Keras Toxic Comment.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Notes\n",
    "* http://scikit-learn.org/stable/tutorial/text_analytics/working_with_text_data.html\n",
    "* http://karpathy.github.io/2015/05/21/rnn-effectiveness/\n",
    "* https://machinelearningmastery.com/sequence-classification-lstm-recurrent-neural-networks-python-keras/\n",
    "* https://blog.keras.io/a-ten-minute-introduction-to-sequence-to-sequence-learning-in-keras.html\n",
    "* `return_state` <- the RNNs need to do this\n",
    "* TODO add dropout\n",
    "* https://github.com/keras-team/keras/issues/4563 - nan loss issue very similar to mine\n",
    "* here next https://machinelearningmastery.com/text-generation-lstm-recurrent-neural-networks-python-keras/\n",
    "\n",
    "# TODO\n",
    "* implement batch size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/johnb/anaconda3/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: compiletime version 3.5 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.6\n",
      "  return f(*args, **kwds)\n",
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tensorflow\n",
    "import keras\n",
    "import sklearn\n",
    "import random\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['axes.labelsize'] = 14\n",
    "plt.rcParams['xtick.labelsize'] = 12\n",
    "plt.rcParams['ytick.labelsize'] = 12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sample_submission = pd.read_csv('/Users/johnb/Personal/data_science/kaggle/toxic-comment/data/sample_submission.csv')\n",
    "test = pd.read_csv('/Users/johnb/Personal/data_science/kaggle/toxic-comment/data/test.csv')\n",
    "train = pd.read_csv('/Users/johnb/Personal/data_science/kaggle/toxic-comment/data/train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "from collections import Counter\n",
    "\n",
    "class DataFrameSelector(BaseEstimator, TransformerMixin):\n",
    "    \"\"\"Allows you to specify a DataFrame as the input to a Pipeline (see below)\"\"\"\n",
    "    def __init__(self, attribute_names):\n",
    "        self.attribute_names = attribute_names\n",
    "\n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "    \n",
    "    def transform(self, X):\n",
    "        return X[self.attribute_names].values\n",
    "    \n",
    "class MapToInts(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, first_n_chars):\n",
    "        self.first_n_chars = first_n_chars or 100\n",
    "    \n",
    "    def translate(self, comment):\n",
    "        return [self.translation.get(char, self.first_n_chars) for char in comment]\n",
    "        \n",
    "    def untranslate(self, translated_comment):\n",
    "        return ''.join([self.untranslation[num] for num in translated_comment])\n",
    "\n",
    "    def fit(self, X, y=None):\n",
    "        char_counter = Counter()\n",
    "        for text in X:\n",
    "            char_counter.update(text)\n",
    "            \n",
    "        for c in '1234567890':\n",
    "            del char_counter[c]\n",
    "        most_popular_chars = dict(char_counter.most_common()[:self.first_n_chars-1]).keys()\n",
    "        self.translation = {char: num for num, char in enumerate(most_popular_chars)}\n",
    "        self.untranslation = {num: char for char,num in self.translation.items()}\n",
    "        for c in '1234567890':\n",
    "            self.translation[c] = self.first_n_chars-1\n",
    "        self.untranslation[self.first_n_chars-1] = '⌗'\n",
    "        self.untranslation[self.first_n_chars] = '≠'   \n",
    "        self.num_symbols = len(self.untranslation)\n",
    "        return self\n",
    "    \n",
    "    def transform(self, X):\n",
    "        y = []\n",
    "        for text in X:\n",
    "            y.append(self.translate(text))\n",
    "        return y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "map_to_ints = MapToInts(first_n_chars=100)\n",
    "pipeline = Pipeline([\n",
    "    ('comment_selector', DataFrameSelector('comment_text')),\n",
    "    ('map_to_ints', map_to_ints),\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = pipeline.fit_transform(train[:2000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class TrainingSetMaker(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, num_samples, num_batches, num_training_steps, num_target_steps, map_to_ints):\n",
    "        \"\"\"Make training set\n",
    "        \n",
    "        num_samples is the number of randomly selected comments used to make the training set (this is the height \n",
    "            of each batch)\n",
    "        num_batches is how many batches we privide to keras during one epoch of training\n",
    "        num_training_steps is the width of each batch\n",
    "        num_target_steps is the width of the target\n",
    "        map_to_ints is the object used to encode the documents into ints - we use it to find the int for a space char\n",
    "        \n",
    "        To make a batch we first get text samples (really integers at this point) of an appropriate length. The length\n",
    "        is num_batches * num_training_steps + num_target_steps. A sample is selected from any starting point in the \n",
    "        input text and then we affix the same comment over and over (space delimited) until it is the proper length. \n",
