Beefed up Chainer LSTM example

chainer_lstm.ipynb

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     "text": [
      "C:\\Python34\\lib\\site-packages\\skcuda\\cublas.py:273: UserWarning: creating CUBLAS context to get version number\n",
      "  warnings.warn('creating CUBLAS context to get version number')\n"
     ]
    }
   ],
   "source": [
    "import argparse\n",
    "import math\n",
    "import sys\n",
    "import time\n",
    "\n",
    "import numpy as np\n",
    "import six\n",
    "\n",
    "import chainer\n",
    "from chainer import cuda\n",
    "import chainer.functions as F\n",
    "from chainer import optimizers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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   "source": [
    "\n",
    "######### CONFIGURATION ###########\n",
    "\n",
    "# training data source\n",
    "train_file = 'chainer.txt'\n",
    "train_chars    = True  # False for word-level training like the original example\n",
    "tokenize_caps  = False # if set to True in train_chars mode, will replace AB with |a|b to get a smaller vocabulary\n",
    "train_fraction = 0.90  # amount of source file data to use for training \n",
    "valid_fraction = 0.03  # amount of source file data to use for validation (too much -> slow training)\n",
    "test_fraction  = 0.07  # amount of source file data to use for training \n",
    "\n",
    "# training parameters\n",
    "n_epoch    = 100       # number of epochs\n",
    "n_units    = 200       # number of units per layer\n",
    "batchsize  = 128       # minibatch size\n",
    "bprop_len  = 120       # length of truncated BPTT\n",
    "grad_clip  = 5         # gradient norm threshold to clip\n",
    "use_GPU_no = 0         # GPU index to use, set to -1 for CPU\n",
    "\n",
    "# reporting parameters\n",
    "generate_every = 180             # every X seconds during training, generate some text to assess progress\n",
    "validate_every = int(n_epoch/8)  # default: do a validation every n-th epoch during training (default=8 times total)\n",
    "perp_every     = 30              # display the current perplexity every x seconds\n",
    "\n",
    "####### END CONFIGURATION #########\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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   "source": [
    "# CANNOT USE THIS IN NOTEBOOK\n",
    "\n",
    "#parser = argparse.ArgumentParser()\n",
    "#parser.add_argument('--gpu', '-g', default=-1, type=int,\n",
    "#                    help='GPU ID (negative value indicates CPU)')\n",
    "#args = parser.parse_args()\n",
    "#mod = cuda if args.gpu >= 0 else np\n",
    "# so let's fake --gpu 0 arg\n",
    "\n",
    "class FakeArg:\n",
    "    def __init__(self):\n",
    "        pass\n",
    "args = FakeArg()\n",
    "mod = cuda\n",
    "args.gpu = use_GPU_no"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#vocab = 88\n",
      "train_data: 74246 items\n",
      "valid_data: 1650 items\n",
      "test_data: 6600 items\n"
     ]
    }
   ],
   "source": [
    "## text generation helper functions\n",
    "\n",
    "def replace_caps(input, identifier=\"|\", identifier_replace=\"}{\"):\n",
    "    input = input.replace(identifier, identifier_replace)\n",
    "    uppers=[chr(n) for n in range(ord(\"A\"),ord(\"Z\"))]\n",
    "    lowers=[identifier+chr(n) for n in range(ord(\"a\"),ord(\"z\"))]\n",
    "    for u, repl in zip(uppers,lowers):\n",
    "        input = input.replace(u,repl)\n",
    "    return input\n",
    "        \n",
    "\n",
    "def rebuild_caps(input, identifier=\"|\", identifier_replace=\"}{\"):\n",
    "    uppers=[chr(n) for n in range(ord(\"A\"),ord(\"Z\"))]\n",
    "    lowers=[identifier+chr(n) for n in range(ord(\"a\"),ord(\"z\"))]\n",
