 5ms/sample - loss: 0.0369 - accuracy: 0.9826 - val_loss: 0.9634 - val_accuracy: 0.7525\n",
      "Epoch 31/50\n",
      "3443/3443 [==============================] - 17s 5ms/sample - loss: 0.0365 - accuracy: 0.9832 - val_loss: 0.9686 - val_accuracy: 0.7529\n",
      "Epoch 32/50\n",
      "3443/3443 [==============================] - 17s 5ms/sample - loss: 0.0368 - accuracy: 0.9817 - val_loss: 0.9743 - val_accuracy: 0.7529\n",
      "Epoch 33/50\n",
      "3443/3443 [==============================] - 18s 5ms/sample - loss: 0.0363 - accuracy: 0.9840 - val_loss: 0.9860 - val_accuracy: 0.7451\n",
      "Epoch 34/50\n",
      "3443/3443 [==============================] - 18s 5ms/sample - loss: 0.0363 - accuracy: 0.9832 - val_loss: 0.9967 - val_accuracy: 0.7433\n",
      "Epoch 35/50\n",
      "3443/3443 [==============================] - 17s 5ms/sample - loss: 0.0363 - accuracy: 0.9834 - val_loss: 1.0017 - val_accuracy: 0.7529\n",
      "Epoch 36/50\n",
      "3443/3443 [==============================] - 17s 5ms/sample - loss: 0.0362 - accuracy: 0.9820 - val_loss: 1.0082 - val_accuracy: 0.7543\n",
      "Epoch 37/50\n",
      "3443/3443 [==============================] - 17s 5ms/sample - loss: 0.0360 - accuracy: 0.9826 - val_loss: 1.0122 - val_accuracy: 0.7529\n",
      "Epoch 38/50\n",
      "3443/3443 [==============================] - 17s 5ms/sample - loss: 0.0359 - accuracy: 0.9832 - val_loss: 1.0172 - val_accuracy: 0.7538\n",
      "Epoch 39/50\n",
      "3443/3443 [==============================] - 18s 5ms/sample - loss: 0.0360 - accuracy: 0.9852 - val_loss: 1.0306 - val_accuracy: 0.7499\n",
      "Epoch 40/50\n",
      "3443/3443 [==============================] - 17s 5ms/sample - loss: 0.0359 - accuracy: 0.9837 - val_loss: 1.0259 - val_accuracy: 0.7543\n",
      "Epoch 41/50\n",
      "3443/3443 [==============================] - 17s 5ms/sample - loss: 0.0360 - accuracy: 0.9832 - val_loss: 1.0376 - val_accuracy: 0.7516\n",
      "Epoch 42/50\n",
      "3443/3443 [==============================] - 17s 5ms/sample - loss: 0.0359 - accuracy: 0.9837 - val_loss: 1.0417 - val_accuracy: 0.7534\n",
      "Epoch 43/50\n",
      "3443/3443 [==============================] - 18s 5ms/sample - loss: 0.0358 - accuracy: 0.9834 - val_loss: 1.0459 - val_accuracy: 0.7538\n",
      "Epoch 44/50\n",
      "3443/3443 [==============================] - 17s 5ms/sample - loss: 0.0360 - accuracy: 0.9826 - val_loss: 1.0532 - val_accuracy: 0.7547\n",
      "Epoch 45/50\n",
      "3443/3443 [==============================] - 17s 5ms/sample - loss: 0.0358 - accuracy: 0.9834 - val_loss: 1.0756 - val_accuracy: 0.7538\n",
      "Epoch 46/50\n",
      "3443/3443 [==============================] - 17s 5ms/sample - loss: 0.0356 - accuracy: 0.9826 - val_loss: 1.0695 - val_accuracy: 0.7569\n",
      "Epoch 47/50\n",
      "3443/3443 [==============================] - 18s 5ms/sample - loss: 0.0359 - accuracy: 0.9826 - val_loss: 1.0865 - val_accuracy: 0.7538\n",
      "Epoch 48/50\n",
      "3443/3443 [==============================] - 18s 5ms/sample - loss: 0.0356 - accuracy: 0.9843 - val_loss: 1.0723 - val_accuracy: 0.7543\n",
      "Epoch 49/50\n",
      "3443/3443 [==============================] - 18s 5ms/sample - loss: 0.0353 - accuracy: 0.9820 - val_loss: 1.1465 - val_accuracy: 0.7490\n",
      "Epoch 50/50\n",
      "3443/3443 [==============================] - 18s 5ms/sample - loss: 0.0361 - accuracy: 0.9832 - val_loss: 1.1003 - val_accuracy: 0.7551\n",
      "Training Accuracy: 0.9837\n",
      "Testing Accuracy:  0.7551\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(X_train, newsgroup_train.target,\n",
    "                    epochs=50,\n",
    "                    verbose=True,\n",
    "                    validation_data=(X_test, newsgroup_test.target),\n",
    "                    batch_size=10)\n",
    "loss, accuracy = model.evaluate(X_train, newsgroup_train.target, verbose=False)\n",
    "print(\"Training Accuracy: {:.4f}\".format(accuracy))\n",
    "loss, accuracy = model.evaluate(X_test, newsgroup_test.target, verbose=False)\n",
    "print(\"Testing Accuracy:  {:.4f}\".format(accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot training & validation accuracy values\n",
    "plt.plot(history.history['accuracy'])\n",
    "plt.plot(history.history['val_accuracy'])\n",
    "plt.title('Model accuracy')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Test'], loc='upper left')\n",
    "plt.show()\n",
    "\n",
    "# Plot training & validation loss values\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('Model loss')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Test'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_22\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding_22 (Embedding)     (None, 1500, 100)         3591800   \n",
      "_________________________________________________________________\n",
      "conv1d (Conv1D)              (None, 1496, 128)         64128     \n",
      "_________________________________________________________________\n",
      "global_max_pooling1d_11 (Glo (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_38 (Dense)             (None, 10)                1290      \n",
      "_________________________________________________________________\n",
      "dense_39 (Dense)             (None, 6)                 66        \n",
      "=================================================================\n",
      "Total params: 3,657,284\n",
      "Trainable params: 3,657,284\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "embedding_dim = 100\n",
    "\n",
    "model = Sequential()\n",
    "model.add(layers.Embedding(vocab_size, embedding_dim, input_length=maxlen))\n",
    "model.add(layers.Conv1D(128, 5, activation='relu'))\n",
    "model.add(layers.GlobalMaxPooling1D())\n",
    "model.add(layers.Dense(10, activation='relu'))\n",
    "model.add(layers.Dense(6, activation='sigmoid'))\n",
    "model.compile(optimizer='adam',\n",
    "              loss='sparse_categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "model.summary()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 3443 samples, validate on 2291 samples\n",
      "Epoch 1/10\n",
      "3443/3443 [==============================] - 48s 14ms/sample - loss: 1.0438 - accuracy: 0.6149 - val_loss: 0.8206 - val_accuracy: 0.7097\n",
      "Epoch 2/10\n",
      "3443/3443 [==============================] - 46s 13ms/sample - loss: 0.2676 - accuracy: 0.9088 - val_loss: 0.8470 - val_accuracy: 0.7372\n",
      "Epoch 3/10\n",
      "3443/3443 [==============================] - 46s 13ms/sample - loss: 0.0727 - accuracy: 0.9779 - val_loss: 0.8245 - val_accuracy: 0.7564\n",
      "Epoch 4/10\n",
      "3443/3443 [==============================] - 46s 13ms/sample - loss: 0.0506 - accuracy: 0.9802 - val_loss: 0.8699 - val_accuracy: 0.7521\n",
      "Epoch 5/10\n",
      "3443/3443 [==============================] - 46s 13ms/sample - loss: 0.0460 - accuracy: 0.9811 - val_loss: 0.8750 - val_accuracy: 0.7577\n",
      "Epoch 6/10\n",
      "3443/3443 [==============================] - 46s 13ms/sample - loss: 0.0428 - accuracy: 0.9814 - val_loss: 0.9305 - val_accuracy: 0.7486\n",
      "Epoch 7/10\n",
      "3443/3443 [==============================] - 46s 13ms/sample - loss: 0.0400 - accuracy: 0.9829 - val_loss: 0.9304 - val_accuracy: 0.7599\n",
      "Epoch 8/10\n",
      "3443/3443 [==============================] - 46s 13ms/sample - loss: 0.0390 - accuracy: 0.9829 - val_loss: 0.9277 - val_accuracy: 0.7599\n",
      "Epoch 9/10\n",
      "3443/3443 [==============================] - 46s 13ms/sample - loss: 0.0387 - accuracy: 0.9829 - val_loss: 0.9602 - val_accuracy: 0.7599\n",
      "Epoch 10/10\n",
      "3443/3443 [==============================] - 47s 14ms/sample - loss: 0.0384 - accuracy: 0.9823 - val_loss: 0.9789 - val_accuracy: 0.7560\n",
      "Training Accuracy: 0.9834\n",
      "Testing Accuracy:  0.7560\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(X_train, newsgroup_train.target,\n",
    "                    epochs=10,\n",
    "                    verbose=True,\n",
    "                    validation_data=(X_test, newsgroup_test.target),\n",
    "                    batch_size=10)\n",
    "loss, accuracy = model.evaluate(X_train, newsgroup_train.target, verbose=False)\n",
    "print(\"Training Accuracy: {:.4f}\".format(accuracy))\n",
    "loss, accuracy = model.evaluate(X_test, newsgroup_test.target, verbose=False)\n",
    "print(\"Testing Accuracy:  {:.4f}\".format(accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot training & validation accuracy values\n",
    "plt.plot(history.history['accuracy'])\n",
    "plt.plot(history.history['val_accuracy'])\n",
    "plt.title('Model accuracy')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Test'], loc='upper left')\n",
    "plt.show()\n",
    "\n",
    "# Plot training & validation loss values\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('Model loss')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Test'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Another Word Embedding Model\n",
    "\n",
    "See https://qiita.com/takeshikondo/items/419bebc4f9e6c78d5ea9\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_41\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding_40 (Embedding)     (None, 1000, 50)          1795900   \n",
      "_________________________________________________________________\n",