    "        The first batch is then the first `num_training_steps` characters from each sample for training and the \n",
    "        subsequent `num_target_steps` for the target.\n",
    "        \n",
    "        Each element of the training and target pieces in the batches then one-hot encoded.\n",
    "        \"\"\"\n",
    "        self.num_samples = num_samples\n",
    "        self.num_batches = num_batches\n",
    "        self.num_training_steps = num_training_steps\n",
    "        self.num_target_steps = num_target_steps\n",
    "        self.map_to_ints = map_to_ints\n",
    "        \n",
    "    def make_a_sample(self, x):\n",
    "        sample_len = self.num_batches * self.num_training_steps + self.num_target_steps\n",
    "        start_index = random.randrange(len(x))\n",
    "        sample = x[start_index:start_index + sample_len]\n",
    "        current_len = len(sample)\n",
    "        while current_len < sample_len:\n",
    "            sample.append(self.space)\n",
    "            current_len += 1\n",
    "            remaining_len = sample_len-current_len\n",
    "            append_str = x[:remaining_len]\n",
    "            sample.extend(append_str)\n",
    "            current_len += len(append_str)\n",
    "        return sample\n",
    "    \n",
    "    def make_same_len_samples(self, X):\n",
    "        samples = []\n",
    "        indices = random.sample(range(len(X)), self.num_samples)\n",
    "        for i in indices:\n",
    "            x = X[i]\n",
    "            samples.append(self.make_a_sample(x))\n",
    "        return np.array(samples)\n",
    "    \n",
    "    def batch_iterator(self, X):\n",
    "        samples = self.make_same_len_samples(X)\n",
    "        for batch_num in range(self.num_batches):\n",
    "            train = samples[:, batch_num*self.num_training_steps:(batch_num+1)*self.num_training_steps]\n",
    "            target = samples[:, (batch_num+1)*self.num_training_steps:(batch_num+1)*self.num_training_steps+self.num_target_steps]\n",
    "            yield (train, target)\n",
    "            \n",
    "    def fit(self, X, y=None):\n",
    "        self.space = map_to_ints.translate([' '])[0]\n",
    "        return self\n",
    "    \n",
    "    def transform(self, X):\n",
    "        train, target = list(zip(*self.batch_iterator(X)))\n",
    "        train = np.concatenate(train)\n",
    "        target = np.concatenate(target)\n",
    "        return train, target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|a bad idea> in |\n",
      "|omise not >to e|\n",
      "|ck, its bi>gger|\n",
      "|re I happe>n to|\n",
      "|ed the mat>eria|\n",
      "--------\n",
      "| in the fi>rst |\n",
      "|to engage >in a|\n",
      "|gger than >your|\n",
      "|n to resid>e. I|\n",
      "|erial and >made|\n",
      "--------\n",
      "|rst place—>runn|\n",
      "|in anymore> per|\n",
      "|yours! hah>aha |\n",
      "|e. I use n>o so|\n",
      "|made corre>ctio|\n",
      "--------\n"
     ]
    }
   ],
   "source": [
    "# prove that the training batches look correct\n",
    "maker = TrainingSetMaker(\n",
    "    num_samples=5, num_batches=3, num_training_steps=10, num_target_steps=4, map_to_ints=map_to_ints\n",
    ")\n",
    "maker.fit(None)\n",
    "X = pipeline.fit_transform(train[:2000])\n",
    "for X_,y in maker.batch_iterator(X):\n",
    "    for i in range(X_.shape[0]):\n",
    "        print('|{}>{}|'.format(map_to_ints.untranslate(X_[i]),map_to_ints.untranslate(y[i])))\n",
    "    print('--------')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "class OneHotEncoder(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, map_to_ints):\n",
    "        self.map_to_ints = map_to_ints\n",
    "\n",
    "    def one_hot_encode(self, X):\n",
    "        shape = list(X.shape)\n",
    "        shape.append(self.num_symbols)\n",
    "        one_hot_X = np.zeros(shape)\n",
    "        i1 = np.tile(np.arange(X.shape[0]).reshape([X.shape[0],1]), [1,X.shape[1]]).flatten()\n",
    "        i2 = np.tile(np.arange(X.shape[1]).reshape([1,X.shape[1]]), [X.shape[0],1]).flatten()\n",
    "        i3 = X.flatten()\n",
    "        one_hot_X[i1,i2,i3] = 1.0\n",
    "        return one_hot_X\n",
    "    \n",
    "    def one_hot_decode(self, one_hot_X):\n",
    "        return np.where(one_hot_X)[2].reshape(one_hot_X.shape[:2])\n",
    "    \n",
    "    def fit(self, X, y=None):\n",
    "        self.num_symbols = self.map_to_ints.num_symbols        \n",
    "        return self\n",
    "    \n",
    "    def transform(self, X):\n",
    "        transformed = []\n",
    "        for x in X:\n",
    "            transformed.append(self.one_hot_encode(x))\n",
    "        # flatten target if target only one char\n",
    "        target_shape = transformed[1].shape\n",
    "        if target_shape[1] == 1:\n",
    "            transformed[1] = transformed[1].reshape((target_shape[0],target_shape[2]))\n",
    "        return transformed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "       [[ 0.,  1.,  0., ...,  0.,  0.,  0.],\n",
       "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
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       "        ..., \n",
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       "        [ 0.,  0.,  0., ...,  0.,  0.,  0.]],\n",
       "\n",
