Classifiers.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow import keras\n",
    "import matplotlib.pyplot as plt\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import * \n",
    "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
    "from collections import Counter\n",
    "import numpy as np\n",
    "from tensorflow.keras.preprocessing.text import Tokenizer\n",
    "\n",
    "## extra imports to set GPU options\n",
    "import tensorflow as tf\n",
    " \n",
    "# TensorFlow wizardry\n",
    "config = tf.ConfigProto()\n",
    " \n",
    "# Don't pre-allocate memory; allocate as-needed\n",
    "config.gpu_options.allow_growth = True\n",
    " \n",
    "# Only allow a total of half the GPU memory to be allocated\n",
    "config.gpu_options.per_process_gpu_memory_fraction = 0.33\n",
    " \n",
    "# Create a session with the above options specified.\n",
    "session = tf.Session(config=config)\n",
    "tf.keras.backend.set_session(session)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def embeddings(word_index, input_dim, input_length, path=\"/home/hellrich/keras-test/embeddings/glove.6B.\", suffix=\"d.txt\",\n",
    "               embedding_dim=200, trainable=False):\n",
    "    def load_embedding_text(dim):\n",
    "        w2e = {}\n",
    "        for line in open(path+str(dim)+suffix):\n",
    "            line = line.strip().split(\" \")\n",
    "            if len(line) > 2:\n",
    "                w = line[0]\n",
    "                e = np.asarray(line[1:], dtype=\"float32\")\n",
    "                w2e[w] = e\n",
    "        return w2e\n",
    "    \n",
    "    index2word = {v : k for k, v in word_index.items()}\n",
    "    weights = np.zeros((input_dim, embedding_dim))\n",
    "    loaded = load_embedding_text(embedding_dim)\n",
    "    entries = 0\n",
    "    for i in range(1,input_dim):\n",
    "        w = index2word[i]\n",
    "        if w in loaded:\n",
    "            weights[i] = np.array(loaded[w])\n",
    "            entries += 1\n",
    "    print(\"entries\", entries, \"found for\", input_dim, \"words in embedding model\")\n",
    "    return Embedding(input_dim, embedding_dim, weights=[weights], \n",
    "                     input_length=input_length, trainable=trainable)  #input_dim + 1 ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from enum import Enum\n",
    "class ToProcess(Enum):\n",
    "    TEXT_ONLY=1\n",
    "    TITLE_ONLY=2\n",
    "    BOTH=3\n",
    "\n",
    "def read_data(data_file = \"20180622processedGoldStandardTopics.tsv\", preprocess=True, \n",
    "              shuffle=False, maxlen=None, vocab_size=None, bow_mode=None, to_process=ToProcess.BOTH,\n",
    "              fold_repeats=True, return_word_index=False, sentence_wise=None):\n",
    "    texts = []\n",
    "    labels = []\n",
    "    with open(data_file) as data_file:\n",
    "        line_number = 0\n",
    "        for line in data_file:\n",
    "            if line_number > 0:\n",
    "                number, trec_topic_number, trec_doc_id, pm_rel_desc, disease_desc, \\\n",
    "                gene1_annotation_desc, gene1_name, gene2_annotation_desc, \\\n",
    "                gene2_name, gene3_annotation_desc, gene3_name, demographics_desc, \\\n",
    "                other_desc, relevance_score, title, abstract, major_mesh, \\\n",
    "                minor_mesh, trec_topic_disease, trec_topic_age, trec_topic_sex, \\\n",
    "                trec_topic_other1, trec_topic_other2, trec_topic_other3 = line.split(\"\\t\")\n",
    "                if to_process == ToProcess.BOTH:\n",
    "                    text = title.replace(\"[\",\"\").replace(\"]\",\"\") + \" \" + abstract\n",
    "                elif to_process == ToProcess.TEXT_ONLY:\n",
    "                    text = abstract\n",
    "                elif to_process == ToProcess.TITLE_ONLY:\n",
    "                    text = title.replace(\"[\",\"\").replace(\"]\",\"\")\n",
    "                else:\n",
    "                    raise Exception(\"not implemented\")\n",
    "                if preprocess:\n",
    "                    text = text.lower().replace(\". \", \" . \").replace(\", \", \" , \").\\\n",
    "                        replace(\"? \", \" ? \").replace(\"! \", \" ! \")\n",
    "                texts.append(text)\n",
    "                \n",
    "                if pm_rel_desc == 'Human PM' or pm_rel_desc == 'Animal PM':\n",
    "                    labels.append(1.)\n",
    "                else:\n",
    "                    labels.append(0.)\n",
    "            line_number += 1\n",
    "               \n",
    "       \n",
    "    if fold_repeats:\n",
    "        text2indices = {}\n",
    "        for i, text in enumerate(texts):\n",
    "            if text in text2indices:\n",
    "                text2indices[text].append(i)\n",
    "            else:\n",
    "                text2indices[text] = [i]\n",
    "        for text, indices in text2indices.items():\n",
    "            if len(indices) > 1:\n",
    "                is_pm = False\n",
    "                for i in indices:\n",
    "                    if labels[i] == 1.:\n",
    "                        is_pm = True\n",
    "                #remove all but first\n",
    "                for i in indices[1:]:\n",
    "                    labels[i] = None\n",
    "                    texts[i] = None\n",
    "                if is_pm:\n",
    "                    labels[indices[0]] = 1.\n",
    "        texts = [t for t in texts if t is not None]\n",
    "        labels = [x for x in labels if x is not None]\n",
    "                        \n",
    "    labels = np.array(labels)  \n",
    "    \n",
    "    #see https://machinelearningmastery.com/prepare-text-data-deep-learning-keras/\n",
    "    t = Tokenizer(num_words=vocab_size)\n",
    "    t.fit_on_texts(texts)\n",
    "    if bow_mode:\n",
    "        #modes:\n",
    "        #‘binary‘: Whether or not each word is present in the document. This is the default.\n",
    "        #‘count‘: The count of each word in the document.\n",
    "        #‘tfidf‘: The Text Frequency-Inverse DocumentFrequency (TF-IDF) scoring for each word in the document.\n",
    "        #‘freq‘: The frequency of each word as a ratio of words within each document.\n",
    "        texts = t.texts_to_matrix(texts, mode=bow_mode)\n",
    "        if sentence_wise:\n",
    "            raise Exception(\"bow_mode and sentence_wise are incompatible\")\n",
    "        if maxlen:\n",
    "            texts = keras.preprocessing.sequence.pad_sequences(texts, maxlen=maxlen)\n",
    "    else:\n",
    "        if sentence_wise:\n",
    "            if not preprocess:\n",
    "                raise Exception(\"preprocess required\")\n",
    "            max_sentences = sentence_wise[0]\n",
    "            max_sentence_length = sentence_wise[1]\n",
    "            list_of_list_of_sentences = [text.split(\" . \") for text in texts]\n",
    "            #trimming number of sentences\n",
    "            trimmed_list_of_list_of_sentences = []\n",
    "            for x in list_of_list_of_sentences:\n",
    "                if len(x) > max_sentences:\n",
    "                    trimmed_list_of_list_of_sentences.append(x[:max_sentences])\n",
    "                else:\n",
    "                    if len(x) < max_sentences:\n",
    "                        to_pad = max_sentences - len(x)\n",
    "                        for i in range(to_pad):\n",
    "                            x.append([\" \"])\n",
    "                    trimmed_list_of_list_of_sentences.append(x)\n",
    "            list_of_sentence_matrices = [t.texts_to_sequences(x) for x in trimmed_list_of_list_of_sentences]\n",
    "            #trimming sentences for length\n",
    "            trimmed_list_of_sentence_matrices = [keras.preprocessing.sequence.pad_sequences(\n",
    "                                x, maxlen = max_sentence_length) for x in list_of_sentence_matrices]\n",
    "            texts = np.asarray(trimmed_list_of_sentence_matrices)\n",
    "        else:\n",
    "            texts = t.texts_to_sequences(texts)\n",
    "            if maxlen:\n",
    "                texts = keras.preprocessing.sequence.pad_sequences(texts, maxlen=maxlen)\n",
    "    \n",
    "    if shuffle:\n",
    "        perm = np.random.permutation(len(texts))\n",
    "        texts = texts[perm]\n",
    "        labels = labels[perm]\n",
    "    if return_word_index:\n",
    "        return texts, labels, t.word_index\n",
    "    else:\n",
    "        return texts, labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "class FineTuneLayer(keras.callbacks.Callback):\n",
    "    ''' \n",
    "        n_epoch = no. of epochs after decay should happen.\n",
    "        decay = decay value\n",
    "        lyaer_nr = layer to unfreeze\n",
    "        verbose = if non-zero there will be a message\n",
    "    '''  \n",
    "    def __init__(self, n_epoch, decay, layer_nr, verbose=1):\n",
    "        super(FineTuneLayer, self).__init__()\n",
    "        self.n_epoch = n_epoch\n",
    "        self.decay = decay\n",
    "        self.layer_nr = layer_nr\n",
    "        self.verbose = verbose\n",
    "\n",
    "    def on_epoch_begin(self, epoch, logs={}):\n",
    "        if epoch == self.n_epoch:\n",
    "            lr = float(keras.backend.get_value(self.model.optimizer.lr)) * self.decay\n",
    "            keras.backend.set_value(self.model.optimizer.lr, lr)\n",
    "            self.model.layers[self.layer_nr].trainable = True\n",
    "            if self.verbose:\n",
    "                print(\"Finetuning layer\", self.layer_nr, \"decreased learning rate with\", self.decay)\n",
    "            \n",
    "\n",
    "def experiment(model, texts, labels, max_epochs=30, patience=4, \n",
    "               fine_tune_embeddings=False, validation_split=0.1, learning_rate=0.001):\n",
    "    model.compile(loss='binary_crossentropy',\n",
    "                optimizer=keras.optimizers.Adam(learning_rate),\n",
    "                metrics=['acc'])\n",
    "    \n",
    "    callbacks = []\n",
    "\n",
    "    if fine_tune_embeddings:\n",
    "        callbacks.append(FineTuneLayer(fine_tune_embeddings[0], fine_tune_embeddings[1], 1))\n",
    "    \n",
    "    if patience:\n",
    "        if patience > 2:\n",
    "            callbacks.append(keras.callbacks.ReduceLROnPlateau(\n",
    "                    monitor='val_loss',\n",
    "                    factor=0.1,\n",
    "                    patience=patience - 2,\n",
    "                    verbose=1\n",
    "                 ))\n",
    "        callbacks.append(keras.callbacks.EarlyStopping(\n",
    "                monitor=\"val_loss\",\n",
    "                patience=patience,\n",
    "                verbose=1\n",
    "             ))\n",
    "    \n",
    "    history = model.fit(texts, labels, validation_split=validation_split, epochs=max_epochs, \n",
    "                        batch_size=50, verbose=2, callbacks=callbacks).history\n",
    "    \n",
    "    epochs = range(1, len(history[\"acc\"]) + 1)\n",
    "    plt.plot(epochs, history[\"val_acc\"], label=\"val accurarcy\")\n",
    "    plt.plot(epochs, history[\"val_loss\"], label=\"val loss\")\n",
    "    plt.xlabel(\"Epochs\")\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "    \n",
    "maxlen = 300\n",
    "vocab_size = 40000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 16839 samples, validate on 1871 samples\n",
      "Epoch 1/30\n",
      " - 5s - loss: 0.5770 - acc: 0.7060 - val_loss: 0.5788 - val_acc: 0.7018\n",
