{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Keras implementation of Character Aware Neural Models by Yoon Kim et al.\n", "\n", "\n", "## Introduction/Disclaimer\n", "\n", "

\n", "This notebook tries to understand the structure and implement the character RNN given in [Kim, 2016](https://arxiv.org/abs/1508.06615). While there is [code](https://github.com/yoonkim/lstm-char-cnn) available online by the authors, that is available in Torch (and there are several PyTorch implementations available). The current notebook tries to aid people in recreating such models in Keras, rather than having a model that works out-of-the-box. I am also currently not very confident about my approach myself, so please if you have any comments, open an issue ticket.

\n", "\n", "\n", "## Description\n", "\n", "The model is a character-input sequential model for next-word prediction. It accepts as input a word as a series of characters (represented as integers), and spits a probability distribution of the next word. It differs from other character RNN models in that it considers the whole word at each timestep, instead of a single character, and from word RNN models in that it accepts characters-as-integers and not words-as-integers. Below we show how we pre-process a corpus `.txt` file to feed into the model, how we build the model into keras and finally how we train & evaluate it.\n", "\n", "## Requirements\n", "Python packages `keras`, `numpy` and `tensorflow` (might also work with Theano but I haven't tried it). Also a GPU with CUDA is *highly* recommended.\n", "\n", "### Preprocessing\n", "\n", "Assume we have a corpus `train.txt` for training, `valid.txt` for validation and `test.txt` for testing. We split each corpus into words.\n", "\n", " Note: The corpus we use here is the same as the corpus used by Kim, Y. et al. in their repo. The prepared corpus have some words that appear only once (e.g. some very rare proper names) and in that case they have been erplaced with the token ``.\n", "\n", "Training has words $W_{tr}=[w_1,w_2,...,w_T]$. Validation has words $W_{vd}$ and testing $W_{ts}$. \n", "\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "\n", "with open('train.txt') as f:\n", " W_tr = f.read().split()\n", " \n", "with open('valid.txt') as f:\n", " W_vd = f.read().split()\n", " \n", "with open('test.txt') as f:\n", " W_ts = f.read().split()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For each word $w_{1...T}$ in training:\n", "1. Convert $w_t$ into a series of characters: $w_t = [c_1, c_2, ..., c_N]$\n", "2. For each word $w_t$ and character $c_n$, assign them an integer $t$ and $n$ respectively in the\n", "Look Up Tables (LUTs) `word_idx` and `char_idx` accordingly. Also create the inverse mapping LUTs `idx_word` and `idx_char` that map from integers to words (and characters).\n", "3. Replace $c_n$ and $w_t$ with $n$ and $t$ respectively.\n", "\n", " Note: We also include characters `begin` and `end` (they are not python characters but for our purposes we can use them as such) to signify the beginning and ending of each word. \n", "\n", " Note 2: We assign the integer `0` to character `''` (no character). Since we use keras, we need to use padding to our models and this is done by filling with `0`. It is important that `0` does not correspond to a valid character or word. " ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# First gather all the words and characters. \n", "words_training = list(set(W_tr))\n", "chars_training = []\n", "for word in words_training:\n", " for char in word:\n", " if char not in chars_training:\n", " chars_training.append(char)\n", " \n", "chars_training.append('begin')\n", "chars_training.append('end')\n", "\n", "# Create the look up table as a python dictionary\n", "word_idx = dict((words_training[n], n) for n in range(len(words_training)))\n", "\n", "char_idx = dict((chars_training[n], n+1) for n in range(len(chars_training)))\n", "char_idx[''] = 0\n", "\n", "# Also create the inverse as a python dictionary\n", "idx_word = dict((word_idx[w], w) for w in word_idx)\n", "idx_char = dict((char_idx[c], c) for c in char_idx)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In order to train the model later, we need to turn our corpora into pairs of (Input, Output). We do this with the function below (accepts a corpus file as a list of words as input). We make this a function since we are going to be using it several times below.\n", "\n", " Note Since `` words appear only once, we omit them from training." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "First 10 (input, output) pairs in training corpus: \n", "[Sample 0] Input: ['begin', 'a', 'e', 'r', 'end'] Output: banknote\n", "[Sample 1] Input: ['begin', 'b', 'a', 'n', 'k', 'n', 'o', 't', 'e', 'end'] Output: berlitz\n", "[Sample 2] Input: ['begin', 'b', 'e', 'r', 'l', 'i', 't', 'z', 'end'] Output: calloway\n", "[Sample 3] Input: ['begin', 'c', 'a', 'l', 'l', 'o', 'w', 'a', 'y', 'end'] Output: centrust\n", "[Sample 4] Input: ['begin', 'c', 'e', 'n', 't', 'r', 'u', 's', 't', 'end'] Output: cluett\n", "[Sample 5] Input: ['begin', 'c', 'l', 'u', 'e', 't', 't', 'end'] Output: fromstein\n", "[Sample 6] Input: ['begin', 'f', 'r', 'o', 'm', 's', 't', 'e', 'i', 'n', 'end'] Output: gitano\n", "[Sample 7] Input: ['begin', 'g', 'i', 't', 'a', 'n', 'o', 'end'] Output: guterman\n", "[Sample 8] Input: ['begin', 'g', 'u', 't', 'e', 'r', 'm', 'a', 'n', 'end'] Output: hydro-quebec\n", "[Sample 9] Input: ['begin', 'h', 'y', 'd', 'r', 'o', '-', 'q', 'u', 'e', 'b', 'e', 'c', 'end'] Output: ipo\n" ] } ], "source": [ "def prepare_inputs_outputs(words):\n", " # Remember that the output is a single word.\n", " output_words = [] \n", " \n", " # The input is a word split into characters. \n", " input_words = []\n", " \n", " for n in range(len(words) - 1):\n", " # are words that appear only once, so omit them.\n", " #if words[n] != '' and words[n+1] != '':\n", " input_words.append(words[n])\n", " output_words.append(words[n+1])\n", " \n", " # The input words split into sequence of characters\n", " input_seqs = []\n", " for n in range(len(input_words)):\n", " input_seqs.append(\n", " # Remember that each word starts with the character begin and ends with end.