       "       [[ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "        ..., \n",
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       "        [ 0.,  1.,  0., ...,  0.,  0.,  0.]]])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# prove that the OneHotEncoder can successfully round-trip encode data\n",
    "one_hot_encoder = OneHotEncoder(map_to_ints)\n",
    "one_hot_encoder.fit(None)\n",
    "one_hot_X = one_hot_encoder.one_hot_encode(X_)\n",
    "X_copy = one_hot_encoder.one_hot_decode(one_hot_X)\n",
    "assert np.all(X_ == X_copy)\n",
    "one_hot_X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# End-to-end pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       " \n",
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       " \n",
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       " \n",
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       " \n",
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       " array([[ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
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       "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "        ..., \n",
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       "        [ 0.,  0.,  0., ...,  0.,  0.,  0.]])]"
      ]
     },
     "execution_count": 339,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "first_n_chars = 100\n",
    "\n",
    "num_samples = 101\n",
    "num_batches = 23\n",
    "num_training_steps = 11\n",
    "num_target_steps = 1\n",
    "\n",
    "map_to_ints = MapToInts(first_n_chars=first_n_chars)\n",
    "full_pipeline = Pipeline([\n",
    "    ('comment_selector', DataFrameSelector('comment_text')),\n",
    "    ('map_to_ints', map_to_ints),\n",
    "    ('train_maker', TrainingSetMaker(\n",
    "        num_samples=num_samples,\n",
    "        num_batches=num_batches,\n",
    "        num_training_steps=num_training_steps,\n",
    "        num_target_steps=num_target_steps,\n",
    "        map_to_ints=map_to_ints,\n",
    "    )),\n",
    "    ('one_hot_encoder', OneHotEncoder(map_to_ints=map_to_ints)),\n",
    "])\n",
    "\n",
    "X = full_pipeline.fit_transform(train[:2000])\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "metadata": {},
   "outputs": [
    {
     "ename": "AssertionError",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-338-c4f1042da376>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# prove it works\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32massert\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mnum_samples\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mnum_batches\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_training_steps\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfirst_n_chars\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----> 3\u001b[0;31m \u001b[0;32massert\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mnum_samples\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mnum_batches\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_target_steps\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfirst_n_chars\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[0m",
      "\u001b[0;31mAssertionError\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# prove it works\n",
    "assert X[0].shape == (num_samples*num_batches, num_training_steps, first_n_chars+1)\n",
    "assert X[1].shape == (num_samples*num_batches, num_target_steps, first_n_chars+1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "------------------------\n",
    "\n",
    "# Finally! Keras stuff."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 420,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "first_n_chars = 100\n",
    "num_symbols = first_n_chars + 1 # (one symbol for 'other' besides the first n chars)\n",
    "\n",
    "num_samples = 230  # e.g. number of comments\n",
    "num_batches = 27  # number of training steps\n",
    "num_training_steps = 2\n",
    "num_target_steps = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First of all, you don't actually want the above pipeline b/c it had to recalculate the mapping each time. That's expensive. So we break it into two parts:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "metadata": {},
   "outputs": [],
   "source": [
    "map_to_ints = MapToInts(first_n_chars=first_n_chars)\n",
    "pipeline_0 = Pipeline([\n",
    "    ('comment_selector', DataFrameSelector('comment_text')),\n",
    "    ('map_to_ints', map_to_ints),\n",
    "])\n",
    "\n",
    "# you run this part once - it takes a while\n",
    "X0 = pipeline_0.fit_transform(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 424,
   "metadata": {},
   "outputs": [],
   "source": [
    "one_hot_encoder = OneHotEncoder(map_to_ints=map_to_ints)\n",
    "\n",
    "def make_pipeline_1(num_training_steps=num_training_steps):\n",
    "    pipeline_1 = Pipeline([\n",
    "        ('train_maker', TrainingSetMaker(\n",
    "            num_samples=num_samples,\n",
    "            num_batches=num_batches,\n",
    "            num_training_steps=num_training_steps,\n",
    "            num_target_steps=num_target_steps,\n",
    "            map_to_ints=map_to_ints,\n",
    "        )),\n",
    "        ('one_hot_encoder', one_hot_encoder),\n",
    "    ])\n",
    "    return pipeline_1\n",