      "Epoch 2/30\n",
      " - 4s - loss: 0.4791 - acc: 0.7813 - val_loss: 0.5738 - val_acc: 0.6970\n",
      "Epoch 3/30\n",
      " - 4s - loss: 0.4311 - acc: 0.8135 - val_loss: 0.6099 - val_acc: 0.6788\n",
      "Epoch 4/30\n",
      " - 4s - loss: 0.3962 - acc: 0.8376 - val_loss: 0.5945 - val_acc: 0.6873\n",
      "\n",
      "Epoch 00004: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
      "Epoch 5/30\n",
      " - 4s - loss: 0.3637 - acc: 0.8615 - val_loss: 0.6046 - val_acc: 0.6804\n",
      "Epoch 6/30\n",
      " - 4s - loss: 0.3609 - acc: 0.8636 - val_loss: 0.6057 - val_acc: 0.6799\n",
      "\n",
      "Epoch 00006: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.\n",
      "Epoch 00006: early stopping\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 16839 samples, validate on 1871 samples\n",
      "Epoch 1/30\n",
      " - 4s - loss: 0.7250 - acc: 0.6360 - val_loss: 0.6624 - val_acc: 0.6921\n",
      "Epoch 2/30\n",
      " - 4s - loss: 0.6511 - acc: 0.6980 - val_loss: 0.6501 - val_acc: 0.7002\n",
      "Epoch 3/30\n",
      " - 3s - loss: 0.6324 - acc: 0.7131 - val_loss: 0.6392 - val_acc: 0.7039\n",
      "Epoch 4/30\n",
      " - 4s - loss: 0.6201 - acc: 0.7175 - val_loss: 0.6337 - val_acc: 0.7018\n",
      "Epoch 5/30\n",
      " - 4s - loss: 0.6111 - acc: 0.7239 - val_loss: 0.6294 - val_acc: 0.6986\n",
      "Epoch 6/30\n",
      " - 3s - loss: 0.6041 - acc: 0.7257 - val_loss: 0.6254 - val_acc: 0.6986\n",
      "Epoch 7/30\n",
      " - 4s - loss: 0.5986 - acc: 0.7284 - val_loss: 0.6265 - val_acc: 0.6948\n",
      "Epoch 8/30\n",
      " - 4s - loss: 0.5940 - acc: 0.7312 - val_loss: 0.6233 - val_acc: 0.6991\n",
      "Epoch 9/30\n",
      " - 4s - loss: 0.5902 - acc: 0.7322 - val_loss: 0.6229 - val_acc: 0.6970\n",
      "Epoch 10/30\n",
      " - 3s - loss: 0.5871 - acc: 0.7318 - val_loss: 0.6226 - val_acc: 0.6964\n",
      "Epoch 11/30\n",
      " - 3s - loss: 0.5843 - acc: 0.7334 - val_loss: 0.6220 - val_acc: 0.6975\n",
      "Epoch 12/30\n",
      " - 4s - loss: 0.5818 - acc: 0.7352 - val_loss: 0.6234 - val_acc: 0.6975\n",
      "Epoch 13/30\n",
      " - 4s - loss: 0.5797 - acc: 0.7356 - val_loss: 0.6244 - val_acc: 0.6970\n",
      "\n",
      "Epoch 00013: ReduceLROnPlateau reducing learning rate to 9.999999747378752e-06.\n",
      "Epoch 14/30\n",
      " - 3s - loss: 0.5764 - acc: 0.7370 - val_loss: 0.6222 - val_acc: 0.6970\n",
      "Epoch 15/30\n",
      " - 4s - loss: 0.5759 - acc: 0.7373 - val_loss: 0.6218 - val_acc: 0.6970\n",
      "Epoch 16/30\n",
      " - 4s - loss: 0.5757 - acc: 0.7373 - val_loss: 0.6215 - val_acc: 0.6980\n",
      "Epoch 17/30\n",
      " - 4s - loss: 0.5755 - acc: 0.7375 - val_loss: 0.6214 - val_acc: 0.6980\n",
      "Epoch 18/30\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-28-fbe78cb87707>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     14\u001b[0m                 \u001b[0mkernel_regularizer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mregularizers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0ml1_l2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ml1\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.001\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ml2\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.001\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m                 input_dim=vocab_size))\n\u001b[0;32m---> 16\u001b[0;31m \u001b[0mexperiment\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbow_texts\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbow_labels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlearning_rate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.0001\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-26-d3f2ee579ffc>\u001b[0m in \u001b[0;36mexperiment\u001b[0;34m(model, texts, labels, max_epochs, patience, fine_tune_embeddings, validation_split, learning_rate)\u001b[0m\n\u001b[1;32m     48\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     49\u001b[0m     history = model.fit(texts, labels, validation_split=validation_split, epochs=max_epochs, \n\u001b[0;32m---> 50\u001b[0;31m                         batch_size=50, verbose=2, callbacks=callbacks).history\n\u001b[0m\u001b[1;32m     51\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     52\u001b[0m     \u001b[0mepochs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"acc\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/bin/anaconda3/envs/keras-tensor-old-cudn/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)\u001b[0m\n\u001b[1;32m   1346\u001b[0m           \u001b[0minitial_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minitial_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1347\u001b[0m           \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1348\u001b[0;31m           validation_steps=validation_steps)\n\u001b[0m\u001b[1;32m   1349\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1350\u001b[0m   def evaluate(self,\n",
      "\u001b[0;32m~/bin/anaconda3/envs/keras-tensor-old-cudn/lib/python3.6/site-packages/tensorflow/python/keras/engine/training_arrays.py\u001b[0m in \u001b[0;36mfit_loop\u001b[0;34m(model, inputs, targets, sample_weights, batch_size, epochs, verbose, callbacks, val_inputs, val_targets, val_sample_weights, shuffle, callback_metrics, initial_epoch, steps_per_epoch, validation_steps)\u001b[0m\n\u001b[1;32m    239\u001b[0m             \u001b[0mins_batch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mslice_arrays\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_ids\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mins\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    240\u001b[0m           \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 241\u001b[0;31m             \u001b[0mins_batch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mslice_arrays\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_ids\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    242\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    243\u001b[0m           raise TypeError('TypeError while preparing batch. '\n",
      "\u001b[0;32m~/bin/anaconda3/envs/keras-tensor-old-cudn/lib/python3.6/site-packages/tensorflow/python/keras/utils/generic_utils.py\u001b[0m in \u001b[0;36mslice_arrays\u001b[0;34m(arrays, start, stop)\u001b[0m\n\u001b[1;32m    503\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'shape'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    504\u001b[0m         \u001b[0mstart\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstart\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 505\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;32mNone\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0marrays\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    506\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    507\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;32mNone\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mstop\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0marrays\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/bin/anaconda3/envs/keras-tensor-old-cudn/lib/python3.6/site-packages/tensorflow/python/keras/utils/generic_utils.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    503\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'shape'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    504\u001b[0m         \u001b[0mstart\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstart\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 505\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;32mNone\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0marrays\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    506\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    507\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;32mNone\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mstop\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0marrays\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "#logistic regression, see https://gist.github.com/fchollet/b7507f373a3446097f26840330c1c378\n",
    "bow_texts, bow_labels = read_data(vocab_size=vocab_size, bow_mode=\"binary\")\n",
    "#texts, labels = read_data(vocab_size=vocab_size, bow_mode=\"binary\", to_process=ToProcess.TITLE_ONLY)\n",
    "\n",
    "model = Sequential()\n",
    "#worked better without regularizer: kernel_regularizer=keras.regularizers.l1_l2(l1=0.01, l2=0.01),\n",
    "model.add(Dense(1, activation='sigmoid', \n",
    "                input_dim=vocab_size))\n",
    "\n",
    "experiment(model, bow_texts, bow_labels)\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Dense(1, activation='sigmoid', \n",
    "                kernel_regularizer=keras.regularizers.l1_l2(l1=0.001, l2=0.001),\n",
    "                input_dim=vocab_size))\n",
    "experiment(model, bow_texts, bow_labels, learning_rate=0.0001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      " - 5s - loss: 0.5752 - acc: 0.7076\n",
      "Epoch 2/5\n",
      " - 4s - loss: 0.4805 - acc: 0.7769\n",
      "Epoch 3/5\n",
      " - 4s - loss: 0.4339 - acc: 0.8075\n",
      "Epoch 4/5\n",
      " - 4s - loss: 0.4004 - acc: 0.8304\n",
      "Epoch 5/5\n",
      " - 4s - loss: 0.3729 - acc: 0.8509\n",
      "18710/18710 [==============================] - 3s 186us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.34352686213402106, 0.8718332442862665]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#reclassification for logistic regression, see https://gist.github.com/fchollet/b7507f373a3446097f26840330c1c378\n",
    "bow_texts, bow_labels = read_data(vocab_size=vocab_size, bow_mode=\"binary\")\n",
    "#texts, labels = read_data(vocab_size=vocab_size, bow_mode=\"binary\", to_process=ToProcess.TITLE_ONLY)\n",
    "\n",
    "model = Sequential()\n",
    "#worked better without regularizer: kernel_regularizer=keras.regularizers.l1_l2(l1=0.01, l2=0.01),\n",
    "model.add(Dense(1, activation='sigmoid', \n",
    "                input_dim=vocab_size))\n",
    "\n",
    "model.compile(loss='binary_crossentropy',\n",
    "                optimizer=\"adam\",\n",
    "                metrics=['acc'])\n",
    "    \n",
    "model.fit(bow_texts, bow_labels, epochs=5, \n",
    "                        batch_size=50, verbose=2)\n",
    "\n",
    "model.evaluate(bow_texts, bow_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 16839 samples, validate on 1871 samples\n",
      "Epoch 1/30\n",
      " - 4s - loss: 0.5515 - acc: 0.7190 - val_loss: 0.6067 - val_acc: 0.6788\n",
      "Epoch 2/30\n",
      " - 3s - loss: 0.4336 - acc: 0.8069 - val_loss: 0.5937 - val_acc: 0.6889\n",
      "Epoch 3/30\n",
      " - 3s - loss: 0.3751 - acc: 0.8474 - val_loss: 0.6236 - val_acc: 0.6697\n",
      "Epoch 4/30\n",
      " - 3s - loss: 0.3340 - acc: 0.8727 - val_loss: 0.6298 - val_acc: 0.6718\n",
      "\n",
      "Epoch 00004: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
      "Epoch 5/30\n",
      " - 3s - loss: 0.2901 - acc: 0.9050 - val_loss: 0.6379 - val_acc: 0.6627\n",
      "Epoch 6/30\n",
      " - 3s - loss: 0.2868 - acc: 0.9068 - val_loss: 0.6339 - val_acc: 0.6670\n",
      "\n",
      "Epoch 00006: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.\n",
      "Epoch 00006: early stopping\n"
     ]
    },
    {
     "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": [
    "#logistic regression with 2 class softmax instead, see https://gist.github.com/fchollet/b7507f373a3446097f26840330c1c378\n",
    "#texts, labels = read_data(vocab_size=vocab_size, bow_mode=\"binary\", to_process=ToProcess.TITLE_ONLY)\n",
