char-rnn_trump_text_generation-TESTING.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Char-RNN Trump-like text generation - TESTING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "import numpy as np\n",
    "import string\n",
    "\n",
    "from keras.models import Sequential\n",
    "from keras.layers import LSTM, Dense, Activation\n",
    "\n",
    "LAYER_COUNT = 4\n",
    "HIDDEN_LAYERS_DIM = 512"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "vocabulary len = 98\n",
      "['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z', '!', '\"', '#', '$', '%', '&', \"'\", '(', ')', '*', '+', ',', '-', '.', '/', ':', ';', '<', '=', '>', '?', '@', '[', '\\\\', ']', '^', '_', '`', '{', '|', '}', '~', ' ', '\\t', '\\n', '\\r']\n"
     ]
    }
   ],
   "source": [
    "# generic vocabulary\n",
    "characters = list(string.printable)\n",
    "characters.remove('\\x0b')\n",
    "characters.remove('\\x0c')\n",
    "\n",
    "VOCABULARY_SIZE = len(characters)\n",
    "characters_to_ix = {c:i for i,c in enumerate(characters)}\n",
    "print(\"vocabulary len = %d\" % VOCABULARY_SIZE)\n",
    "print(characters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test_model = Sequential()\n",
    "for i in range(LAYER_COUNT):\n",
    "    test_model.add(\n",
    "            LSTM(\n",
    "                HIDDEN_LAYERS_DIM, \n",
    "                return_sequences=True if (i!=(LAYER_COUNT-1)) else False,\n",
    "                batch_input_shape=(1, 1, VOCABULARY_SIZE),\n",
    "                stateful=True\n",
    "            )\n",
    "        )\n",
    "test_model.add(Dense(VOCABULARY_SIZE))\n",
    "test_model.add(Activation('softmax'))\n",
    "test_model.compile(loss='categorical_crossentropy', optimizer=\"adam\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test_model.load_weights(\n",
    "    \"./weights/weights_4-512-lstm_loss1.2550_val-loss1.2443\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def sample(preds, temperature=1.0):\n",
    "    \"\"\"Helper function to sample an index from a probability array\"\"\"\n",
    "    # from fchollet/keras\n",
    "    preds = np.asarray(preds).astype('float64')\n",
    "    preds = np.log(preds) / temperature\n",
    "    exp_preds = np.exp(preds)\n",
    "    preds = exp_preds / np.sum(exp_preds)\n",
    "    probas = np.random.multinomial(1, preds, 1)\n",
    "    return np.argmax(probas)\n",
    "\n",
    "def predict_next_char(model, current_char, diversity=1.0):\n",
    "    \"\"\"Predict the next character from the current one\"\"\"\n",
    "    x = np.zeros((1, 1, VOCABULARY_SIZE))\n",
    "    x[:,:,characters_to_ix[current_char]] = 1\n",
    "    y = model.predict(x, batch_size=1)\n",
    "    next_char_ix = sample(y[0,:], temperature=diversity)\n",
    "    next_char = characters[next_char_ix]\n",
    "    return next_char\n",
    "\n",
    "def generate_text(model, seed=\"I am\", count=140):\n",
    "    \"\"\"Generate characters from a given seed\"\"\"\n",
    "    model.reset_states()\n",
    "    for s in seed[:-1]:\n",
    "        next_char = predict_next_char(model, s)\n",
    "    current_char = seed[-1]\n",
    "\n",
    "    sys.stdout.write(\"[\"+seed+\"]\")\n",
    "    \n",
    "    for i in range(count - len(seed)):\n",
    "        next_char = predict_next_char(model, current_char, diversity=0.5)\n",
    "        current_char = next_char\n",
    "        sys.stdout.write(next_char)\n",
    "    print(\"...\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[It is ]a great deal. What a new facts in the world! \"@markasimonce: @realDonaldTrump @seanhannity is the only person to save this country.\" \"...\n",
      "\n",
      "[It is ]the best season of the @WhiteHouse. #Some \"@STPolitics: @realDonaldTrump The Donald Trump is a vision in 2016. We need you! #trump2016...\n",
      "\n",
      "[It is ]a total disaster! \"@michaelsser: @realDonaldTrump why don't you run for President?\" Very special interests are already proud of you - ...\n",
      "\n",
      "[It is ]right. I will be back soon! @BarackObama is a strong response to the U.S. across the truth of the U.S. and an economy http://t.co/IruH...\n",
      "\n",
      "[It is ]a complete person they will be heading to line that voted for @realDonaldTrump\" Thank you. \"@David_Bobby: @realDonaldTrump @Jackshalli...\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for i in range(5):\n",
    "    generate_text(\n",
    "        test_model,\n",
    "        seed=\"It is \"\n",
    "    )"
   ]
  }
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