    "    for u, repl in zip(uppers,lowers):\n",
    "        input = input.replace(repl,u)\n",
    "    input = input.replace(identifier_replace, identifier)\n",
    "    return input\n",
    "\n",
    "\n",
    "def load_data(filename):\n",
    "    global vocab, n_vocab\n",
    "    words = open(filename).read().replace('\\n', ' <eos> ').strip().split()\n",
    "    dataset = np.ndarray((len(words),), dtype=np.int32)\n",
    "    for i, word in enumerate(words):\n",
    "        if word not in vocab:\n",
    "            vocab[word] = len(vocab)\n",
    "        dataset[i] = vocab[word]\n",
    "    return dataset\n",
    "\n",
    "def load_data_onechar(filename, nocaps=True):\n",
    "    global vocab, n_vocab\n",
    "    with open(filename,\"rb\") as f:\n",
    "        raw = f.read().decode('utf-8',errors='replace')\n",
    "        if nocaps: \n",
    "            raw = replace_caps(raw)\n",
    "        chars = [char for char in raw.replace('\\n', '<br>').replace(\"\\r\",\"\").strip() ]\n",
    "    dataset = np.ndarray((len(chars),), dtype=np.int32)\n",
    "    vocab[\"\"] = 0\n",
    "    for i, char in enumerate(chars):\n",
    "        if char not in vocab:\n",
    "            vocab[char] = len(vocab)\n",
    "        dataset[i] = vocab[char]\n",
    "    return dataset\n",
    "\n",
    "# Prepare dataset (preliminary download dataset by ./download.py)\n",
    "vocab = {}\n",
    "\n",
    "#all_train_data = load_data('emilydickinson.txt')\n",
    "if train_chars:\n",
    "    all_train_data = load_data_onechar(train_file, nocaps=tokenize_caps)\n",
    "else:\n",
    "    all_train_data = load_data(train_file)\n",
    "    \n",
    "\n",
    "#valid_data = load_data('ptb.valid.txt')\n",
    "#test_data = load_data('ptb.test.txt')\n",
    "\n",
    "total_len = len(all_train_data)\n",
    "\n",
    "#potentially truncate the data for faster prototype training\n",
    "total_len = total_len #/5\n",
    "\n",
    "# chop parts off for validation and test\n",
    "train = total_len * train_fraction\n",
    "valid = total_len * valid_fraction\n",
    "test = total_len * test_fraction\n",
    "\n",
    "train_data = all_train_data[:int(train)]\n",
    "valid_data = all_train_data[int(train):int(train+valid)]\n",
    "test_data = all_train_data[int(train+valid):int(train+valid+test)]\n",
    "\n",
    "print('#vocab =', len(vocab))\n",
    "print(\"train_data:\",len(train_data),\"items\")\n",
    "print(\"valid_data:\",len(valid_data),\"items\")\n",
    "print(\"test_data:\",len(test_data),\"items\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
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   "outputs": [],
   "source": [
    "# Neural net architecture\n",
    "\n",
    "def forward_one_step(x_data, y_data, state, train=True):\n",
    "    if args.gpu >= 0:\n",
    "        x_data = cuda.to_gpu(x_data)\n",
    "        y_data = cuda.to_gpu(y_data)\n",
    "    \n",
    "    x = chainer.Variable(x_data, volatile=not train)\n",
    "    t = chainer.Variable(y_data, volatile=not train)\n",
    "    \n",
    "    h0 = model.embed(x)\n",
    "    \n",
    "    h1_in = model.l1_x(F.dropout(h0, train=train)) + model.l1_h(state['h1'])\n",
    "    c1, h1 = F.lstm(state['c1'], h1_in)\n",
    "    h2_in = model.l2_x(F.dropout(h1, train=train)) + model.l2_h(state['h2'])\n",
    "    c2, h2 = F.lstm(state['c2'], h2_in)\n",
    "    y = model.l3(F.dropout(h2, train=train))\n",
    "    \n",
    "    state = {'c1': c1, 'h1': h1, 'c2': c2, 'h2': h2}\n",
    "    return state, F.softmax_cross_entropy(y, t)\n",
    "\n",
    "\n",
    "def make_initial_state(batchsize=batchsize, train=True):\n",
    "    return {name: chainer.Variable(mod.zeros((batchsize, n_units), dtype=np.float32), volatile=not train)\n",