      "conv1d_28 (Conv1D)           (None, 996, 128)          32128     \n",
      "_________________________________________________________________\n",
      "max_pooling1d_27 (MaxPooling (None, 199, 128)          0         \n",
      "_________________________________________________________________\n",
      "conv1d_29 (Conv1D)           (None, 195, 128)          82048     \n",
      "_________________________________________________________________\n",
      "max_pooling1d_28 (MaxPooling (None, 39, 128)           0         \n",
      "_________________________________________________________________\n",
      "conv1d_30 (Conv1D)           (None, 35, 128)           82048     \n",
      "_________________________________________________________________\n",
      "max_pooling1d_29 (MaxPooling (None, 1, 128)            0         \n",
      "_________________________________________________________________\n",
      "flatten_22 (Flatten)         (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_69 (Dense)             (None, 128)               16512     \n",
      "_________________________________________________________________\n",
      "dense_70 (Dense)             (None, 6)                 774       \n",
      "=================================================================\n",
      "Total params: 2,009,410\n",
      "Trainable params: 213,510\n",
      "Non-trainable params: 1,795,900\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "\n",
    "model.add(layers.InputLayer(input_shape=(maxlen,), dtype='int32'))\n",
    "model.add(layers.Embedding(input_dim=vocab_size, output_dim=embedding_dim, \n",
    "                           weights=[embedding_matrix], \n",
    "                           input_length=maxlen, \n",
    "                           trainable=False))\n",
    "model.add(layers.Conv1D(128, 5, activation='relu'))\n",
    "model.add(layers.MaxPooling1D(5))\n",
    "model.add(layers.Conv1D(128, 5, activation='relu'))\n",
    "model.add(layers.MaxPooling1D(5))\n",
    "model.add(layers.Conv1D(128, 5, activation='relu'))\n",
    "model.add(layers.MaxPooling1D(35))  # global max pooling\n",
    "\n",
    "model.add(layers.Flatten())\n",
    "model.add(layers.Dense(128, activation='relu'))\n",
    "model.add(layers.Dense(6, activation='softmax'))\n",
    "\n",
    "model.compile(optimizer='adam',\n",
    "              loss='sparse_categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "model.summary()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 3443 samples, validate on 2291 samples\n",
      "Epoch 1/10\n",
      "3443/3443 [==============================] - 17s 5ms/sample - loss: 1.4081 - accuracy: 0.4243 - val_loss: 1.0654 - val_accuracy: 0.5378\n",
      "Epoch 2/10\n",
      "3443/3443 [==============================] - 16s 5ms/sample - loss: 0.8551 - accuracy: 0.6306 - val_loss: 0.9257 - val_accuracy: 0.6242\n",
      "Epoch 3/10\n",
      "3443/3443 [==============================] - 16s 5ms/sample - loss: 0.7286 - accuracy: 0.6759 - val_loss: 0.9142 - val_accuracy: 0.6281\n",
      "Epoch 4/10\n",
      "3443/3443 [==============================] - 16s 5ms/sample - loss: 0.6470 - accuracy: 0.7241 - val_loss: 0.9157 - val_accuracy: 0.6342\n",
      "Epoch 5/10\n",
      "3443/3443 [==============================] - 16s 5ms/sample - loss: 0.5790 - accuracy: 0.7488 - val_loss: 0.8716 - val_accuracy: 0.6704\n",
      "Epoch 6/10\n",
      "3443/3443 [==============================] - 16s 5ms/sample - loss: 0.4772 - accuracy: 0.8132 - val_loss: 0.8620 - val_accuracy: 0.6709\n",
      "Epoch 7/10\n",
      "3443/3443 [==============================] - 16s 5ms/sample - loss: 0.4224 - accuracy: 0.8330 - val_loss: 0.9106 - val_accuracy: 0.6652\n",
      "Epoch 8/10\n",
      "3443/3443 [==============================] - 16s 5ms/sample - loss: 0.3554 - accuracy: 0.8670 - val_loss: 0.8616 - val_accuracy: 0.6962\n",
      "Epoch 9/10\n",
      "3443/3443 [==============================] - 16s 5ms/sample - loss: 0.2762 - accuracy: 0.8963 - val_loss: 0.9979 - val_accuracy: 0.6792\n",
      "Epoch 10/10\n",
      "3443/3443 [==============================] - 16s 5ms/sample - loss: 0.2352 - accuracy: 0.9184 - val_loss: 0.9476 - val_accuracy: 0.6945\n",
      "Training Accuracy: 0.9445\n",
      "Testing Accuracy:  0.6945\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(X_train, newsgroup_train.target,\n",
    "                    epochs=10,\n",
    "                    verbose=True,\n",
    "                    validation_data=(X_test, newsgroup_test.target),\n",
    "                    batch_size=128)\n",
    "loss, accuracy = model.evaluate(X_train, newsgroup_train.target, verbose=False)\n",
    "print(\"Training Accuracy: {:.4f}\".format(accuracy))\n",
    "loss, accuracy = model.evaluate(X_test, newsgroup_test.target, verbose=False)\n",
    "print(\"Testing Accuracy:  {:.4f}\".format(accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot training & validation accuracy values\n",
    "plt.plot(history.history['accuracy'])\n",
    "plt.plot(history.history['val_accuracy'])\n",
    "plt.title('Model accuracy')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Test'], loc='upper left')\n",
    "plt.show()\n",
    "\n",
    "# Plot training & validation loss values\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('Model loss')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Test'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Another WE model\n",
    "\n",
    "See https://towardsdatascience.com/cnn-sentiment-analysis-1d16b7c5a0e7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_9\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding_9 (Embedding)      (None, 2000, 100)         3591800   \n",
      "_________________________________________________________________\n",
      "conv1d_22 (Conv1D)           (None, 1997, 128)         51328     \n",
      "_________________________________________________________________\n",
      "global_max_pooling1d_3 (Glob (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_18 (Dense)             (None, 10)                1290      \n",
      "_________________________________________________________________\n",
      "dropout_12 (Dropout)         (None, 10)                0         \n",
      "_________________________________________________________________\n",
      "dense_19 (Dense)             (None, 6)                 66        \n",
      "=================================================================\n",
      "Total params: 3,644,484\n",
      "Trainable params: 3,644,484\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "\n",
    "model.add(layers.Embedding(input_dim=vocab_size, output_dim=embedding_dim, \n",
    "                           weights=[embedding_matrix], \n",
    "                           input_length=maxlen, \n",
    "                           trainable=True))\n",
    "\n",
    "#model.add(layers.Conv1D(128, 2, activation='relu'))\n",
    "#model.add(layers.Conv1D(128, 3, activation='relu'))\n",
    "model.add(layers.Conv1D(128, 4, activation='relu'))\n",
    "#model.add(layers.Conv1D(128, 5, activation='relu'))\n",
    "#model.add(layers.Conv1D(128, 6, activation='relu'))\n",
    "\n",
    "#model.add(layers.MaxPooling1D())\n",
    "#model.add(layers.MaxPooling1D())\n",
    "#model.add(layers.MaxPooling1D())\n",
    "#model.add(layers.MaxPooling1D())\n",
    "#model.add(layers.MaxPooling1D())\n",
    "\n",
    "model.add(layers.GlobalMaxPool1D())\n",
    "model.add(layers.Dense(10, activation='relu'))\n",
    "model.add(layers.Dropout(0.3))\n",
    "model.add(layers.Dense(6, activation='softmax'))\n",
    "\n",
    "model.compile(optimizer='adam',\n",
    "              loss='sparse_categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 3443 samples, validate on 2291 samples\n",
      "Epoch 1/150\n",
      "3443/3443 [==============================] - 51s 15ms/sample - loss: 1.3834 - accuracy: 0.4447 - val_loss: 1.0352 - val_accuracy: 0.6172\n",
      "Epoch 2/150\n",
      "3443/3443 [==============================] - 50s 15ms/sample - loss: 0.9006 - accuracy: 0.6550 - val_loss: 0.7260 - val_accuracy: 0.7259\n",
      "Epoch 3/150\n",
      "3443/3443 [==============================] - 50s 14ms/sample - loss: 0.6419 - accuracy: 0.7482 - val_loss: 0.6842 - val_accuracy: 0.7412\n",
      "Epoch 4/150\n",
      "3443/3443 [==============================] - 50s 14ms/sample - loss: 0.4980 - accuracy: 0.8034 - val_loss: 0.6986 - val_accuracy: 0.7442\n",
      "Epoch 5/150\n",
      "3443/3443 [==============================] - 50s 14ms/sample - loss: 0.4308 - accuracy: 0.8301 - val_loss: 0.7002 - val_accuracy: 0.7564\n",
      "Epoch 6/150\n",
      "3443/3443 [==============================] - 50s 14ms/sample - loss: 0.3763 - accuracy: 0.8443 - val_loss: 0.7082 - val_accuracy: 0.7656\n",
      "Epoch 7/150\n",
      "3443/3443 [==============================] - 50s 14ms/sample - loss: 0.3413 - accuracy: 0.8507 - val_loss: 0.7708 - val_accuracy: 0.7639\n",
      "Epoch 8/150\n",
      "3443/3443 [==============================] - 50s 14ms/sample - loss: 0.3395 - accuracy: 0.8394 - val_loss: 0.7873 - val_accuracy: 0.7674\n",
      "Epoch 9/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.3392 - accuracy: 0.8385 - val_loss: 0.8232 - val_accuracy: 0.7577\n",
      "Epoch 10/150\n",
      "3443/3443 [==============================] - 50s 14ms/sample - loss: 0.3012 - accuracy: 0.8609 - val_loss: 0.8445 - val_accuracy: 0.7704\n",