    "\n",
    "pipeline_1 = make_pipeline_1()\n",
    "    \n",
    "# you run this part several times, once for each training epoch\n",
    "X1 = pipeline_1.fit_transform(X0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 440,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.layers import Input, LSTM, Dense\n",
    "from keras.models import Model\n",
    "\n",
    "\n",
    "def make_model_v1(num_samples, num_training_steps=num_training_steps):\n",
    "    lstm_state_size = 20 # TODO see blog post for number\n",
    "\n",
    "    inputs = Input(\n",
    "        batch_shape=(num_samples,num_training_steps, num_symbols),\n",
    "        dtype='float',\n",
    "        name='inputs',\n",
    "    )\n",
    "    intermediate_0 = LSTM(\n",
    "        units=lstm_state_size, \n",
    "        batch_size=num_samples, # gotcha\n",
    "        name='intermediate_0', \n",
    "        # return_sequences=True,  you only need this on intermediate layers of RNNs\n",
    "        stateful=True,  # gotcha - have to specify this\n",
    "        activation='relu',\n",
    "        dropout=0.5,\n",
    "        recurrent_dropout=0.5,\n",
    "    )(inputs)\n",
    "    outputs = Dense(num_symbols, activation='softmax')(intermediate_0)\n",
    "    model = Model(inputs=inputs, output=outputs)\n",
    "    model.compile(optimizer='sgd', loss='categorical_crossentropy', metrics=['accuracy'])\n",
    "    # optimizer rmsprop adam sgd\n",
    "    return model\n",
    "\n",
    "def make_model_v2(num_samples, num_training_steps=num_training_steps):\n",
    "    lstm_state_size = 101 # TODO see blog post for number\n",
    "\n",
    "    inputs = Input(\n",
    "        batch_shape=(num_samples,num_training_steps, num_symbols),\n",
    "        dtype='float',\n",
    "        name='inputs',\n",
    "    )\n",
    "    intermediate_0 = LSTM(\n",
    "        units=lstm_state_size, \n",
    "        batch_size=num_samples, # gotcha\n",
    "        name='intermediate_0', \n",
    "        return_sequences=True,  #  you only need this on intermediate layers of RNNs\n",
    "        stateful=True,  # gotcha - have to specify this\n",
    "        activation='relu',\n",
    "        dropout=0.5,\n",
    "        recurrent_dropout=0.5,\n",
    "    )(inputs)\n",
    "    intermediate_1 = LSTM(\n",
    "        units=lstm_state_size, \n",
    "        batch_size=num_samples, # gotcha\n",
    "        name='intermediate_1', \n",
    "        # return_sequences=True,  you only need this on intermediate layers of RNNs\n",
    "        stateful=True,  # gotcha - have to specify this\n",
    "        activation='relu',\n",
    "        dropout=0.5,\n",
    "        recurrent_dropout=0.5,\n",
    "    )(intermediate_0)\n",
    "    \n",
    "    outputs = Dense(num_symbols, activation='softmax')(intermediate_1)\n",
    "    model = Model(inputs=inputs, output=outputs)\n",
    "    model.compile(optimizer='sgd', loss='categorical_crossentropy', metrics=['accuracy'])\n",
    "    # optimizer rmsprop adam sgd\n",
    "    return model\n",
    "\n",
    "\n",
    "make_model = make_model_v1\n",
    "\n",
    "def clone_model_with_different_size(trained_model, num_samples=1, num_training_steps=1):\n",
    "    \"\"\"takes a trained model and makes a new model with num_samples reduced to 1\"\"\"\n",
    "    new_model = make_model(num_samples, num_training_steps)\n",
    "    new_model.set_weights(trained_model.get_weights())\n",
    "    return new_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 441,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "inputs (InputLayer)          (230, 10, 101)            0         \n",
      "_________________________________________________________________\n",
      "intermediate_0 (LSTM)        (230, 20)                 9760      \n",
      "_________________________________________________________________\n",
      "dense_64 (Dense)             (230, 101)                2121      \n",
      "=================================================================\n",
      "Total params: 11,881\n",
      "Trainable params: 11,881\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/johnb/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:24: UserWarning: Update your `Model` call to the Keras 2 API: `Model(inputs=Tensor(\"in..., outputs=Tensor(\"de...)`\n"
     ]
    }
   ],
   "source": [
    "model = make_model(num_samples)\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 457,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1\n",
      "6210/6210 [==============================] - 1s 222us/step - loss: 2.5091 - acc: 0.2921\n",
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      "Epoch 1/1\n",
      "6210/6210 [==============================] - 1s 203us/step - loss: 2.5713 - acc: 0.2878\n",
      "Epoch 1/1\n",
      "6210/6210 [==============================] - 1s 218us/step - loss: 2.5140 - acc: 0.3013\n",
      "Epoch 1/1\n",
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      "Epoch 1/1\n",
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      "Epoch 1/1\n",
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      "Epoch 1/1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6210/6210 [==============================] - 1s 201us/step - loss: 2.5232 - acc: 0.3026\n",
