BalanceNet-1.0.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## BalanceNet prototype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[i] Loaded Parameters:\n",
      " 40000 35 0.2 200 \n",
      " dataset/glove/glove.twitter.27B.200d.txt\n",
      "[i] Importing Modules...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[+] Using Keras version 2.1.5\n",
      "[+] Finished Importing Modules\n",
      "[i] Reading from csv file...Done!\n",
      "[i] Found 34359 unique tokens.\n",
      "[+] Shape of data tensor: (47288, 30)\n",
      "[+] Shape of label tensor: (47288, 5)\n",
      "[i] Number of entries in each category:\n",
      "[+] Training:\n",
      " [ 7716. 13053. 12748.  3446.   868.]\n",
      "[+] Validation:\n",
      " [1927. 3244. 3190.  855.  241.]\n",
      "[i] Loading GloVe from: dataset/glove/glove.twitter.27B.200d.txt ...Done.\n",
      "[+] Proceeding with Embedding Matrix...[i] Completed!\n",
      "[i] Finished running setup.\n"
     ]
    }
   ],
   "source": [
    "%run Setup.ipynb\n",
    "%run ExtraFunctions.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Completed!\n"
     ]
    }
   ],
   "source": [
    "# second embedding matrix for non-static channel\n",
    "embedding_matrix_ns = np.random.random((len(word_index) + 1, EMBEDDING_DIM))\n",
    "for word, i in word_index.items():\n",
    "    embedding_vector = embeddings_index.get(word)\n",
    "    if embedding_vector is not None:\n",
    "        # words not found in embedding index will be all-zeros.\n",
    "        embedding_matrix_ns[i] = embedding_vector\n",
    "print(\"Completed!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "sequence_input = Input(shape=(MAX_SEQUENCE_LENGTH,), dtype='int32')\n",
    "\n",
    "# static channel\n",
    "embedding_layer_frozen = Embedding(len(word_index) + 1,\n",
    "                            EMBEDDING_DIM,\n",
    "                            weights=[embedding_matrix],\n",
    "                            input_length=MAX_SEQUENCE_LENGTH,\n",
    "                            trainable=False)\n",
    "embedded_sequences_frozen = embedding_layer_frozen(sequence_input)\n",
    "\n",
    "# non-static channel\n",
    "embedding_layer_train = Embedding(len(word_index) + 1,\n",
    "                            EMBEDDING_DIM,\n",
    "                            weights=[embedding_matrix_ns],\n",
    "                            input_length=MAX_SEQUENCE_LENGTH,\n",
    "                            trainable=True)\n",
    "embedded_sequences_train = embedding_layer_train(sequence_input)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First Half: LSTM > CNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "l_lstm1f = Bidirectional(LSTM(6,return_sequences=True,dropout=0.3, recurrent_dropout=0.0))(embedded_sequences_frozen)\n",
    "l_lstm1t = Bidirectional(LSTM(6,return_sequences=True,dropout=0.3, recurrent_dropout=0.0))(embedded_sequences_train)\n",
    "l_lstm1 = Concatenate(axis=1)([l_lstm1f, l_lstm1t])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "l_conv_2 = Conv1D(filters=24,kernel_size=2,activation='relu')(l_lstm1)\n",
    "l_conv_2 = Dropout(0.3)(l_conv_2)\n",
    "l_conv_3 = Conv1D(filters=24,kernel_size=3,activation='relu')(l_lstm1)\n",
    "l_conv_3 = Dropout(0.3)(l_conv_3)\n",
    "\n",
    "l_conv_5 = Conv1D(filters=24,kernel_size=5,activation='relu',)(l_lstm1)\n",
    "l_conv_5 = Dropout(0.3)(l_conv_5)\n",
    "l_conv_6 = Conv1D(filters=24,kernel_size=6,activation='relu',kernel_regularizer=regularizers.l2(0.0001))(l_lstm1)\n",
    "l_conv_6 = Dropout(0.3)(l_conv_6)\n",
    "\n",
    "l_conv_8 = Conv1D(filters=24,kernel_size=8,activation='relu',kernel_regularizer=regularizers.l2(0.0001))(l_lstm1)\n",
    "l_conv_8 = Dropout(0.3)(l_conv_8)\n",
    "\n",
    "conv_1 = [l_conv_6,l_conv_5, l_conv_8,l_conv_2,l_conv_3]\n",
    "\n",
    "l_lstm_c = Concatenate(axis=1)(conv_1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Second Half: CNN > LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "l_conv_4f = Conv1D(filters=12,kernel_size=4,activation='relu',kernel_regularizer=regularizers.l2(0.0001))(embedded_sequences_frozen)\n",
    "l_conv_4f = Dropout(0.3)(l_conv_4f)\n",
    "l_conv_4t = Conv1D(filters=12,kernel_size=4,activation='relu',kernel_regularizer=regularizers.l2(0.0001))(embedded_sequences_train)\n",
    "l_conv_4t = Dropout(0.3)(l_conv_4t)\n",
    "\n",
    "l_conv_3f = Conv1D(filters=12,kernel_size=3,activation='relu',)(embedded_sequences_frozen)\n",
    "l_conv_3f = Dropout(0.3)(l_conv_3f)\n",
    "l_conv_3t = Conv1D(filters=12,kernel_size=3,activation='relu',)(embedded_sequences_train)\n",
    "l_conv_3t = Dropout(0.3)(l_conv_3t)\n",
    "\n",
    "l_conv_2f = Conv1D(filters=12,kernel_size=2,activation='relu')(embedded_sequences_frozen)\n",
    "l_conv_2f = Dropout(0.3)(l_conv_2f)\n",
    "l_conv_2t = Conv1D(filters=12,kernel_size=2,activation='relu')(embedded_sequences_train)\n",
    "l_conv_2t = Dropout(0.3)(l_conv_2t)\n",
    "\n",
    "conv_2 = [l_conv_4f, l_conv_4t,l_conv_3f, l_conv_3t, l_conv_2f, l_conv_2t]\n",