    "labels = []\n",
    "for b in bow_labels:\n",
    "    if b:\n",
    "        labels.append([0., 1.])\n",
    "    else:\n",
    "        labels.append([1., 0.])\n",
    "labels = np.asarray(labels)\n",
    "\n",
    "model = Sequential()\n",
    "#worked better without regularizer: kernel_regularizer=keras.regularizers.l1_l2(l1=0.01, l2=0.01),\n",
    "model.add(Dense(2, activation='softmax', \n",
    "                input_dim=vocab_size))\n",
    "\n",
    "model.compile(loss='categorical_crossentropy',\n",
    "                optimizer=\"adam\",\n",
    "                metrics=['acc'])\n",
    "callbacks = [keras.callbacks.ReduceLROnPlateau(\n",
    "                    monitor='val_loss',\n",
    "                    factor=0.1,\n",
    "                    patience=2,\n",
    "                    verbose=1\n",
    "                 ),\n",
    "        keras.callbacks.EarlyStopping(\n",
    "                monitor=\"val_loss\",\n",
    "                patience=4,\n",
    "                verbose=1\n",
    "             )]\n",
    "history = model.fit(bow_texts, labels, validation_split=0.1, epochs=30, \n",
    "                        batch_size=50, verbose=2, callbacks=callbacks).history\n",
    "    \n",
    "epochs = range(1, len(history[\"acc\"]) + 1)\n",
    "plt.plot(epochs, history[\"val_acc\"], label=\"val accurarcy\")\n",
    "plt.plot(epochs, history[\"val_loss\"], label=\"val loss\")\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#text and labels in embedding format\n",
    "texts, labels, word_index = read_data(maxlen=maxlen, vocab_size=vocab_size, return_word_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding (Embedding)        (None, 300, 20)           800000    \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 6000)              0         \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 6000)              0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1)                 6001      \n",
      "=================================================================\n",
      "Total params: 806,001\n",
      "Trainable params: 806,001\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hellrich/bin/anaconda3/envs/keras-tensor-old-cudn/lib/python3.6/site-packages/tensorflow/python/ops/gradients_impl.py:100: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.\n",
      "  \"Converting sparse IndexedSlices to a dense Tensor of unknown shape. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 16839 samples, validate on 1871 samples\n",
      "Epoch 1/30\n",
      " - 3s - loss: 0.7314 - acc: 0.5343 - val_loss: 0.6567 - val_acc: 0.5986\n",
      "Epoch 2/30\n",
      " - 2s - loss: 0.6316 - acc: 0.6386 - val_loss: 0.7972 - val_acc: 0.4912\n",
      "Epoch 3/30\n",
      " - 2s - loss: 0.5344 - acc: 0.7258 - val_loss: 0.5856 - val_acc: 0.6825\n",
      "Epoch 4/30\n",
      " - 2s - loss: 0.4662 - acc: 0.7758 - val_loss: 0.6781 - val_acc: 0.6472\n",
      "Epoch 5/30\n",
      " - 1s - loss: 0.4171 - acc: 0.8046 - val_loss: 0.6540 - val_acc: 0.6627\n",
      "\n",
      "Epoch 00005: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
      "Epoch 6/30\n",
      " - 2s - loss: 0.3515 - acc: 0.8517 - val_loss: 0.6475 - val_acc: 0.6676\n",
      "Epoch 7/30\n",
      " - 2s - loss: 0.3436 - acc: 0.8588 - val_loss: 0.5968 - val_acc: 0.6948\n",
      "\n",
      "Epoch 00007: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.\n",
      "Epoch 00007: early stopping\n"
     ]
    },
    {
     "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": [
    "#simple embedding net\n",
    "model = Sequential()\n",
    "model.add(Embedding(input_dim=vocab_size, output_dim=20, input_length=maxlen))\n",
    "model.add(Flatten())\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "model.summary()\n",
    "experiment(model, texts, labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Layer weight shape (40001, 200) not compatible with provided weight shape (40000, 200)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-65-5ff2fea5f806>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m#simple embedding with pre-trained embeddings\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSequential\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0membeddings\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mword_index\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_dim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvocab_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_length\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmaxlen\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0membedding_dim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m200\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFlatten\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mDropout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/bin/anaconda3/envs/keras-tensor-old-cudn/lib/python3.6/site-packages/tensorflow/python/keras/engine/sequential.py\u001b[0m in \u001b[0;36madd\u001b[0;34m(self, layer)\u001b[0m\n\u001b[1;32m    161\u001b[0m           \u001b[0;31m# and create the node connecting the current layer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    162\u001b[0m           \u001b[0;31m# to the input layer we just created.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 163\u001b[0;31m           \u001b[0mlayer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    164\u001b[0m           \u001b[0mset_inputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    165\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/bin/anaconda3/envs/keras-tensor-old-cudn/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs, *args, **kwargs)\u001b[0m\n\u001b[1;32m    746\u001b[0m     \u001b[0;31m# TODO(fchollet): consider enabling this with eager execution too.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    747\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'_initial_weights'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_initial_weights\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 748\u001b[0;31m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_weights\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_initial_weights\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    749\u001b[0m       \u001b[0;32mdel\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_initial_weights\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    750\u001b[0m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_post_build_cleanup\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/bin/anaconda3/envs/keras-tensor-old-cudn/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py\u001b[0m in \u001b[0;36mset_weights\u001b[0;34m(self, weights)\u001b[0m\n\u001b[1;32m   1469\u001b[0m         raise ValueError('Layer weight shape ' + str(pv.shape) +\n\u001b[1;32m   1470\u001b[0m                          \u001b[0;34m' not compatible with '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1471\u001b[0;31m                          'provided weight shape ' + str(w.shape))\n\u001b[0m\u001b[1;32m   1472\u001b[0m       \u001b[0mweight_value_tuples\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1473\u001b[0m     \u001b[0mbackend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbatch_set_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mweight_value_tuples\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Layer weight shape (40001, 200) not compatible with provided weight shape (40000, 200)"
     ]
    }
   ],
   "source": [
    "#simple embedding with pre-trained embeddings\n",
    "model = Sequential()\n",
    "model.add(embeddings(word_index, input_dim=vocab_size, input_length=maxlen, embedding_dim=200))\n",
    "model.add(Flatten())\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "#model.layers[1].trainable=False\n",
    "model.summary()\n",
    "experiment(model, texts, labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding_10 (Embedding)     (None, 300, 20)           800000    \n",
      "_________________________________________________________________\n",
      "dropout_12 (Dropout)         (None, 300, 20)           0         \n",
      "_________________________________________________________________\n",
      "conv1d_4 (Conv1D)            (None, 296, 128)          12928     \n",
      "_________________________________________________________________\n",
      "global_max_pooling1d_4 (Glob (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_20 (Dense)             (None, 128)               16512     \n",
      "_________________________________________________________________\n",
      "dropout_13 (Dropout)         (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_21 (Dense)             (None, 1)                 129       \n",
      "=================================================================\n",
      "Total params: 829,569\n",
      "Trainable params: 829,569\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hellrich/bin/anaconda3/envs/keras-tensor-old-cudn/lib/python3.6/site-packages/tensorflow/python/ops/gradients_impl.py:100: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.\n",
      "  \"Converting sparse IndexedSlices to a dense Tensor of unknown shape. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 16839 samples, validate on 1872 samples\n",
      "Epoch 1/100\n",
      " - 3s - loss: 0.6934 - acc: 0.5382 - val_loss: 0.6822 - val_acc: 0.6469\n",
      "Epoch 2/100\n",
      " - 2s - loss: 0.6801 - acc: 0.5677 - val_loss: 0.6460 - val_acc: 0.6229\n",
      "Epoch 3/100\n",
      " - 2s - loss: 0.6051 - acc: 0.6708 - val_loss: 0.6059 - val_acc: 0.6485\n",
      "Epoch 4/100\n",
      " - 2s - loss: 0.5301 - acc: 0.7247 - val_loss: 0.5828 - val_acc: 0.6538\n",
      "Epoch 5/100\n",
      " - 2s - loss: 0.4999 - acc: 0.7473 - val_loss: 0.6013 - val_acc: 0.6640\n",
      "Epoch 6/100\n",
      " - 2s - loss: 0.4811 - acc: 0.7594 - val_loss: 0.5858 - val_acc: 0.6581\n",
      "Epoch 7/100\n",
      " - 2s - loss: 0.4572 - acc: 0.7762 - val_loss: 0.5927 - val_acc: 0.6741\n",
      "Epoch 8/100\n",
      " - 2s - loss: 0.4391 - acc: 0.7864 - val_loss: 0.5973 - val_acc: 0.6768\n",
      "Epoch 9/100\n",
      " - 2s - loss: 0.4207 - acc: 0.8002 - val_loss: 0.5904 - val_acc: 0.6795\n",
      "Epoch 10/100\n",
      " - 2s - loss: 0.4019 - acc: 0.8145 - val_loss: 0.6616 - val_acc: 0.6864\n",
      "Epoch 11/100\n",
      " - 2s - loss: 0.3802 - acc: 0.8270 - val_loss: 0.6283 - val_acc: 0.6928\n",
      "Epoch 12/100\n",
      " - 2s - loss: 0.3617 - acc: 0.8363 - val_loss: 0.6165 - val_acc: 0.6918\n",
      "Epoch 13/100\n",
      " - 2s - loss: 0.3445 - acc: 0.8479 - val_loss: 0.7100 - val_acc: 0.6982\n",
      "\n",
      "Epoch 00013: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
      "Epoch 14/100\n",
      " - 2s - loss: 0.3074 - acc: 0.8677 - val_loss: 0.6829 - val_acc: 0.6960\n",
      "Epoch 00014: early stopping\n"
     ]
    },
    {
     "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": [
    "#convolution a la sven\n",
    "embedding_dimensions=20\n",
    "model = Sequential()\n",
    "model.add(Embedding(input_dim=vocab_size, output_dim=embedding_dimensions, input_length=maxlen))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Conv1D(128, 5, activation='relu'))\n",
    "#model.add(MaxPooling1D(5))\n",
    "#model.add(Conv1D(64, 5, activation='relu'))\n",
    "#model.add(MaxPooling1D(5))\n",
    "#model.add(Conv1D(32, 5, activation='relu'))\n",
    "model.add(GlobalMaxPooling1D())\n",