      "Epoch 1/1\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6210/6210 [==============================] - 1s 205us/step - loss: 2.4666 - acc: 0.3135\n",
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      "5060/6210 [=======================>......] - ETA: 0s - loss: 2.4995 - acc: 0.3053"
     ]
    },
    {
     "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-457-1104e3584755>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      6\u001b[0m         \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnum_samples\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;31m# gotcha - confused what this is\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m         \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;31m# do you want to train more than once on the same data?\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m         \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;31m# gotcha - shuffling doesn't make sense here b/c of prescribed format\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m         \u001b[0;31m# validation_data=(Xs_valid, ys_valid),\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m     )\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/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   1655\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   1656\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-> 1657\u001b[0;31m                               validation_steps=validation_steps)\n\u001b[0m\u001b[1;32m   1658\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1659\u001b[0m     def evaluate(self, x=None, y=None,\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36m_fit_loop\u001b[0;34m(self, f, ins, out_labels, batch_size, epochs, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics, initial_epoch, steps_per_epoch, validation_steps)\u001b[0m\n\u001b[1;32m   1211\u001b[0m                     \u001b[0mbatch_logs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'size'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\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[1;32m   1212\u001b[0m                     \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_index\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_logs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1213\u001b[0;31m                     \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins_batch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1214\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1215\u001b[0m                         \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2355\u001b[0m         \u001b[0msession\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_session\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2356\u001b[0m         updated = session.run(fetches=fetches, feed_dict=feed_dict,\n\u001b[0;32m-> 2357\u001b[0;31m                               **self.session_kwargs)\n\u001b[0m\u001b[1;32m   2358\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mupdated\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2359\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    887\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    888\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 889\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    890\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    891\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1118\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mhandle\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mfeed_dict_tensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1119\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m-> 1120\u001b[0;31m                              feed_dict_tensor, options, run_metadata)\n\u001b[0m\u001b[1;32m   1121\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1122\u001b[0m       \u001b[0mresults\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[0;32m~/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_run\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1315\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1316\u001b[0m       return self._do_call(_run_fn, self._session, feeds, fetches, targets,\n\u001b[0;32m-> 1317\u001b[0;31m                            options, run_metadata)\n\u001b[0m\u001b[1;32m   1318\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1319\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_prun_fn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeeds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetches\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m   1321\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1322\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1323\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1324\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1325\u001b[0m       \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(session, feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m   1300\u001b[0m           return tf_session.TF_Run(session, options,\n\u001b[1;32m   1301\u001b[0m                                    \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1302\u001b[0;31m                                    status, run_metadata)\n\u001b[0m\u001b[1;32m   1303\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1304\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "for i in range(999999):\n",