    "            for name in ('c1', 'h1', 'c2', 'h2')}\n",
    "\n",
    "# Evaluation routine\n",
    "def evaluate(dataset):\n",
    "    sum_log_perp = mod.zeros(())\n",
    "    state = make_initial_state(batchsize=1, train=False)\n",
    "    for i in six.moves.range(0,dataset.size - 1,1):\n",
    "        x_batch = dataset[i:i + 1]\n",
    "        y_batch = dataset[i + 1:i + 2]\n",
    "        state, loss = forward_one_step(x_batch, y_batch, state, train=False)\n",
    "        sum_log_perp += loss.data.reshape(())\n",
    "\n",
    "    return math.exp(cuda.to_cpu(sum_log_perp) / (dataset.size - 1))\n",
    "\n",
    "\n",
    "# Prepare RNNLM model\n",
    "model = chainer.FunctionSet(embed=F.EmbedID(len(vocab), n_units),\n",
    "                            l1_x=F.Linear(n_units, 4 * n_units), \n",
    "                            l1_h=F.Linear(n_units, 4 * n_units),\n",
    "                            l2_x=F.Linear(n_units, 4 * n_units), \n",
    "                            l2_h=F.Linear(n_units, 4 * n_units),\n",
    "                            l3=F.Linear(n_units, len(vocab)))\n",
    "\n",
    "for param in model.parameters:\n",
    "    param[:] = np.random.uniform(-0.1, 0.1, param.shape)\n",
    "if args.gpu >= 0:\n",
    "    cuda.init(args.gpu)\n",
    "    model.to_gpu()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# helper functions\n",
    "def to_word(idx):\n",
    "    global vocab\n",
    "    return list(vocab.keys())[list(vocab.values()).index(idx)]\n",
    "\n",
    "def to_index(word):\n",
    "    global vocab\n",
    "    return vocab.get(word.lower(),vocab.get('<unk>',0))\n",
    "\n",
    "\n",
    "def produce_one_step(x_data,state, train=False):\n",
    "    if args.gpu >= 0:\n",
    "        x_data = cuda.to_gpu(x_data)\n",
    "    x = chainer.Variable(x_data, volatile=not train)\n",
    "    h0 = model.embed(x)\n",
    "    h1_in = model.l1_x(F.dropout(h0, train=train)) + model.l1_h(state['h1'])\n",
    "    c1, h1 = F.lstm(state['c1'], h1_in)\n",
    "    h2_in = model.l2_x(F.dropout(h1, train=train)) + model.l2_h(state['h2'])\n",
    "    c2, h2 = F.lstm(state['c2'], h2_in)\n",
    "    y = model.l3(F.dropout(h2, train=train))\n",
    "    state = {'c1': c1, 'h1': h1, 'c2': c2, 'h2': h2}\n",
    "    return state, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#### TEXT PRODUCTION HELPERS\n",
    "# helper functions\n",
    "def to_word(idx):\n",
    "    global vocab\n",
    "    return list(vocab.keys())[list(vocab.values()).index(idx)]\n",
    "\n",
    "def to_index(word):\n",
    "    global vocab\n",
    "    return vocab.get(word.lower(),vocab.get('<unk>',0))\n",
    "\n",
    "\n",
    "def produce_one_step(x_data,state, train=False):\n",
    "    if args.gpu >= 0:\n",
    "        x_data = cuda.to_gpu(x_data)\n",
    "    x = chainer.Variable(x_data, volatile=not train)\n",
    "    h0 = model.embed(x)\n",
    "    h1_in = model.l1_x(F.dropout(h0, train=train)) + model.l1_h(state['h1'])\n",
    "    c1, h1 = F.lstm(state['c1'], h1_in)\n",
    "    h2_in = model.l2_x(F.dropout(h1, train=train)) + model.l2_h(state['h2'])\n",
    "    c2, h2 = F.lstm(state['c2'], h2_in)\n",
    "    y = model.l3(F.dropout(h2, train=train))\n",
    "    state = {'c1': c1, 'h1': h1, 'c2': c2, 'h2': h2}\n",
    "    return state, y\n",
    "\n",
    "import sys\n",
    "\n",
    "def generate(primer_text,amount,dictionary,randomness=0.5,start_state=None,stop_count=3, separator=\" \"):\n",
    "    state = start_state or make_initial_state(1,False)\n",
    "    \n",
    "    primer_text.replace(\"\\n\",\"<eos>\")\n",
    "    print(primer_text + \" => \")\n",
    "    if separator: # don't split if it's a character based model!\n",