      "Epoch 11/150\n",
      "3443/3443 [==============================] - 50s 15ms/sample - loss: 0.2932 - accuracy: 0.8652 - val_loss: 1.0157 - val_accuracy: 0.7438\n",
      "Epoch 12/150\n",
      "3443/3443 [==============================] - 50s 14ms/sample - loss: 0.2897 - accuracy: 0.8678 - val_loss: 1.0177 - val_accuracy: 0.7604\n",
      "Epoch 13/150\n",
      "3443/3443 [==============================] - 50s 14ms/sample - loss: 0.3072 - accuracy: 0.8644 - val_loss: 1.0216 - val_accuracy: 0.7652\n",
      "Epoch 14/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.2997 - accuracy: 0.8702 - val_loss: 1.0524 - val_accuracy: 0.7656\n",
      "Epoch 15/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.3031 - accuracy: 0.8591 - val_loss: 1.0106 - val_accuracy: 0.7674\n",
      "Epoch 16/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.2770 - accuracy: 0.8769 - val_loss: 1.0877 - val_accuracy: 0.7682\n",
      "Epoch 17/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.2912 - accuracy: 0.8687 - val_loss: 1.0700 - val_accuracy: 0.7678\n",
      "Epoch 18/150\n",
      "3443/3443 [==============================] - 50s 14ms/sample - loss: 0.2973 - accuracy: 0.8716 - val_loss: 1.2452 - val_accuracy: 0.7647\n",
      "Epoch 19/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.2698 - accuracy: 0.8818 - val_loss: 1.2649 - val_accuracy: 0.7543\n",
      "Epoch 20/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.2689 - accuracy: 0.8891 - val_loss: 1.4468 - val_accuracy: 0.7503\n",
      "Epoch 21/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.2514 - accuracy: 0.8937 - val_loss: 1.1598 - val_accuracy: 0.7791\n",
      "Epoch 22/150\n",
      "3443/3443 [==============================] - 48s 14ms/sample - loss: 0.2428 - accuracy: 0.8850 - val_loss: 1.3286 - val_accuracy: 0.7748\n",
      "Epoch 23/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.2359 - accuracy: 0.9076 - val_loss: 1.3380 - val_accuracy: 0.7800\n",
      "Epoch 24/150\n",
      "3443/3443 [==============================] - 50s 14ms/sample - loss: 0.2384 - accuracy: 0.9111 - val_loss: 1.4781 - val_accuracy: 0.7678\n",
      "Epoch 25/150\n",
      "3443/3443 [==============================] - 50s 15ms/sample - loss: 0.2272 - accuracy: 0.9129 - val_loss: 1.4706 - val_accuracy: 0.7770\n",
      "Epoch 26/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.2341 - accuracy: 0.9108 - val_loss: 1.2731 - val_accuracy: 0.7765\n",
      "Epoch 27/150\n",
      "3443/3443 [==============================] - 50s 14ms/sample - loss: 0.2256 - accuracy: 0.9129 - val_loss: 1.6618 - val_accuracy: 0.7756\n",
      "Epoch 28/150\n",
      "3443/3443 [==============================] - 50s 15ms/sample - loss: 0.2248 - accuracy: 0.9140 - val_loss: 1.4281 - val_accuracy: 0.7787\n",
      "Epoch 29/150\n",
      "3443/3443 [==============================] - 50s 14ms/sample - loss: 0.2323 - accuracy: 0.9103 - val_loss: 1.7213 - val_accuracy: 0.7717\n",
      "Epoch 30/150\n",
      "3443/3443 [==============================] - 50s 14ms/sample - loss: 0.2144 - accuracy: 0.9190 - val_loss: 1.6281 - val_accuracy: 0.7783\n",
      "Epoch 31/150\n",
      "3443/3443 [==============================] - 50s 14ms/sample - loss: 0.2084 - accuracy: 0.9213 - val_loss: 1.6770 - val_accuracy: 0.7809\n",
      "Epoch 32/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.2322 - accuracy: 0.9129 - val_loss: 1.6415 - val_accuracy: 0.7835\n",
      "Epoch 33/150\n",
      "3443/3443 [==============================] - 50s 14ms/sample - loss: 0.2212 - accuracy: 0.9140 - val_loss: 1.9372 - val_accuracy: 0.7660\n",
      "Epoch 34/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.2287 - accuracy: 0.9158 - val_loss: 1.6702 - val_accuracy: 0.7765\n",
      "Epoch 35/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.2157 - accuracy: 0.9181 - val_loss: 1.9778 - val_accuracy: 0.7761\n",
      "Epoch 36/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.2114 - accuracy: 0.9204 - val_loss: 2.0820 - val_accuracy: 0.7621\n",
      "Epoch 37/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.2127 - accuracy: 0.9195 - val_loss: 1.9569 - val_accuracy: 0.7726\n",
      "Epoch 38/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.2067 - accuracy: 0.9195 - val_loss: 2.1211 - val_accuracy: 0.7687\n",
      "Epoch 39/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.2100 - accuracy: 0.9187 - val_loss: 1.9362 - val_accuracy: 0.7778\n",
      "Epoch 40/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.2345 - accuracy: 0.9103 - val_loss: 2.3704 - val_accuracy: 0.7652\n",
      "Epoch 41/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.2234 - accuracy: 0.9184 - val_loss: 1.8522 - val_accuracy: 0.7848\n",
      "Epoch 42/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.2138 - accuracy: 0.9248 - val_loss: 2.3042 - val_accuracy: 0.7560\n",
      "Epoch 43/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.2215 - accuracy: 0.9164 - val_loss: 1.8417 - val_accuracy: 0.7778\n",
      "Epoch 44/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.2150 - accuracy: 0.9190 - val_loss: 1.8765 - val_accuracy: 0.7848\n",
      "Epoch 45/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.2086 - accuracy: 0.9233 - val_loss: 1.8911 - val_accuracy: 0.7818\n",
      "Epoch 46/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1993 - accuracy: 0.9274 - val_loss: 1.9305 - val_accuracy: 0.7844\n",
      "Epoch 47/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.2180 - accuracy: 0.9172 - val_loss: 1.9468 - val_accuracy: 0.7765\n",
      "Epoch 48/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1895 - accuracy: 0.9288 - val_loss: 2.0217 - val_accuracy: 0.7870\n",
      "Epoch 49/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.2142 - accuracy: 0.9187 - val_loss: 1.9494 - val_accuracy: 0.7687\n",
      "Epoch 50/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.2063 - accuracy: 0.9233 - val_loss: 2.1296 - val_accuracy: 0.7848\n",
      "Epoch 51/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1766 - accuracy: 0.9320 - val_loss: 2.1921 - val_accuracy: 0.7822\n",
      "Epoch 52/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1642 - accuracy: 0.9306 - val_loss: 2.4748 - val_accuracy: 0.7826\n",
      "Epoch 53/150\n",
      "3443/3443 [==============================] - 50s 14ms/sample - loss: 0.1641 - accuracy: 0.9315 - val_loss: 2.5373 - val_accuracy: 0.7835\n",
      "Epoch 54/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1602 - accuracy: 0.9320 - val_loss: 2.6823 - val_accuracy: 0.7852\n",
      "Epoch 55/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1719 - accuracy: 0.9349 - val_loss: 2.4626 - val_accuracy: 0.7804\n",
      "Epoch 56/150\n",
      "3443/3443 [==============================] - 50s 14ms/sample - loss: 0.1523 - accuracy: 0.9364 - val_loss: 2.6440 - val_accuracy: 0.7730\n",
      "Epoch 57/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1652 - accuracy: 0.9256 - val_loss: 2.6789 - val_accuracy: 0.7800\n",
      "Epoch 58/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1443 - accuracy: 0.9323 - val_loss: 2.5239 - val_accuracy: 0.7844\n",
      "Epoch 59/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1480 - accuracy: 0.9344 - val_loss: 2.8613 - val_accuracy: 0.7809\n",
      "Epoch 60/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1555 - accuracy: 0.9329 - val_loss: 2.9545 - val_accuracy: 0.7839\n",
      "Epoch 61/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1747 - accuracy: 0.9256 - val_loss: 3.0594 - val_accuracy: 0.7756\n",
      "Epoch 62/150\n",
      "3443/3443 [==============================] - 50s 14ms/sample - loss: 0.1651 - accuracy: 0.9283 - val_loss: 2.6443 - val_accuracy: 0.7726\n",
      "Epoch 63/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1413 - accuracy: 0.9361 - val_loss: 3.1205 - val_accuracy: 0.7787\n",
      "Epoch 64/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1567 - accuracy: 0.9294 - val_loss: 3.1798 - val_accuracy: 0.7756\n",
      "Epoch 65/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1570 - accuracy: 0.9309 - val_loss: 2.9465 - val_accuracy: 0.7892\n",
      "Epoch 66/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1511 - accuracy: 0.9265 - val_loss: 3.4987 - val_accuracy: 0.7708\n",
      "Epoch 67/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1540 - accuracy: 0.9341 - val_loss: 3.3117 - val_accuracy: 0.7783\n",
      "Epoch 68/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1607 - accuracy: 0.9251 - val_loss: 3.3214 - val_accuracy: 0.7735\n",
      "Epoch 69/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1315 - accuracy: 0.9402 - val_loss: 3.4201 - val_accuracy: 0.7809\n",
      "Epoch 70/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1490 - accuracy: 0.9303 - val_loss: 3.0786 - val_accuracy: 0.7900\n",
      "Epoch 71/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1493 - accuracy: 0.9309 - val_loss: 2.9610 - val_accuracy: 0.7804\n",
      "Epoch 72/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1541 - accuracy: 0.9309 - val_loss: 3.8852 - val_accuracy: 0.7669\n",
      "Epoch 73/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1510 - accuracy: 0.9358 - val_loss: 3.4160 - val_accuracy: 0.7787\n",
      "Epoch 74/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1455 - accuracy: 0.9364 - val_loss: 3.9579 - val_accuracy: 0.7708\n",