    "    X1_train, X1_target = pipeline_1.fit_transform(X0)\n",
    "    model.fit(\n",
    "        X1_train, \n",
    "        X1_target, \n",
    "        batch_size=num_samples, # gotcha - confused what this is\n",
    "        epochs=1, # do you want to train more than once on the same data?\n",
    "        shuffle=False, # gotcha - shuffling doesn't make sense here b/c of prescribed format\n",
    "        # validation_data=(Xs_valid, ys_valid),\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 450,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/johnb/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:24: UserWarning: Update your `Model` call to the Keras 2 API: `Model(inputs=Tensor(\"in..., outputs=Tensor(\"de...)`\n"
     ]
    }
   ],
   "source": [
    "num_training_steps = 50\n",
    "model = clone_model_with_different_size(model, num_samples=num_samples, num_training_steps=num_training_steps)\n",
    "pipeline_1 = make_pipeline_1(num_training_steps=num_training_steps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 456,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.optimizer = 'cfgh'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluation attempts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 458,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/johnb/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:24: UserWarning: Update your `Model` call to the Keras 2 API: `Model(inputs=Tensor(\"in..., outputs=Tensor(\"de...)`\n"
     ]
    }
   ],
   "source": [
    "new_model = clone_model_with_different_size(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 459,
   "metadata": {},
   "outputs": [],
   "source": [
    "def one_hot_encode(chars):\n",
    "    return one_hot_encoder.one_hot_encode(np.array(\n",
    "        [map_to_ints.translate(chars)]\n",
    "    ))\n",
    "\n",
    "def get_next_char(char):\n",
    "    assert isinstance(char, str)\n",
    "    assert len(char) == 1\n",
    "    one_hot_vect = one_hot_encode(char)\n",
    "    one_hot_encoder.one_hot_encode(np.array(\n",
    "        [map_to_ints.translate(char)]\n",
    "    ))\n",
    "    return map_to_ints.untranslate([np.argmax(new_model.predict(one_hot_vect))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 460,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "an an the the the the the the the the the the the the the the the the the the the the the the the th\n",
      "be the the the the the the the the the the the the the the the the the the the the the the the the t\n",
      "coure the the the the the the the the the the the the the the the the the the the the the the the th\n",
      "d the the the the the the the the the the the the the the the the the the the the the the the the th\n",
      "e the the the the the the the the the the the the the the the the the the the the the the the the th\n",
      "f the the the the the the the the the the the the the the the the the the the the the the the the th\n",
      "g the the the the the the the the the the the the the the the the the the the the the the the the th\n",
      "h the the the the the the the the the the the the the the the the the the the the the the the the th\n",
      "it an the the the the the the the the the the the the the the the the the the the the the the the th\n",
      "jut the the the the the the the the the the the the the the the the the the the the the the the the \n",
      "king the the the the the the the the the the the the the the the the the the the the the the the the\n",
      "l the the the the the the the the the the the the the the the the the the the the the the the the th\n",
      "me the the the the the the the the the the the the the the the the the the the the the the the the t\n",
      "not the the the the the the the the the the the the the the the the the the the the the the the the \n",
      "on the the the the the the the the the the the the the the the the the the the the the the the the t\n",
      "ped the the the the the the the the the the the the the the the the the the the the the the the the \n",
      "qued the the the the the the the the the the the the the the the the the the the the the the the the\n",
      "r the the the the the the the the the the the the the the the the the the the the the the the the th\n",
      "s the the the the the the the the the the the the the the the the the the the the the the the the th\n",
      "t an the the the the the the the the the the the the the the the the the the the the the the the the\n",
      "us the the the the the the the the the the the the the the the the the the the the the the the the t\n",
      "ve the the the the the the the the the the the the the the the the the the the the the the the the t\n",
      "we the the the the the the the the the the the the the the the the the the the the the the the the t\n",
      "xe the the the the the the the the the the the the the the the the the the the the the the the the t\n",
      "y the the the the the the the the the the the the the the the the the the the the the the the the th\n",
      "ze the the the the the the the the the the the the the the the the the the the the the the the the t\n",