    "        primer_text = primer_text.split()\n",
    "    primer_x = [to_index(word) for word in primer_text]\n",
    "    \n",
    "    # priming until before the last item, that goes into generation loop since we want the result\n",
    "    for item in primer_x[:-1]:\n",
    "        state, y = produce_one_step(np.array([item],dtype=\"int32\"),state)\n",
    "        yarr = [float(x) for x in cuda.to_cpu(y.data.ravel())]\n",
    "        #print(to_word(item),\"->\",to_word(np.argmax(yarr)),end=\" # \")\n",
    "        sys.stdout.flush()\n",
    "        \n",
    "    #print(\"\\nLooping:\\n\")\n",
    "    \n",
    "    current = primer_x[-1]\n",
    "    end_count=0\n",
    "    for i in range(amount):\n",
    "        # feed x to model\n",
    "        state, y = produce_one_step(np.array([current],dtype=\"int32\"),state)\n",
    "        # append y to x - wait, we DON'T NEED TO APPEND; THIS IS A REAL RNN\n",
    "        # print y\n",
    "        yarr = cuda.to_cpu(y.data.ravel())\n",
    "        result_word = \"<unk>\"\n",
    "        while result_word == \"<unk>\":\n",
    "            yarr *= (1+np.random.uniform(high=randomness,size=len(yarr)))\n",
    "            yidx = np.argmax([float(i) for i in yarr])\n",
    "            result_word = to_word(yidx)\n",
    "        print(result_word.replace(\"\\r\",\"<r>\"),end=separator)\n",
    "        sys.stdout.flush()\n",
    "        current = to_index(result_word)\n",
    "\n",
    "        if i >= 5: # look for production stoppers after 5 words\n",
    "            if \".\" in result_word or \"!\" in result_word or \"?\" in result_word:\n",
    "                end_count += 1\n",
    "            if \"<eos>\" in result_word:\n",
    "                end_count += 3\n",
    "            if end_count >= stop_count:\n",
    "                break\n",
    "        \n",
    "        #y_cpu = cuda.to_cpu(y)\n",
    "        #print(\"y.data-\",y.data[0][-1],end=\"#\")\n",
    "        #current=int(y_cpu.data[0][-1])\n",
    "    print(\"\\n\",\"-\" * 16)\n",
    "        \n",
    "    return state\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# plotting \n",
    "%matplotlib inline\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def plot_progress(perp_dic, title=\"perplexity\", start_at=0, log_axis=True):\n",
    "    trn = list(perp_dic.items())\n",
    "    trn.sort()\n",
    "    plt.plot([x[0] for x in trn[start_at:]],[x[1] for x in trn[start_at:]] )\n",
    "    plt.title(title)\n",
    "    if log_axis: plt.yscale('log')\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Setup optimizer\n",
    "#optimizer = optimizers.SGD(lr=1.0)\n",
    "#optimizer = optimizers.SGD(lr=1.)\n",
    "optimizer = optimizers.Adam()\n",
    "optimizer.setup(model.collect_parameters())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Learning loop prep\n",
    "\n",
    "# training data range init\n",
    "whole_len = train_data.shape[0]\n",
    "jump = whole_len // batchsize\n",
    "epoch = 0\n",
    "start_iter = 0 # use 'start_iter = i' if you want to continue a previous training!\n",
    "# time stamps for throughput calc\n",
    "start_at = time.time()\n",
    "cur_at = start_at \n",
    "# loss and state initialization\n",
    "cur_log_perp = mod.zeros(())\n",
    "accum_loss = chainer.Variable(mod.zeros((), dtype=np.float32)) \n",
    "state = make_initial_state()\n",
    "# logging initialization\n",
    "training_perps = {}\n",
    "valid_perps = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# uncomment the next line to continue previously interrupted training!\n",
    "#start_iter = i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "batch size is 580\n",
      "going to train 100 epochs = 58000 iterations\n",