    "model.add(Dense(128, activation=\"relu\"))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "model.summary()\n",
    "experiment(model, texts, labels, 100, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding_24 (Embedding)     (None, 300, 200)          8000000   \n",
      "_________________________________________________________________\n",
      "dropout_7 (Dropout)          (None, 300, 200)          0         \n",
      "_________________________________________________________________\n",
      "conv1d_5 (Conv1D)            (None, 296, 128)          128128    \n",
      "_________________________________________________________________\n",
      "global_max_pooling1d_5 (Glob (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_7 (Dense)              (None, 128)               16512     \n",
      "_________________________________________________________________\n",
      "dropout_8 (Dropout)          (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_8 (Dense)              (None, 1)                 129       \n",
      "=================================================================\n",
      "Total params: 8,144,769\n",
      "Trainable params: 144,769\n",
      "Non-trainable params: 8,000,000\n",
      "_________________________________________________________________\n",
      "Train on 16839 samples, validate on 1872 samples\n",
      "Epoch 1/100\n",
      " - 3s - loss: 0.6042 - acc: 0.6627 - val_loss: 0.5746 - val_acc: 0.6581\n",
      "Epoch 2/100\n",
      " - 2s - loss: 0.5273 - acc: 0.7227 - val_loss: 0.5900 - val_acc: 0.6635\n",
      "Epoch 3/100\n",
      " - 2s - loss: 0.4908 - acc: 0.7511 - val_loss: 0.5801 - val_acc: 0.6822\n",
      "Epoch 4/100\n",
      " - 2s - loss: 0.4637 - acc: 0.7696 - val_loss: 0.5827 - val_acc: 0.6822\n",
      "Epoch 5/100\n",
      " - 2s - loss: 0.4375 - acc: 0.7916 - val_loss: 0.5455 - val_acc: 0.6998\n",
      "Epoch 6/100\n",
      " - 2s - loss: 0.4047 - acc: 0.8069 - val_loss: 0.5716 - val_acc: 0.7115\n",
      "Epoch 7/100\n",
      " - 2s - loss: 0.3791 - acc: 0.8258 - val_loss: 0.6012 - val_acc: 0.6987\n",
      "Epoch 8/100\n",
      " - 2s - loss: 0.3461 - acc: 0.8508 - val_loss: 0.6232 - val_acc: 0.7014\n",
      "Epoch 9/100\n",
      " - 2s - loss: 0.3179 - acc: 0.8607 - val_loss: 0.7055 - val_acc: 0.6912\n",
      "Epoch 10/100\n",
      " - 2s - loss: 0.2835 - acc: 0.8802 - val_loss: 0.6178 - val_acc: 0.7105\n",
      "Epoch 11/100\n",
      " - 2s - loss: 0.2571 - acc: 0.8915 - val_loss: 0.7774 - val_acc: 0.6811\n",
      "Epoch 12/100\n",
      " - 2s - loss: 0.2416 - acc: 0.8998 - val_loss: 0.8316 - val_acc: 0.6859\n",
      "Epoch 13/100\n",
      " - 2s - loss: 0.2206 - acc: 0.9090 - val_loss: 0.7342 - val_acc: 0.6987\n",
      "\n",
      "Epoch 00013: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
      "Epoch 14/100\n",
      " - 2s - loss: 0.1600 - acc: 0.9395 - val_loss: 0.8897 - val_acc: 0.7035\n",
      "Epoch 15/100\n",
      " - 2s - loss: 0.1415 - acc: 0.9461 - val_loss: 0.9271 - val_acc: 0.6902\n",
      "Epoch 00015: early stopping\n"
     ]
    },
    {
     "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": [
    "#convolution a la sven with pretrained\n",
    "embedding_dimensions=20\n",
    "model = Sequential()\n",
    "model.add(embeddings(word_index, input_dim=vocab_size, input_length=maxlen, embedding_dim=200))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Conv1D(128, 5, activation='relu'))\n",
    "#model.add(MaxPooling1D(5))\n",
    "#model.add(Conv1D(64, 5, activation='relu'))\n",
    "#model.add(MaxPooling1D(5))\n",
    "#model.add(Conv1D(32, 5, activation='relu'))\n",
    "model.add(GlobalMaxPooling1D())\n",
    "model.add(Dense(128, activation=\"relu\"))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "model.summary()\n",
    "experiment(model, texts, labels, 100, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding_27 (Embedding)     (None, 300, 20)           800000    \n",
      "_________________________________________________________________\n",
      "conv1d_10 (Conv1D)           (None, 296, 64)           6464      \n",
      "_________________________________________________________________\n",
      "global_max_pooling1d_10 (Glo (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dropout_11 (Dropout)         (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_10 (Dense)             (None, 1)                 65        \n",
      "=================================================================\n",
      "Total params: 806,529\n",
      "Trainable params: 806,529\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hellrich/bin/anaconda3/envs/keras-tensor-old-cudn/lib/python3.6/site-packages/tensorflow/python/ops/gradients_impl.py:100: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.\n",
      "  \"Converting sparse IndexedSlices to a dense Tensor of unknown shape. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 16839 samples, validate on 1872 samples\n",
      "Epoch 1/30\n",
      " - 3s - loss: 0.6833 - acc: 0.5652 - val_loss: 0.6245 - val_acc: 0.6635\n",
      "Epoch 2/30\n",
      " - 2s - loss: 0.5975 - acc: 0.6790 - val_loss: 0.5873 - val_acc: 0.6672\n",
      "Epoch 3/30\n",
      " - 2s - loss: 0.5257 - acc: 0.7329 - val_loss: 0.5740 - val_acc: 0.6624\n",
      "Epoch 4/30\n",
      " - 2s - loss: 0.4702 - acc: 0.7725 - val_loss: 0.6016 - val_acc: 0.6709\n",
      "Epoch 5/30\n",
      " - 2s - loss: 0.4243 - acc: 0.8046 - val_loss: 0.5816 - val_acc: 0.6848\n",
      "\n",
      "Epoch 00005: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
      "Epoch 6/30\n",
      " - 2s - loss: 0.3719 - acc: 0.8442 - val_loss: 0.5943 - val_acc: 0.6784\n",
      "Epoch 7/30\n",
      " - 2s - loss: 0.3639 - acc: 0.8462 - val_loss: 0.5841 - val_acc: 0.6880\n",
      "\n",
      "Epoch 00007: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.\n",
      "Epoch 00007: early stopping\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#simple convolution\n",
    "embedding_dimensions=20\n",
    "model = Sequential()\n",
    "model.add(Embedding(input_dim=vocab_size, output_dim=embedding_dimensions, input_length=maxlen))\n",
    "model.add(Conv1D(64, 5, activation='relu'))\n",
    "#model.add(MaxPooling1D(5))\n",
    "#model.add(Conv1D(64, 5, activation='relu'))\n",
    "#model.add(MaxPooling1D(5))\n",
    "#model.add(Conv1D(32, 5, activation='relu'))\n",
    "model.add(GlobalMaxPooling1D())\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "model.summary()\n",
    "experiment(model, texts, labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding_28 (Embedding)     (None, 300, 200)          8000000   \n",
      "_________________________________________________________________\n",
      "conv1d_11 (Conv1D)           (None, 296, 64)           64064     \n",
      "_________________________________________________________________\n",
      "global_max_pooling1d_11 (Glo (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dropout_12 (Dropout)         (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_11 (Dense)             (None, 1)                 65        \n",
      "=================================================================\n",
      "Total params: 8,064,129\n",
      "Trainable params: 64,129\n",
      "Non-trainable params: 8,000,000\n",
      "_________________________________________________________________\n",
      "Train on 16839 samples, validate on 1872 samples\n",
      "Epoch 1/30\n",
      " - 2s - loss: 0.6123 - acc: 0.6679 - val_loss: 0.5571 - val_acc: 0.6960\n",
      "Epoch 2/30\n",
      " - 2s - loss: 0.5086 - acc: 0.7423 - val_loss: 0.5887 - val_acc: 0.6779\n",
      "Epoch 3/30\n",
      " - 2s - loss: 0.4664 - acc: 0.7753 - val_loss: 0.6359 - val_acc: 0.6709\n",
      "\n",
      "Epoch 00003: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
      "Epoch 4/30\n",
      " - 2s - loss: 0.4039 - acc: 0.8182 - val_loss: 0.5836 - val_acc: 0.6923\n",
      "Epoch 5/30\n",
      " - 2s - loss: 0.3905 - acc: 0.8323 - val_loss: 0.5755 - val_acc: 0.6955\n",
      "\n",
      "Epoch 00005: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.\n",
      "Epoch 00005: early stopping\n"
     ]
    },
    {
     "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": [
    "#simple convolution pretrained\n",
    "embedding_dimensions=20\n",
    "model = Sequential()\n",
    "model.add(embeddings(word_index, input_dim=vocab_size, \n",
    "                    input_length=maxlen, embedding_dim=200))\n",
    "model.add(Conv1D(64, 5, activation='relu'))\n",
    "#model.add(MaxPooling1D(5))\n",
    "#model.add(Conv1D(64, 5, activation='relu'))\n",
    "#model.add(MaxPooling1D(5))\n",
    "#model.add(Conv1D(32, 5, activation='relu'))\n",
    "model.add(GlobalMaxPooling1D())\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "model.summary()\n",
    "experiment(model, texts, labels)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_2 (InputLayer)         (None, 300)               0         \n",
      "_________________________________________________________________\n",
      "embedding_12 (Embedding)     (None, 300, 20)           800000    \n",
      "_________________________________________________________________\n",
      "average_pooling1d_1 (Average (None, 15, 20)            0         \n",
      "_________________________________________________________________\n",
      "flatten_6 (Flatten)          (None, 300)               0         \n",
      "_________________________________________________________________\n",
      "dense_23 (Dense)             (None, 1)                 301       \n",
      "=================================================================\n",
      "Total params: 800,301\n",
      "Trainable params: 800,301\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hellrich/bin/anaconda3/envs/keras-tensor-old-cudn/lib/python3.6/site-packages/tensorflow/python/ops/gradients_impl.py:100: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.\n",
      "  \"Converting sparse IndexedSlices to a dense Tensor of unknown shape. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 16839 samples, validate on 1872 samples\n",
      "Epoch 1/20\n",
      " - 2s - loss: 0.6790 - acc: 0.5572 - val_loss: 0.6413 - val_acc: 0.6597\n",
      "Epoch 2/20\n",
      " - 1s - loss: 0.6425 - acc: 0.6426 - val_loss: 0.6150 - val_acc: 0.6806\n",
      "Epoch 3/20\n",
      " - 1s - loss: 0.5844 - acc: 0.7155 - val_loss: 0.5972 - val_acc: 0.6955\n",
      "Epoch 4/20\n",
      " - 1s - loss: 0.5259 - acc: 0.7512 - val_loss: 0.5806 - val_acc: 0.6907\n",
      "Epoch 5/20\n",
      " - 1s - loss: 0.4832 - acc: 0.7767 - val_loss: 0.5919 - val_acc: 0.6896\n",
      "Epoch 6/20\n",
      " - 1s - loss: 0.4501 - acc: 0.7950 - val_loss: 0.5885 - val_acc: 0.6928\n",
      "Epoch 7/20\n",
      " - 1s - loss: 0.4221 - acc: 0.8121 - val_loss: 0.6252 - val_acc: 0.6704\n",
      "\n",
      "Epoch 00007: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
      "Epoch 8/20\n",
      " - 1s - loss: 0.4005 - acc: 0.8298 - val_loss: 0.5976 - val_acc: 0.6864\n",
      "Epoch 00008: early stopping\n"
     ]
    },
    {
     "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": [
    "#mean of embeddings\n",
    "    \n",
    "embedding_dimensions=20\n",
    "sequence_input = Input(shape=(maxlen,), dtype='int32')\n",
    "embedded_sequences = Embedding(input_dim=vocab_size, output_dim=embedding_dimensions)(sequence_input)\n",
    "l_mean = Flatten()(AveragePooling1D(20)(embedded_sequences))\n",
    "\n",
    "preds = Dense(1, activation='sigmoid')(l_mean)\n",
    "model = keras.Model(sequence_input, preds)\n",