      "Epoch 75/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1574 - accuracy: 0.9306 - val_loss: 3.2856 - val_accuracy: 0.7791\n",
      "Epoch 76/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1587 - accuracy: 0.9294 - val_loss: 3.3235 - val_accuracy: 0.7778\n",
      "Epoch 77/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1449 - accuracy: 0.9329 - val_loss: 3.7195 - val_accuracy: 0.7791\n",
      "Epoch 78/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1528 - accuracy: 0.9338 - val_loss: 3.8275 - val_accuracy: 0.7665\n",
      "Epoch 79/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1376 - accuracy: 0.9387 - val_loss: 3.9915 - val_accuracy: 0.7700\n",
      "Epoch 80/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1670 - accuracy: 0.9320 - val_loss: 3.5222 - val_accuracy: 0.7791\n",
      "Epoch 81/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1521 - accuracy: 0.9312 - val_loss: 4.5890 - val_accuracy: 0.7433\n",
      "Epoch 82/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1470 - accuracy: 0.9364 - val_loss: 3.8492 - val_accuracy: 0.7787\n",
      "Epoch 83/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1569 - accuracy: 0.9349 - val_loss: 3.3873 - val_accuracy: 0.7835\n",
      "Epoch 84/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1435 - accuracy: 0.9355 - val_loss: 4.1951 - val_accuracy: 0.7604\n",
      "Epoch 85/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1545 - accuracy: 0.9358 - val_loss: 3.5806 - val_accuracy: 0.7804\n",
      "Epoch 86/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1398 - accuracy: 0.9425 - val_loss: 3.8403 - val_accuracy: 0.7748\n",
      "Epoch 87/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1551 - accuracy: 0.9335 - val_loss: 3.2725 - val_accuracy: 0.7822\n",
      "Epoch 88/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1615 - accuracy: 0.9294 - val_loss: 3.6024 - val_accuracy: 0.7822\n",
      "Epoch 89/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1526 - accuracy: 0.9341 - val_loss: 3.6449 - val_accuracy: 0.7791\n",
      "Epoch 90/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1445 - accuracy: 0.9396 - val_loss: 3.9943 - val_accuracy: 0.7787\n",
      "Epoch 91/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1282 - accuracy: 0.9437 - val_loss: 3.9156 - val_accuracy: 0.7778\n",
      "Epoch 92/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1662 - accuracy: 0.9288 - val_loss: 4.2416 - val_accuracy: 0.7656\n",
      "Epoch 93/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1591 - accuracy: 0.9294 - val_loss: 3.9529 - val_accuracy: 0.7739\n",
      "Epoch 94/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1432 - accuracy: 0.9352 - val_loss: 3.7699 - val_accuracy: 0.7674\n",
      "Epoch 95/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1440 - accuracy: 0.9352 - val_loss: 4.3314 - val_accuracy: 0.7538\n",
      "Epoch 96/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1391 - accuracy: 0.9370 - val_loss: 3.9169 - val_accuracy: 0.7643\n",
      "Epoch 97/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1636 - accuracy: 0.9355 - val_loss: 3.5309 - val_accuracy: 0.7809\n",
      "Epoch 98/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1490 - accuracy: 0.9358 - val_loss: 3.7551 - val_accuracy: 0.7717\n",
      "Epoch 99/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1407 - accuracy: 0.9410 - val_loss: 5.0083 - val_accuracy: 0.7564\n",
      "Epoch 100/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1480 - accuracy: 0.9387 - val_loss: 4.2884 - val_accuracy: 0.7669\n",
      "Epoch 101/150\n",
      "3443/3443 [==============================] - 50s 15ms/sample - loss: 0.1472 - accuracy: 0.9332 - val_loss: 4.5322 - val_accuracy: 0.7691\n",
      "Epoch 102/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1507 - accuracy: 0.9335 - val_loss: 4.4649 - val_accuracy: 0.7700\n",
      "Epoch 103/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1520 - accuracy: 0.9329 - val_loss: 5.1226 - val_accuracy: 0.7726\n",
      "Epoch 104/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1576 - accuracy: 0.9294 - val_loss: 5.1544 - val_accuracy: 0.7634\n",
      "Epoch 105/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1314 - accuracy: 0.9442 - val_loss: 5.4866 - val_accuracy: 0.7678\n",
      "Epoch 106/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1605 - accuracy: 0.9268 - val_loss: 5.4301 - val_accuracy: 0.7700\n",
      "Epoch 107/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1511 - accuracy: 0.9332 - val_loss: 6.0329 - val_accuracy: 0.7660\n",
      "Epoch 108/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1422 - accuracy: 0.9341 - val_loss: 5.4818 - val_accuracy: 0.7726\n",
      "Epoch 109/150\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1435 - accuracy: 0.9355 - val_loss: 5.8074 - val_accuracy: 0.7525\n",
      "Epoch 110/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1471 - accuracy: 0.9390 - val_loss: 5.6275 - val_accuracy: 0.7599\n",
      "Epoch 111/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1443 - accuracy: 0.9364 - val_loss: 5.3960 - val_accuracy: 0.7643\n",
      "Epoch 112/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1486 - accuracy: 0.9347 - val_loss: 5.7158 - val_accuracy: 0.7556\n",
      "Epoch 113/150\n",
      "3443/3443 [==============================] - 50s 15ms/sample - loss: 0.1373 - accuracy: 0.9422 - val_loss: 5.4206 - val_accuracy: 0.7621\n",
      "Epoch 114/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1523 - accuracy: 0.9320 - val_loss: 5.6690 - val_accuracy: 0.7647\n",
      "Epoch 115/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1391 - accuracy: 0.9344 - val_loss: 5.4203 - val_accuracy: 0.7652\n",
      "Epoch 116/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1493 - accuracy: 0.9332 - val_loss: 5.4124 - val_accuracy: 0.7617\n",
      "Epoch 117/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1569 - accuracy: 0.9297 - val_loss: 5.1167 - val_accuracy: 0.7708\n",
      "Epoch 118/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1542 - accuracy: 0.9286 - val_loss: 6.0926 - val_accuracy: 0.7612\n",
      "Epoch 119/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1465 - accuracy: 0.9323 - val_loss: 6.6528 - val_accuracy: 0.7420\n",
      "Epoch 120/150\n",
      "3443/3443 [==============================] - 50s 14ms/sample - loss: 0.1442 - accuracy: 0.9361 - val_loss: 5.6775 - val_accuracy: 0.7687\n",
      "Epoch 121/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1619 - accuracy: 0.9315 - val_loss: 6.0858 - val_accuracy: 0.7647\n",
      "Epoch 122/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1498 - accuracy: 0.9347 - val_loss: 6.8435 - val_accuracy: 0.7460\n",
      "Epoch 123/150\n",
      "3443/3443 [==============================] - 50s 14ms/sample - loss: 0.1565 - accuracy: 0.9329 - val_loss: 6.5095 - val_accuracy: 0.7551\n",
      "Epoch 124/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1500 - accuracy: 0.9309 - val_loss: 6.5131 - val_accuracy: 0.7516\n",
      "Epoch 125/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1504 - accuracy: 0.9335 - val_loss: 7.1089 - val_accuracy: 0.7346\n",
      "Epoch 126/150\n",
      "3443/3443 [==============================] - 50s 14ms/sample - loss: 0.1476 - accuracy: 0.9320 - val_loss: 6.7401 - val_accuracy: 0.7529\n",
      "Epoch 127/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1525 - accuracy: 0.9323 - val_loss: 6.8493 - val_accuracy: 0.7569\n",
      "Epoch 128/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1374 - accuracy: 0.9410 - val_loss: 7.5843 - val_accuracy: 0.7499\n",
      "Epoch 129/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1574 - accuracy: 0.9283 - val_loss: 7.9115 - val_accuracy: 0.7525\n",
      "Epoch 130/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1583 - accuracy: 0.9306 - val_loss: 6.9572 - val_accuracy: 0.7560\n",
      "Epoch 131/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1405 - accuracy: 0.9352 - val_loss: 7.0904 - val_accuracy: 0.7634\n",
      "Epoch 132/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1323 - accuracy: 0.9402 - val_loss: 7.6072 - val_accuracy: 0.7512\n",
      "Epoch 133/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1401 - accuracy: 0.9355 - val_loss: 7.8570 - val_accuracy: 0.7447\n",
      "Epoch 134/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1414 - accuracy: 0.9378 - val_loss: 7.0927 - val_accuracy: 0.7569\n",
      "Epoch 135/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1491 - accuracy: 0.9349 - val_loss: 7.6405 - val_accuracy: 0.7569\n",
      "Epoch 136/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1457 - accuracy: 0.9378 - val_loss: 7.8919 - val_accuracy: 0.7521\n",
      "Epoch 137/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1634 - accuracy: 0.9251 - val_loss: 7.6609 - val_accuracy: 0.7560\n",
      "Epoch 138/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1450 - accuracy: 0.9370 - val_loss: 7.9503 - val_accuracy: 0.7525\n",
      "Epoch 139/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1483 - accuracy: 0.9323 - val_loss: 7.6047 - val_accuracy: 0.7503\n",
      "Epoch 140/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1349 - accuracy: 0.9393 - val_loss: 7.7687 - val_accuracy: 0.7547\n",
      "Epoch 141/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1461 - accuracy: 0.9352 - val_loss: 7.5881 - val_accuracy: 0.7560\n",