      "iter    193 training perplexity:   89.73 (   6.62 iters/sec) ETA: Mon Aug  3 19:32:28 2015\n",
      "iter   2427 training perplexity:   39.66 (  27.56 iters/sec) ETA: Mon Aug  3 17:41:30 2015\n",
      "iter   3536 training perplexity:   25.92 (  30.21 iters/sec) ETA: Mon Aug  3 17:38:26 2015\n",
      "iter   4679 training perplexity:   22.56 (  32.04 iters/sec) ETA: Mon Aug  3 17:36:37 2015\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x18299ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- Generating text: \n",
      "i...\n",
      "                                                                                                                                                      \n",
      " ----------------------------------------\n",
      "Validating.. epoch 12 validation perplexity: 17.88\n",
      "iter   7946 training perplexity:   18.24 (  31.67 iters/sec) ETA: Mon Aug  3 17:36:57 2015\n",
      "iter   9077 training perplexity:   14.79 (  32.43 iters/sec) ETA: Mon Aug  3 17:36:15 2015\n",
      "iter  11363 training perplexity:   12.99 (  33.53 iters/sec) ETA: Mon Aug  3 17:35:16 2015\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x182e0198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- Generating text: \n",
      "i...\n",
      "ettarray                                                                                                                                              \n",
      " ----------------------------------------\n",
      "iter  12360 training perplexity:   11.63 (  33.59 iters/sec) ETA: Mon Aug  3 17:35:13 2015\n",
      "iter  13494 training perplexity:   11.05 (  34.00 iters/sec) ETA: Mon Aug  3 17:34:52 2015\n",
      "Validating.. epoch 25 validation perplexity: 11.08\n",
      "iter  15720 training perplexity:   10.17 (  33.33 iters/sec) ETA: Mon Aug  3 17:35:27 2015\n",
      "iter  16864 training perplexity:    9.40 (  33.68 iters/sec) ETA: Mon Aug  3 17:35:08 2015\n",
      "iter  19098 training perplexity:    8.85 (  34.12 iters/sec) ETA: Mon Aug  3 17:34:46 2015\n",
      "iter  20234 training perplexity:    8.34 (  34.37 iters/sec) ETA: Mon Aug  3 17:34:34 2015\n",
      "iter  21360 training perplexity:    8.02 (  34.52 iters/sec) ETA: Mon Aug  3 17:34:26 2015\n",
      "Validating.. epoch 37 validation perplexity: 7.85\n",
      "iter  22512 training perplexity:    7.65 (  33.96 iters/sec) ETA: Mon Aug  3 17:34:54 2015\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x184837f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- Generating text: \n",
      "i...\n",
      "nentet or                                                                                                                                             \n",
      " ----------------------------------------\n",
      "iter  25800 training perplexity:    7.02 (  34.32 iters/sec) ETA: Mon Aug  3 17:34:36 2015\n",
      "iter  26927 training perplexity:    6.48 (  34.49 iters/sec) ETA: Mon Aug  3 17:34:28 2015\n",
      "iter  28070 training perplexity:    6.25 (  34.66 iters/sec) ETA: Mon Aug  3 17:34:20 2015\n",
      "Validating.. epoch 50 validation perplexity: 5.84\n",
      "iter  29056 training perplexity:    6.06 (  34.03 iters/sec) ETA: Mon Aug  3 17:34:51 2015\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x17034358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- Generating text: \n",
      "i...\n",
      "nter sonction tor is the tore the setine the the array. on array.<br><br>                                                                             \n",
      " ----------------------------------------\n",
      "iter  31320 training perplexity:    5.76 (  34.28 iters/sec) ETA: Mon Aug  3 17:34:38 2015\n",