    "model.summary()\n",
    "experiment(model, texts, labels, 20)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_3 (InputLayer)         (None, 300)               0         \n",
      "_________________________________________________________________\n",
      "embedding_13 (Embedding)     (None, 300, 20)           800000    \n",
      "_________________________________________________________________\n",
      "global_average_pooling1d (Gl (None, 20)                0         \n",
      "_________________________________________________________________\n",
      "dropout_15 (Dropout)         (None, 20)                0         \n",
      "_________________________________________________________________\n",
      "dense_24 (Dense)             (None, 1)                 21        \n",
      "=================================================================\n",
      "Total params: 800,021\n",
      "Trainable params: 800,021\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hellrich/bin/anaconda3/envs/keras-tensor-old-cudn/lib/python3.6/site-packages/tensorflow/python/ops/gradients_impl.py:100: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.\n",
      "  \"Converting sparse IndexedSlices to a dense Tensor of unknown shape. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 16839 samples, validate on 1872 samples\n",
      "Epoch 1/30\n",
      " - 2s - loss: 0.7057 - acc: 0.5260 - val_loss: 0.6449 - val_acc: 0.6522\n",
      "Epoch 2/30\n",
      " - 1s - loss: 0.6662 - acc: 0.5900 - val_loss: 0.6408 - val_acc: 0.6709\n",
      "Epoch 3/30\n",
      " - 1s - loss: 0.6443 - acc: 0.6238 - val_loss: 0.6259 - val_acc: 0.6859\n",
      "Epoch 4/30\n",
      " - 1s - loss: 0.6231 - acc: 0.6513 - val_loss: 0.6217 - val_acc: 0.6838\n",
      "Epoch 5/30\n",
      " - 1s - loss: 0.6029 - acc: 0.6741 - val_loss: 0.5929 - val_acc: 0.6960\n",
      "Epoch 6/30\n",
      " - 1s - loss: 0.5791 - acc: 0.6946 - val_loss: 0.5897 - val_acc: 0.6987\n",
      "Epoch 7/30\n",
      " - 1s - loss: 0.5559 - acc: 0.7166 - val_loss: 0.5814 - val_acc: 0.7014\n",
      "Epoch 8/30\n",
      " - 1s - loss: 0.5399 - acc: 0.7261 - val_loss: 0.5887 - val_acc: 0.6982\n",
      "Epoch 9/30\n",
      " - 1s - loss: 0.5260 - acc: 0.7336 - val_loss: 0.5839 - val_acc: 0.6982\n",
      "Epoch 10/30\n",
      " - 1s - loss: 0.5099 - acc: 0.7492 - val_loss: 0.5798 - val_acc: 0.7046\n",
      "Epoch 11/30\n",
      " - 1s - loss: 0.4966 - acc: 0.7594 - val_loss: 0.5890 - val_acc: 0.6987\n",
      "Epoch 12/30\n",
      " - 1s - loss: 0.4846 - acc: 0.7679 - val_loss: 0.5841 - val_acc: 0.6939\n",
      "Epoch 13/30\n",
      " - 1s - loss: 0.4756 - acc: 0.7704 - val_loss: 0.5872 - val_acc: 0.6960\n",
      "\n",
      "Epoch 00013: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
      "Epoch 14/30\n",
      " - 1s - loss: 0.4660 - acc: 0.7775 - val_loss: 0.5887 - val_acc: 0.6960\n",
      "Epoch 00014: early stopping\n"
     ]
    },
    {
     "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": [
    "#mean of embeddings + drop\n",
    "    \n",
    "embedding_dimensions=20\n",
    "sequence_input = Input(shape=(maxlen,), dtype='int32')\n",
    "embedded_sequences = Embedding(input_dim=vocab_size, output_dim=embedding_dimensions)(sequence_input)\n",
    "l_mean = Dropout(0.2)(GlobalAveragePooling1D()(embedded_sequences))\n",
    "\n",
    "preds = Dense(1, activation='sigmoid')(l_mean)\n",
    "model = keras.Model(sequence_input, preds)\n",
    "model.summary()\n",
    "experiment(model, texts, labels)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_4 (InputLayer)         (None, 300)               0         \n",
      "_________________________________________________________________\n",
      "embedding_14 (Embedding)     (None, 300, 20)           800000    \n",
      "_________________________________________________________________\n",
      "global_average_pooling1d_1 ( (None, 20)                0         \n",
      "_________________________________________________________________\n",
      "dense_25 (Dense)             (None, 1)                 21        \n",
      "=================================================================\n",
      "Total params: 800,021\n",
      "Trainable params: 800,021\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hellrich/bin/anaconda3/envs/keras-tensor-old-cudn/lib/python3.6/site-packages/tensorflow/python/ops/gradients_impl.py:100: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.\n",
      "  \"Converting sparse IndexedSlices to a dense Tensor of unknown shape. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 16839 samples, validate on 1872 samples\n",
      "Epoch 1/30\n",
      " - 2s - loss: 0.6906 - acc: 0.5352 - val_loss: 0.6535 - val_acc: 0.6747\n",
      "Epoch 2/30\n",
      " - 1s - loss: 0.6499 - acc: 0.6427 - val_loss: 0.6317 - val_acc: 0.6864\n",
      "Epoch 3/30\n",
      " - 1s - loss: 0.6205 - acc: 0.6847 - val_loss: 0.6172 - val_acc: 0.6934\n",
      "Epoch 4/30\n",
      " - 1s - loss: 0.5885 - acc: 0.7073 - val_loss: 0.6020 - val_acc: 0.6998\n",
      "Epoch 5/30\n",
      " - 1s - loss: 0.5580 - acc: 0.7259 - val_loss: 0.5890 - val_acc: 0.6993\n",
      "Epoch 6/30\n",
      " - 1s - loss: 0.5322 - acc: 0.7419 - val_loss: 0.5913 - val_acc: 0.6998\n",
      "Epoch 7/30\n",
      " - 1s - loss: 0.5110 - acc: 0.7553 - val_loss: 0.5761 - val_acc: 0.7051\n",
      "Epoch 8/30\n",
      " - 1s - loss: 0.4936 - acc: 0.7645 - val_loss: 0.5839 - val_acc: 0.6998\n",
      "Epoch 9/30\n",
      " - 1s - loss: 0.4786 - acc: 0.7751 - val_loss: 0.5853 - val_acc: 0.6976\n",
      "Epoch 10/30\n",
      " - 1s - loss: 0.4653 - acc: 0.7826 - val_loss: 0.5838 - val_acc: 0.6971\n",
      "\n",
      "Epoch 00010: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
      "Epoch 11/30\n",
      " - 1s - loss: 0.4558 - acc: 0.7898 - val_loss: 0.5848 - val_acc: 0.6976\n",
      "Epoch 00011: early stopping\n"
     ]
    },
    {
     "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": [
    "#avg and max\n",
    "\n",
    "in_layer = Input(shape=(maxlen,), dtype='int32')\n",
    "embed_layer = Embedding(input_dim=vocab_size, output_dim=20, input_length=maxlen)(in_layer)\n",
    "avg_layer = GlobalAveragePooling1D()(embed_layer)\n",
    "max_layer = GlobalMaxPooling1D()(embed_layer)\n",
    "concat_layer = Dropout(0.2)(Concatenate()([avg_layer, max_layer]))\n",
    "out_layer = Dense(1, activation='sigmoid')(avg_layer)#(concat_layer)\n",
    "model = keras.Model(in_layer, out_layer)\n",
    "\n",
    "model.compile(loss='binary_crossentropy',\n",
    "                optimizer=\"adam\",\n",
    "                metrics=['acc'])\n",
    "model.summary()\n",
    "experiment(model, texts, labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_9 (InputLayer)            (None, 300)          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "embedding_19 (Embedding)        (None, 300, 20)      800000      input_9[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_14 (Conv1D)              (None, 299, 64)      2624        embedding_19[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_15 (Conv1D)              (None, 298, 64)      3904        embedding_19[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_16 (Conv1D)              (None, 297, 64)      5184        embedding_19[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "dropout_28 (Dropout)            (None, 299, 64)      0           conv1d_14[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "dropout_29 (Dropout)            (None, 298, 64)      0           conv1d_15[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "dropout_30 (Dropout)            (None, 297, 64)      0           conv1d_16[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "global_max_pooling1d_20 (Global (None, 64)           0           dropout_28[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "global_max_pooling1d_21 (Global (None, 64)           0           dropout_29[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "global_max_pooling1d_22 (Global (None, 64)           0           dropout_30[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_4 (Concatenate)     (None, 192)          0           global_max_pooling1d_20[0][0]    \n",
      "                                                                 global_max_pooling1d_21[0][0]    \n",
      "                                                                 global_max_pooling1d_22[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "global_average_pooling1d_6 (Glo (None, 20)           0           embedding_19[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "global_max_pooling1d_19 (Global (None, 20)           0           embedding_19[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "dropout_31 (Dropout)            (None, 192)          0           concatenate_4[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_5 (Concatenate)     (None, 232)          0           global_average_pooling1d_6[0][0] \n",
      "                                                                 global_max_pooling1d_19[0][0]    \n",
      "                                                                 dropout_31[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "dropout_32 (Dropout)            (None, 232)          0           concatenate_5[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dense_27 (Dense)                (None, 1)            233         dropout_32[0][0]                 \n",
      "==================================================================================================\n",
      "Total params: 811,945\n",
      "Trainable params: 811,945\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hellrich/bin/anaconda3/envs/keras-tensor-old-cudn/lib/python3.6/site-packages/tensorflow/python/ops/gradients_impl.py:100: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.\n",
      "  \"Converting sparse IndexedSlices to a dense Tensor of unknown shape. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 16839 samples, validate on 1872 samples\n",
      "Epoch 1/30\n",
      " - 4s - loss: 0.6766 - acc: 0.5845 - val_loss: 0.6205 - val_acc: 0.6667\n",
      "Epoch 2/30\n",
      " - 3s - loss: 0.5748 - acc: 0.6933 - val_loss: 0.6031 - val_acc: 0.6747\n",
      "Epoch 3/30\n",
      " - 3s - loss: 0.5275 - acc: 0.7296 - val_loss: 0.5914 - val_acc: 0.6779\n",
      "Epoch 4/30\n",
      " - 3s - loss: 0.4853 - acc: 0.7587 - val_loss: 0.5598 - val_acc: 0.6725\n",
      "Epoch 5/30\n",
      " - 3s - loss: 0.4530 - acc: 0.7809 - val_loss: 0.5727 - val_acc: 0.6832\n",
      "Epoch 6/30\n",
      " - 3s - loss: 0.4245 - acc: 0.7978 - val_loss: 0.5831 - val_acc: 0.6827\n",
      "Epoch 7/30\n",
      " - 3s - loss: 0.3876 - acc: 0.8246 - val_loss: 0.5509 - val_acc: 0.6955\n",
      "Epoch 8/30\n",
      " - 3s - loss: 0.3492 - acc: 0.8485 - val_loss: 0.5701 - val_acc: 0.6982\n",
      "Epoch 9/30\n",
      " - 3s - loss: 0.3114 - acc: 0.8699 - val_loss: 0.6180 - val_acc: 0.6806\n",
      "Epoch 10/30\n",
      " - 3s - loss: 0.2779 - acc: 0.8850 - val_loss: 0.6125 - val_acc: 0.6923\n",
      "\n",
      "Epoch 00010: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
      "Epoch 11/30\n",
      " - 3s - loss: 0.2276 - acc: 0.9154 - val_loss: 0.6143 - val_acc: 0.6928\n",
      "Epoch 00011: early stopping\n"
     ]
    },
    {
     "data": {