      "Epoch 142/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1431 - accuracy: 0.9367 - val_loss: 7.4404 - val_accuracy: 0.7538\n",
      "Epoch 143/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1475 - accuracy: 0.9326 - val_loss: 8.8156 - val_accuracy: 0.7433\n",
      "Epoch 144/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1509 - accuracy: 0.9286 - val_loss: 8.7309 - val_accuracy: 0.7433\n",
      "Epoch 145/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1309 - accuracy: 0.9396 - val_loss: 8.2660 - val_accuracy: 0.7508\n",
      "Epoch 146/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1627 - accuracy: 0.9309 - val_loss: 7.8825 - val_accuracy: 0.7529\n",
      "Epoch 147/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1372 - accuracy: 0.9367 - val_loss: 8.1027 - val_accuracy: 0.7429\n",
      "Epoch 148/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1439 - accuracy: 0.9364 - val_loss: 7.5495 - val_accuracy: 0.7490\n",
      "Epoch 150/150\n",
      "3443/3443 [==============================] - 49s 14ms/sample - loss: 0.1422 - accuracy: 0.9341 - val_loss: 9.2412 - val_accuracy: 0.7420\n",
      "Training Accuracy: 0.9834\n",
      "Testing Accuracy:  0.7420\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(X_train, newsgroup_train.target,\n",
    "                    epochs=150,\n",
    "                    verbose=True,\n",
    "                    validation_data=(X_test, newsgroup_test.target),\n",
    "                    batch_size=10)\n",
    "loss, accuracy = model.evaluate(X_train, newsgroup_train.target, verbose=False)\n",
    "print(\"Training Accuracy: {:.4f}\".format(accuracy))\n",
    "loss, accuracy = model.evaluate(X_test, newsgroup_test.target, verbose=False)\n",
    "print(\"Testing Accuracy:  {:.4f}\".format(accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot training & validation accuracy values\n",
    "plt.plot(history.history['accuracy'])\n",
    "plt.plot(history.history['val_accuracy'])\n",
    "plt.title('Model accuracy')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Test'], loc='upper left')\n",
    "plt.show()\n",
    "\n",
    "# Plot training & validation loss values\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('Model loss')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Test'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Same model w/ slight adjustments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding (Embedding)        (None, 2000, 100)         3591800   \n",
      "_________________________________________________________________\n",
      "conv1d (Conv1D)              (None, 1999, 128)         25728     \n",
      "_________________________________________________________________\n",
      "conv1d_1 (Conv1D)            (None, 1997, 128)         49280     \n",
      "_________________________________________________________________\n",
      "conv1d_2 (Conv1D)            (None, 1994, 128)         65664     \n",
      "_________________________________________________________________\n",
      "conv1d_3 (Conv1D)            (None, 1990, 128)         82048     \n",
      "_________________________________________________________________\n",
      "conv1d_4 (Conv1D)            (None, 1985, 128)         98432     \n",
      "_________________________________________________________________\n",
      "max_pooling1d (MaxPooling1D) (None, 992, 128)          0         \n",
      "_________________________________________________________________\n",
      "max_pooling1d_1 (MaxPooling1 (None, 496, 128)          0         \n",
      "_________________________________________________________________\n",
      "max_pooling1d_2 (MaxPooling1 (None, 248, 128)          0         \n",
      "_________________________________________________________________\n",
      "max_pooling1d_3 (MaxPooling1 (None, 124, 128)          0         \n",
      "_________________________________________________________________\n",
      "max_pooling1d_4 (MaxPooling1 (None, 62, 128)           0         \n",
      "_________________________________________________________________\n",
      "global_max_pooling1d (Global (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 10)                1290      \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 10)                0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 6)                 66        \n",
      "=================================================================\n",
      "Total params: 3,914,308\n",
      "Trainable params: 3,914,308\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "\n",
    "model.add(layers.Embedding(input_dim=vocab_size, output_dim=embedding_dim, \n",
    "                           weights=[embedding_matrix], \n",
    "                           input_length=maxlen, \n",
    "                           trainable=True))\n",
    "\n",
    "model.add(layers.Conv1D(128, 2, activation='relu'))\n",
    "model.add(layers.Conv1D(128, 3, activation='relu'))\n",
    "model.add(layers.Conv1D(128, 4, activation='relu'))\n",
    "model.add(layers.Conv1D(128, 5, activation='relu'))\n",
    "model.add(layers.Conv1D(128, 6, activation='relu'))\n",
    "\n",
    "model.add(layers.MaxPooling1D())\n",
    "model.add(layers.MaxPooling1D())\n",
    "model.add(layers.MaxPooling1D())\n",
    "model.add(layers.MaxPooling1D())\n",
    "model.add(layers.MaxPooling1D())\n",
    "\n",
    "model.add(layers.GlobalMaxPool1D())\n",
    "model.add(layers.Dense(10, activation='relu'))\n",
    "model.add(layers.Dropout(0.3))\n",
    "model.add(layers.Dense(6, activation='softmax'))\n",
    "\n",
    "model.compile(optimizer='adam',\n",
    "              loss='sparse_categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 3443 samples, validate on 2291 samples\n",
      "Epoch 1/150\n",
      "3443/3443 [==============================] - 232s 67ms/sample - loss: 1.7914 - accuracy: 0.1693 - val_loss: 1.7899 - val_accuracy: 0.1737\n",
      "Epoch 2/150\n",
      "3443/3443 [==============================] - 225s 65ms/sample - loss: 1.7899 - accuracy: 0.1661 - val_loss: 1.7892 - val_accuracy: 0.1737\n",
      "Epoch 3/150\n",
      "3443/3443 [==============================] - 224s 65ms/sample - loss: 1.7894 - accuracy: 0.1626 - val_loss: 1.7890 - val_accuracy: 0.1737\n",
      "Epoch 4/150\n",
      "3443/3443 [==============================] - 222s 65ms/sample - loss: 1.7893 - accuracy: 0.1702 - val_loss: 1.7889 - val_accuracy: 0.1729\n",
      "Epoch 5/150\n",
      "3443/3443 [==============================] - 222s 65ms/sample - loss: 1.7893 - accuracy: 0.1696 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 6/150\n",
      "3443/3443 [==============================] - 222s 64ms/sample - loss: 1.7893 - accuracy: 0.1676 - val_loss: 1.7889 - val_accuracy: 0.1729\n",
      "Epoch 7/150\n",
      "3443/3443 [==============================] - 223s 65ms/sample - loss: 1.7893 - accuracy: 0.1589 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 8/150\n",
      "3443/3443 [==============================] - 222s 65ms/sample - loss: 1.7892 - accuracy: 0.1653 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 9/150\n",
      "3443/3443 [==============================] - 223s 65ms/sample - loss: 1.7892 - accuracy: 0.1693 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 10/150\n",
      "3443/3443 [==============================] - 223s 65ms/sample - loss: 1.7892 - accuracy: 0.1711 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 11/150\n",
      "3443/3443 [==============================] - 222s 65ms/sample - loss: 1.7892 - accuracy: 0.1667 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 12/150\n",
      "3443/3443 [==============================] - 222s 64ms/sample - loss: 1.7892 - accuracy: 0.1632 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 13/150\n",
      "3443/3443 [==============================] - 222s 65ms/sample - loss: 1.7893 - accuracy: 0.1719 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 14/150\n",
      "3443/3443 [==============================] - 223s 65ms/sample - loss: 1.7893 - accuracy: 0.1597 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 15/150\n",
      "3443/3443 [==============================] - 222s 65ms/sample - loss: 1.7892 - accuracy: 0.1661 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 16/150\n",
      "3443/3443 [==============================] - 223s 65ms/sample - loss: 1.7892 - accuracy: 0.1699 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 17/150\n",
      "3443/3443 [==============================] - 223s 65ms/sample - loss: 1.7892 - accuracy: 0.1687 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 18/150\n",
      "3443/3443 [==============================] - 223s 65ms/sample - loss: 1.7892 - accuracy: 0.1705 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 19/150\n",
      "3443/3443 [==============================] - 223s 65ms/sample - loss: 1.7893 - accuracy: 0.1685 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 20/150\n",
      "3443/3443 [==============================] - 223s 65ms/sample - loss: 1.7893 - accuracy: 0.1676 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 21/150\n",
      "3443/3443 [==============================] - 222s 65ms/sample - loss: 1.7893 - accuracy: 0.1679 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 22/150\n",