      "iter  32411 training perplexity:    5.52 (  34.38 iters/sec) ETA: Mon Aug  3 17:34:33 2015\n",
      "iter  33505 training perplexity:    5.38 (  34.48 iters/sec) ETA: Mon Aug  3 17:34:28 2015\n",
      "iter  34597 training perplexity:    5.25 (  34.57 iters/sec) ETA: Mon Aug  3 17:34:24 2015\n",
      "iter  35755 training perplexity:    5.12 (  34.72 iters/sec) ETA: Mon Aug  3 17:34:17 2015\n",
      "Validating.. epoch 62 validation perplexity: 4.81\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x170210f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- Generating text: \n",
      "i...\n",
      "nters):<br>                                                                                                                                           \n",
      " ----------------------------------------\n",
      "iter  37907 training perplexity:    4.93 (  34.33 iters/sec) ETA: Mon Aug  3 17:34:36 2015\n",
      "iter  40150 training perplexity:    4.68 (  34.52 iters/sec) ETA: Mon Aug  3 17:34:27 2015\n",
      "iter  42360 training perplexity:    4.48 (  34.65 iters/sec) ETA: Mon Aug  3 17:34:20 2015\n",
      "iter  43465 training perplexity:    4.33 (  34.73 iters/sec) ETA: Mon Aug  3 17:34:16 2015\n",
      "Validating.. epoch 75 validation perplexity: 4.18\n",
      "iter  44592 training perplexity:    4.24 (  34.42 iters/sec) ETA: Mon Aug  3 17:34:31 2015\n",
      "iter  46815 training perplexity:    4.12 (  34.56 iters/sec) ETA: Mon Aug  3 17:34:24 2015\n",
      "iter  47880 training perplexity:    4.02 (  34.61 iters/sec) ETA: Mon Aug  3 17:34:22 2015\n"
     ]
    },
    {
     "data": {
      "image/png": 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dTFJJxV5EADjwQHj6aXjtNX9RlWbN/GieL74InUxSQcVeRHbSsSNMmAD//re/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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1829a320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- Generating text: \n",
      "i...\n",
      "ntext is nont of inition in setine the corater<br>                                                                                                    \n",
      " ----------------------------------------\n",
      "Validating.. epoch 87 validation perplexity: 3.80\n",
      "iter  51120 training perplexity:    3.86 (  34.32 iters/sec) ETA: Mon Aug  3 17:34:36 2015\n",
      "iter  52200 training perplexity:    3.73 (  34.37 iters/sec) ETA: Mon Aug  3 17:34:34 2015\n",
      "iter  53352 training perplexity:    3.68 (  34.47 iters/sec) ETA: Mon Aug  3 17:34:29 2015\n",
      "iter  55560 training perplexity:    3.58 (  34.57 iters/sec) ETA: Mon Aug  3 17:34:24 2015\n",
      "iter  56676 training perplexity:    3.51 (  34.64 iters/sec) ETA: Mon Aug  3 17:34:21 2015\n",
      "iter  57810 training perplexity:    3.47 (  34.72 iters/sec) ETA: Mon Aug  3 17:34:17 2015\n",
      "Validating.. epoch 100 validation perplexity: 3.52\n",
      "\n",
      "Training complete! Evaluating test data..done!\n",
      "\n",
      "Test perplexity: 3.4527037704247037\n"
     ]
    }
   ],
   "source": [
    "# Learning loop \n",
    "print('batch size is {}'.format(jump))\n",
    "total_iterations = jump * n_epoch\n",
    "print('going to train {} epochs = {} iterations'.format(n_epoch,total_iterations))\n",
    "last_iter = 0 # to count throughput, for restarting iteration loop after interrupting training\n",
    "sys.stdout.flush()\n",
    "for i in six.moves.range(start_iter,jump * n_epoch):\n",
    "    x_batch = np.array([train_data[(jump * j + i) % whole_len]\n",
    "                        for j in six.moves.range(batchsize)])\n",
    "    y_batch = np.array([train_data[(jump * j + i + 1) % whole_len]\n",
    "                        for j in six.moves.range(batchsize)])\n",