    "\n",
    "l_merge_2 = Concatenate(axis=1)(conv_2)\n",
    "l_c_lstm = Bidirectional(LSTM(12,return_sequences=True,dropout=0.3, recurrent_dropout=0.0))(l_merge_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Combine both halfs of the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "l_merge = Concatenate(axis=1)([l_lstm_c, l_c_lstm])\n",
    "l_pool = MaxPooling1D(4)(l_merge)\n",
    "l_drop = Dropout(0.5)(l_pool)\n",
    "l_flat = Flatten()(l_drop)\n",
    "l_dense = Dense(26, activation='relu')(l_flat)\n",
    "preds = Dense(5, activation='softmax')(l_dense)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Model(sequence_input, preds)\n",
    "adadelta = optimizers.Adadelta(lr=0.9, rho=0.95, epsilon=None, decay=0.002)\n",
    "lr_metric = get_lr_metric(adadelta)\n",
    "model.compile(loss='categorical_crossentropy',\n",
    "              optimizer=adadelta,\n",
    "              metrics=['acc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rm: cannot remove 'logs': No such file or directory\r\n"
     ]
    }
   ],
   "source": [
    "!rm -r logs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "tensorboard = callbacks.TensorBoard(log_dir='./logs', histogram_freq=0, batch_size=16, write_grads=True , write_graph=True)\n",
    "model_checkpoints = callbacks.ModelCheckpoint(\"checkpoint-{val_loss:.3f}.h5\", monitor='val_loss', verbose=0, save_best_only=True, save_weights_only=False, mode='auto', period=0)\n",
    "lr_schedule = callbacks.LearningRateScheduler(initial_boost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            (None, 30)           0                                            \n",
      "__________________________________________________________________________________________________\n",
      "embedding_1 (Embedding)         (None, 30, 200)      6872000     input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "embedding_2 (Embedding)         (None, 30, 200)      6872000     input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "bidirectional_1 (Bidirectional) (None, 30, 12)       9936        embedding_1[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "bidirectional_2 (Bidirectional) (None, 30, 12)       9936        embedding_2[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1 (Concatenate)     (None, 60, 12)       0           bidirectional_1[0][0]            \n",
      "                                                                 bidirectional_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_6 (Conv1D)               (None, 27, 12)       9612        embedding_1[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_7 (Conv1D)               (None, 27, 12)       9612        embedding_2[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_8 (Conv1D)               (None, 28, 12)       7212        embedding_1[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_9 (Conv1D)               (None, 28, 12)       7212        embedding_2[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_10 (Conv1D)              (None, 29, 12)       4812        embedding_1[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_11 (Conv1D)              (None, 29, 12)       4812        embedding_2[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_4 (Conv1D)               (None, 55, 24)       1752        concatenate_1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_3 (Conv1D)               (None, 56, 24)       1464        concatenate_1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_5 (Conv1D)               (None, 53, 24)       2328        concatenate_1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_1 (Conv1D)               (None, 59, 24)       600         concatenate_1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_2 (Conv1D)               (None, 58, 24)       888         concatenate_1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dropout_6 (Dropout)             (None, 27, 12)       0           conv1d_6[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "dropout_7 (Dropout)             (None, 27, 12)       0           conv1d_7[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "dropout_8 (Dropout)             (None, 28, 12)       0           conv1d_8[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "dropout_9 (Dropout)             (None, 28, 12)       0           conv1d_9[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "dropout_10 (Dropout)            (None, 29, 12)       0           conv1d_10[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "dropout_11 (Dropout)            (None, 29, 12)       0           conv1d_11[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "dropout_4 (Dropout)             (None, 55, 24)       0           conv1d_4[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "dropout_3 (Dropout)             (None, 56, 24)       0           conv1d_3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "dropout_5 (Dropout)             (None, 53, 24)       0           conv1d_5[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "dropout_1 (Dropout)             (None, 59, 24)       0           conv1d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "dropout_2 (Dropout)             (None, 58, 24)       0           conv1d_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_3 (Concatenate)     (None, 168, 12)      0           dropout_6[0][0]                  \n",