      " the the the the the the the the the the the the the the the the the the the the the the the the the\n",
      "A the the the the the the the the the the the the the the the the the the the the the the the the th\n",
      "Bre the the the the the the the the the the the the the the the the the the the the the the the the \n",
      "Cous the the the the the the the the the the the the the the the the the the the the the the the the\n",
      "D an the the the the the the the the the the the the the the the the the the the the the the the the\n",
      "E an the the the the the the the the the the the the the the the the the the the the the the the the\n",
      "F an the the the the the the the the the the the the the the the the the the the the the the the the\n",
      "H an the the the the the the the the the the the the the the the the the the the the the the the the\n",
      "G an the the the the the the the the the the the the the the the the the the the the the the the the\n",
      "I the the the the the the the the the the the the the the the the the the the the the the the the th\n",
      "Je an the the the the the the the the the the the the the the the the the the the the the the the th\n",
      "K an the the the the the the the the the the the the the the the the the the the the the the the the\n",
      "L an the the the the the the the the the the the the the the the the the the the the the the the the\n",
      "M an the the the the the the the the the the the the the the the the the the the the the the the the\n",
      "N an the the the the the the the the the the the the the the the the the the the the the the the the\n",
      "O an the the the the the the the the the the the the the the the the the the the the the the the the\n",
      "P an the the the the the the the the the the the the the the the the the the the the the the the the\n",
      "Q an the the the the the the the the the the the the the the the the the the the the the the the the\n",
      "R an the the the the the the the the the the the the the the the the the the the the the the the the\n",
      "S the the the the the the the the the the the the the the the the the the the the the the the the th\n",
      "T an the the the the the the the the the the the the the the the the the the the the the the the the\n",
      "U an the the the the the the the the the the the the the the the the the the the the the the the the\n",
      "V an the the the the the the the the the the the the the the the the the the the the the the the the\n",
      "W an the the the the the the the the the the the the the the the the the the the the the the the the\n",
      "X an the the the the the the the the the the the the the the the the the the the the the the the the\n",
      "Y an the the the the the the the the the the the the the the the the the the the the the the the the\n",
      "Z an the the the the the the the the the the the the the the the the the the the the the the the the\n"
     ]
    }
   ],
   "source": [
    "new_model.reset_states()\n",
    "for char in 'abcdefghijklmnopqrstuvwxyz ABCDEFHGIJKLMNOPQRSTUVWXYZ':\n",
    "    next_char = char\n",
    "    chars = []\n",
    "    for i in range(100):\n",
    "        chars.append(next_char)\n",
    "        next_char = get_next_char(next_char)\n",
    "    print(''.join(chars))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 493,
   "metadata": {},
   "outputs": [
    {
     "ename": "AssertionError",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-493-7bb9019affae>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0mchars\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnext_char\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m     \u001b[0mnext_char\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_next_char\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnext_char\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m''\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchars\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-459-84e75eeb37d1>\u001b[0m in \u001b[0;36mget_next_char\u001b[0;34m(char)\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mget_next_char\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchar\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0;32massert\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchar\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m     \u001b[0;32massert\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchar\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m     \u001b[0mone_hot_vect\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mone_hot_encode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchar\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m     one_hot_encoder.one_hot_encode(np.array(\n",
      "\u001b[0;31mAssertionError\u001b[0m: "
     ]
    }
   ],
   "source": [
    "new_model.reset_states()\n",
    "for char in one_hot_encode('hello')[0]:\n",
    "    new_model.predict(np.array([[char]]))\n",
    "next_char = ''\n",
    "chars = []\n",
    "for i in range(100):\n",
    "    chars.append(next_char)\n",
    "    next_char = get_next_char(next_char)\n",