    "    state, loss_i = forward_one_step(x_batch, y_batch, state)\n",
    "    accum_loss += loss_i\n",
    "    cur_log_perp += loss_i.data.reshape(())\n",
    "    \n",
    "    \n",
    "    if (i + 1) % bprop_len == 0:  # Run truncated BPTT\n",
    "        optimizer.zero_grads()\n",
    "        accum_loss.backward()\n",
    "        accum_loss.unchain_backward()  # truncate\n",
    "        accum_loss = chainer.Variable(mod.zeros((), dtype=np.float32))\n",
    "\n",
    "        optimizer.clip_grads(grad_clip)\n",
    "        optimizer.update()\n",
    "\n",
    "    if int(time.time() - cur_at + 1) % perp_every == 0:\n",
    "        now = time.time()\n",
    "        throuput = i / (now - start_at)\n",
    "        perp = math.exp(cuda.to_cpu(cur_log_perp) / (i-last_iter))\n",
    "        training_perps[i] = perp\n",
    "        print('iter {:6d} training perplexity: {:7.2f} ({:7.2f} iters/sec)'.format(\n",
    "            i + 1, perp, throuput),end=\" \")\n",
    "        #print('ETA final: {:.1f} minutes'.format((total_iterations-i)/throuput/60.0))\n",
    "        if i> jump/4: \n",
    "            print(\"ETA:\",time.ctime(time.time()+(total_iterations-i)/throuput))\n",
    "        else: \n",
    "            print(\"\")\n",
    "        cur_at = now\n",
    "        last_iter = i\n",
    "        cur_log_perp.fill(0)\n",
    "\n",
    "    if int(time.time() - start_at + 1) % generate_every == 0:       \n",
    "        plot_progress(training_perps,\n",
    "                      title=\"Training perplexity (vocab size: {})\".format(len(vocab)),\n",
    "                      start_at=2) \n",
    "        print(\"--- Generating text: \")\n",
    "        generate(to_word(1), 150, vocab, start_state=None,separator=\"\")\n",
    "        \n",
    "    if (i+1) % jump == 0:\n",
    "        epoch += 1\n",
    "        \n",
    "    if (i + 1) % int(validate_every) == 0:\n",
    "        print('Validating..',end=\" \")\n",
    "        sys.stdout.flush()\n",
    "        now = time.time()\n",
    "        perp = evaluate(valid_data)\n",
    "        valid_perps[i] = perp\n",
    "        #print('ETA for end of training: in {:.1f} minutes'.format((total_iterations-i)/throuput/60.0))\n",
    "        print('epoch {} validation perplexity: {:.2f}'.format(epoch, perp))\n",
    "        cur_at += time.time() - now  # skip time of evaluation\n",
    "\n",
    "        if \"SGD\" in str(type(optimizer)) and epoch >= 8:\n",
    "            optimizer.lr /= 1.1\n",
    "            print('learning rate =', optimizer.lr)\n",
    "\n",
    "    sys.stdout.flush()\n",
    "    \n",
    "print('\\nTraining complete! Evaluating test data..',end=\"\")\n",
    "sys.stdout.flush()\n",
    "test_perp = evaluate(test_data)\n",
    "print('done!\\n\\nTest perplexity:', test_perp)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Now, wait for the training to complete\n",
    "\n",
    "* After a couple iterations, you will get an estimated ETA for completion\n",
    "* You can interrupt it with Kernel -> interrupt and continue after some tests! \n",
    "* (uncomment and run the cell before the training loop to reinstate the iteration counter if you do)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Once it's done, generate a complete report about the run:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n_epoch : 100\n",
      "n_units : 200\n",
      "batchsize : 128\n",
      "bprop_len : 120\n",
      "grad_clip : 5\n",
      "train_file : chainer.txt\n",
      "optimizer : <chainer.optimizers.adam.Adam object at 0x000000000FA147F0>\n",
      "train_chars : True\n",
      "tokenize_caps : False\n"
     ]
    },
    {
     "data": {