      "                                                                 dropout_7[0][0]                  \n",
      "                                                                 dropout_8[0][0]                  \n",
      "                                                                 dropout_9[0][0]                  \n",
      "                                                                 dropout_10[0][0]                 \n",
      "                                                                 dropout_11[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_2 (Concatenate)     (None, 281, 24)      0           dropout_4[0][0]                  \n",
      "                                                                 dropout_3[0][0]                  \n",
      "                                                                 dropout_5[0][0]                  \n",
      "                                                                 dropout_1[0][0]                  \n",
      "                                                                 dropout_2[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "bidirectional_3 (Bidirectional) (None, 168, 24)      2400        concatenate_3[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_4 (Concatenate)     (None, 449, 24)      0           concatenate_2[0][0]              \n",
      "                                                                 bidirectional_3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_1 (MaxPooling1D)  (None, 112, 24)      0           concatenate_4[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dropout_12 (Dropout)            (None, 112, 24)      0           max_pooling1d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "flatten_1 (Flatten)             (None, 2688)         0           dropout_12[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "dense_1 (Dense)                 (None, 26)           69914       flatten_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "dense_2 (Dense)                 (None, 5)            135         dense_1[0][0]                    \n",
      "==================================================================================================\n",
      "Total params: 13,886,625\n",
      "Trainable params: 7,014,625\n",
      "Non-trainable params: 6,872,000\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()\n",
    "model.save('Intialmodel.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Variable *= will be deprecated. Use `var.assign(var * other)` if you want assignment to the variable value or `x = x * y` if you want a new python Tensor object.\n"
     ]
    }
   ],
   "source": [
    "model = keras.models.load_model(\"checkpoint-0.910.h5\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Time to train!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Progress:\n",
      "Train on 37831 samples, validate on 9457 samples\n",
      "Epoch 1/1\n",
      "37831/37831 [==============================] - 71s 2ms/step - loss: 0.9192 - acc: 0.6242 - val_loss: 0.8849 - val_acc: 0.6432\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'pandas' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-16-b0561f6260cb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m           callbacks=[tensorboard, model_checkpoints])\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_log\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"history-balance.csv\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'pandas' is not defined"
     ]
    }
   ],
   "source": [
    "print(\"Training Progress:\")\n",
    "model_log = model.fit(x_train, y_train, validation_data=(x_val, y_val),\n",
    "          epochs=1, batch_size=256,\n",
    "          callbacks=[tensorboard, model_checkpoints])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Testing and Inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import load_model\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "import itertools, pickle\n",
    "\n",
    "with open('tokenizer.pickle', 'rb') as handle:\n",
    "    tokenizer = pickle.load(handle)\n",
    "\n",
    "classes = [\"neutral\", \"happy\", \"sad\", \"hate\",\"anger\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BalanceNet-1.0.ipynb  _config.yml\t    images\t       README.md\r\n",
      "BalanceNet.h5\t      data.csv\t\t    logs\t       Setup.ipynb\r\n",
      "checkpoint-0.885.h5   dataset\t\t    Predict.ipynb      tokenizer.pickle\r\n",
      "checkpoint-0.910.h5   ExtraFunctions.ipynb  ProcessData.ipynb\r\n"
     ]
    }
   ],
   "source": [
    "! ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#model_test = load_model('checkpoint-0.866.h5')\n",