\n", " ['begin'] + [c for c in input_words[n]] + ['end']\n", " )\n", " \n", " # Final input, output\n", " inputs = input_seqs\n", " outputs = output_words\n", " \n", " return inputs, outputs\n", "\n", " \n", "inputs_tr, outputs_tr = prepare_inputs_outputs(W_tr)\n", "inputs_vd, outputs_vd = prepare_inputs_outputs(W_vd)\n", "inputs_ts, outputs_ts = prepare_inputs_outputs(W_ts)\n", "\n", "\n", "print(\"First 10 (input, output) pairs in training corpus: \")\n", "for n in range(10):\n", "\n", " print('[Sample {}]'.format(n), 'Input:',inputs_tr[n], 'Output:',outputs_tr[n])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the paper we see that training is done in groups of `seq_len=30` timesteps for non-arabic corpora, so we group the inputs and outputs accordingly:\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[Sample 0]\n", "Input:\n", " [['begin', 'a', 'e', 'r', 'end'], ['begin', 'b', 'a', 'n', 'k', 'n', 'o', 't', 'e', 'end'], ['begin', 'b', 'e', 'r', 'l', 'i', 't', 'z', 'end'], ['begin', 'c', 'a', 'l', 'l', 'o', 'w', 'a', 'y', 'end'], ['begin', 'c', 'e', 'n', 't', 'r', 'u', 's', 't', 'end'], ['begin', 'c', 'l', 'u', 'e', 't', 't', 'end'], ['begin', 'f', 'r', 'o', 'm', 's', 't', 'e', 'i', 'n', 'end'], ['begin', 'g', 'i', 't', 'a', 'n', 'o', 'end'], ['begin', 'g', 'u', 't', 'e', 'r', 'm', 'a', 'n', 'end'], ['begin', 'h', 'y', 'd', 'r', 'o', '-', 'q', 'u', 'e', 'b', 'e', 'c', 'end'], ['begin', 'i', 'p', 'o', 'end'], ['begin', 'k', 'i', 'a', 'end'], ['begin', 'm', 'e', 'm', 'o', 't', 'e', 'c', 'end'], ['begin', 'm', 'l', 'x', 'end'], ['begin', 'n', 'a', 'h', 'b', 'end'], ['begin', 'p', 'u', 'n', 't', 's', 'end'], ['begin', 'r', 'a', 'k', 'e', 'end'], ['begin', 'r', 'e', 'g', 'a', 't', 't', 'a', 'end'], ['begin', 'r', 'u', 'b', 'e', 'n', 's', 'end'], ['begin', 's', 'i', 'm', 'end'], ['begin', 's', 'n', 'a', 'c', 'k', '-', 'f', 'o', 'o', 'd', 'end'], ['begin', 's', 's', 'a', 'n', 'g', 'y', 'o', 'n', 'g', 'end'], ['begin', 's', 'w', 'a', 'p', 'o', 'end'], ['begin', 'w', 'a', 'c', 'h', 't', 'e', 'r', 'end'], ['begin', 'p', 'i', 'e', 'r', 'r', 'e', 'end'], ['begin', '<', 'u', 'n', 'k', '>', 'end'], ['begin', 'N', 'end'], ['begin', 'y', 'e', 'a', 'r', 's', 'end'], ['begin', 'o', 'l', 'd', 'end'], ['begin', 'w', 'i', 'l', 'l', 'end'], ['begin', 'j', 'o', 'i', 'n', 'end'], ['begin', 't', 'h', 'e', 'end'], ['begin', 'b', 'o', 'a', 'r', 'd', 'end'], ['begin', 'a', 's', 'end'], ['begin', 'a', 'end']]\n", "Output:\n", " ['banknote', 'berlitz', 'calloway', 'centrust', 'cluett', 'fromstein', 'gitano', 'guterman', 'hydro-quebec', 'ipo', 'kia', 'memotec', 'mlx', 'nahb', 'punts', 'rake', 'regatta', 'rubens', 'sim', 'snack-food', 'ssangyong', 'swapo', 'wachter', 'pierre', '', 'N', 'years', 'old', 'will', 'join', 'the', 'board', 'as', 'a', 'nonexecutive']\n", "\n", "[Sample 1]\n", "Input:\n", " [['begin', 'n', 'o', 'n', 'e', 'x', 'e', 'c', 'u', 't', 'i', 'v', 'e', 'end'], ['begin', 'd', 'i', 'r', 'e', 'c', 't', 'o', 'r', 'end'], ['begin', 'n', 'o', 'v', '.', 'end'], ['begin', 'N', 'end'], ['begin', 'm', 'r', '.', 'end'], ['begin', '<', 'u', 'n', 'k', '>', 'end'], ['begin', 'i', 's', 'end'], ['begin', 'c', 'h', 'a', 'i', 'r', 'm', 'a', 'n', 'end'], ['begin', 'o', 'f', 'end'], ['begin', '<', 'u', 'n', 'k', '>', 'end'], ['begin', 'n', '.', 'v', '.', 'end'], ['begin', 't', 'h', 'e', 'end'], ['begin', 'd', 'u', 't', 'c', 'h', 'end'], ['begin', 'p', 'u', 'b', 'l', 'i', 's', 'h', 'i', 'n', 'g', 'end'], ['begin', 'g', 'r', 'o', 'u', 'p', 'end'], ['begin', 'r', 'u', 'd', 'o', 'l', 'p', 'h', 'end'], ['begin', '<', 'u', 'n', 'k', '>', 'end'], ['begin', 'N', 'end'], ['begin', 'y', 'e', 'a', 'r', 's', 'end'], ['begin', 'o', 'l', 'd', 'end'], ['begin', 'a', 'n', 'd', 'end'], ['begin', 'f', 'o', 'r', 'm', 'e', 'r', 'end'], ['begin', 'c', 'h', 'a', 'i', 'r', 'm', 'a', 'n', 'end'], ['begin', 'o', 'f', 'end'], ['begin', 'c', 'o', 'n', 's', 'o', 'l', 'i', 'd', 'a', 't', 'e', 'd', 'end'], ['begin', 'g', 'o', 'l', 'd', 'end'], ['begin', 'f', 'i', 'e', 'l', 'd', 's', 'end'], ['begin', 'p', 'l', 'c', 'end'], ['begin', 'w', 'a', 's', 'end'], ['begin', 'n', 'a', 'm', 'e', 'd', 'end'], ['begin', 'a', 'end'], ['begin', 'n', 'o', 'n', 'e', 'x', 'e', 'c', 'u', 't', 'i', 'v', 'e', 'end'], ['begin', 'd', 'i', 'r', 'e', 'c', 't', 'o', 'r', 'end'], ['begin', 'o', 'f', 'end'], ['begin', 't', 'h', 'i', 's', 'end']]\n", "Output:\n", " ['director', 'nov.', 'N', 'mr.', '', 'is', 'chairman', 'of', '', 'n.v.', 'the', 'dutch', 'publishing', 'group', 'rudolph', '', 'N', 'years', 'old', 'and', 'former', 'chairman', 'of', 'consolidated', 'gold', 'fields', 'plc', 'was', 'named', 'a', 'nonexecutive', 'director', 'of', 'this', 'british']\n", "\n" ] } ], "source": [ "seq_len = 35\n", "\n", "inputs_tr_seq = []\n", "outputs_tr_seq = []\n", "\n", "for n in range(0, len(inputs_tr)-seq_len, seq_len):\n", " # rearrange input into groups of 25 samples\n", " # let's say input has words-as-character-sequences W = [W_1, W_2, ..., W_N]\n", " input_seq = inputs_tr[n:n+seq_len]\n", " \n", " # Then since the goal is to predict the next word, the output \n", " # has the sequence of words-as-words W' = [W_2, W_3, ..., W_N+1]\n", " output_seq = outputs_tr[n:n+seq_len]\n", " \n", " inputs_tr_seq.append(input_seq)\n", " outputs_tr_seq.append(output_seq)\n", " \n", "# Before each (charachters, word) pair was a single sample. Now a single sample\n", "# is ([(characters, ..., characters, characters), (word, ..., word, word)]).\n", "\n", "# Example.\n", "for n in range(2):\n", " print('[Sample {}]'.format(n))\n", " print('Input:\\n',inputs_tr_seq[n])\n", " print('Output:\\n',outputs_tr_seq[n]) \n", " print('')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In order to actually use the inputs and outputs with our model, we convert them to integers. This is as straightforward as looking using LUTs defined above (`word_idx` for words, `char_idx` for characters) and replacing each word and character with its corresponding integer. We can try that, and we will get a `MemoryError`. That is because the output has a total number of elements: `len(outputs_tr_seq)*seq_len*len(words_training)) = 26721*35*9999 = 8015498370` which requires\n", "over `32GB` to store for 4 byte integers! We will consider a different approach by constructing a `generator`. This is a function that yields a single batch of input paired by a single batch of output every time. We will later use that generator with the `fit_generator` method of our model.\n", "\n", " Note: Since we are using a generator, we need to specify the number of samples in a batch in advance." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[Batch: 0] Input: (20, 35, 21) Output: (20, 35, 9999)\n", "[Batch: 1] Input: (20, 35, 21) Output: (20, 35, 9999)\n", "[Batch: 2] Input: (20, 35, 21) Output: (20, 35, 9999)\n", "[Batch: 3] Input: (20, 35, 21) Output: (20, 35, 9999)\n", "[Batch: 4] Input: (20, 35, 21) Output: (20, 35, 9999)\n", "[Batch: 5] Input: (20, 35, 21) Output: (20, 35, 9999)\n", "[Batch: 6] Input: (20, 35, 21) Output: (20, 35, 9999)\n", "[Batch: 7] Input: (20, 35, 21) Output: (20, 35, 9999)\n", "[Batch: 8] Input: (20, 35, 21) Output: (20, 35, 9999)\n", "[Batch: 9] Input: (20, 35, 21) Output: (20, 35, 9999)\n" ] } ], "source": [ "import numpy as np\n", "\n", "# Since in keras pretty much everything \n", "# has to be of fixed size, figure out the\n", "# maximum number of characters there is in a \n", "# word, and add 2 (for the characters begin and end)\n", "max_word_len = max([len(w) for w in words_training]) + 2\n", "\n", "## Uncomment the code below to get a MemoryError\n", "\n", "# Create the output for the inputs (will probably give a MemoryError. If it doesn't \n", "# still use the method below since people are going to start whining of you taking \n", "# too much memory (assuming it is a memory machine)).\n", "# y = np.zeros(\n", "# (len(outputs_tr_seq), # Same number of samples as input\n", "# seq_len, # Same number of timesteps\n", "# len(words_training) # Categorical distribution\n", "# )\n", "# )\n", "\n", "batch_size = 20\n", "\n", "def generator(words, # The corpora to \n", " batch_size=batch_size, # The desired batch size\n", " seq_len=seq_len, # The timesteps at each batch\n", " char_idx=char_idx, # The character-to-integer LUT\n", " word_idx=word_idx, # The word-to-integer LUT\n", " ):\n", " \n", " inputs, outputs = prepare_inputs_outputs(words) # Prepare the inputs and outputs as above\n", " \n", " x = np.zeros((batch_size, seq_len, max_word_len))\n", " y = np.zeros((batch_size, seq_len, len(word_idx)))\n", " \n", " I = 0 # Since the generator never \"terminates\"\n", " # we traverse through our samples from the\n", " # beginning to the end, back to the \n", " # beginning, etc...