      "image/png": 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zttH6DsCRwGwS0r6oriVmtne06QjgX8Ak4mpfgXZOHEWY+bEAGN1UO0VyqPNB\nwhHA6wljX2cD5YSdYvOByUD7rOdfHrVpHvC9rO0HEP5QFwA3Z23fHngIeJcwU6RHE7fv24Tx3pnA\nm9EyPCltBPoB/wRmRbX9n2h7ItqXVcMwqmfjJKJthDHtmdHydiYnktK+6PP7A69Hf58TCTttY2uf\nDqoSEUkBXZZQRCQFFPYiIimgsBcRSQGFvYhICijsRURSQGEvIpICCnsRkRRQ2IuIpMD/B3Yn64bU\nAXnvAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16f57048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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kDOSc7M1sJzObaWavm9lrZvajfAYmIiL5U5+a/ZfAxe6+F3AAcJGZ7ZGfsJIv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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x197c4358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "items = \"n_epoch n_units batchsize bprop_len grad_clip train_file optimizer train_chars tokenize_caps\"\n",
    "for x in items.split(\" \"):\n",
    "    print(x,\":\",locals()[x])\n",
    "\n",
    "plot_progress(training_perps,title=\"Training perplexity\",start_at=3, log_axis=False)\n",
    "plot_progress(valid_perps,title=\"Validation perplexity\",start_at=0, log_axis=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "#### Generate some text like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "def  => \n",
      "out_device):<br>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        \n",
      " ----------------\n",
      "def  => \n",
      "= gx.gpu(array)<br>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     \n",
      " ----------------\n",
      "def  => \n",
      "= is is self.thes = stream()<br>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        \n",
      " ----------------\n",
      "def  => \n",
      "= grad_outputs)<br><br><br>    def __gpu(self, in_stric):<br>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           \n",
      " ----------------\n",
      "def  => \n",
      "in___is__init____self.inport):<br>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      \n",
      " ----------------\n"
     ]
    }
   ],
   "source": [
    "prompt = \"def \"\n",
    "prod_state = None\n",
    "for randomness in [0.2,0.35,0.5,0.8,1.2]:\n",
    "    prod_state = generate(prompt,600,vocab,randomness=randomness,start_state=prod_state,separator=\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Development stuff below .."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "########## DEVELOPMENT SECTION BELOW, IGNORE ;) ##########"
   ]
  },
  
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# alternative generation text\n",
    "#prompt = to_word(0)\n",
    "#prod_state = None\n",
    "#for randomness in [0.1,0.3,0.5,1.0,1.4]:\n",
    "#    prod_state = generate_choice(prompt,600,vocab,randomness=randomness,start_state=prod_state,separator=\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# how to save / load trained networks?\n",
    "###from pickle import dumps, loads\n",
    "#import json\n",
    "#members = dict(state.items())\n",
    "#json.dumps(members)??\n",
    "#str(to_word(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#generate(str(to_word(1)), 150, vocab, start_state=None,separator=\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#from collections import Counter\n",
    "#Counter(open(\"chainer.txt\").read())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#state,result = produce_one_step(np.array([3]),make_initial_state(1,False))\n",
    "#y=cuda.to_cpu(result.data.ravel())\n",
    "#y = np.array(y,dtype='float64')\n",
    "#y -= y.min()\n",
    "#y /= sum(y) \n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}

The narrow main column in the notebook layout on github breaks the formatting somewhat, it should look just fine (without all the line wrapping going on) when running it on a local machine; at least it does for me.