    "model_test = load_model('checkpoint-0.910.h5')\n",
    "Y_test = np.argmax(y_val, axis=1) # Convert one-hot to index\n",
    "y_pred = model_test.predict(x_val)\n",
    "y_pred_class = np.argmax(y_pred,axis=1)\n",
    "cnf_matrix = confusion_matrix(Y_test, y_pred_class)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "    neutral       0.51      0.36      0.42      1927\n",
      "      happy       0.66      0.72      0.69      3244\n",
      "        sad       0.62      0.72      0.67      3190\n",
      "       hate       0.88      0.69      0.77       855\n",
      "      anger       0.92      0.69      0.79       241\n",
      "\n",
      "avg / total       0.64      0.65      0.64      9457\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(Y_test, y_pred_class, target_names=classes))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 725,
       "width": 839
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_confusion_matrix(cm, labels,\n",
    "                          normalize=True,\n",
    "                          title='Confusion Matrix (Validation Set)',\n",
    "                          cmap=plt.cm.Blues):\n",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    Normalization can be applied by setting `normalize=True`.\n",
    "    \"\"\"\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        #print(\"Normalized confusion matrix\")\n",
    "    else:\n",
    "        #print('Confusion matrix, without normalization')\n",
    "        pass\n",
    "\n",
    "    #print(cm)\n",
    "\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(labels))\n",
    "    plt.xticks(tick_marks, labels, rotation=45)\n",
    "    plt.yticks(tick_marks, labels)\n",
    "\n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, format(cm[i, j], fmt),\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')\n",
    "\n",
    "plt.figure(figsize=(20,10))\n",
    "plot_confusion_matrix(cnf_matrix, labels=classes)\n",
    "\n",
    "# precision = true_pos / (true_pos + false_pos)\n",
    "# recall = true_pos / (true_pos + false_neg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "text = [\"I salute you for the bravery and sacrifice! A true hero indeed.\",\n",
    "        \"I am sorry but I trust HRW I damned sight more than the PAP and it's cronies! Off course the PAP will say that they (HRW) made things up...despite of the fact that SG is a dictator state!?\",\n",
    "        \"PAP are taking the piss again!\",\n",
    "        \"Thought he sold his kidney to buy it; Instead, he bought a kidney then bought the car Filthy rich This is why we need communism\",\n",
    "        \"Somebody needs to water Tharman's head, hair needs to be grown there\",\n",
    "        \"what a nuisance fk. a proper clean and flat footpath,,now obstructed by sharedbikes..! which idiotic MP allowed this to happen?\",\n",
    "        \"What baby bonus scheme ??? To grow up a kid in Singapore you think is easy now bo ??? Both parent need to work to grow up a kid until 21 , you think tats easy bo ??? Think la\"\n",
    "       ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I salute you for the bravery and sacrifice! A true hero indeed. \n",
      "Prediction: happy \n",
      "\n",
      "I am sorry but I trust HRW I damned sight more than the PAP and it's cronies! Off course the PAP will say that they (HRW) made things up...despite of the fact that SG is a dictator state!? \n",
      "Prediction: sad \n",
      "\n",
      "PAP are taking the piss again! \n",
      "Prediction: sad \n",
      "\n",
      "Thought he sold his kidney to buy it; Instead, he bought a kidney then bought the car Filthy rich This is why we need communism \n",
      "Prediction: hate \n",
      "\n",
      "Somebody needs to water Tharman's head, hair needs to be grown there \n",
      "Prediction: sad \n",
      "\n",
      "what a nuisance fk. a proper clean and flat footpath,,now obstructed by sharedbikes..! which idiotic MP allowed this to happen? \n",
      "Prediction: sad \n",
      "\n",
      "What baby bonus scheme ??? To grow up a kid in Singapore you think is easy now bo ??? Both parent need to work to grow up a kid until 21 , you think tats easy bo ??? Think la \n",
      "Prediction: sad \n",
      "\n"
     ]
    }
   ],
   "source": [
    "sequences_test = tokenizer.texts_to_sequences(text)\n",
    "data_int_t = pad_sequences(sequences_test, padding='pre', maxlen=(MAX_SEQUENCE_LENGTH-5))\n",
    "data_test = pad_sequences(data_int_t, padding='post', maxlen=(MAX_SEQUENCE_LENGTH))\n",
    "y_prob = model.predict(data_test)\n",
    "for n, prediction in enumerate(y_prob):\n",
    "    pred = y_prob.argmax(axis=-1)[n]\n",
    "    print(text[n],\"\\nPrediction:\",classes[pred],\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   1, 6097,    5,   12,    3,    6, 4841,    4,  515, 1476, 1238])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(sequences_test[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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