\n", " while 1:\n", " # Clear the vectors at each yield.\n", " x[:,:,:] = 0.0\n", " y[:,:,:] = 0.0\n", " \n", " for sample in range(batch_size):\n", " for time in range(seq_len):\n", " if I >= len(inputs):\n", " I = 0 # If we are at the last sample and last timestep, \n", " # move to the beginning.\n", " \n", " for n, char in enumerate(inputs[I]):\n", " x[sample, time, n] = char_idx[char] # Replace with integer\n", " \n", " y[sample, time, word_idx[outputs[I]]] = 1.0\n", " # Set 1.0 to the position of \n", " # the vector given by the integer\n", " # from the word LUT for the word\n", " # at outputs[I]\n", " \n", " I += 1 \n", " yield x, y\n", " \n", "# Example: Show the first 10 input/output shapes as vectors\n", "gen = generator(W_tr)\n", "\n", "I = 0\n", "for x, y in gen:\n", " if I >= 10:\n", " break\n", " print(\"[Batch: {}]\".format(I),\"Input: \",x.shape,\"Output:\",y.shape)\n", " I += 1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Implementation \n", "\n", "The model is given in Fig ? in the paper and looks like the figure below: \n", "\n", "![model figure](model.png). \n", "\n", "Note that this is for one time step. At time $t$ the input is a word-as-string-of-characters $w_t = [c_1 c_2 \\dots c_N]$ (where $c_n$ is an integer), and the output is the probability distribution over all words $W_{t+1}$. \n", "\n", "Note the LSTM stage has an output to itself. Its input at time $t$ isn't output of the Highway Network at time $t$ but the output of the LSTM itself at time $t-1$. \n", "\n", "In order to train it, we will \"unroll\" the LSTM for `seq_len=35` time steps. Since the LSTM at each timestep also depends on the outputs of the highway network at this timestep we also have to distribute the previous stages across time. The final model will then be: \n", "\n", "![model_unrolled](model_unrolled.png)\n", "\n", "In the block diagram above, with pink we have the inputs/outputs and with teal the various components of the model. We will go about building each component separately as a keras model and then we will put them together in order to build the composite model.\n", "\n", "### Embeddings layer\n", "\n", "The embeddings layer is quite simple, it inputs a series of characters as integers $[c_1, c_2, ... c_N]$ and it outputs a matrix $V = [v_1 v_2 \\dots v_N]$ where each row $v_i$ (of `embeddings_size = 15` columns) is an embedding of character $c_i$. Let's implement it:\n", "\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/mmxgn/.local/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n", " from ._conv import register_converters as _register_converters\n", "Using TensorFlow backend.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", "Embeddings (Embedding) (None, 21, 15) 780 \n", "=================================================================\n", "Total params: 780\n", "Trainable params: 780\n", "Non-trainable params: 0\n", "_________________________________________________________________\n" ] }, { "data": { "image/svg+xml": [ "\n", "\n", "G\n", "\n", "\n", "\n", "140498816003824\n", "\n", "Embeddings_input: InputLayer\n", "\n", "input:\n", "\n", "output:\n", "\n", "(None, 21)\n", "\n", "(None, 21)\n", "\n", "\n", "\n", "140498816002256\n", "\n", "Embeddings: Embedding\n", "\n", "input:\n", "\n", "output:\n", "\n", "(None, 21)\n", "\n", "(None, 21, 15)\n", "\n", "\n", "\n", "140498816003824->140498816002256\n", "\n", "\n", "\n", "\n", "" ], "text/plain": [ "" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# First import the relevant keras modules\n", "import keras\n", "from keras import layers\n", "\n", "# embeddings_size is the dimension of the embeddings vector of each character\n", "embeddings_size = 15\n", "\n", "# Because we want to build the composite model step-by-step\n", "# each layer we define is going to be wrapped into a model\n", "Embeddings = keras.Sequential()\n", "\n", "Embeddings.add(\n", " layers.Embedding(\n", " len(char_idx) + 1, # Input is the number of possible characters + 1\n", " embeddings_size, # Output is the dimension of the embeddings vector\n", " name = 'Embeddings',\n", " #embeddings_initializer='random_normal',\n", " input_shape=(max_word_len,)\n", " )\n", ")\n", "Embeddings.name = 'Embeddings'\n", "\n", "# Change to False to keep the embeddings as are (as used in the paper). Setting it to True\n", "# leads to a massive increase in performance.\n", "Embeddings.trainable = True\n", "\n", "# Show details to check on input/output dimensions\n", "Embeddings.summary()\n", "\n", "from IPython.display import SVG\n", "from keras.utils.vis_utils import model_to_dot\n", "\n", "SVG(model_to_dot(Embeddings, show_shapes=True).create(prog='dot', format='svg'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the paper $V$ is a stack of the embeddings as column-vectors instead of row-vectors. The first dimension in `(None, None, 15)` is the batch dimension. The second is the number of characters $N$ in each word. Let's see initially how the embeddings look like (they will change during training).\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0,0.5,'character as integer')" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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ZLV9YrI37W1V7tNPpOAAAYJT1DQ7phbpmLS0vkDHG6Tgh5SseHsrP1koAADAs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\nZBMRERGpEFQcExERETm2NOfct/7br1FYPOoAbHbOJfvvnwUMprB4lg1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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# We generated some x's and y's above, use a slice for visualization purposes\n", "sample_input = x[:, 0, :]\n", "embeddings_output = Embeddings.predict(sample_input)\n", "\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "plt.figure(figsize=(21,5))\n", "# Plot first word as integer characters\n", "plt.plot(sample_input[0,:])\n", "plt.xlabel('position in word')\n", "plt.ylabel('character as integer')\n", "\n", "plt.figure(figsize=(21,5))\n", "plt.imshow(embeddings_output[0,:,:].T, aspect='auto')\n", "plt.xlabel('position in word')\n", "plt.ylabel('character as integer')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Convolutional\n", "\n", "The convolutional layer takes the embeddings and applies a number of 1D filters of various shapes across the character dimension. For the small model described in the paper, it filters it through the following number of 1D filters of the following sizes:\n", "\n", "|`num_filters` | `w` |\n", "|------------|-------|\n", "|25 | 1 |\n", "|50 | 2|\n", "|75 | 3|\n", "|100 | 4|\n", "|125 | 5|\n", "|150 | 6|\n", "\n", " This can be implemented in keras using the functional API and a for loop. Foreach number of filters `num_filters` and width `w` in the matrix above:\n", "\n", "1. Create a `Conv1D` layer of size `num_filters` and shape `w` \n", "2. Apply max pooling over time using `GlobalMaxPool1D`\n", "3. Append output to a list `L`\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "__________________________________________________________________________________________________\n", "Layer (type) Output Shape Param # Connected to \n", "==================================================================================================\n", "InputFromEmbeddings (InputLayer (None, 21, 15) 0 \n", "__________________________________________________________________________________________________\n", "Conv1D_25_1 (Conv1D) (None, 21, 25) 400 InputFromEmbeddings[0][0] \n", "__________________________________________________________________________________________________\n", "Conv1D_50_2 (Conv1D) (None, 20, 50) 1550 InputFromEmbeddings[0][0] \n", "__________________________________________________________________________________________________\n", "Conv1D_75_3 (Conv1D) (None, 19, 75) 3450 InputFromEmbeddings[0][0] \n", "__________________________________________________________________________________________________\n", "Conv1D_100_4 (Conv1D) (None, 18, 100) 6100 InputFromEmbeddings[0][0] \n", "__________________________________________________________________________________________________\n", "Conv1D_125_5 (Conv1D) (None, 17, 125) 9500 InputFromEmbeddings[0][0] \n", "__________________________________________________________________________________________________\n", "Conv1D_150_6 (Conv1D) (None, 16, 150) 13650 InputFromEmbeddings[0][0] \n", "==================================================================================================\n", "Total params: 34,650\n", "Trainable params: 34,650\n", "Non-trainable params: 0\n", "__________________________________________________________________________________________________\n" ] }, { "data": { "image/svg+xml": [ "\n", "\n", "G\n", "\n", "\n", "\n", "140496709029784\n", "\n", "InputFromEmbeddings: InputLayer\n", "\n", "input:\n", "\n", "output:\n", "\n", "(None, 21, 15)\n", "\n", "(None, 21, 15)\n", "\n", "\n", "\n", "140496709028888\n", "\n", "Conv1D_25_1: Conv1D\n", "\n", "input:\n", "\n", "output:\n", "\n", "(None, 21, 15)\n", "\n", "(None, 21, 25)\n", "\n", "\n", "\n", "140496709029784->140496709028888\n", "\n", "\n", "\n", "\n", "\n", "140496709321896\n", "\n", "Conv1D_50_2: Conv1D\n", "\n", "input:\n", "\n", "output:\n", "\n", "(None, 21, 15)\n", "\n", "(None, 20, 50)\n", "\n", "\n", "\n", "140496709029784->140496709321896\n", "\n", "\n", "\n", "\n", "\n", "140496709029728\n", "\n", "Conv1D_75_3: Conv1D\n", "\n", "input:\n", "\n", "output:\n", "\n", "(None, 21, 15)\n", "\n", "(None, 19, 75)\n", "\n", "\n", "\n", "140496709029784->140496709029728\n", "\n", "\n", "\n", "\n", "\n", "140499251436960\n", "\n", "Conv1D_100_4: Conv1D\n", "\n", "input:\n", "\n", "output:\n", "\n", "(None, 21, 15)\n", "\n", "(None, 18, 100)\n", "\n", "\n", "\n", "140496709029784->140499251436960\n", "\n", "\n", "\n", "\n", "\n", "140496703249880\n", "\n", "Conv1D_125_5: Conv1D\n", "\n", "input:\n", "\n", "output:\n", "\n", "(None, 21, 15)\n", "\n", "(None, 17, 125)\n", "\n", "\n", "\n", "140496709029784->140496703249880\n", "\n", "\n", "\n", "\n", "\n", "140496702985776\n", "\n", "Conv1D_150_6: Conv1D\n", "\n", "input:\n", "\n", "output:\n", "\n", "(None, 21, 15)\n", "\n", "(None, 16, 150)\n", "\n", "\n", "\n", "140496709029784->140496702985776\n", "\n", "\n", "\n", "\n", "" ], "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "num_filters_per_layer = [25, 50, 75, 100, 125, 150]\n", "\n", "filter_widths_per_layer = [1, 2, 3, 4, 5 ,6]\n", "\n", "inputs = layers.Input(\n", " shape=(max_word_len, embeddings_size), # The input to this layer comes from the output\n", " # of the embeddings layer, therefore must have \n", " # the same shape as the embedding's output\n", " name='InputFromEmbeddings'\n", ")\n", "\n", "L = [] # Will keep record of the outputs of maxpooling\n", "\n", "for n in range(len(num_filters_per_layer)):\n", " num_filters = num_filters_per_layer[n]\n", " w = filter_widths_per_layer[n]\n", " \n", " # Create a Conv1D layer\n", " x = layers.Conv1D(num_filters, \n", " w,\n", " activation='tanh', # Hyperbolic tangent is used as the activation layer\n", " name='Conv1D_{}_{}'.format(num_filters, w) # Give the layer a representative name\n", " )(inputs) # for debugging purposes.\n", " \n", " # Append to outputs that will be concatenated\n", " L.append(x)\n", "\n", "# Again, wrap it into a model.\n", "\n", "Convolutional = keras.Model(\n", " inputs=inputs,\n", " outputs=L,\n", " name='Convolutional'\n", ")\n", "\n", "Convolutional.summary()\n", "SVG(model_to_dot(Convolutional, show_shapes=True).create(prog='dot', format='svg'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's see how it transforms our input at this stage (remember the output from embeddings in in `embeddings_output` of shape `(30,21,15)`):" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/lib/python3.6/site-packages/matplotlib/cbook/deprecation.py:106: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance. In a future version, a new instance will always be created and returned. Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n", " warnings.warn(message, mplDeprecation, stacklevel=1)\n" ] }, { "data": { "image/png": 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vevs/ICJNwFHA82g1+X3AoyKSB7wV+MIwr3c/UDyI7f8NHbb4m7i0a4B16GPN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\n0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/X6Ugvuwl5kKroduHC/vKHONRcBS7z/Pg/8ZJzaKDKR3E5in3kSWG6tPQZ4H7gBwBiz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\nGF0r/dlyZX/KiacbT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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "convolutional_output = Convolutional.predict(embeddings_output)\n", "plt.figure(figsize=(21,5))\n", "plt.subplot(1, len(convolutional_output), 1)\n", "\n", "for n, outp in enumerate(convolutional_output):\n", " plt.subplot(1, len(convolutional_output), n+1)\n", " plt.imshow(outp[0,:,:].T)\n", " plt.title('Conv1D_{}_{}'.format(num_filters_per_layer[n],filter_widths_per_layer[n]))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## MaxPoolingOverTime\n", "\n", "MaxPooling over time is implemented using a `GlobalMaxPool1D` layer on the outputs of the Convolutional layer. Then those outputs are concatenated to provide a fixed representation. " ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "__________________________________________________________________________________________________\n", "Layer (type) Output Shape Param # Connected to \n", "==================================================================================================\n", "FromConvolutional_0 (InputLayer (None, 21, 25) 0 \n", "__________________________________________________________________________________________________\n", "FromConvolutional_1 (InputLayer (None, 20, 50) 0 \n", "__________________________________________________________________________________________________\n", "FromConvolutional_2 (InputLayer (None, 19, 75) 0 \n", "__________________________________________________________________________________________________\n", "FromConvolutional_3 (InputLayer (None, 18, 100) 0 \n", "__________________________________________________________________________________________________\n", "FromConvolutional_4 (InputLayer (None, 17, 125) 0 \n", "__________________________________________________________________________________________________\n", "FromConvolutional_5 (InputLayer (None, 16, 150) 0 \n", "__________________________________________________________________________________________________\n", "MaxPoolingOverTime_0 (GlobalMax (None, 25) 0 FromConvolutional_0[0][0] \n", "__________________________________________________________________________________________________\n", "MaxPoolingOverTime_1 (GlobalMax (None, 50) 0 FromConvolutional_1[0][0] \n", "__________________________________________________________________________________________________\n", "MaxPoolingOverTime_2 (GlobalMax (None, 75) 0 FromConvolutional_2[0][0] \n", "__________________________________________________________________________________________________\n", "MaxPoolingOverTime_3 (GlobalMax (None, 100) 0 FromConvolutional_3[0][0] \n", "__________________________________________________________________________________________________\n", "MaxPoolingOverTime_4 (GlobalMax (None, 125) 0 FromConvolutional_4[0][0] \n", "__________________________________________________________________________________________________\n", "MaxPoolingOverTime_5 (GlobalMax (None, 150) 0 FromConvolutional_5[0][0] \n", "__________________________________________________________________________________________________\n", "concatenate_1 (Concatenate) (None, 525) 0 MaxPoolingOverTime_0[0][0] \n", " MaxPoolingOverTime_1[0][0] \n", " MaxPoolingOverTime_2[0][0] \n", " MaxPoolingOverTime_3[0][0] \n", " MaxPoolingOverTime_4[0][0] \n", " MaxPoolingOverTime_5[0][0] \n", "==================================================================================================\n", "Total params: 0\n", "Trainable params: 0\n", "Non-trainable params: 0\n", "__________________________________________________________________________________________________\n" ] }, { "data": { "image/svg+xml": [ "\n", "\n", "G\n", "\n", "\n", "\n", "140496703596304\n", "\n", "FromConvolutional_0: InputLayer\n", "\n", "input:\n", "\n", "output:\n", "\n", "(None, 21, 25)\n", "\n", "(None, 21, 25)\n", "\n", "\n", "\n", "140496703598152\n", "\n", "MaxPoolingOverTime_0: GlobalMaxPooling1D\n", "\n", "input:\n", "\n", "output:\n", "\n", "(None, 21, 25)\n", "\n", "(None, 25)\n", "\n", "\n", "\n", "140496703596304->140496703598152\n", "\n", "\n", "\n", "\n", "\n", "140496703724232\n", "\n", "FromConvolutional_1: InputLayer\n", "\n", "input:\n", "\n", "output:\n", "\n", "(None, 20, 50)\n", "\n", "(None, 20, 50)\n", "\n", "\n", "\n", "140495944195824\n", "\n", "MaxPoolingOverTime_1: GlobalMaxPooling1D\n", "\n", "input:\n", "\n", "output:\n", "\n", "(None, 20, 50)\n", "\n", "(None, 50)\n", "\n", "\n", "\n", "140496703724232->140495944195824\n", "\n", "\n", "\n", "\n", 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"\n", "\n", "140495943926336\n", "\n", "MaxPoolingOverTime_4: GlobalMaxPooling1D\n", "\n", "input:\n", "\n", "output:\n", "\n", "(None, 17, 125)\n", "\n", "(None, 125)\n", "\n", "\n", "\n", "140496703694608->140495943926336\n", "\n", "\n", "\n", "\n", "\n", "140495944121984\n", "\n", "FromConvolutional_5: InputLayer\n", "\n", "input:\n", "\n", "output:\n", "\n", "(None, 16, 150)\n", "\n", "(None, 16, 150)\n", "\n", "\n", "\n", "140496703676824\n", "\n", "MaxPoolingOverTime_5: GlobalMaxPooling1D\n", "\n", "input:\n", "\n", "output:\n", "\n", "(None, 16, 150)\n", "\n", "(None, 150)\n", "\n", "\n", "\n", "140495944121984->140496703676824\n", "\n", "\n", "\n", "\n", "\n", "140496703597536\n", "\n", "concatenate_1: Concatenate\n", "\n", "input:\n", "\n", "output:\n", "\n", "[(None, 25), (None, 50), (None, 75), (None, 100), (None, 125), (None, 150)]\n", "\n", "(None, 525)\n", "\n", "\n", "\n", "140496703598152->140496703597536\n", "\n", "\n", "\n", "\n", "\n", "140495944195824->140496703597536\n", "\n", "\n", "\n", "\n", "\n", "140495952949712->140496703597536\n", "\n", "\n", "\n", "\n", "\n", "140495943989512->140496703597536\n", "\n", "\n", "\n", "\n", "\n", "140495943926336->140496703597536\n", "\n", "\n", "\n", "\n", "\n", "140496703676824->140496703597536\n", "\n", "\n", "\n", "\n", "" ], "text/plain": [ "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "inputs = [] # list that holds the inputs, \n", " # must be of similar size as the outputs of the Convolutional block\n", "L = [] # Stores the outputs of the maxpooling in order to concatenate them later\n", " \n", "for n in range(len(convolutional_output)):\n", " # This looks complicated but is really an input layer that matches\n", " # the output of the convolutional layer.\n", " inputs.append(\n", " layers.Input(\n", " shape=(\n", " convolutional_output[n].shape[1], \n", " convolutional_output[n].shape[2]\n", " ),\n", " name = 'FromConvolutional_{}'.format(n)\n", " \n", " )\n", " )\n", " \n", " # Max Pooling over time for input n\n", " x = layers.GlobalMaxPool1D(name='MaxPoolingOverTime_{}'.format(n))(inputs[n])\n", " \n", " # Append the output of max pooling to L\n", " L.append(x)\n", " \n", "outputs = layers.Concatenate()(L) # Concatenated the outputs in L\n", "\n", "# Wrap it into a model \n", "MaxPoolingOverTime = keras.Model(\n", " inputs = inputs,\n", " outputs = outputs,\n", " name = 'MaxPoolingOverTime'\n", ")\n", "\n", "MaxPoolingOverTime.summary()\n", "\n", "# Block diagram\n", "SVG(model_to_dot(MaxPoolingOverTime, show_shapes=True).create(prog='dot', format='svg'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here is an example of the output at this layer for one word:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5,1,'Concatenate')" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, 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RrK/TbEPsmus1KyvG0sTUVSAU7vMoqSvQQoD24hd0zUDLG62j8W2M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\nayQ80IIMtFsMQOO7GfO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QKp/xrcHN5UIEQgw0d67CgTx1rQ3PoymcPQ0GQI9BO4cFPvDo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DWOublCb2ag9T2JZ65uKQKtYkBM71n3PCIb3/44xKDnYBxGU+efAdn99nASWmUN6W1NFWhH\nQUmJgJsR0buj0LBwCv2MBoolArzhk6AUQnklsOdI3eBXZeEMIp6BpiYO9LaOzDe1HYyjSoXAA+eH\n+M53PGS1X8rgGM9nOl7Z/cHOPkmTukkU48Ep7aRVsM1hOy2cb1CgAcrGSSUCYZTk7GAEfr7f2lUk\nwTjMFkx4+7IJykg6siCpAHU/DqLqxR7+XCy7JxCB1piBxp5Lowr1HEeTAm1MFk6mvK7KCPSN/VaG\nO/uTXP4ZkI4Po8zCSeRwWYlQWQGDCdqXd1Ki6dpWH//+q7u4f6TIEHNMxNWBnIQvU4fzghxpKNCa\njg23cAJqX/aZgjvkJQI1CjQiBreHPQx7rlageVJoojZOklYLz3QP58gsnEHu30dhZDVfoONEhLSb\n2hG5hZNITZMMMxcochloU5QI8PM7jJRauqyF01SgKTt//lwnS3eVAo3UwQfjEAfjEEJAEWqTjFAr\ns0CXgcjldiUCEn/05XtIEuDND54rfU2uRCAsV6AFZQo0GwtnUwtnqvLsuRLjM65AO71ReoczgywD\nze6BwPORmqwodBOm1Zblb+Gkibzd9yArgT/H1HGpV8XsJscEavo5qWPCJ5/8M3W+VM35l1k46Tya\nz/4c9Bz84+96ix5QV6HnSvyDT7wJH3/uuvV7u47MWQaDKC6deLQBPfAPJsXAVZok1uX0mKuH1MJF\nh4e3+Nkr0OxUl/10hay0RIBbONlKclUGGg8JD2NVIrDhNyvQuH01iliJQHpNlSn7pBAnUiJQhZ0N\nH3/zLTcK//7C4zv48fc/hjfd2AaQBbFnFmmRlQhYZKAdBZG26ZxmY6A+5trCaSjQ9GBVtXD6roOn\nr6mQ6qpVdHrPuvuIDu1On10TiwlzHQZMkaOUnnbvReqFMjWOTMkp1cKZV11KkanE6X1yvytKVJgl\nmaa8qaxo4aT3UAHIZRZOKdSK+yS91somTbOCaRV66MIQP/fRp/H+110GAJwf9vCp5x/GB566VPs+\nfAL24Iyz/mjbhgvawnm+IQMNUKq6+0cBkiQ9L0qeDXSNHowjrVbjGYW8fdkEZSQFUWKVFec5iqDQ\nduUCiVNcsOO4ZqFAUxmuavvjVLXSFPau2zXrMtBKFGhlChquNq3CnYMigUYE1tEkb+EsLRGKmstN\naFvvHU6w7rvYGqpz4f5RgO0S5RTZKwFgUkG2EriFs1AiUDM+NC2cQHE/5UoEchloan/QfZBIp3ND\nD4Oeo3N3aWGRLPitFWhsPwPcwplXoI0sm2c9NuYB1DM9SBda6LlHZGATGdbvFUsEytpxy2BaBclW\nuV7SSsmJtChWJKJJFtO2agLNLBFICeC9kSLQLqSlJgeTEPujEMOeY31sSHwwNsaRdSALpxDAsw9u\nV74voBZ/y5pte67ISgTCjEArK/ExUdfCmSRJZuFsyEo7C+gItA5zB12rtgsqZgtn3b1qoAdR6UNo\nRSyc7RVo87VwTlMiQAP4EyPQXE6aZf+vJ/g125GVCKQKtAU4j775LTdahV17jsg9+MI4KZ14tAE9\nmA/T64sPAD741GX84nc8h0d21it/X1k4SxRozMLZtgBEWzibCLQ0H3GcDvhEavUCSiychgKNB68C\nWQ6IUgkk2B0FtQq0shIBlbWWWTXjBHqVkJ+vF9Z7U1vM5olzaz385Ief1JNFOjd5Xh+tSDaRoefT\nIOiv3jsCkDXqnQa0yikmBVo+eJwr0NTqvcQ7H72AYc+pvD7NrKwy0MSKJjqTKJ6JAm0URAhje/Wp\nK0U6OYxLJ1a+K7F7FGAcxrkWTpG2YeprRJqkQlGBFifFwbvrKFt0GBUtrAUFmi4RyDd6+o7EJIxS\n2045QTALmFYhIQQ++fzDWpknpcDPfPTp2nsikLcAzVqBRsd9UUsEzusWzupzfaPvYm8UZIsrJa+l\n7/fi3UOtRiwjOMoUaI4Uesxos5+IoAgrFhE9fT6Uj3eupeUVdaShK2Vh0juTDDQnr7w27W8EGkPW\nTYpvlxBoWQZa3sJZViISxEmjqp7y2u4cTLDmO9jse5iEMV6+P8L2oFd4PR9jBGH9PXTAFWipck5t\nY73ThVs4/Yp9zhcC+eIF7Q/6OS9DGPYc7JECzZFasR8l9ZlsJrgdlUAKtNslJQJ2GWh5la2XXgOj\nININ2bQA1jQWG3gqoiBKn6FAuxIBTlbujdX+4vdQba1nRFocJ6qts2DhVNuqLZzGttOC8H5q2by0\nob7r4TjCwaS64bkMdDwnbBG3CXQdPn5pvTImhFs4J1HxmsqVCGgCzW4OV6dAC+MESaKOic9Kuc4q\nOgKtw9yhq9+tFWjqzzhl1+tW0ulmSA8L21X3RQV9d+sMtPQX5t7CmUAr0KwtnIuiQLMI9O57ShV1\nX1s4T59AawtPGgq0sLgy1RY0uNhPB8ec6Ol7Dj76xmu1v+9KmauxNzPQ4iQbWNqSlkRu9BpW5zWB\nxlqidFCyaeHUCrR0wp6qkcyVQzddhd09Cmsz0MrsK9xWRyvNE4OgA4B/9ePvwfe9+2btd1sEvO/J\nHfzMNzylFWlSMgVaw/VDOUhfSQm007RwZse83MLJ7RLjQKlCnri8gc/83a/DwxfXat+zbj/QQJys\nT+PgmBlo2tKmSnVs4wwcKfXzs4xM8D2JW3tKxcAJNCnVinQYqUUuc8JXZuFUKjIUXqdKBIq2U5cp\nQuO0mY7sz5xAo0E/tZpuzEuBNqOSDz4Jm5eFs7+gBBo1ONadnxu+h71RqFU9Zc8xGl/81e1D/W88\nv6tOgSaF0MS1zb3HS0nejNAzCDRWbFNmHbTJQOOZQlk2ZP39oIrMIegMtPR1R0GEURA3WDjLM9BU\ne2SJAo1aOCkDzc9Hd+QVaOVqQg561lIAPJHTX7pzWKpAU5+fRS7whVQTOgPNUKDFSYMCjVk4aXtM\nlQ7FNThSZOeDKwsLCXe5As1zWAYau7c1KOJMUNsjx15NiYCNAo1IGV0ikFrtj4JInwO2CjS+uJPF\nNrRQoHECLd1fPAsvs9bTn+qZsj8KC88CbeHU9lMjAy11FuyPQhxMIlzazBRoZaUEdXCkRBQVy6Lq\nQNdhlX1TvS9ToEWxbuMl5EoEQspAy0oE6jajrhyAZ7nRotVZxnKzDR2WAnRp2445TQtnUwsnkE3w\nl5w/K6xw27zeleJY7W1NoMYquqfaTsz6J65A4zYKZomTItf8WAYhBNb8LI9iGQk0XucOoHRlqi1o\nPx6MQ0jRLpNN/X6JAo21NiqSvF0Lp29r4fSkbuH02QQHMDLQ4nw7piOFDl41w197aamCUqBVE2h6\nMsIUEaHxORFr4eTk78V1f67X86zQ9xx86j2P6HNCZaAR0Vp/bGgA/tK9kX6v04JZ+KDOl6KFk2eg\nAfWt0jR5rLPik1XkgCvQjkGg6VD1SVRKRlXBkZkKrpRAc52MQOOr/qmVMqxQlXCVKSGOi9k+bqps\nKC8RyF+vSoGmlG/0PCICLYgyFczcLZzHVCjTJEwI4PqUjaBVyCycixlx7DkSm323dpFQKdBCvfhS\ndh3R2O+Ltw/0v3ELZ5MCrY40Lnu9styXxw3Q36vUT9csMtCGPQeHgdqmkWVOlC4RaGrhNIL5y66P\nphbO3SOVrVimQIuZhZNs4HRP4Ne/jYWT3wM4gfbV+0elz1wKtwdQeg/h4CUCUmbZaWEU1x4bV0oc\njEPESdYCau7zIE50oQ6RhH1X6vNcK9BYBtqg57Dm97wCrc0Y1MyaBYD9VKl1lxFoSZr7Z2fhzC8q\netqqG+HCujoHiJxrVKCxeAGdgWY5xvENAk0H+ft8MYfGVVL/PU6KZQNAtrBA+9185tLrbx+MEcVJ\npkCbhK3VzWRvbpeBpravjkDjz9bS9mqX25qZhTNOcr9fhjprZo5A8+SZV6At5hO2w0qBTxptQK+j\nfJW6B66vCbTsIbTMyBRo7eTbc23hFIqYaVsiYEr55w2+D8paspq2e913tQLtpIoPZgnPePCFcXFl\nqi1oUH0wCadSs7mOzJF6YZppmF3jrBnIlkBzJYRoHrT5rqObzGgiIqXQljWCmX+SH5CrP7kCbRRE\nOJxEtQq0SxvKsvPKbpY/EsfFFk79/nO8fk8KjhB68tWoQCML5/3UwnmKBJomVZl9iuf6cQWaSa5V\nwTEG9GVYNyyc4zA+lpK4zyYpkYVViuBKqbMfByXhyL4n8VpKoBVLBKpVJbpIg6swS5Qe6ppSQctm\nflGmGFWTLkeS/Zkp0GRW7rFfYu+ZJdqOZapABMbVzf7MyXKa+C6qhRMAfvh9j+Kpq5uVP9/oe0ot\nNakm5I+rQDuY2BNorlTK4yyE2zhPmeKoDGu+i4vrPtZqPmvYc3E4Ud+FgtqbCDSyO1a15pGF05UC\nQmSER5lCkyIOqibFt9MsLSJPCETekJLWLI/i138Qx1hvIHb5Plzvu5o0ixNgu4RA42OMJtU9bZvv\nOXBT4g9oLhHouUIrn6oINE7C0Tb4nqPvd7Qf7h4GWOs56LkSw57DVJZC5w3Hcbv2eiJ4ObiFU5W3\nZCU/NvZJ+g40hqSszFEQ6wiGu4eWGWhetrhDJLdt7IypiCLL67qxmAMwhbBQx+dwEhWeBYUSgQoC\n7eX7anGPxnKHk6iygKMK07ZwAsCbHyrPP6P3BVTM0SRKChEYOQWaLhFI2P2rnkCrvJ8wIrDLQOsI\ntA4ngCwDzVZVpf4kBVrdgyRToKmHxZLzZ1MN0D0p52rhlJLy6Owmx4RsIHUyByXXEFnIKJGN2z1k\nga5t8icWBbRCSFDtPLOxcB6Mw6nymRRZxVagYzXIpHtBnCCTlVvfH4Su0a5D35MYBXkFGpC3BCRJ\noq1hBE5EmgMfV0q9oluXgXZhrQfPEfhqOgjj3x3gLZz20v5Fh5RZBl9jBtoCWThNq9EoiLCz4bOf\nS/3zURDj4nrzsImOZ91+GPrFEoE6VWMTBmyS0qaB15HZ5LBKgfbiHXWc8hlomYK07N5KmUIxuycl\nSTELVVmwVPGBeY+hxTOaBGQlAuraBUiB5mASxfp7zM/Cmf55zOeD70p4jmiVcWkLOl8XtYUTAP7W\n+x6r/TkR2GR3K1tEtVWglU3UHQmdm2Zz73GlzOX9mdtD+7zuPv5Ln3wbdmqyLQc9RxNntjY3k/w3\nQapWIVQZCGVWVSk0fVdWWjiJfDu/lv8ORBIcpvlgOv+RlL08l9VCGWs2WHLSvtzCmY0xJlFSS1LQ\nws320EsXhlMCLanfLs+RWrFE22DuJ5U5my20Aer40X2Qxs/3DifYTrdjwMhENyU6KRe2zZCAbPCE\nJEmwNwrhOYo0O5xEWPNdZg1uQaCxsQ+d/0PfQd+T1go0eq5QJi1gr0Azi6j0PT5n4aR5k/q7lEIf\nr2KJgHpRVYMoEW4v76YE2maWgbY3Clvds7N4AlUKYPNM3tnwcXnTxyMXq3M06XtWWjgZ6cj/tBFB\neK5oVqA5SoF21gm05R+1d1h4iNTE2bqFM6YWTnsL58oo0Fooh65tDxpbHY8DV0pECbNw2hJoWoE2\nry3Lgw+cCqGabjOBts4snMuoQCu0cMbJsYmZzMIZTdXo6aW5GQQaQGcraOoaF6Idadn3HLsSgTCf\ngQbkBxc0ieLH28/J3/Pto54rdSjvRo0CTUqBy5t9vJwqrIC80k0K9XedgXZMpeAiwBH2GWhbAw9S\nqAlyz+LanCecVJ0RMAUaP19o06KELJw2+THNCyHrvaICbRYlAqRAsy6ikUKv6peRLn0vu17yLZxZ\niUDZfcZUXgDlSg9l4YzT9ymq04DM0uYIoYk7buG8vj3Ai3cP8Uo66ZmbAm1GGWhCCGwPe5UZeseB\nuwQKtCbQ5Jgm6KUKNJb5R+AlAk0tnASzebYMtE9pe8xJuba71eRvPXNtC5c2+5U/H/Yc/V1sg9bp\nuVSlGiMLJ6AmvbT4U3V9UANgGei5d6GQgaZIgsNJvv22VIEWNS/q8XvAet/N2cbLSgT4GGMSRrX3\n0Bee2MGv/fA78ejOulYR2rReulJqUnOTKdCSJMHHfvF38X/84Ys5e6pWoLlOYT/cY22i/H5L46Jo\nCgunqUAbhzHCOMGNtCmbztuMmLVTXfLvQnEcR2mL51rP1QS3bQbaURBhVENsl8FUOtHzki+S6BIB\npkQrI9qAbJ9XlQhQtMKt1D1ApPdBauFsszjjpFZSUoLaiEh+/AOP41/+2Htqx8OmhdOc93IXBS0+\nhczCWa9Ac3KL3hxcwWiWO5xFLDfb0GEpQNeq7fOAB4xXrW4T6Ga4n04AlpD3yIFusG2IwF//0Xfj\nR9736Lw2SWdFaP+85U7WWRgnRGpWlQiobajPQAPUqiwNYNvI5xcFFHRLyoyyCWlbZCUCUyrQDFUc\nhZvTodA5hy33d991GuXwysJZokBjA7IySTtf8aQyAT1BkgK394t5UGW4tjXIKdB4sDupaOhzjkOc\nLAocmRFoTde8IwVbhT/dib4QIj3mmQKtz84XypmMYtVUaaP2tSkuyTLQKG8vOpaSmAiTw0lUmUtW\nhpwCraKFk5C3cKYKtIpco8z6yhpvk5IMNEdkEwFz4UPmc5mowTcxLJzvf90lBFGCf/WnLwNQypV5\nwJyoHQf/9Lvfip/40BPHfh8Ti97CaQNanCCLWNlzTEqRawAG8kRSXQYaP3429x86l798V1ksr27l\nibDM7nYMAjynQLNT6fB2zX/++1/C83//d/Tzn/6d20tJgVZlQzPzpjgyBVqexKK8qYNxPlvL0Rbu\ndLEqVvlbTWMS/lzf8PMKtK0yBRobYwRRUvv+jhR4283z6f8j3b5mAo0XE3AL5ySK8ccv3scf/NXd\nVGGeVyL2vWxxiBo/lQJNvQe/37pORqCVZUXWgUouCHQ/p4ISOnZHpECzaL2m+weRomSVpQy1Qc/R\n16dNCydAFs52CrSqEoH1XMwC0m0sLnDwrDT+uZpAM65ZPx1bkgJto++h78nMwtkmAy3djsNJZB3T\n0fecnAK+DDwCJYySkugDRysZsxKBhJUI1J3rFhloabNvp0Dr0GHOaN3CmZ6VNLmuU2PRjflgHOWs\nYcuKaVa4+55zbKteHRyhBj/0gLZVZ73n8Yv46b/2Orz++tbcto2DP8TNQZTnyEaSJrd6uoSKIE9P\nWNV1kyTHtwbSfjychLWr69XblJffK/TdoN0AACAASURBVEJc5lbQ2qhlCE9e2cBjO9USdyAtEQgq\nFGghKdCKknZOpoyN8Fdl5UhboBrsdle2+gUCLcvGSls4W7QzLTp4iYDN9XMunUTYtnHNE57McvEo\nN48jy3+xU6Dx41wF11E25MNJViIwiww0WqFvY+HMSgTK2vmy78tty7kSgRLCtLqF0yTQ1D1iUqLA\nowIYIkNkWiIQx3kL55sf3Ma5oYff+qwi0EzVwazg6AWu4z8f3vTANi7XKJKmBY0FyvLslgW0OHG7\nRoEGZJP8G2kRwzQKNBurKx3vF+8qRfHVrbziP1PpTH/9rvVcTKIYYRRrwtjWwhlEMf70pft48e6R\nvpaTRCmcfWbFu3tQbmsj1AWDVxFotG/2RkGOtOXW+CCK8bd/7Y/xxduHeO6B6nB0ICPNATUm48/Z\nrbIMNJk9r5tKBDiI7IpiFeNQd6/m70kq3HEU4zBd/Hhld6yIDEbgqJzWYgbaURBr62ZegSZ1udBU\nCjSmGqJz4KELpgLNnry6sO7jf/7UO/DX06Z1bpXt9xwMe44moRoz0HIlAuo+b7sQbxI1+6MQjhR5\nstaYY/L3LmSgpb+3NwrTPN3idmz4rs5AW/OV2m5/rBRobQpq6Bwje/Os4LDx86Qkg5RnI9K+sy8R\nEHoh/h/99ufwuVf29M9Iia4iVJxKu/dZwemPXDusPLIMNLvXmy2cdRd738smDcvYnGhCWzgX6LtQ\nRXbbEoG+5+AHX3j0xI4LH+SUlghYWDgJy6hAo4lTGCeVWS1tQQP0/XE0ZYlAsYXTlSKnMp2GQPul\nT74dP/nhJ2tf0/ecQgsnoAZkRIxlCrTs9zwny/KiwYeftp3x/VlXIgAolcLL90d6os8D1GmgHBgE\n3TKD7AqA3f2LJmKnrUAD8q1VoyAuTFzddIJikrFVoPOkaT+s+25m4QyO18Kp4wxSgtf22uf2nyoL\nJ/1pkhFKJV6udCWLzhHLpVLh2PnXUe5PGJerR1wp9MSPJqY0NgDUZMJ1JL72yUvaotQm6LkNtAJt\ngZ7PJlZKgZZO/KsUrXTOUy6RdQsne77blggAwJfvHMKRoqAQoWttFi26hwFT6VhaOCdhrJtyidQw\nm/88R2YNhhUEc8+RueZojjsHEwx7Tsnignr/3VGYJ9B09leCn/rVP8b//ocv4ac+9AQ+9Z6Ha78T\nFf3QdnqO1O9bViKgrIWxXuS1PQb0zOcNv1Xg5x9XoB2m++rW3qhQeOY5En1PprmN2WIdVxrz/eWl\nCrQkUWq1NmNQs4Vzv0GB1re8N7z7sYv6mcD3wcBzMOy5ILGjrQJtlJ7bbcgkPl4DsmZNTnyZ2dH8\nWJrPAnMsWIY138WtvZH+/aHv4N7hBEGUtHq20Hl8FEzn4KgCfd84ThVoxnvzbMQxI9CsSgTSffLK\n7hj/6Lf/Ar/5p1/VP6N7Q8/tFGhAR6B1OAEIY3WgCVkGWjbhrgLd3A8m4bGr5RcBWoG2QAooFbjb\nvi3xpJHLQCtkAsjGySxZqtTvL+Z3rANNPidpnhBwfGvgcUsEHGNgR0H6OZVpy9VWW6gSgRIFGrdw\nVinQwkyNBGRkAN8HdSUCgFKgTaJYD17DKPue1LaVNXAt/6OYH0Kb40mBzqdZIEAgFUOS5pyZRBFX\noFlZONPjWdfCCaiBOpUIHE3s1G11n+k5WZ6Z7T2Mv66qRAAoEsZkpeTnNcfAU1aYe6nNB6ho4Uz3\nfZV6xGNZK7xEgG4rdO1+8OnL+nfaqATagDZvkccaWoG2ANfVtFg3MtB6FepnIgIe1ARaRv6MaxRo\nnAC1snCm+/TLd49wacMvnO/0XDiuhRNIbW6hXdi7nigzAo3C0c0CnFy7ZYU60feqM9DuHEwK6jOA\nK9DCnILVZeqb//PTX8V3fc1D+LEPPG7lEqH7AJEVRFqR7T/3+al9MYjbqbnpvkFEY92Ym6vvsxKB\nGIfpvfvW7ljdv9j93pNCn3uqKKVoMx3kLJyZS2LMGj1tYGag7aVtxCaB1tY+ycHJQUWgZe/RlGfG\nM9DGYWzVAqrfOx2v0ULk7igoKIwLLZxs3xVey6zfVQ3I676rx2Zrvou1nqsz0dqom2k7DifTZQg3\nvS8twpqLZZxYzxRoCWgtu+75Rb/7l6+pchZ+P+CkvF/T2HtWsPyj9g4LD52BZnm2OWxyTZavKlBW\nTZIsJ+lhgu5ri/RdaPIYLgmBJkRJBpojGwlcPula1O9YBxo4KstEO7tt03seBfYZDubv83YoXSJg\nqEznsb9VBlpxxZPnvJDtgX9+j6mRxkGUWjHSrB2HD8yaFGjK5kM2Tv49aUU6s3Au3/lmgu9Dm9xD\nrUBbAKVMz1EWziBSdp6CAi09j20H/2YTXRWGPQf74wj74xB74/DYlr6+5+iMGNvsSaeRQFPvY1qW\npYTO8SubtAohcG7o6ZwcQK2YmxNoV2b7viyKwHUya7AjFPnOM9BE+ivvefwiPEfo1fF5QFs4F/h6\nJTvPcivQ8gRa1blM35GsarYZaHwCaXP/oX364t1DXNkqXqNmePw04BmGRxO7sHc1rlET21fTzKbd\nVIFGY4Ayco8vFnLUtXDePpgUCgSA7P5RZeH885f3EEQJ3vbw+drvwkHPwyKBVqJAS+8fPJ/JBrR9\nmkCrayasUKAdpJl1r+6PMQnzpJebKtAAda8kgovn0vFzz5NCk3jme9l8Fz7OIgXa1a0BPEdoK7Ru\n4Zzi3sDHKINenkBrIuQycjhOYxDaKdCA7HzeH4UFFRhvN+d/B8rVyLQ9VYthXKG55rsY9hxNULfJ\n16T7wuGkvtyiLcwSAfO+Q38PDAValmNd/d70u5pAY6peMwONZ2OfRXQEWoe5o20Lp8hNruNaEsB1\npL4xLbKtwhZZBtriXJoqc2YJFGjpeVB2vvQcixKB3nITaLw2nlqpjrvqxSeiU1k4Zd7CSaUg2TWe\nb6ecJfqexCgskh5cel4WqpoLlA/jXE4G7QMh0NjGREHTmkBLzAy0rOhh2bMbgfw9y0qBtkAWTjdt\nciPlR1kGGuXd2Az+eRZOHdZTBRq1tV7bPh6BNvAcndFnS/LkrDmlFk71b2b+kFag1eSUbg96WhED\nqGd6cXEjs2j2St6HK9Acmao3GYFG946NvoeveeRCY7nHcdBWTX8acDSBtrwZaAUCreL8onsHtQ3a\nZqBlZRDN9jMg26cv3j0qFAgArETgWBZs9Z0PJyGzcNqpXSdhjFfTcpt7R6YCzcltW9+TlZm5daqS\nOwfjcgVaemx2j4J8plf673/y0n0AwNNXNxu/C4G2mRY1Sf1amoGWxkRkam67a5OO6ZjZw6vAxz45\nC2eqQIviBK/sjXP71XNkXoFWktM2NMacfBzU5h7jSKlUuekYXTdV9l2cG/a0FXpkma1X9RmEfmrh\nJDQp0OgZcjgJMbYs4iFwMghQ361KgeaWEWglzwMi/Kq2g4/t1noO1nx3qoZn2q6jFiUCNuAlAmXF\nGTQXGgdxLo7EtkQAAP7ytX31HoxQ56pWOrcn0dlVoS3vE7bD0oCuVdtJIs9AC+Ok0c7Y91T74CKp\ntqaF+SBYBLgs2BRYXPsKkUWlgdaOzEncy7D0CjSZ5R6Y/zb1e7IB4TQraLzRElADTS9dNQfsKuSn\nRd9VTUQH47CQe0EDybJQVZ6BZobGc2tJE2FPEy0iR7gdnUiASWgferzoMHPkmkBqhkWwcFLte2bZ\nzW+TKwUO0rB/G/sL3YOabeMu7h5O8JV7anBuhpO3hVK0qQm07TXFSd2ya1wr0IyJg0itlElSXiIA\nKMUIJ9CipDwDja7HUgsny0CTQujPNS2cAPCzH30aX75zWPd1jwWbcojTRlYicPrX1bSgJjxt4ay4\nR1JRwuXNvrqvs8neOIwhRfm9iN5u4DmtLIWTMMaVzeI16s1QgcYtnDb3xp4rcWtvrAmk+wUCLa9A\nM1sJOXzX0fvcxJ39CZ68XCTB6FrYHxsZaOk94XOv7KHvSTx8ca3xu+jvlO5PIjI2B6oJsWx/kDo4\nyxO1O+8dY7xUS6CVtnBGurUdAF66e5RbAHl0Zw2P7qzp96Y24rwCjcWOODK3DW1OJZ03lySQELms\nu/NrPa1Ao5bXaRatcgo008LZpEBjGWjjsF1UAbcjrvnKKmxmENK+KmtJLlOMEYFYl4FGr3PTDD4i\nlpsWTjmyDLRopsU2WYkAZZAaCrT0e9F5AGRts0BTiUBegcZzJbmFk/bdOLDLhV1FdARah7mDr/ZZ\nvV5PrtUEt4mwGfTUqvsiD2ptQV9hkb6LTPMVbAIoTxPUNlU2abWZ0K8zW8Oifsc60CCPK76mac7k\n4BOXadRsvGIeyBRofAVtbgSaXvWMChlou0fpynGFAo0GHqMgyhEmdG41FQgAqsXKlUIr0MIoyVkM\nJqEa9K9CgQCQJ9bbZKAtggKNiF7dfmccE0cKHIzzeXh1KFsJL8O67+LFu4e68atM3dIG3MJpS55r\nxVIFmUDft2DhFNn1W3V/PTfs4T+8uq//XtXCSQPzMgLCzSnQBByhSLuoxI7yxOUNPHF5o/b7HgfL\nQKDRsVhmCyegCFuy/1YppgbpuXlpw0fflTm7kbLul5/TdK+ybSrlx7vsGiWyqCqrzQbcwkmTVhul\nju9KvHg3I42zEoEs8Jv/uV5h36T3KlOgJUmiLJzr1RlocVJUVAFq4ej117daXTM01iAiY2fDx6WN\n8nujsnC2j0MwSwTqFjtKLZxRrBdVAOC1/TEeOJ+Rq//ih96p/5+H/I/ZM59Uh7TdOQKtlQIt29ee\nA/0MWPddXFjv4c6BUie2IWZNmEplOtbmdpeBXnMUqHO7bYkAkJE3++OwQMbq7DPt4FH/vtZzSreN\nvn9VHAOpzMj+yUm4di2cmQKtzP48LehQUI5usUQgI7UJ3MJpo0D7gs5Ayy9KAOo+oQm0KALQPB5e\nRXQEWocTQ+sSAbKHNNycaQK2yINaW2RWscX5Lk4aeD4Jm1fqThN0Qy9TLP7Qex/N1XyXgT8YF0kB\naAsa4IRxrNuRbHOQqsDJnTJ7lc025Vs441wLJ7W7zqtEgMAHbGR5oc8HjAw0J9/IyAkTGtibZEIZ\nHClwebOvyZE4SXLESpQkmJQMfpYVnBhpk4G2CKuXRKBVtd+5MlvVt1Kg1ZD5HGu+g4NxhK/cP4IQ\nOHYG2qDn4Pa+Ih3aKtCqFEv0fcstnKgsEQCAc2se7n6RWThLWrX5Pioj6V1HaGLEkVQikOhQ6ZO0\nU5ph1YsIV6oyiWW/r2z0Pbx0T6l3q8ZDNPa7tOmnrcv5yV6VVU3qc75dXhaA0gw0beGcQYnA4STC\nOM3ttFHH9RyJF+8e6b+T4nPM8ooARqDVKGGqMtAOJyr8vczCya19ZRloQDv7JsCU3um2/uSHnsAn\n332z9LVkvzdbR5ugFWh6XFv9e/z82+QWzkl+X1UtWlDIf5KGvtMxMRV7fJ+1iaSh3yOSbn8cwnNU\nWP75NR+fvqdstKRAm4pAq1Cg2TwPhRAYeA7LQGuhQHPyx2lvFBbO4aoWzqpzXRNoFdcrqcxoTsCf\njW0snFkGWjgXCyddqwULJynQRoxA4xZOCwUaKblzJQI5C2emQDur6Ai0DnMHDTrtFWjZipaNOqW/\nQgRapkBbnMGv66iH/7/74l08srO2sPuZHhplk/evffJS4+9zAm2RM26qoFs4Q6ZAO+Ykij+YpysR\nEDkLJynQaMCTWF7j04CvLnISrOdmihfK3eUDCtXCmWagGQo0CpO2zVm6utXPFGisEEWkuYJ8ML3s\naK1A0yUCp//9SSnJV1g5HClwSBZOi8E/DZyb7uPDnspA++q9ES6u+8ceZA97Dr44bpuBVq9Y0gq0\nQgtnmsESx1j3yq+H7WEP9w4nSJIktV6WtHCy7fRKzpuek03qpUgJtFjZV+jfTgq6hXOBFrhMvP91\nl7CEj68CNvouUxVVWYR7uLThw3cd9D0nZzciBVoZ6BwcVpy3JrgCrjwDjRY+j2PhVNtyFIStSAbP\nlfjSnRIFmnEvM4P5y+B7snRCTLbOuhZO9R2ybVaNuWoc//S1KQk0pkAzbXv68x2BIH2W8t9tgqlA\nq/s1j6n4FLGpiIUDpvChbSndxpRAC+MESYJSAs1zRI40m0qBxoL2N/oehBA4P/RwO83Ho+fbNKpv\nfpz7npMF8VvmqfU9R7dw2ixAErTSSRNoNS2c5HaS9ec6EetV224qz/jcoI2Fk57/hxO79m7r96W2\n1or7I/2dFv1ULEnCSgRq1JY6cy4bAxP0PcVx9PfpMtA6dJgj6DlgnYGW3gu0Aq1hsEo38kUldtpg\nETPQpFDqi//3C7fx/e95+LQ3pxIZgTbdvuMP20VuWauCbuFkCrTjWErU7+eVW21RsHCmLZzapk0W\nzrmUCJS3RDWWCPAWTkPFQPugqYGTcGWrj0+nIcqKKFT/7gjeYLh851oZ8gq05u90PrVw2qxgzxuk\nSqxWoGW2XpuBMH3/pv2w7rs4mIT4yv3ycPK2GHgO9kZB+tntLJxVdja/QoFGhBhd02U4N/QQxkka\n/OwhilEgdzjJWG7hFNgbZQo0kRJ3sVagWXzJGWEZFGjPP34Rzz9+8bQ349jgk+SqZ8+Pvf8xfPvb\nHwSgrst8iYCNAs3u3mOrQDtOaY9p4bQlOXqO1M/7a1t93cJZyEBLr+NaAs11Si2cRKDVtXACxXuI\nm95Xn77azlad2U2bp6ieVAo0WvSyXZCi+844slCgSbJcKktwL/1epOjaGni4fxRUWo0pBkWTfLrQ\nIZu7CCFmoEDLbI60786v+dgdhQgitb1VuYCNn8G+26DnYE03Wdqdp4OeLG1Fb4ImakLVtDoO4wKJ\nZVrrMwVa+TiNrq2qc8W0cHKis42Fk47LOJxtVAedG1SAYZ539L32xpmVN4xjK7eHuZ05BVrUKdA4\nTn/pt8PKg67XaSycZZYPE6tk4TRXUhYBSn0RIYwTfPjpy6e9OZWgQey05FeuhXOBJ0hVcNnKEQ3U\njmvh5BOX6Vo4iyUCjpRZCOocM9D4IM037Jw0EIhKVuQ8R+hBwyiIcko22sebg3YKNMpsouPhsAH1\nslutCFlBgt3g/9yaGtwuQth5z1GV7FlLWbGFU1s4bRRo7DjXYc13EScqsHcWBFrfc/TKse01RffL\nKgVa3ys/50ldoha5KhRCAzXhJltZUjKA56qzUgunzHKZSIGWpOUFQtgvzM0Ci/h8XlVssLD7KoL2\nwrqPJ68ockYp0LiFs1qBRsfRlqSi4y0ESrO46BqahYWTSgRsmxI52XRlq497R4rsMi2NWYlAewsn\nEWjnGhRoaz1z4UER3k9eaadA6znq92xy/FxHlYpoO5uthVOQYt9GgZY/X3ppVtzBJELPkbi+rbLP\nqs5TykDTpKahQOPlQnr7WtxjiPyj8czeiBFoaW7d3cMJRkFkXZxhgt+nB6yF01aBpiycUbooOUWJ\nQJQp/sxzOHM5mQq0qmdavXpuTVs40zbY9LvanpMEM1t3VqBzl8YrZryKLhFILZxrvqssnHHz3KaO\nQCOyjJcInGUF2mqM3DssNI5j4bTJQKOb4SKptqYF3dsW6bvQ8biw1sOzD5w75a2pBg1Kpt13a8te\nIpBucxDFrNL9eLd4PiGY1sLJM9BUY5DQAzhbm/Y0qFSgsQy0uCQTIpeBZgz2aKBiUyIAqFbFcRjj\n3mGQ+56U4zSZ8crkaYK+my1pu+67+MEXHsGHFoCUd1OrcaZAk4WfH2gCrfn70Qp/E5lPA/wX7x4d\nu4ETyBMC9mHaRTsRB5EQRQunQJIkCGtUlNtD9TtEoJWtgHM7ZFnOIrdwKvt3qlxNmhfXZo1lKBFY\nFfCsIZt7ZN/LB+DXKdDosWY7GabruMpmbeaMTYNheu0ejCMcTewtnPSZOxu+VkIBKJA1pEavU9D4\nqTqb8gUJt60VaMWFh5sX1qyUZByeI7Hec1s1pFIemf19zyTQ6jLQUgWaVl2pRbjDSYih7+DyprKX\nVo09acHMJDWJhNKKZbbtbRZx6fcDnYEW6OuHjtlrexMctcwfy30GV6AxC6etgnyQWjiVPdn+Osks\nhbEuRzAdAFrZbyi/q847ek5WEexmBtrQz9SbbchHfj7MMqrDzECrUqBRI/e67yKIkrTEp/69+Zxh\nZ8PXKjdAFZNQCVjPUftkUqJYPStYjZF7h6WAvQJN/ZlodUr9aUo3w2XMrTJxZauPjz17DW9/+Pxp\nb4oGPQQ+8NSlhZ44kOKoSg3RBD6wXOTvWQVadQojngdyvO8hZWYrmGYAQBXzBCKRtIUzVg/1uRBo\nFQo0buEMtVoHuZ9rC2cQ5d4nU6DZEmhKrfDV+yOEcZybgKsB9QqVCLRU5wgh8NN