      "3443/3443 [==============================] - 223s 65ms/sample - loss: 1.7892 - accuracy: 0.1609 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 23/150\n",
      "3443/3443 [==============================] - 227s 66ms/sample - loss: 1.7892 - accuracy: 0.1670 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 24/150\n",
      "3443/3443 [==============================] - 225s 65ms/sample - loss: 1.7892 - accuracy: 0.1696 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 25/150\n",
      "3443/3443 [==============================] - 225s 65ms/sample - loss: 1.7892 - accuracy: 0.1696 - val_loss: 1.7889 - val_accuracy: 0.1724\n",
      "Epoch 26/150\n",
      "3443/3443 [==============================] - 226s 66ms/sample - loss: 1.7893 - accuracy: 0.1690 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 27/150\n",
      "3443/3443 [==============================] - 223s 65ms/sample - loss: 1.7892 - accuracy: 0.1536 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 28/150\n",
      "3443/3443 [==============================] - 223s 65ms/sample - loss: 1.7893 - accuracy: 0.1670 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 29/150\n",
      "3443/3443 [==============================] - 223s 65ms/sample - loss: 1.7892 - accuracy: 0.1702 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 30/150\n",
      "3443/3443 [==============================] - 224s 65ms/sample - loss: 1.7893 - accuracy: 0.1679 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 31/150\n",
      "3443/3443 [==============================] - 224s 65ms/sample - loss: 1.7893 - accuracy: 0.1629 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 32/150\n",
      "3443/3443 [==============================] - 224s 65ms/sample - loss: 1.7892 - accuracy: 0.1690 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 33/150\n",
      "3443/3443 [==============================] - 223s 65ms/sample - loss: 1.7892 - accuracy: 0.1696 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 34/150\n",
      "3443/3443 [==============================] - 223s 65ms/sample - loss: 1.7893 - accuracy: 0.1717 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 35/150\n",
      "3443/3443 [==============================] - 222s 64ms/sample - loss: 1.7892 - accuracy: 0.1673 - val_loss: 1.7889 - val_accuracy: 0.1729\n",
      "Epoch 36/150\n",
      "3443/3443 [==============================] - 223s 65ms/sample - loss: 1.7893 - accuracy: 0.1563 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 37/150\n",
      "3443/3443 [==============================] - 224s 65ms/sample - loss: 1.7893 - accuracy: 0.1711 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 38/150\n",
      "3443/3443 [==============================] - 223s 65ms/sample - loss: 1.7893 - accuracy: 0.1717 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 39/150\n",
      "3443/3443 [==============================] - 223s 65ms/sample - loss: 1.7893 - accuracy: 0.1542 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 40/150\n",
      "3443/3443 [==============================] - 223s 65ms/sample - loss: 1.7892 - accuracy: 0.1687 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 41/150\n",
      "3443/3443 [==============================] - 224s 65ms/sample - loss: 1.7893 - accuracy: 0.1632 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 42/150\n",
      "3443/3443 [==============================] - 224s 65ms/sample - loss: 1.7893 - accuracy: 0.1632 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 43/150\n",
      "3443/3443 [==============================] - 223s 65ms/sample - loss: 1.7893 - accuracy: 0.1740 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 44/150\n",
      "3443/3443 [==============================] - 223s 65ms/sample - loss: 1.7892 - accuracy: 0.1545 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 45/150\n",
      "3443/3443 [==============================] - 223s 65ms/sample - loss: 1.7893 - accuracy: 0.1719 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 46/150\n",
      "3443/3443 [==============================] - 225s 65ms/sample - loss: 1.7892 - accuracy: 0.1722 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 47/150\n",
      "3443/3443 [==============================] - 223s 65ms/sample - loss: 1.7892 - accuracy: 0.1687 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 48/150\n",
      "3443/3443 [==============================] - 223s 65ms/sample - loss: 1.7892 - accuracy: 0.1699 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 49/150\n",
      "3443/3443 [==============================] - 223s 65ms/sample - loss: 1.7892 - accuracy: 0.1647 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 50/150\n",
      "3443/3443 [==============================] - 224s 65ms/sample - loss: 1.7892 - accuracy: 0.1603 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 51/150\n",
      "3443/3443 [==============================] - 224s 65ms/sample - loss: 1.7892 - accuracy: 0.1740 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 52/150\n",
      "3443/3443 [==============================] - 224s 65ms/sample - loss: 1.7892 - accuracy: 0.1702 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 53/150\n",
      "3443/3443 [==============================] - 223s 65ms/sample - loss: 1.7892 - accuracy: 0.1740 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 54/150\n",
      "3443/3443 [==============================] - 224s 65ms/sample - loss: 1.7893 - accuracy: 0.1696 - val_loss: 1.7889 - val_accuracy: 0.1724\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 55/150\n",
      "3443/3443 [==============================] - 223s 65ms/sample - loss: 1.7893 - accuracy: 0.1653 - val_loss: 1.7889 - val_accuracy: 0.1724\n",
      "Epoch 56/150\n",
      "3443/3443 [==============================] - 223s 65ms/sample - loss: 1.7892 - accuracy: 0.1612 - val_loss: 1.7889 - val_accuracy: 0.1724\n",
      "Epoch 57/150\n",
      "3443/3443 [==============================] - 223s 65ms/sample - loss: 1.7892 - accuracy: 0.1734 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 58/150\n",
      "3443/3443 [==============================] - 223s 65ms/sample - loss: 1.7892 - accuracy: 0.1650 - val_loss: 1.7889 - val_accuracy: 0.1729\n",
      "Epoch 59/150\n",
      "3443/3443 [==============================] - 224s 65ms/sample - loss: 1.7892 - accuracy: 0.1644 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 60/150\n",
      "3443/3443 [==============================] - 223s 65ms/sample - loss: 1.7892 - accuracy: 0.1592 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 61/150\n",
      "3443/3443 [==============================] - 223s 65ms/sample - loss: 1.7892 - accuracy: 0.1702 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 62/150\n",
      "3443/3443 [==============================] - 224s 65ms/sample - loss: 1.7893 - accuracy: 0.1621 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 63/150\n",
      "3443/3443 [==============================] - 224s 65ms/sample - loss: 1.7892 - accuracy: 0.1708 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 64/150\n",
      "3443/3443 [==============================] - 223s 65ms/sample - loss: 1.7893 - accuracy: 0.1632 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 65/150\n",
      "3443/3443 [==============================] - 225s 65ms/sample - loss: 1.7892 - accuracy: 0.1740 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 66/150\n",
      "3443/3443 [==============================] - 224s 65ms/sample - loss: 1.7892 - accuracy: 0.1717 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 67/150\n",
      "3443/3443 [==============================] - 224s 65ms/sample - loss: 1.7893 - accuracy: 0.1676 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 68/150\n",
      "3443/3443 [==============================] - 223s 65ms/sample - loss: 1.7893 - accuracy: 0.1658 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 69/150\n",
      "3443/3443 [==============================] - 223s 65ms/sample - loss: 1.7892 - accuracy: 0.1629 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 70/150\n",
      "3443/3443 [==============================] - 224s 65ms/sample - loss: 1.7892 - accuracy: 0.1740 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 71/150\n",
      "3443/3443 [==============================] - 224s 65ms/sample - loss: 1.7893 - accuracy: 0.1702 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 72/150\n",
      "3443/3443 [==============================] - 225s 65ms/sample - loss: 1.7892 - accuracy: 0.1696 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 73/150\n",
      "3443/3443 [==============================] - 223s 65ms/sample - loss: 1.7893 - accuracy: 0.1685 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 74/150\n",
      "3443/3443 [==============================] - 224s 65ms/sample - loss: 1.7892 - accuracy: 0.1711 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 75/150\n",
      "3443/3443 [==============================] - 224s 65ms/sample - loss: 1.7892 - accuracy: 0.1667 - val_loss: 1.7889 - val_accuracy: 0.1729\n",
      "Epoch 76/150\n",
      "3443/3443 [==============================] - 224s 65ms/sample - loss: 1.7892 - accuracy: 0.1635 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 77/150\n",
      "3443/3443 [==============================] - 225s 65ms/sample - loss: 1.7893 - accuracy: 0.1664 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 78/150\n",