      "image/png": 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KjKPeCVwXyM9Xpi3fxmvfr2b+xh3UqBzFH89pzHVdGtnNNMaUAkv6JWTX/hwmLd7C+AWpzFybgSq0SYil1+m1aFLTl9zjKpWZ0TTZB/NYsGmH70Mgk3kbsth7MA+A+tUq0imxuu+6QBzNalX+3XWBnLx8vljoxtiv3LqHhOoVubVHU67saGPsjSlNlvQDaH9OHtOXb2PCgjS+W7GNg7n5JMbHMLBdfQa0q0fTcjS5cm5ePsu37HZdQuuz+HV9Jum+6wKxFSNJalT98MXhJWm7GDljLak7sjm9dhVu79mU/m3q2hh7YzxgSf8U5eUrs9ZmMH5+KpN+28LuA7nUqFyBS9rW5dJ29WmTEBsSY8pVlY2Z+5izPst3gTiTtb7rAgAdG1Xnjp5N6d2iVkj8PowJVjaJyklQVRan7mTCgjS+WJjGtt0HqFwhgn6t6jCwXT3ObhIfcmexIkKjeHfR+YqO7sbqQ9cF4itXoGOj6h5HaIw5EZb0cePIJyxIZeKCNNZu30tUeBg9T6/Jpe3r07tFLeubLiS+cgX6nlnH6zCMMSchZJP+tt37+WLhZiYuSGVhyk5EoEvjeG7p3oQLW9UtMxdijTHmRIRU0t+1P4fJv21hwoI0flmznXyFM+tV5e8XnUH/tnWpGxt8pQuMMSaQyn3SP5Cbx/Tl6UxYkMq05W7kTcO4GIb3Oo0B7epxWq2ip2YzxpjyqFwm/bx8ZfbaDCYsSOPr3zaze38uNSpHcU3nhgxsV492DarZSBNjTEgqN0lfVVmStovx81P5YlEaW3cdoFJUOBe0qsPAdvXp1jT0Rt4YY0xh5Sbpb8zcR/9XfiIyXOjRvBaP9K9Hnxa1bfJkY4wpoNwk/UbxlRhxTQe6nRZPtRibS9MYY4pSbpI+wMVtbHo1Y4w5HuvkNsaYEGJJ3xhjQoglfWOMCSF+JX0R6SciK0RktYg8cIw2V4nIUhFZIiIfFVjfUES+FZFlvu2JgQndGGPMiSr2Qq6IhAMjgPOBFGCOiExU1aUF2jQDHgS6qWqWiNQqsIv3gCdUdYqIVAbyA3oExhhj/ObPmX5nYLWqrlXVg8AYYGChNsOAEaqaBaCq2wBEpCUQoapTfOv3qOq+gEVvjDHmhPiT9OsDmwosp/jWFdQcaC4iP4vILBHpV2D9DhH5TETmi8izvm8ORxGRW0QkWUSS09PTT+Y4jDHG+MGfpF9UkZrC021FAM2AnsAQ4G0RqeZbfy5wD9AJaAIM/d3OVEeqapKqJtWsWdPv4I0xxpwYf5J+CtCgwHICkFZEmwmqmqOq64AVuA+BFGC+r2soFxgPdDj1sI0xxpwMf5L+HKCZiDQWkShgMDCxUJvxQC8AEamB69ZZ63ttdRE5dPreG1iKMcYYTxSb9H1n6MOBycAy4BNVXSIij4nIAF+zyUCGiCwFpgP3qmqGqubhunamichiXFfRWyVxIMYYY4onqoW7572VlJSkycnJXodhjDFliojMVdWk4trZHbnGGBNCLOkbY0wIsaRvjDEhxJK+McaEEEv6xhgTQizpG2NMCLGkb4wxIcSSvjHGhBBL+sYYE0Is6RtjTAixpG+MMSHEkr4xxoQQS/rGGBNCLOkbY0wIsaRvjDEhxJK+McaEEEv6xhgTQizpG2NMCPEr6YtIPxFZISKrReSBY7S5SkSWisgSEfmo0LaqIpIqIq8GImhjjDEnJ6K4BiISDowAzgdSgDkiMlFVlxZo0wx4EOimqlkiUqvQbh4Hfghc2MYYY06GP2f6nYHVqrpWVQ8CY4CBhdoMA0aoahaAqm47tEFEOgK1gW8DE7IxxpiT5U/Srw9sKrCc4ltXUHOguYj8LCKzRKQfgIiEAc8D9x7vDUTkFhFJFpHk9PR0/6M3xhhzQvxJ+lLEOi20HAE0A3oCQ4C3RaQacAfwtapu4jhUdaSqJqlqUs2aNf0IyRhjzMkotk8fd2bfoMByApBWRJtZqpoDrBORFbgPgbOBc0XkDqAyECUie1S1yIvBxhhjSpY/Z/pzgGYi0lhEooDBwMRCbcYDvQBEpAauu2etql6rqg1VNRG4B3jPEr4xxnin2KSvqrnAcGAysAz4RFWXiMhjIjLA12wykCEiS4HpwL2qmlFSQRtjjDk5olq4e95bSUlJmpyc7HUYJkBycnJISUlh//79XodiCoiOjiYhIYHIyEivQzEBIiJzVTWpuHb+9Okbc9JSUlKoUqUKiYmJiBQ1JsCUNlUlIyODlJQUGjdu7HU4ppRZGQZTovbv3098fLwl/CAiIsTHx9u3rxBlSd+UOEv4wcf+JqHLkr4xhVSuXNnrEIwpMZb0jQkyubm5AW1nTEGW9E25dv/99/Paa68dXn700Ud5/vnn2bNnD3369KFDhw60bt2aCRMmFLuvSy+9lI4dO3LmmWcycuTIw+snTZpEhw4daNu2LX369AFgz5493HjjjbRu3Zo2bdrw6aefAkd/ixg3bhxDhw4FYOjQofz1r3+lV69e3H///fz666907dqV9u3b07VrV1asWAHAO++8w5VXXskll1xC3759AXjmmWdo3bo1bdu25YEHHmDNmjV06NDh8PusWrWKjh07nuRv0JQ3NnrHlJp/frGEpWm7ArrPlvWq8o9Lzjzm9sGDB3P33Xdzxx13APDJJ58wadIkoqOj+fzzz6latSrbt2+nS5cuDBgw4Lh93aNGjSIuLo7s7Gw6derEoEGDyM/PZ9iwYcyYMYPGjRuTmZkJwOOPP05sbCyLFy8GICsrq9hjWblyJVOnTiU8PJxdu3YxY8YMIiIimDp1Kg899NDhD46ZM2eyaNEi4uLi+Oabbxg/fjyzZ88mJiaGzMxM4uLiiI2NZcGCBbRr147Ro0cf/nAxxpK+Kdfat2/Ptm3bSEtLIz09nerVq9OwYUNycnJ46KGHmDFjBmFhYaSmprJ161bq1KlzzH29/PLLfP755wBs2rSJVatWkZ6eTvfu3Q8PfYyLiwNg6tSpjBkz5vBrq1evXmysV155JeHh4QDs3LmTG264gVWrViEi5OTkHG53/vnnH/U+N954IzExMUe9/80338zo0aP5z3/+w9ixY/n111/9/p2Z8s2Svik1xzsjL0lXXHEF48aNY8uWLQwePBiADz/8kPT0dObOnUtkZCSJiYnHHcL4/fffM3XqVGbOnElMTAw9e/Zk//79qGqR3w6Otb7gusLvV6lSpcPPH3nkEXr16sXnn3/O+vXr6dmzZ5HtjvU+gwYN4p///Ce9e/emY8eOxMfHH/PYTGixPn1T7g0ePJgxY8Ywbtw4rrjiCsCdSdeqVYvIyEimT5/Ohg0bjruPnTt3Ur16dWJiYli+fDmzZs0C4Oyzz+aHH35g3bp1AIe7d/r27currx6ZKO5Q907t2rVZtmwZ+fn5h781HOv96td3FczfeeedY7br27cvo0aNYt++fUe9f3R0NBdccAG33347N95443GPzYQWS/qm3DvzzDPZvXs39evXp27dugBce+21JCcnk5SUxIcffkiLFi2Ou49+/fqRm5tLmzZteOSRR+jSpQsANWvWZOTIkVx++eW0bduWq6++GoCHH36YrKwsWrVqRdu2bZk+fToATz31FP3796d3796HYynKfffdx4MPPki3bt3Iy8s7blwDBgwgKSmJdu3a8dxzzx3edu211yIihy/4GgNWe8eUsGXLlnHGGWd4HUZIeu6559i5cyePP/54kdvtb1O+WO0dY0LYZZddxpo1a/juu++8DsUEGUv6xpRDx7teYEKb9ekbY0wIsaRvjDEhxJK+McaEEEv6xhgTQvxK+iLST0RWiMhqESlyYnMRuUpElorIEhH5yLeunYjM9K1bJCJXBzJ4Y0rCsUorW8llUx4UO3pHRMKBEcD5QAowR0QmqurSAm2aAQ8C3VQ1S0Rq+TbtA/6gqqtEpB4wV0Qmq+qOgB8JwOJxcPqFEFWp+LbGGBOC/DnT7wysVtW1qnoQGAMMLNRmGDBCVbMAVHWb7+dKVV3le54GbANqBir4o6SvhM+GwdvnwfZVJfIWpuwJZGnlQ1SVe++9l1atWtG6dWvGjh0LwObNm+nevTvt2rWjVatW/Pjjj+Tl5TF06NDDbV944YWAH6MxJ8Kfcfr1gU0FllOAswq1aQ4gIj8D4cCjqjqpYAMR6QxEAWsKv4GI3ALcAtCwYUN/Yz9azeZw7Tj49GYY2QsGvgpnXnpy+zIl45sHYMviwO6zTmu48Kljbg5kaeVDPvvsMxYsWMDChQvZvn07nTp1onv37nz00UdccMEF/P3vfycvL499+/axYMECUlNT+e233wDYsaNkvuQa4y9/zvSL+l9QuHZDBNAM6AkMAd4WkWqHdyBSF3gfuFFV83+3M9WRqpqkqkk1a57CF4HT+sBtP0LN0+F/N8CkByEvp/jXmXKrYGnlhQsXHi6trKo89NBDtGnThvPOO+9waWV//PTTTwwZMoTw8HBq165Njx49mDNnDp06dWL06NE8+uijLF68mCpVqtCkSRPWrl3LnXfeyaRJk6hatWoJH7Exx+fPmX4K0KDAcgKQVkSbWaqaA6wTkRW4D4E5IlIV+Ap4WFVnBSDm44tNgBu/gW8fhlmvQepcuGI0xNYv8bc2xTjOGXlJCkRp5YKOVa+qe/fuzJgxg6+++orrr7+ee++9lz/84Q8sXLiQyZMnM2LECD755BNGjRoVsGMrt5JHwdx3oUFnaNQVGnaFKrW9jqpc8OdMfw7QTEQai0gUMBiYWKjNeKAXgIjUwHX3rPW1/xx4T1X/F7iwixERBRc9A4P+C1t+gze7w5rppfb2JrgEorRyQd27d2fs2LHk5eWRnp7OjBkz6Ny5Mxs2bKBWrVoMGzaMm266iXnz5rF9+3by8/MZNGgQjz/+OPPmzSupwyw/DuyBaY/B7s0w/wP431B4vjm83AEmDIcFH0HWegiyYpFlRbFn+qqaKyLDgcm4/vpRqrpERB4DklV1om9bXxFZCuQB96pqhohcB3QH4kVkqG+XQ1V1QUkczO+0vsL1+Y69Ht6/DHr/Hc75G4TZ7Qmh5FillS+55JLDJYmLK61c0GWXXcbMmTNp27YtIsIzzzxDnTp1ePfdd3n22WeJjIykcuXKvPfee6SmpnLjjTeSn+96Nf/973+XyDGWK/PehewsuGkq1GsHmxfChl/cY9kXMP99165KPfct4NCjxun2f9sPoVFa+cAe+PJuWPw/aNYXLnsTYuIC+x6mSFa+N3gF5d8m9wC81BbiT4OhX/5+e34+pC878iGw4RfYs8VtqxgHDc8+8iFQpw2Eh05NSSutXFCFynD5W9DgLHdx980ecNW7UL+D15EZYwpaOMZ161z6WtHbw8Kg9pnu0XmY6+LJWnf0h8CKr1zbqMpHXxOo3xEio0vvWIJUaCR9ABH3j6ReBzeyZ9QF0O8pSPqj22aM8VZ+Hvz8ItRtB016+fcaEYhr4h7tr3PrdqW55L9xpvv53b/c+vAol/gPfRNocBZUqFIyx3JIfj4c2An7Mt0j2/dzX4bvecbR2+JPg6vfL9GQQifpH5LQEW6d4W7k+uqvsGk29H/B7uI1xmtLx0PmWrjqvVM7Eataz13Pa+0u2rMvEzbOgo2+bwI/vQg/Pg8S5rqADn0INDwbKtU49n7z8yB7R6FkXSh5Z2cV2pYFeozpLiXcdTNXjIOYePfBVavlyR+3n0Iv6YP7RV/zP/jxOZj+JGxe5D5dazTzOrJySVX9uunJlJ5gu5aHKvz4AtRoDi0uCey+Y+KgxUXuAe4aX8qcI91ByaPc8G5wF4MbdHLxFE7s2Tv4/S1KPmGRLnHH+BJ4zdOPXj6U2GPioGJ197xCVU8uPIdm0gf3y+5xHyQk+e7i7em7i/cyryMrV6Kjo8nIyCA+Pt4Sf5BQVTIyMoiODqL+7dVTYetiGPhaySfCCpWhaS/3AHfxOG1+gWsC30BEtC9Rx0HdNr9P3IfP0H3roiqXmW7i0Bi9U5ydKW4scMoc6HIHnP8YhEeWbgzlVE5ODikpKX7f+GRKR3R0NAkJCURGBsm/81H9YMcmuGu+u8/GnDAbvXMiYhNg6Ncw5RG7izfAIiMjady4sddhmGC2Yaa76HrhM5bwS4HdyXBIRBRc+DQPCzv0AAAYEklEQVRcMQq2LrG7eI0pLT/9B2JqQPvrvY4kJFjSL6zVIBg23V3Ff/8y+OFZN+zKGBN4mxfBqm+hy20QFeN1NCHBkn5RajaHm6e5IV/T/wUfX+2u4htjAuunFyCqCnQa5nUkIcOS/rEcuov34udh7ffuLt5UK5ZlTMBkrHFj8zvdBBWrFd/eBIQl/eMRgU43wx8nAeru4p3ztlX3MyYQfn7RjW/vcofXkYQUS/r+qO+7i7dxD/jqb/D5rXBwr9dRGVN27UqDBR+70glWJ79UWdL3V0wcXPMJ9HoYFn0Cb/WxuXiNOVkzR4DmQ7e7vI4k5FjSPxFhYdDjXrj+M9i7zd3Fu+Rzr6MypmzZlwnJo91AieqJXkcTcizpn4ymvV13T60z3J283zwAuQe9jsqYsmH2m5CzF875i9eRhCRL+ifr0F28Z90Os1+Ht/vA6ml2kdeY4zmwG2a/Aadf7E6aTKnzK+mLSD8RWSEiq0XkgWO0uUpElorIEhH5qMD6G0Rkle9xQ6ACDwoRUW6y76vecyVUP7gc3rnY3VZujPm9ue/A/h1w7l+9jiRkFVtwTUTCgZXA+UAKbqL0Iaq6tECbZsAnQG9VzRKRWqq6TUTigGQgCVeTdC7QUVWzjvV+nhRcC4TcAzDvPZjxLOzZCk37QO+HbXYuYw4pbipEc0r8Lbjmz5l+Z2C1qq5V1YPAGGBgoTbDgBGHkrmqbvOtvwCYoqqZvm1TgH7+HkSZElHBzcx11wI4/3FXqvWtXjDmWti6tPjXG1PeLfzYTYV47t+8jiSk+ZP06wObCiyn+NYV1BxoLiI/i8gsEel3Aq8tX6Ji3DC0Py+Eng/BuhnweldXsz9jjdfRGeONvFw3Y1W99tCkp9fRhDR/kn5RMwMU7hOKAJoBPYEhwNsiUs3P1yIit4hIsogkp6en+xFSGRBdFXre75L/OXfD8q/g1U4wYbirG25MKFk63k1gfs5fy8xkI+WVP0k/BWhQYDkBSCuizQRVzVHVdcAK3IeAP69FVUeqapKqJtWsWfNE4g9+MXFw3qOu26fzLbBoLLzSAb6+D3Zv9To6Y0qeqiusVqM5tOjvdTQhz5+kPwdoJiKNRSQKGAxMLNRmPNALQERq4Lp71gKTgb4iUl1EqgN9fetCT5XabqTPXfOh7RBXw+eltjDl/6yCpynfVn0LW39z4/I9mBPWHK3Yv4Cq5gLDccl6GfCJqi4RkcdEZICv2WQgQ0SWAtOBe1U1Q1UzgcdxHxxzgMd860JXbAIMeBmGz4GWA+Dnl+HFNjD937B/l9fRGRN4P/4HYhtA6yu9jsRgc+R6b9symP4ELPsCKlaHbne7biCbUMKUBxt+gdEXuqkQz7rV62jKtUAO2TQlqdYZcPUHcMv3UD8Jpv7DdfvMftONazamLPvxeZsKMchY0g8W9drDdePgxknugtc398ErHd0NX3m5XkdnzInbvBBWT4Uut9s31yBiST/YNDrb3a14/XioXAsm3gkjOsGi/9lcvaZsOTwV4s1eR2IKsKQfjESgaS83T+/gjyEyBj67Gd7o5vr+g+w6jDG/s301LBkPnW+2qRCDjCX9YCYCLS6CW3+EK0ZB3kEYe50r77B6qiV/E7x+ftGVJrGpEIOOJf2yICwMWg2CO2bDwNdgXwZ8MAhGXwTrf/Y6OmOOtjMVFo5xUyFWruV1NKYQS/plSXgEtL8Whs+Fi56DzLXwzkVurlFjgsXMV91UiF1tKsRgZEm/LIqIchU9/7wAGp3jJmu3Ym4mGOzNcDXzW18J1Rt5HY0pgiX9siyyIlz+JoRHwqc32ZSNxnu/vgk5+1yRQROULOmXdbEJMOAVV7//+ye9jsaEskNTIbbob1MhBjFL+uVBywHQcairV772B6+jMaEqeTTs3+nKJ5ugZUm/vLjgSajRDD6/1ap2mtKXsx9mjoDG3SGho9fRmOOwpF9eRFWCQW+74ZwT77Qx/KZ0LfwI9myxqRDLAEv65Undtm7CluVfwtzRXkdjQkVeLvz8EtTrAI17eB2NKYYl/fLmrNuhaR+Y9BBsW+51NCYULB0PWevhXJsKsSywpF/ehIXBpa+77p5Pb3Z9rcaUFFU3SUqN0+H0i72OxvjBkn55VKW2S/xbF8O0f3odjSnPVk6GbUtsKsQyxP5K5VXzvnDWbTDrNVg1xetoyrb9u+CXV9y0lpP/7nU0wUMVfvoPxDaE1ld4HY3xk19JX0T6icgKEVktIg8UsX2oiKSLyALf4+YC254RkSUiskxEXhaxTr9Sc94/odaZMP522LPN62jKnl2b3cT1L5wJ3z7sktzMV93ZrXFTIW6aDV3vdHeFmzKh2KQvIuHACOBCoCUwRERaFtF0rKq28z3e9r22K9ANaAO0AjoBdnm/tERGwxX/dXdKjr/dJmHx17ZlMP4OeLG1O8M/rQ8Mmw53JkPtVjDhT7An3esovffj81CpJnSwqRDLEn/O9DsDq1V1raoeBMYAA/3cvwLRQBRQAYgEtp5MoOYk1ToDLnjC1d+f/YbX0QQvVVj3I3x4JbzWBX77zN3lfOc8uPIdqN/B1Ye//C3X3TNxeGjfC5G2ANZMc1MhRlb0OhpzAvxJ+vWBTQWWU3zrChskIotEZJyINABQ1ZnAdGCz7zFZVZedYszmRCXdBKdf5CZd37zI62iCS16uS/Bv9YZ3+0PqPOj5EPxlCVz8HMQ1Prp97ZZw/j9h5SRIHuVNzMHgpxegQlWbCrEM8ifpF9UHX/gU5wsgUVXbAFOBdwFE5DTgDCAB90HRW0S6/+4NRG4RkWQRSU5Pt6/NAScCA16FinGuGufBfV5H5L2D++DXt+CVDjDuRti/Ay7+D/zlN+h5P1SKP/ZrO98KTXu7i7rbV5VezMFi+ypYOsEl/OhYr6MxJ8ifpJ8CNCiwnACkFWygqhmqesC3+BZwqPjGZcAsVd2jqnuAb4Auhd9AVUeqapKqJtWsWfNEj8H4o1K8K8O8fRVMfsjraLyzdztMf9JdnP36Hjez01Xvw/Bk6HSTf10VYWFuBrPIiu5eiFAraW1TIZZp/iT9OUAzEWksIlHAYGBiwQYiUrfA4gDgUBfORqCHiESISCTuIq5173ilSU/odpcr0bDsC6+jKV0Za+DLv7hk/8PT0LAL3DgJbpriqpSGhZ/Y/qrWhUtegs0L4Pt/l0zMwWhnCiwcC+2vh8p2glYWRRTXQFVzRWQ4MBkIB0ap6hIReQxIVtWJwF0iMgDIBTKBob6XjwN6A4txXUKTVDXEsk2Q6fWwK7888U5XKyW2qMsz5cimOfDLS7DsSzessO1gOPtOqNn81PfdcoCbB/anF6DZ+dCo66nvM9j98iqg7uTBlEmiQTYCISkpSZOTk70Oo3zbvhre7O5GpPxhwomf5Qa7/HxYNdkVAds40/U7d7rZ9cVXqR3Y9zqwB944B/Lz4Pafyncf994MeLEVtBwIl9lIsGAjInNVNam4dnZHbiiqcRpc9Ays/9ElxvIi9wDMew9eOws+Huy6Ii74txuJ0+f/Ap/wASpUdsM4d6XC1/cGfv/BZPYbbirEbjYVYllWbPeOKafaXevG7k9/wpXDLcsTX2RnueGTs9+EPVuhTmu4/G0489LSuVO0QSfocZ/r22/Wt3yWJNi/y81/26I/1GrhdTTmFFjSD1Ui0P8FSEl2wzhv+xEqVPE6qhOzY5OrLTTvPTi4xw2jvOxNd8G6tKt9nHuP+xD98q/Q4Cyo1qD415Qlc20qxPLCundCWcXqcPlI2LEBvr7P62j8t3mRGyr5Ulv4dSS0uBhu+wmu/xya9vKmpnt4hPtdap6v5EVe6cdQUg5PhVjGvxEawJK+adQVut/rprtbPM7raI4vaz18cgO8eS6s+MaVAPjzQpds67T2OjqIawIXPu2ulcx81etoAmfBh67b7Fw7yy8PrHvHQPf7YM10N449oRNUb+R1REfbv8uV8J35mhtp1OMBl/ArVvM6st9rd60r0TDtcdfNVLet1xGdmkNTIdbvaFMhlhN2pm9c18Sgt93zz4a5/+jBID8P5r7jSiX89AK0uhzunAu9HgzOhA+ua+mSlyEmHj4dBjnZXkd0apZ87rr/zrGpEMsLS/rGqd7IXdjdNBtmPOt1NO4Gsje7wxd/hrimMOw7Nza8aj2vIyteTBxc9jpsX+Hq8ZdVB/bAj89BzRauYJ8pFyzpmyNaXwFtr4EZz8CGmd7EkLEGPh4C7w2AA7tcWeM/TnLdC2VJ096uNs2vI8vmzGU7U2F0P9i+0t3jYFMhlhv2lzRHu+gZqNbIdfNk7yi9983OgkkPwYizXF37Pv+AP82BMy8ru90Kff4BtVq6CVn2bvc6Gv+lzYe3+0DmerjmEzc6ypQblvTN0SpUgUH/hd2b4cu7S36ikLwcmD0SXu7gxty3uwbumudGikRGl+x7l7TIaN+kKztcraMgK3lSpGVfwOiLICwCbprsagqZcsWSvvm9hI7Q6+/uIt6Cj0rufVZNgde7wTf3Qp1W7gaxAS+7csflRZ1W7ox/xdcw712vozk2VfjpRRh7vft2cvM0qH2m11GZEmBJ3xSt258h8VxXTyZjTWD3vW0ZvH85fHgF5OfA4I/hDxODY6x9SehyhxvuOOlBV+wu2OQedN9Epv7Dla4Y+mXJ1CkyQcGSvilaWLgraRARBeP+GJiJQvZmwFd/c2f3KclwwZNwx2xocVHZ7bf3R1gYXPo6hEf5hsTmeB3REdlZ8MHlMP99d5PeoFE25205Z0nfHFtsfRjwipsoZPq/Tn4/uQddHfaX20PyaEj6I9w1H87+k/tQCQWx9d2kK2nz3CQuwSBjDbx9nhume9mb0PthG6UTAuyOXHN8Z1wCHW90d2U26eVq2/hLFZZ/BVMegcy1cNp50PeJ0K3SeOalsPIa+PF597to+LuZQ0vP+p9h7LWAuDkVQmECGAPYmb7xxwVPQo3m8PltrovGH5sXwbuXuMQSFgnXfgrXfRq6Cf+QC5+G2Aaum2f/Lm9iWPARvDcQKtWEYdMs4YcYS/qmeFExbhhndiZMHH78oYe7t8KE4e5u2q1L4KLn4PZfoNl5pRdvMIuu6oZx7kyBb+4v3ffOz4ep/3RVQBt1hZu+dUXiTEjxK+mLSD8RWSEiq0XkgSK2DxWRdBFZ4HvcXGBbQxH5VkSWichSEUkMXPim1NRtA+f90w09TP7v77fn7HfdFq90gIVjXH/9XfOg8zBX28cc0fAsV39/4UduWGxpOLgPxg11hes63OC+dVWsXjrvbYJKsf8bRSQcGAGcD6QAc0RkoqouLdR0rKoOL2IX7wFPqOoUEakM5J9q0MYjZ90Ga6bB5L9Do25Q6wx31r/kM5jyKOzcCKdfDH0fh/imXkcb3Hrc536XX9wNCZ1LdoL63Vvd9JFp86Hvv+Ds4eV7tJQ5Ln/O9DsDq1V1raoeBMYAA/3ZuYi0BCJUdQqAqu5R1X0nHa3x1qGhhxWqwLibYMMvMKqfG9IZHevG2g/5yBK+P8IjXTdPXo5v0pUSOhfa8hu81RvSl8PgD6HrnZbwQ5w/Sb8+sKnAcopvXWGDRGSRiIwTkUNzxTUHdojIZyIyX0Se9X1zMGVV5Vow8DXYtgRGX+hG5VzyMtz6AzSxeusnJL4p9Ps3rPvBlaAItJWTYdQFoPmuaJ3V0DH4l/SLOi0ofCXvCyBRVdsAU4FD95tHAOcC9wCdgCbA0N+9gcgtIpIsIsnp6el+hm4807wv9Hsaetzv6tt3vMHdzGVOXIc/uC6xaf+ELYsDs09VmPW669KJb+pG6JT1yVxMwPiT9FOAgrM8JwBpBRuoaoaqHvAtvgV0LPDa+b6uoVxgPNCh8Buo6khVTVLVpJo1a57oMRgvdLkNej3kRqOYkyfiboCrWD0wk67k5bq7nic94Grg3/hN2ZiDwJQaf5L+HKCZiDQWkShgMDCxYAMRqVtgcQCwrMBrq4vIoUzeGyh8AdiY0FYp3nWZpS9zQypP1v6d8NGVbnRV17vgqvchqlLg4jTlQrGjd1Q1V0SGA5OBcGCUqi4RkceAZFWdCNwlIgOAXCATXxeOquaJyD3ANBERYC7um4AxpqBm50HnW2H2666c8Wl9Tuz1Wevho6shY7W7xtLxhhIJ05R9okFW4zspKUmTk5O9DsOY0peTDSN7uiJot8903wD8selXN9tYfo47u7cL6iFJROaqalJx7eyOXGOCRWRFN4xzXyZ8cZd/k64sHgfv9HfDaG+eZgnfFMuSvjHBpG4bNyft8i9h/gfHbqcK3z8Fn97k5g++eRrUaFZ6cZoyy5K+McHm7OFuAptv7i96Apuc/a5g2/f/hrZD4A/j/e8KMiHPkr4xwSYsDC57w9Us+uwWNwzzkL3b4b0BsPh/0PsRd4d0RAXvYjVljiV9Y4JRbAL0fwFSk2HGs27dtuWupMLmhXDlO9D9HiupYE6YlT80Jli1GgQrv4UZz7jy1jOeg4hoGPq1m7zemJNgZ/rGBLOLnoGqCTDl/9zkK8OmWcI3p8TO9I0JZtGxMPgDNzSz+71W9sKcMkv6xgS7um2tYJoJGOveMcaYEGJJ3xhjQoglfWOMCSGW9I0xJoRY0jfGmBBiSd8YY0KIJX1jjAkhlvSNMSaEBN3MWSKSDmzwOo6TUAPY7nUQpcyOOTTYMZcNjVS1ZnGNgi7pl1UikuzPVGXliR1zaLBjLl+se8cYY0KIJX1jjAkhlvQDZ6TXAXjAjjk02DGXI9anb4wxIcTO9I0xJoRY0j9FItJARKaLyDIRWSIif/Y6ptIgIuEiMl9EvvQ6ltIgItVEZJyILPf9rc/2OqaSJiJ/8f2b/k1EPhaRaK9jCjQRGSUi20TktwLr4kRkiois8v2s7mWMgWZJ/9TlAn9T1TOALsCfRKSlxzGVhj8Dy7wOohS9BExS1RZAW8r5sYtIfeAuIElVWwHhwGBvoyoR7wD9Cq17AJimqs2Aab7lcsOS/ilS1c2qOs/3fDcuGdT3NqqSJSIJwMXA217HUhpEpCrQHfgvgKoeVNUd3kZVKiKAiiISAcQAaR7HE3CqOgPILLR6IPCu7/m7wKWlGlQJs6QfQCKSCLQHZnsbSYl7EbgPyPc6kFLSBEgHRvu6tN4WkUpeB1WSVDUVeA7YCGwGdqrqt95GVWpqq+pmcCd1QC2P4wkoS/oBIiKVgU+Bu1V1l9fxlBQR6Q9sU9W5XsdSiiKADsDrqtoe2Es5+8pfmK8feyDQGKgHVBKR67yNygSCJf0AEJFIXML/UFU/8zqeEtYNGCAi64ExQG8R+cDbkEpcCpCiqoe+wY3DfQiUZ+cB61Q1XVVzgM+Arh7HVFq2ikhdAN/PbR7HE1CW9E+RiAiur3eZqv7H63hKmqo+qKoJqpqIu7D3naqW6zNAVd0CbBKR032r+gBLPQypNGwEuohIjO/feB/K+cXrAiYCN/ie3wBM8DCWgIvwOoByoBtwPbBYRBb41j2kql97GJMJvDuBD0UkClgL3OhxPCVKVWeLyDhgHm6E2nzK4V2qIvIx0BOoISIpwD+Ap4BPROQm3Iffld5FGHh2R64xxoQQ694xxpgQYknfGGNCiCV9Y4wJIZb0jTEmhFjSN8aYEGJJ34QMEckTkQUFHgG7q1ZEEgtWajQmWNk4fRNKslW1nddBGOMlO9M3IU9E1ovI0yLyq+9xmm99IxGZJiKLfD8b+tbXFpHPRWSh73GoPEG4iLzlq0H/rYhU9LW/S0SW+vYzxqPDNAawpG9CS8VC3TtXF9i2S1U7A6/iqojie/6eqrYBPgRe9q1/GfhBVdviavAs8a1vBoxQ1TOBHcAg3/oHgPa+/dxWUgdnjD/sjlwTMkRkj6pWLmL9eqC3qq71Fc/boqrxIrIdqKuqOb71m1W1hoikAwmqeqDAPhKBKb6JNxCR+4FIVf2XiEwC9gDjgfGquqeED9WYY7IzfWMcPcbzY7UpyoECz/M4cs3sYmAE0BGY65uUxBhPWNI3xrm6wM+Zvue/cGSKwGuBn3zPpwG3w+G5gqsea6ciEgY0UNXpuIlnqgG/+7ZhTGmxMw4TSioWqIQKbs7bQ8M2K4jIbNyJ0BDfuruAUSJyL27mrEOVNf8MjPRVYczDfQBsPsZ7hgMfiEgsIMALITLVoglS1qdvQp6vTz9JVbd7HYsxJc26d4wxJoTYmb4xxoQQO9M3xpgQYknfGGNCiCV9Y4wJIZb0jTEmhFjSN8aYEGJJ3xhjQsj/A2cTDWuHNUvGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#avg and max + conv\n",
    "\n",
    "in_layer = Input(shape=(maxlen,), dtype='int32')\n",
    "embed_layer = Embedding(input_dim=vocab_size, output_dim=20, input_length=maxlen)(in_layer)\n",
    "avg_layer = GlobalAveragePooling1D()(embed_layer)\n",
    "max_layer = GlobalMaxPooling1D()(embed_layer)\n",
    "\n",
    "conv_layers = []\n",
    "for window_size in [2,3,4]:\n",
    "    conv_layer = Conv1D(64,window_size)(embed_layer)\n",
    "    conv_layer = Dropout(0.2)(conv_layer)\n",
    "    conv_layer = GlobalMaxPooling1D()(conv_layer)\n",
    "    conv_layers.append(conv_layer)\n",
    "convs_layer = Dropout(0.2)(Concatenate()(conv_layers))\n",
    "    \n",
    "concat_layer = Dropout(0.2)(Concatenate()([avg_layer, max_layer, convs_layer]))\n",
    "out_layer = Dense(1, activation='sigmoid')(concat_layer)\n",
    "model = keras.Model(in_layer, out_layer)\n",
    "\n",
    "model.compile(loss='binary_crossentropy',\n",
    "                optimizer=\"adam\",\n",
    "                metrics=['acc'])\n",
    "model.summary()\n",
    "experiment(model, texts, labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_4 (InputLayer)            (None, 300)          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "embedding_29 (Embedding)        (None, 300, 20)      800000      input_4[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dropout_13 (Dropout)            (None, 300, 20)      0           embedding_29[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_12 (Conv1D)              (None, 299, 128)     5248        dropout_13[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_13 (Conv1D)              (None, 298, 128)     7808        dropout_13[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_14 (Conv1D)              (None, 297, 128)     10368       dropout_13[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_15 (Conv1D)              (None, 296, 128)     12928       dropout_13[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "global_max_pooling1d_12 (Global (None, 128)          0           conv1d_12[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "global_max_pooling1d_13 (Global (None, 128)          0           conv1d_13[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "global_max_pooling1d_14 (Global (None, 128)          0           conv1d_14[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "global_max_pooling1d_15 (Global (None, 128)          0           conv1d_15[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_2 (Concatenate)     (None, 512)          0           global_max_pooling1d_12[0][0]    \n",
      "                                                                 global_max_pooling1d_13[0][0]    \n",
      "                                                                 global_max_pooling1d_14[0][0]    \n",
      "                                                                 global_max_pooling1d_15[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "dropout_14 (Dropout)            (None, 512)          0           concatenate_2[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dense_12 (Dense)                (None, 1)            513         dropout_14[0][0]                 \n",
      "==================================================================================================\n",
      "Total params: 836,865\n",
      "Trainable params: 836,865\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/hellrich/bin/anaconda3/envs/keras-tensor-old-cudn/lib/python3.6/site-packages/tensorflow/python/ops/gradients_impl.py:100: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.\n",
      "  \"Converting sparse IndexedSlices to a dense Tensor of unknown shape. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 16839 samples, validate on 1872 samples\n",
      "Epoch 1/50\n",
      " - 4s - loss: 0.7199 - acc: 0.5381 - val_loss: 0.6688 - val_acc: 0.6587\n",
      "Epoch 2/50\n",
      " - 3s - loss: 0.6254 - acc: 0.6475 - val_loss: 0.6226 - val_acc: 0.6384\n",
      "Epoch 3/50\n",
      " - 3s - loss: 0.5625 - acc: 0.7082 - val_loss: 0.5999 - val_acc: 0.6528\n",
      "Epoch 4/50\n",
      " - 3s - loss: 0.5328 - acc: 0.7270 - val_loss: 0.5793 - val_acc: 0.6683\n",
      "Epoch 5/50\n",
      " - 3s - loss: 0.5078 - acc: 0.7448 - val_loss: 0.5743 - val_acc: 0.6763\n",
      "Epoch 6/50\n",
      " - 3s - loss: 0.4859 - acc: 0.7601 - val_loss: 0.5700 - val_acc: 0.6822\n",
      "Epoch 7/50\n",
      " - 3s - loss: 0.4672 - acc: 0.7717 - val_loss: 0.5703 - val_acc: 0.6928\n",
      "Epoch 8/50\n",
      " - 3s - loss: 0.4517 - acc: 0.7853 - val_loss: 0.5990 - val_acc: 0.6880\n",
      "Epoch 9/50\n",
      " - 3s - loss: 0.4314 - acc: 0.7942 - val_loss: 0.5667 - val_acc: 0.6950\n",
      "Epoch 10/50\n",
      " - 3s - loss: 0.4142 - acc: 0.8100 - val_loss: 0.5726 - val_acc: 0.7019\n",
      "Epoch 11/50\n",
      " - 3s - loss: 0.3917 - acc: 0.8207 - val_loss: 0.5656 - val_acc: 0.7099\n",
      "Epoch 12/50\n",
      " - 3s - loss: 0.3704 - acc: 0.8355 - val_loss: 0.5894 - val_acc: 0.7110\n",
      "Epoch 13/50\n",
      " - 3s - loss: 0.3521 - acc: 0.8419 - val_loss: 0.6054 - val_acc: 0.7014\n",
      "Epoch 14/50\n",
      " - 3s - loss: 0.3264 - acc: 0.8571 - val_loss: 0.6175 - val_acc: 0.6960\n",
      "Epoch 15/50\n",
      " - 3s - loss: 0.3051 - acc: 0.8674 - val_loss: 0.6078 - val_acc: 0.7083\n",
      "\n",
      "Epoch 00015: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
      "Epoch 16/50\n",
      " - 3s - loss: 0.2851 - acc: 0.8797 - val_loss: 0.6275 - val_acc: 0.6971\n",
      "Epoch 17/50\n",
      " - 3s - loss: 0.2749 - acc: 0.8846 - val_loss: 0.6310 - val_acc: 0.7003\n",
      "Epoch 00017: early stopping\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Yoon Kim inspired architecture with cutom embeddings, \n",
    "#see (deprectaed code!)\n",
    "#https://richliao.github.io/supervised/classification/2016/11/26/textclassifier-convolutional/\n",
    " \n",
    "in_layer = Input(shape=(maxlen,), dtype='int32')\n",
    "embed_layer = Dropout(0.2)(Embedding(input_dim=vocab_size, output_dim=20, \n",
    "                                     input_length=maxlen)(in_layer))\n",
    "\n",
    "convs = []\n",
    "for window_size in [2,3,4,5]:\n",
    "    conv_layer = Conv1D(128,window_size,activation='relu')(embed_layer)\n",
    "    conv_layer = GlobalMaxPooling1D()(conv_layer)\n",
    "    convs.append(conv_layer)\n",
    "    \n",
    "layer = Dropout(0.5)(Concatenate()(convs))\n",
    "out_layer = Dense(1, activation='sigmoid')(layer)\n",
    "model = keras.Model(in_layer, out_layer)\n",
    "\n",
    "model.summary()\n",
    "experiment(model, texts, labels, 50, 6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "entries 22696 found for 40000 words in embedding model\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_31 (InputLayer)           (None, 300)          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "embedding_56 (Embedding)        (None, 300, 200)     8000000     input_31[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "dropout_87 (Dropout)            (None, 300, 200)     0           embedding_56[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_120 (Conv1D)             (None, 299, 128)     51328       dropout_87[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_121 (Conv1D)             (None, 298, 128)     76928       dropout_87[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_122 (Conv1D)             (None, 297, 128)     102528      dropout_87[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_123 (Conv1D)             (None, 296, 128)     128128      dropout_87[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "global_max_pooling1d_120 (Globa (None, 128)          0           conv1d_120[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "global_max_pooling1d_121 (Globa (None, 128)          0           conv1d_121[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "global_max_pooling1d_122 (Globa (None, 128)          0           conv1d_122[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "global_max_pooling1d_123 (Globa (None, 128)          0           conv1d_123[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_29 (Concatenate)    (None, 512)          0           global_max_pooling1d_120[0][0]   \n",
      "                                                                 global_max_pooling1d_121[0][0]   \n",
      "                                                                 global_max_pooling1d_122[0][0]   \n",
      "                                                                 global_max_pooling1d_123[0][0]   \n",
      "__________________________________________________________________________________________________\n",
      "dropout_88 (Dropout)            (None, 512)          0           concatenate_29[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "dense_41 (Dense)                (None, 1)            513         dropout_88[0][0]                 \n",