    "print(''.join(chars))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 491,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ \"Explanation\\nWhy the edits made under my username Hardcore Metallica Fan were reverted? They weren't vandalisms, just closure on some GAs after I voted at New York Dolls FAC. And please don't remove the template from the talk page since I'm retired now.89.205.38.27\"], dtype=object)"
      ]
     },
     "execution_count": 491,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[:1]['comment_text'].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scratch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/johnb/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:31: UserWarning: Update your `Model` call to the Keras 2 API: `Model(inputs=Tensor(\"in..., outputs=Tensor(\"in...)`\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "Cannot feed value of shape (6, 7, 11) for Tensor 'inputs_63:0', which has shape '(5, 7, 11)'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-117-f12b86af4240>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     34\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     35\u001b[0m \u001b[0mstart\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mround\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnum_samples\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_training_steps\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_symbols\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 36\u001b[0;31m \u001b[0mend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     37\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msummary\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     38\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, x, batch_size, verbose, steps)\u001b[0m\n\u001b[1;32m   1788\u001b[0m         \u001b[0mf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict_function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1789\u001b[0m         return self._predict_loop(f, ins, batch_size=batch_size,\n\u001b[0;32m-> 1790\u001b[0;31m                                   verbose=verbose, steps=steps)\n\u001b[0m\u001b[1;32m   1791\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1792\u001b[0m     def train_on_batch(self, x, y,\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36m_predict_loop\u001b[0;34m(self, f, ins, batch_size, verbose, steps)\u001b[0m\n\u001b[1;32m   1297\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1298\u001b[0m                     \u001b[0mins_batch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_slice_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[0;32m-> 1299\u001b[0;31m                 \u001b[0mbatch_outs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins_batch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1300\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_outs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1301\u001b[0m                     \u001b[0mbatch_outs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mbatch_outs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2355\u001b[0m         \u001b[0msession\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_session\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2356\u001b[0m         updated = session.run(fetches=fetches, feed_dict=feed_dict,\n\u001b[0;32m-> 2357\u001b[0;31m                               **self.session_kwargs)\n\u001b[0m\u001b[1;32m   2358\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mupdated\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2359\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    887\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    888\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 889\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    890\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    891\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1094\u001b[0m                 \u001b[0;34m'Cannot feed value of shape %r for Tensor %r, '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1095\u001b[0m                 \u001b[0;34m'which has shape %r'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1096\u001b[0;31m                 % (np_val.shape, subfeed_t.name, str(subfeed_t.get_shape())))\n\u001b[0m\u001b[1;32m   1097\u001b[0m           \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgraph\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_feedable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msubfeed_t\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1098\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Tensor %s may not be fed.'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0msubfeed_t\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Cannot feed value of shape (6, 7, 11) for Tensor 'inputs_63:0', which has shape '(5, 7, 11)'"
     ]
    }
   ],
   "source": [
    "num_samples = 5\n",
    "num_training_steps = 7\n",
    "num_symbols = 11\n",
    "\n",
    "lstm_state_size = 13\n",
    "\n",
    "X1_train\n",
    "\n",
    "# start = np.round(np.random.random([num_samples, num_training_steps, num_symbols])*10)\n",
    "# end = model.predict(start)\n",
    "# print(model.summary())\n",
    "# print(start)\n",
    "# print(end)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2323, 11, 101)"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X1_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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