      "3443/3443 [==============================] - 224s 65ms/sample - loss: 1.7893 - accuracy: 0.1632 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 79/150\n",
      "3443/3443 [==============================] - 224s 65ms/sample - loss: 1.7893 - accuracy: 0.1699 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 80/150\n",
      "3443/3443 [==============================] - 223s 65ms/sample - loss: 1.7893 - accuracy: 0.1740 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 81/150\n",
      "3443/3443 [==============================] - 223s 65ms/sample - loss: 1.7892 - accuracy: 0.1600 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 82/150\n",
      "3443/3443 [==============================] - 224s 65ms/sample - loss: 1.7893 - accuracy: 0.1722 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 83/150\n",
      "3443/3443 [==============================] - 223s 65ms/sample - loss: 1.7892 - accuracy: 0.1740 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 84/150\n",
      "3443/3443 [==============================] - 223s 65ms/sample - loss: 1.7892 - accuracy: 0.1670 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 85/150\n",
      "3443/3443 [==============================] - 227s 66ms/sample - loss: 1.7893 - accuracy: 0.1687 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 86/150\n",
      "3443/3443 [==============================] - 224s 65ms/sample - loss: 1.7893 - accuracy: 0.1685 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 87/150\n",
      "3443/3443 [==============================] - 223s 65ms/sample - loss: 1.7893 - accuracy: 0.1653 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 88/150\n",
      "3443/3443 [==============================] - 223s 65ms/sample - loss: 1.7893 - accuracy: 0.1699 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 89/150\n",
      "3443/3443 [==============================] - 222s 65ms/sample - loss: 1.7892 - accuracy: 0.1702 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 90/150\n",
      "3443/3443 [==============================] - 223s 65ms/sample - loss: 1.7893 - accuracy: 0.1740 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 91/150\n",
      "3443/3443 [==============================] - 223s 65ms/sample - loss: 1.7892 - accuracy: 0.1565 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 92/150\n",
      "3443/3443 [==============================] - 223s 65ms/sample - loss: 1.7893 - accuracy: 0.1685 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 93/150\n",
      "3443/3443 [==============================] - 223s 65ms/sample - loss: 1.7892 - accuracy: 0.1647 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 94/150\n",
      "3443/3443 [==============================] - 222s 64ms/sample - loss: 1.7892 - accuracy: 0.1612 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 95/150\n",
      "3443/3443 [==============================] - 223s 65ms/sample - loss: 1.7893 - accuracy: 0.1702 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 96/150\n",
      "3443/3443 [==============================] - 224s 65ms/sample - loss: 1.7893 - accuracy: 0.1717 - val_loss: 1.7889 - val_accuracy: 0.1729\n",
      "Epoch 97/150\n",
      "3443/3443 [==============================] - 224s 65ms/sample - loss: 1.7892 - accuracy: 0.1670 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 98/150\n",
      "3443/3443 [==============================] - 224s 65ms/sample - loss: 1.7893 - accuracy: 0.1621 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 99/150\n",
      "3443/3443 [==============================] - 224s 65ms/sample - loss: 1.7892 - accuracy: 0.1696 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 100/150\n",
      "3443/3443 [==============================] - 224s 65ms/sample - loss: 1.7893 - accuracy: 0.1603 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 101/150\n",
      "3443/3443 [==============================] - 223s 65ms/sample - loss: 1.7892 - accuracy: 0.1658 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 102/150\n",
      "3443/3443 [==============================] - 224s 65ms/sample - loss: 1.7892 - accuracy: 0.1699 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 103/150\n",
      "3443/3443 [==============================] - 224s 65ms/sample - loss: 1.7893 - accuracy: 0.1708 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 104/150\n",
      "3443/3443 [==============================] - 222s 64ms/sample - loss: 1.7892 - accuracy: 0.1638 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 105/150\n",
      "3443/3443 [==============================] - 225s 65ms/sample - loss: 1.7892 - accuracy: 0.1740 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 106/150\n",
      "3443/3443 [==============================] - 223s 65ms/sample - loss: 1.7893 - accuracy: 0.1711 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 107/150\n",
      "3443/3443 [==============================] - 223s 65ms/sample - loss: 1.7892 - accuracy: 0.1650 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 108/150\n",
      "3443/3443 [==============================] - 224s 65ms/sample - loss: 1.7892 - accuracy: 0.1685 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 109/150\n",
      "3443/3443 [==============================] - 223s 65ms/sample - loss: 1.7892 - accuracy: 0.1740 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 110/150\n",
      "3443/3443 [==============================] - 224s 65ms/sample - loss: 1.7892 - accuracy: 0.1647 - val_loss: 1.7889 - val_accuracy: 0.1729\n",
      "Epoch 111/150\n",
      "3443/3443 [==============================] - 224s 65ms/sample - loss: 1.7893 - accuracy: 0.1644 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 112/150\n",
      "3443/3443 [==============================] - 224s 65ms/sample - loss: 1.7893 - accuracy: 0.1664 - val_loss: 1.7889 - val_accuracy: 0.1724\n",
      "Epoch 113/150\n",
      "3443/3443 [==============================] - 223s 65ms/sample - loss: 1.7892 - accuracy: 0.1635 - val_loss: 1.7889 - val_accuracy: 0.1724\n",
      "Epoch 114/150\n",
      "3443/3443 [==============================] - 226s 66ms/sample - loss: 1.7893 - accuracy: 0.1685 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 115/150\n",
      "3443/3443 [==============================] - 223s 65ms/sample - loss: 1.7892 - accuracy: 0.1650 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 116/150\n",
      "3443/3443 [==============================] - 223s 65ms/sample - loss: 1.7892 - accuracy: 0.1629 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 117/150\n",
      "3443/3443 [==============================] - 224s 65ms/sample - loss: 1.7893 - accuracy: 0.1740 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 118/150\n",
      "3443/3443 [==============================] - 223s 65ms/sample - loss: 1.7892 - accuracy: 0.1667 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 119/150\n",
      "3443/3443 [==============================] - 223s 65ms/sample - loss: 1.7892 - accuracy: 0.1740 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 120/150\n",
      "3443/3443 [==============================] - 223s 65ms/sample - loss: 1.7892 - accuracy: 0.1740 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 121/150\n",
      "3443/3443 [==============================] - 222s 65ms/sample - loss: 1.7893 - accuracy: 0.1650 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 122/150\n",
      "3443/3443 [==============================] - 222s 64ms/sample - loss: 1.7893 - accuracy: 0.1612 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 123/150\n",
      "3443/3443 [==============================] - 224s 65ms/sample - loss: 1.7893 - accuracy: 0.1626 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 124/150\n",
      "3443/3443 [==============================] - 223s 65ms/sample - loss: 1.7892 - accuracy: 0.1705 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 125/150\n",
      "3443/3443 [==============================] - 224s 65ms/sample - loss: 1.7893 - accuracy: 0.1638 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 126/150\n",
      "3443/3443 [==============================] - 226s 65ms/sample - loss: 1.7892 - accuracy: 0.1635 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 127/150\n",
      "3443/3443 [==============================] - 224s 65ms/sample - loss: 1.7892 - accuracy: 0.1714 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 128/150\n",
      "3443/3443 [==============================] - 223s 65ms/sample - loss: 1.7892 - accuracy: 0.1719 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 129/150\n",
      "3443/3443 [==============================] - 223s 65ms/sample - loss: 1.7892 - accuracy: 0.1661 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 130/150\n",
      "3443/3443 [==============================] - 223s 65ms/sample - loss: 1.7892 - accuracy: 0.1708 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 131/150\n",
      "3443/3443 [==============================] - 224s 65ms/sample - loss: 1.7892 - accuracy: 0.1644 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 132/150\n",
      "3443/3443 [==============================] - 220s 64ms/sample - loss: 1.7892 - accuracy: 0.1682 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 133/150\n",
      "3443/3443 [==============================] - 219s 64ms/sample - loss: 1.7893 - accuracy: 0.1667 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 134/150\n",
      "3443/3443 [==============================] - 221s 64ms/sample - loss: 1.7893 - accuracy: 0.1699 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 135/150\n",