      "==================================================================================================\n",
      "Total params: 8,359,425\n",
      "Trainable params: 359,425\n",
      "Non-trainable params: 8,000,000\n",
      "__________________________________________________________________________________________________\n",
      "Train on 16839 samples, validate on 1872 samples\n",
      "Epoch 1/50\n",
      " - 6s - loss: 0.7065 - acc: 0.6489 - val_loss: 0.6258 - val_acc: 0.6683\n",
      "Epoch 2/50\n",
      " - 3s - loss: 0.5538 - acc: 0.7174 - val_loss: 0.5635 - val_acc: 0.6934\n",
      "Epoch 3/50\n",
      " - 3s - loss: 0.5171 - acc: 0.7342 - val_loss: 0.6086 - val_acc: 0.6806\n",
      "Epoch 4/50\n",
      " - 3s - loss: 0.4884 - acc: 0.7576 - val_loss: 0.5667 - val_acc: 0.6987\n",
      "Epoch 5/50\n",
      " - 3s - loss: 0.4756 - acc: 0.7689 - val_loss: 0.5532 - val_acc: 0.6944\n",
      "Epoch 6/50\n",
      " - 3s - loss: 0.4587 - acc: 0.7764 - val_loss: 0.6048 - val_acc: 0.6891\n",
      "Epoch 7/50\n",
      " - 3s - loss: 0.4399 - acc: 0.7926 - val_loss: 0.5897 - val_acc: 0.6891\n",
      "Epoch 8/50\n",
      " - 3s - loss: 0.4244 - acc: 0.7993 - val_loss: 0.5861 - val_acc: 0.7025\n",
      "Epoch 9/50\n",
      " - 3s - loss: 0.4009 - acc: 0.8166 - val_loss: 0.5915 - val_acc: 0.6998\n",
      "\n",
      "Epoch 00009: ReduceLROnPlateau reducing learning rate to 0.00010000000474974513.\n",
      "Epoch 10/50\n",
      " - 3s - loss: 0.3586 - acc: 0.8379 - val_loss: 0.6062 - val_acc: 0.6982\n",
      "Epoch 11/50\n",
      " - 3s - loss: 0.3545 - acc: 0.8412 - val_loss: 0.6201 - val_acc: 0.6944\n",
      "Epoch 00011: early stopping\n"
     ]
    },
    {
     "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": [
    "#Yoon Kim inspired architecture with pretrained embeddings, \n",
    "#see (deprectaed code!)\n",
    "#https://richliao.github.io/supervised/classification/2016/11/26/textclassifier-convolutional/\n",
    " \n",
    "in_layer = Input(shape=(maxlen,), dtype='int32')\n",
    "embed_layer = Dropout(0.2)((embeddings(word_index, input_dim=vocab_size, \n",
    "                    input_length=maxlen, embedding_dim=200))(in_layer))\n",
    "\n",
    "convs = []\n",
    "for window_size in [2,3,4,5]:\n",
    "    conv_layer = Conv1D(128,window_size,activation='relu')(embed_layer)\n",
    "    conv_layer = GlobalMaxPooling1D()(conv_layer)\n",
    "    convs.append(conv_layer)\n",
    "    \n",
    "layer = Dropout(0.5)(Concatenate()(convs))\n",
    "out_layer = Dense(1, activation='sigmoid')(layer)\n",
    "model = keras.Model(in_layer, out_layer)\n",
    "\n",
    "model.summary()\n",
    "experiment(model, texts, labels, 50, 6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "entries 29714 found for 40000 words in embedding model\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            (None, 300)          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "embedding_1 (Embedding)         (None, 300, 200)     8000000     input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dropout_1 (Dropout)             (None, 300, 200)     0           embedding_1[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "conv1d (Conv1D)                 (None, 299, 128)     51328       dropout_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_1 (Conv1D)               (None, 298, 128)     76928       dropout_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_2 (Conv1D)               (None, 297, 128)     102528      dropout_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_3 (Conv1D)               (None, 296, 128)     128128      dropout_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "global_max_pooling1d (GlobalMax (None, 128)          0           conv1d[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "global_max_pooling1d_1 (GlobalM (None, 128)          0           conv1d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "global_max_pooling1d_2 (GlobalM (None, 128)          0           conv1d_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "global_max_pooling1d_3 (GlobalM (None, 128)          0           conv1d_3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "dropout_2 (Dropout)             (None, 128)          0           global_max_pooling1d[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "dropout_3 (Dropout)             (None, 128)          0           global_max_pooling1d_1[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "dropout_4 (Dropout)             (None, 128)          0           global_max_pooling1d_2[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "dropout_5 (Dropout)             (None, 128)          0           global_max_pooling1d_3[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "concatenate (Concatenate)       (None, 512)          0           dropout_2[0][0]                  \n",
      "                                                                 dropout_3[0][0]                  \n",
      "                                                                 dropout_4[0][0]                  \n",
      "                                                                 dropout_5[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "dropout_6 (Dropout)             (None, 512)          0           concatenate[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "dense_2 (Dense)                 (None, 1)            513         dropout_6[0][0]                  \n",
      "==================================================================================================\n",
      "Total params: 8,359,425\n",
      "Trainable params: 359,425\n",
      "Non-trainable params: 8,000,000\n",
      "__________________________________________________________________________________________________\n",
      "Train on 16839 samples, validate on 1871 samples\n",
      "Epoch 1/50\n",
      " - 9s - loss: 0.6127 - acc: 0.6703 - val_loss: 0.6163 - val_acc: 0.6654\n",
      "Epoch 2/50\n",
      " - 5s - loss: 0.5221 - acc: 0.7293 - val_loss: 0.6023 - val_acc: 0.6756\n",
      "Epoch 3/50\n",
      " - 5s - loss: 0.4966 - acc: 0.7481 - val_loss: 0.6120 - val_acc: 0.6654\n",
      "Epoch 4/50\n",
      " - 5s - loss: 0.4837 - acc: 0.7552 - val_loss: 0.6688 - val_acc: 0.6526\n",
      "Epoch 5/50\n",
      "Finetuning layer 1 decreased learning rate with 0.5\n",
      " - 5s - loss: 0.4583 - acc: 0.7773 - val_loss: 0.6045 - val_acc: 0.6820\n",
      "Epoch 6/50\n",
      " - 5s - loss: 0.4475 - acc: 0.7832 - val_loss: 0.6220 - val_acc: 0.6831\n",
      "\n",
      "Epoch 00006: ReduceLROnPlateau reducing learning rate to 5.0000002374872565e-05.\n",
      "Epoch 7/50\n",
      " - 5s - loss: 0.4328 - acc: 0.7943 - val_loss: 0.6268 - val_acc: 0.6863\n",
      "Epoch 8/50\n",
      " - 5s - loss: 0.4267 - acc: 0.7992 - val_loss: 0.6216 - val_acc: 0.6825\n",
      "Epoch 00008: early stopping\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Yoon Kim inspired architecture with pretrained embeddings + fine tuning \n",
    "#see (deprectaed code!)\n",
    "#https://richliao.github.io/supervised/classification/2016/11/26/textclassifier-convolutional/\n",
    "\n",
    "#fasttext\n",
    "path = \"/home/hellrich/keras-test/embeddings/wiki-news-\"\n",
    "suffix = \"d-1M.vec\"\n",
    "embedding_dim=300\n",
    "\n",
    "#bionlp\n",
    "path = \"/home/hellrich/keras-test/embeddings/bio_nlp_win2_\"\n",
    "path = \"/home/hellrich/keras-test/embeddings/bio_nlp_win30_\"\n",
    "embedding_dim = 200\n",
    "suffix = \"d.vec\"\n",
    "\n",
    "in_layer = Input(shape=(maxlen,), dtype='int32')\n",
    "embed_layer = Dropout(0.2)((embeddings(word_index, path=path, suffix=suffix, input_dim=vocab_size, \n",
    "                    input_length=maxlen, embedding_dim=embedding_dim))(in_layer))\n",
    "\n",
    "convs = []\n",
    "for window_size in [2,3,4,5]:\n",
    "    conv_layer = Conv1D(128,window_size,activation='relu')(embed_layer)\n",
    "    conv_layer = Dropout(0.2)(GlobalMaxPooling1D()(conv_layer))\n",
    "    convs.append(conv_layer)\n",
    "    \n",
    "layer = Dropout(0.5)(Concatenate()(convs))\n",
    "out_layer = Dense(1, activation='sigmoid')(layer)\n",
    "model = keras.Model(in_layer, out_layer)\n",
    "\n",
    "model.summary()\n",
    "experiment(model, texts, labels, 50, 6, fine_tune_embeddings=(4,0.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "entries 29714 found for 40000 words in embedding model\n"
     ]
    }
   ],
   "source": [
    "#loading stuff for cell below\n",
    "max_sentences = 10\n",
    "MAX_SEQUENCE_LENGTH = 30\n",
    "texts, labels, word_index = read_data(maxlen=maxlen, vocab_size=vocab_size, \n",
    "                                      return_word_index=True, sentence_wise=(max_sentences,MAX_SEQUENCE_LENGTH))\n",
    "\n",
    "#bionlp\n",
    "path = \"/home/hellrich/keras-test/embeddings/bio_nlp_win2_\"\n",
    "#path = \"/home/hellrich/keras-test/embeddings/bio_nlp_win30_\"\n",
    "embedding_dim = 200\n",
    "suffix = \"d.vec\"\n",
    "e =  embeddings(word_index, path=path, suffix=suffix, input_dim=vocab_size, input_length=maxlen, embedding_dim=embedding_dim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_6 (InputLayer)         (None, 10, 30)            0         \n",
      "_________________________________________________________________\n",
      "time_distributed_1 (TimeDist (None, 10, 64)            8067840   \n",
      "_________________________________________________________________\n",
      "dropout_8 (Dropout)          (None, 10, 64)            0         \n",
      "_________________________________________________________________\n",
      "lstm_3 (LSTM)                (None, 64)                33024     \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 1)                 65        \n",
      "=================================================================\n",
      "Total params: 8,100,929\n",
      "Trainable params: 100,929\n",
      "Non-trainable params: 8,000,000\n",
      "_________________________________________________________________\n",
      "Train on 16839 samples, validate on 1871 samples\n",
      "Epoch 1/50\n",
      " - 54s - loss: 0.5789 - acc: 0.6819 - val_loss: 0.5620 - val_acc: 0.6815\n",
      "Epoch 2/50\n",
      " - 52s - loss: 0.5197 - acc: 0.7286 - val_loss: 0.5749 - val_acc: 0.6734\n",
      "Epoch 3/50\n",
      " - 52s - loss: 0.4973 - acc: 0.7439 - val_loss: 0.5636 - val_acc: 0.6841\n",
      "Epoch 4/50\n",
      " - 52s - loss: 0.4804 - acc: 0.7540 - val_loss: 0.5926 - val_acc: 0.6921\n",
      "Epoch 5/50\n"
     ]
    }
   ],
   "source": [
    "#test with sentence and document level lstms, see https://github.com/keras-team/keras/issues/5516#issuecomment-295016548\n",
    "\n",
    "\n",
    "# Encode each timestep\n",
    "in_sentence = Input(shape=(MAX_SEQUENCE_LENGTH,), dtype='int64')\n",
    "embedded_sentence = e(in_sentence)\n",
    "lstm_sentence = LSTM(64)(embedded_sentence)\n",
    "encoded_model = keras.Model(in_sentence, lstm_sentence)\n",
    "\n",
    "sequence_input = Input(shape=(max_sentences, MAX_SEQUENCE_LENGTH), dtype='int64')\n",
    "seq_encoded = TimeDistributed(encoded_model)(sequence_input)\n",
    "seq_encoded = Dropout(0.2)(seq_encoded)\n",
    "\n",
    "# Encode entire sentence\n",
    "seq_encoded = LSTM(64)(seq_encoded)\n",
    "\n",
    "# Prediction\n",
    "out_layer = Dense(1, activation='sigmoid')(seq_encoded)\n",
    "model = keras.Model(sequence_input, out_layer)\n",
    "\n",
    "model.summary()\n",
    "experiment(model, texts, labels, 50, 6)"
   ]
  }
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