      "3443/3443 [==============================] - 220s 64ms/sample - loss: 1.7893 - accuracy: 0.1679 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 136/150\n",
      "3443/3443 [==============================] - 220s 64ms/sample - loss: 1.7892 - accuracy: 0.1740 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 137/150\n",
      "3443/3443 [==============================] - 220s 64ms/sample - loss: 1.7892 - accuracy: 0.1653 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 138/150\n",
      "3443/3443 [==============================] - 220s 64ms/sample - loss: 1.7892 - accuracy: 0.1685 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 139/150\n",
      "3443/3443 [==============================] - 220s 64ms/sample - loss: 1.7893 - accuracy: 0.1687 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 140/150\n",
      "3443/3443 [==============================] - 220s 64ms/sample - loss: 1.7893 - accuracy: 0.1638 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 141/150\n",
      "3443/3443 [==============================] - 220s 64ms/sample - loss: 1.7893 - accuracy: 0.1647 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 142/150\n",
      "3443/3443 [==============================] - 221s 64ms/sample - loss: 1.7892 - accuracy: 0.1740 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 143/150\n",
      "3443/3443 [==============================] - 222s 65ms/sample - loss: 1.7892 - accuracy: 0.1693 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 144/150\n",
      "3443/3443 [==============================] - 222s 65ms/sample - loss: 1.7892 - accuracy: 0.1664 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 145/150\n",
      "3443/3443 [==============================] - 223s 65ms/sample - loss: 1.7892 - accuracy: 0.1690 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 146/150\n",
      "3443/3443 [==============================] - 228s 66ms/sample - loss: 1.7892 - accuracy: 0.1626 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 147/150\n",
      "3443/3443 [==============================] - 223s 65ms/sample - loss: 1.7893 - accuracy: 0.1740 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 148/150\n",
      "3443/3443 [==============================] - 223s 65ms/sample - loss: 1.7892 - accuracy: 0.1740 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 149/150\n",
      "3443/3443 [==============================] - 223s 65ms/sample - loss: 1.7892 - accuracy: 0.1656 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Epoch 150/150\n",
      "3443/3443 [==============================] - 221s 64ms/sample - loss: 1.7893 - accuracy: 0.1679 - val_loss: 1.7889 - val_accuracy: 0.1737\n",
      "Training Accuracy: 0.1740\n",
      "Testing Accuracy:  0.1737\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(X_train, newsgroup_train.target,\n",
    "                    epochs=150,\n",
    "                    verbose=True,\n",
    "                    validation_data=(X_test, newsgroup_test.target),\n",
    "                    batch_size=10)\n",
    "loss, accuracy = model.evaluate(X_train, newsgroup_train.target, verbose=False)\n",
    "print(\"Training Accuracy: {:.4f}\".format(accuracy))\n",
    "loss, accuracy = model.evaluate(X_test, newsgroup_test.target, verbose=False)\n",
    "print(\"Testing Accuracy:  {:.4f}\".format(accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Another model\n",
    "\n",
    "https://blog.keras.io/using-pre-trained-word-embeddings-in-a-keras-model.html\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_4\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding_3 (Embedding)      (None, 2000, 100)         3591800   \n",
      "_________________________________________________________________\n",
      "conv1d_6 (Conv1D)            (None, 1996, 128)         64128     \n",
      "_________________________________________________________________\n",
      "max_pooling1d_4 (MaxPooling1 (None, 399, 128)          0         \n",
      "_________________________________________________________________\n",
      "conv1d_7 (Conv1D)            (None, 395, 128)          82048     \n",
      "_________________________________________________________________\n",
      "max_pooling1d_5 (MaxPooling1 (None, 79, 128)           0         \n",
      "_________________________________________________________________\n",
      "conv1d_8 (Conv1D)            (None, 75, 128)           82048     \n",
      "_________________________________________________________________\n",
      "global_max_pooling1d_2 (Glob (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "flatten_3 (Flatten)          (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_6 (Dense)              (None, 128)               16512     \n",
      "_________________________________________________________________\n",
      "dense_7 (Dense)              (None, 6)                 774       \n",
      "=================================================================\n",
      "Total params: 3,837,310\n",
      "Trainable params: 3,837,310\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "\n",
    "#model.add(layers.Embedding(input_dim=vocab_size, output_dim=embedding_dim, \n",
    "#                           weights=[embedding_matrix], \n",
    "#                           input_length=maxlen, \n",
    "#                           trainable=False))\n",
    "\n",
    "## --> 71.5% accuracy w/ GloVe\n",
    "\n",
    "model.add(layers.Embedding(input_dim=vocab_size, output_dim=embedding_dim, \n",
    "                           input_length=maxlen))\n",
    "\n",
    "## --> 68.8% accuracy w/o GloVe\n",
    "\n",
    "\n",
    "model.add(layers.Conv1D(128, 5, activation='relu'))\n",
    "model.add(layers.MaxPooling1D(5))\n",
    "model.add(layers.Conv1D(128, 5, activation='relu'))\n",
    "model.add(layers.MaxPooling1D(5))\n",
    "model.add(layers.Conv1D(128, 5, activation='relu'))\n",
    "\n",
    "model.add(layers.GlobalMaxPool1D())\n",
    "\n",
    "model.add(layers.Flatten())\n",
    "\n",
    "model.add(layers.Dense(128, activation='relu'))\n",
    "#model.add(layers.Dropout(0.3))\n",
    "model.add(layers.Dense(6, activation='softmax'))\n",
    "\n",
    "model.compile(optimizer='adam',\n",
    "              loss='sparse_categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "model.summary()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 3443 samples, validate on 2291 samples\n",
      "Epoch 1/10\n",
      "3443/3443 [==============================] - 73s 21ms/sample - loss: 1.4748 - accuracy: 0.3352 - val_loss: 1.2522 - val_accuracy: 0.4321\n",
      "Epoch 2/10\n",
      "3443/3443 [==============================] - 73s 21ms/sample - loss: 0.8391 - accuracy: 0.6262 - val_loss: 0.9459 - val_accuracy: 0.6229\n",
      "Epoch 3/10\n",
      "3443/3443 [==============================] - 71s 21ms/sample - loss: 0.4011 - accuracy: 0.8440 - val_loss: 1.0490 - val_accuracy: 0.6652\n",
      "Epoch 4/10\n",
      "3443/3443 [==============================] - 69s 20ms/sample - loss: 0.2144 - accuracy: 0.9233 - val_loss: 1.3709 - val_accuracy: 0.6726\n",
      "Epoch 5/10\n",
      "3443/3443 [==============================] - 70s 20ms/sample - loss: 0.1162 - accuracy: 0.9582 - val_loss: 1.8273 - val_accuracy: 0.6517\n",
      "Epoch 6/10\n",
      "3443/3443 [==============================] - 70s 20ms/sample - loss: 0.0864 - accuracy: 0.9678 - val_loss: 2.0931 - val_accuracy: 0.6656\n",
      "Epoch 7/10\n",
      "3443/3443 [==============================] - 70s 20ms/sample - loss: 0.1092 - accuracy: 0.9640 - val_loss: 1.4877 - val_accuracy: 0.6997\n",
      "Epoch 8/10\n",
      "3443/3443 [==============================] - 70s 20ms/sample - loss: 0.0764 - accuracy: 0.9724 - val_loss: 1.9078 - val_accuracy: 0.6735\n",
      "Epoch 9/10\n",
      "3443/3443 [==============================] - 70s 20ms/sample - loss: 0.0606 - accuracy: 0.9762 - val_loss: 1.9802 - val_accuracy: 0.7097\n",
      "Epoch 10/10\n",
      "3443/3443 [==============================] - 71s 20ms/sample - loss: 0.0652 - accuracy: 0.9771 - val_loss: 2.0657 - val_accuracy: 0.6879\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(X_train, newsgroup_train.target,\n",
    "                    epochs=10,\n",
    "                    verbose=True,\n",
    "                    validation_data=(X_test, newsgroup_test.target),\n",
    "                    batch_size=10)\n",
    "loss, accuracy = model.evaluate(X_train, newsgroup_train.target, verbose=False)\n",
    "print(\"Training Accuracy: {:.4f}\".format(accuracy))\n",
    "loss, accuracy = model.evaluate(X_test, newsgroup_test.target, verbose=False)\n",
    "print(\"Testing Accuracy:  {:.4f}\".format(accuracy))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy: 0.9750\n",
      "Testing Accuracy:  0.6879\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot training & validation accuracy values\n",
    "plt.plot(history.history['accuracy'])\n",
    "plt.plot(history.history['val_accuracy'])\n",
    "plt.title('Model accuracy')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Test'], loc='upper left')\n",
    "plt.show()\n",
    "\n",
    "# Plot training & validation loss values\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('Model loss')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Test'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}