/7Sm88cb2PDfL\nCtrCWdFS5sjMimxjWbElWvg9ZxYKNE4IWJcIpC+rUuPQfbVo4cwWuaqe0aRYoTbFOCkqxnjwdhn5\nmi8RyMjnODl5pbBpEeowP3ALp82iWBsFWtbCaWvhVOdl1TVKz4XjPG9dR6LnSBwGIUZhXNkQaIKe\nHzsbPraHvSKBZirQavI7e67U1zQHtTiWZqCx7zw0LJw9R+KplvZNQH0nW6scnRuUUWlv4TQItJpr\nmo5/nxEvkzDGwTjC0HN0+UvVs9yVahxkkpp0/tH5w+cwbSychQy0cahJICL3XtkbpaTytARatj19\nT+oFZ2sFWi8j0Gxtn0C+RGCPCKGqDDRjDLLul4/T/JYWTlKgtSWCc+VUc7BwkgKt0MJZokCbRLFV\nXAp3bjy6s1YoEaB9Rse9TLF6VtBloHWYO+jmZjvmPJdm49zaHSOM48ZVfK1AW4EsIc+R+G++7bnT\n3owc6CHwwadOXylSh1lmoC0jgZatQmYryLMgZzxHIoyjqSYHVDFPIeKkKKX9SzmHJ6pA4yUCJU1+\nqpGRlQjkVD0pgWZZInBFE2hHiGMWcitUC2cQrlCJgGXu1yLCS7P6tIXTGODnA5RtFGiphbPhoZcj\n0LaPr0Dr94rnahOaFGgPXVhD35N46OJa7t+JyAqiuDT8H1AZaAAj0OIE5m3EzB804TrZ9SqFSLPX\n1LV70jyWaRHqMD9wlYmNLc93JW7v5xVoZtg4QWegWZIJNLa8UtGS62kL5/Hs6IOesrmNjIWbOtCE\n91KqQCO1Z8HCSVbPiqxD9V5q+8dhPlrg9sEEPUeWEghVLZwA8He+8Rk8trNu9T04+p6sPHYmiHwi\nBZotUUH39InOQKu+pnvawpnty3EYI4pjDH0Xl9KCg6r30AtmBqlJ5x+dP/z32wy36Pykxcq9UYhH\nd9T+u5Iqm1+5P2qt/sp9BrtmVKumev82CrTbBxOMw9iadAOYhTOMNSFkZqBpAs3JWzmrziHrEgEj\nA60tgcYXhGbawqlLBMpVl3Tt7hmW10kYNz67+P3i+vYQX7qdFZRwx4TZjnoW0RFoHeaOthlo59Z6\nuLrVx2e+ct/K3kUtbt2q8Hxwbq2Hjb678MHEetI65eTGd6Ue6Cwjgaal7mEMWkCeBanccyWOgmjK\nEoEsm8N1BKJIqVVMC+c81KOc+Mop0BwHUZzo/4BiVgUNqk0bkNfSwvlwSjr8xa19tRggswl4kpYI\ntBlMLjLMQewywUttu0epgsU8Jvz8tFnBv749wI+9/zG8/3X17b88d3FWJQIE6ww0HahePhx8+tom\n/uy//PrCvxORFUbVRT/bw3wGWlxiu+SD/zKSnpNzpF5N0nzUk75P0y1wGUniZQNfpPAsbOG+5+TU\nEOMwxsWmFs6WJQJV16jvOlj3XVxYLyq02mDYc3A4iTAOIn3tNIEmspc2+tjou9gbhYhYozD9XDdb\n1hBTWlUSRDmy4M7+BOfXeqX2taoWTgD4xjdds/oOJn70ax/HblqG0gS6Z2QWTrvnKREJRADUXdOm\nhVPFQKiyh7Weg0spsVrZwpku0JjRGj1XwpVCEy2uca+zhVagkYWTZaBd2vAhhFLBHwXR1JmjtI30\n+3Ss2yjQDu+qDLRWCjTWwknfr2jhrFKgNVk47Qg0+rPu2inD/BRo6k9bBRpt9yiIGs8req/r2wMM\nerJQIkD7LFOgdQRahw5zAz1020ySn7m2ic98ZddKcrpKJQKLiO979038jTdfL8jzFw1agTalokcI\ngbWeg91RuJTnUtbCmTXUzULdxAd7bZGtjCZwHaWOcx2mQIspXH8eBJpk/59XoAFq4FzWwtlL87CS\nJEnDnFmJQPo62xKB7WEP17cH+PRL9xEn3GoAXSLQdlC2qMjIheUjBF0pEUYJ/vzlXaz1nELWD58Y\n2TSISSnwUx9+svF1PHdx1hZO6yyghhKBKkgBQDdll++T7YGRgRYnhRVwrmApu1/xyYGTlggoBdrJ\nxzZ0JQInh5yF0+Jc7ruOnlACqf2+oYWzb3nO0/G+UpFT2HMlfusnX8CFNd/q/aqQKdCq89vKPhsA\nLm36WfveKChp4UwJtIYMNKA4Kb5zMCm1bwL5a2FWY8Q33Niyfi09b45aEmjFEoFmCycvEZiEaQZa\nz80snBXPvioFGqCOuesU50jTtXCmJQLjUI8rPEfiwpqPV3ZTBdqUrdf0PKF9QM8u2/fre462F7dR\nwekWzijGKFDfzxwzXVz34buZrVSXCFQq0LLjWIbMwpknC1sr0Pi4YQ4lAjoDzThX6FhR8RGpTkdh\nbF0icH17AN81bfGdAo1jNUbuHRYamQLN/neevraF3/mzWyrnpEmB1hFoc4XvOri0cfpNeU2gxqfj\nkDHrvqsItCVUM9LDOohiTaBNSyZy0ABmGgUaDShpYFfIQEtzDudhicop0NhAiRNocYUCLUmVcQUL\np0sWTjsFGqAUPH/60n0AyCnQSCWwKhbOslX0ZUHPFZhEMf7tX97Fmx86V7huOMljm01kAxqQCwE9\nCTsO8gq0dlam9gRaqkCLqy2criOx4bvawpkkKIQYu07+2iu+R35SKVPyOYpP0cK5hM+HZQNXmdhl\noMmCAq0yA43Oec9uCkSNoA+eKb1VzwAAIABJREFUH1a+ZhYlIEqBFhYWbuqgM9DWM/Lu3mFQUKDZ\nEWiZhZPjzuGkUl2XJ9BOfpzoGgq01hloLSycnHhRrZARrm55WYlABdHriPISAUDts7IG+TZjUN7C\nOQ4jTMI4Z3O8suXj5V2lQGtLAhHomUjk12CKFs576XOgDYnHw+pJIW5+h4+8/greevNr9T2D9l3V\nd9UWzortuLTRx9947jqef2wHQEYML0oGGr2vVqAZ752VCGQZaIBaVGi0cDqcQJPFDDStQOtKBDoC\nrcPcQQq0Nu0lz1zbzGxoDRc8rSJ2BNrZhhACPUceawJPD5plPJeIrKK2HeD4JQL8PaYhemg/hunA\nMYwTeFLoCWiUqG2dR5A+J83KFGjjKMosnDwDLbfiGeeyaGgf25YIAOpe9luffUV9DmWd8PyoFWnh\npIHZMl47rpS4fxTg1b0xPvrGqyU/5xbO2R0vut/srPszuQbK1JJNoGuxrbVHijTDMKov+tle8/TE\nKUqSgkqD36PKJqA5BZqkDDSVq3jyFk71eauQt7ro2NAKGmE1dvQNBVpd3lNbC+eDF4b4lR/4Grzt\n5nxbyIeei8M0A802n40r0Ggye/8owFfuHaHvSV3+YVMi4LPFJY47B5NK8rCuhfMkkFk4FVnguZbK\nW0kqnmYCTVs4mfWPCJ2hnynQ6lo4Q6ZA4/e8gefkslEJ0yrQDsZFkunK5gAv3j2EEAIX1qa1cOaf\nE2tTtHCShXXaDLS91NZrZps5UuQWoOiRUZWB1mThdKTAP/zWZ/Xfp89Am5eFM69AM8fmBQtnqqSz\nUaDRsblxboAE6pwKo1hlkbIFX/rzLJcIrMbIvcNCg67XVgq0q5v6/5sydehmuIzWoQ6zRc+Rx5rc\nDH0XUrQjexcFNHAMo5i1cJ6uhdPTqjiVWZQkarWUBoqJbvE7OQWaz6TnZOHkg1X6vjQQ5YqjG+cH\nGHhOK7vdM9cyO4rDFCxRmomyKgo0uuyWkVygFk4AeFtJAzKdn0LMto6eCLRZFAgAeQuV7XHQCrSW\nyjrKQAuiuJaoPzfs4W5NBlqThZNPQqSgDLRTsnCWTHQ7zAekJrF9hrVRoLW1cALAOx+9MBNFdx2o\nqfBoYq9A0wTaRl+TZfeOAnz+1j4eubiuvyu9rq7dMsubyk+K7+xPdLmXCX59rp1CzAeN+6dWoLWx\ncPYyBdo4jHEwDrXlX4hqxb/rCB3ZAOTHI4Oeq/chn8O0U6ClC5JxzHKvskU+UqCNjpOBZlg4B1Nk\noBHaKNA85qy4fxjAd2VjjIJsUKD5DRZOE2YWmi1MZ8OsQOcGLRgULZyplXvcPgNts+/h57/1Tfi2\ntz+Ys8/Sn/RvPJvurKJToHWYO6ZprrpxboCtgYf7R4F1BlrXjNWh50pr61IZ1n1naYlY2u4gTrQ1\ncRYPbXpQHqdEIIxjbeN0nczCGaXbOo8JaZMCTVk41b+ZGWgA9Gonf593PXoRf/Kff7jVvnjmGlsM\nYBZOG/JhmbDMCjQ6Bp4j8OwD24Wf0wDVd+VMyXVaxb86A/smkBXqAPYKtCwDrd1wkML8w7i6RABQ\nOYD3WAtnoUSgYZLBFZqOVPmJcZIgSpJWi3KzQBZSfbKfexZBk1/b87ifKlwoJqBegab+bEsazxvD\nnoOX7kUYtWgqzEoEfH1e3k8JtDc/dK7wOrPBkKPMwjkOI+yNw0IuJIEvLsxSnWuLQomAJSnShkDL\nWjh5iUCMw0mEYc+F60j89Nc/hXc8Ulx8Ue+tmswzBVrewkn/zoeebZ6jWoEWJdgbq3FLXoHWx73D\nAAKwbnctfgZZOLN94DnCWimZa0Vvcd3x8doruyNc2vQbn8H2JQJ227FwLZyGAs085ynOJlOgKTJV\nWTib3//jz90AkI19R0GMYQ8YB8zC2RFonQKtw/xBt7o2k2QhhFahNbdwkgJt+SZuHWaLnisr83hs\nsNZzrR4wiwgaSHIF2izUQDTYm4bo4QM73nipSwRoAj6Ha1cIkTUGcRsmKdCiWNe+83uTXr1LBx/m\nYK8tkXh1q49zwzSbQ2YTcG3hXJHZOJELNo15iwY6Bm+8sV06uKfj1mbgbwPXkdgeenjoYnW2Uhvk\nLZztMtDaWziFziKr+6xzQ48p0IpK9NwqfckkI9fCKfIWzpMvEUi3YwnP8WUDtXDaTjyJvBkFEcJI\nLdg0ZqCdguWwDoOeg8NxiEkYW6t0Ngce1n0X20NPRwu8fP8IL907wmM76/p1b7yxjbffPI8b56rv\nNVkLZzYpvnugrt3zFRloPGD/NJT7ukQgUM9rWwUa3fcmkSIhbBRouQy0MMbBJNTn0A+88AjeeKO4\n+EKfFcVxaQba5U1fFzTw+2gbQQBv4STShNsXqfzi7mEwtQLNLBEQQuDnv/VZfMc7HrT6/UGFI6AJ\nPaaCurU3xqWN5sUmTaA1lAjYbkffc/CffOR1+Niz7Vpl8wuzM7RwGgo0c8wlhIDnSJ0ZR+UKYwsL\nJwcp9Yiom0RdiQBHp0DrMHdkwbvtfu+Za5v4vS/cbs5AIwVaZ6s481AKtOnPg3XfXVoFGpEAQRQj\nHafN5KFttnhNu01BnMnNaaAdJ+XNfLNC33MwDuNqBVpJC2cTgdYWQgg8c20Lv/v51/S9jEKFwySZ\n6crkaYLI2uVUoKlj8Lab5QoCOm7TNpjV4Vd/6J0zKRAA8ioy2+NAZNA0JQKZVbzewqkz0Ers2pxA\nLlv84LYoKdXnUsnHaVk4l7FkZtlAFk7b5zGRZbw1blYtnCeFYc/RZLPtc+f7n38Y3/CGqxBCaAvn\nH37pHgDgsUsZgfb0tU386g+/s/a9MlVJtg9f2x8DQGXD6GmTkfTcocgF2wUpundoBVrNNU3EPrdw\n7o4CJAkw9Ju/tyMFwiizcPJx2d/7+Bt03jPf9Db3GN52Xha0f4U9X2wVYyackoWWj77RnlDKWTjb\nKNB01pYi0J64vN7wG8A7Hr6AT7zlRu7856D7Qpux14+871Hr1xLmlYFm5veV5f6RSlKI7JiPLEoE\nOPoGoT4JY72wIaXKnO4UaB06zBFZBlq7Qecz11MFWsMDMctA6wa1Zx3Xtgat8qlMbA68VqtjiwSX\n5Y1pBdoMrgkzNHSabQrjBFGUJ6scKRDHqkRgXteuHiixbecEGhGNZSUCZOGchS2FbJxEVsj0u0+i\n1WnhpPv7Mmag0Ta//eHykHA6bm3Cj23xxOUNPfE9LvjkyHYiOa0CTQhuf6r+rO2hh91RiDBtBzZV\nKk05MblmOil0eYFNQ/es4SzxOb5s6HuqEMg2FF5P9sJYT+oqQ8KpRGDhLJyuJkBsnztbAw9PXtkA\noEjEgefg333xLgBUEghVKLNwvrqnCLRLm+UEWtbiezp6DLpnHE2inLq9CXQN21g4yfZIlnvflbiX\nEp02uW9uajsfl1g4t4c9rUDj99E2wwLewrlv5F4BKgONMG2LtFmk0BY5BVqL5yiVg2kLp4UC7cpW\nH//1J95UqUBtKhGYFfg5NcvPorcdp/eKskUGrhSj/x8FLRVotChBCjQjV5KUmGcVnQKtw9whtAKt\n3aDzDddV+HbTjYcG/suofOgwW/xP3/e2Y6kSfuCFR/B1z1yZ4RadHEjGreySauVpFtdElg81BYGm\nm0F5BlpKIonMwjlPBVrPlbn359JzbivNfq7+XyvQZqA6eloTaOrvUghEiSoRWBkL5xJnoF3Z7GPD\nd/GWh05egTZL8EmKvQJtugmwEEJbkuoUaNspOXj/KECcJIUBPCejyiycnGB2pWrwjROkZFyrTT42\nytryOswHQghs9F1rS3jfK1Og1ZcInEZrZB2GU6p0OLYGHl7eHUEK4GZLa3hZC6cm0DYWVIGWfv5h\nELaKmdAKNIu4iwvrPn7h25/Dex/fAaCIBRrP2HxvKVULZ1mJAAe/N7a5x/AWzj2de5Xdzy/PQIGm\nn4FT/n4uA63lc5QUf3ujEDsV52EbPHllAx96+nJp3ukswc+pWSrQRFqmQ4Rs2SIsXQu+K/XPR2GE\nbWm/WKcVqaRAYxZO+jlZoM8iOgKtw9xB4/i2Y87HLm3gv/vON+P5xy/Wvo4eCMs4ceswWxzXbnd9\ne4DrM2rEO2nIdPWVyCpPzibw/HgWzmIGGg3EREoixXFxUj0rlDU28UyNOgvn7qidlaYOzz6wDSGg\nlUZk6Yji1SHQtAJtCe/DH3/uOj70zGVs9ssHlxS0fxoh2W3ACQHrEoEpJ8B8AF/3WedSdcXdwyC1\na+d/zlfPyybAuVwgocKR42R+5SN1yIoyTvRjzyw2+p612i+zcMb6uVeldNEKtIUm0KY7ybaHikB7\n6MKadUg6oayF89beCABwcb1CgUbZWKdm4VTbfDiJWj1LdQaahYUTAL7xTZldkY+FbBYeVAZaUloi\nwMHHIW3mM7yF8ygtU+DHY6OvcvL2x+HU5xXt52mP8+AY53bPlXjx7hEAzCTuYKPv4b//7rce+32a\nkF+Yne31QUUpQDn5q8P+PUcfu3EQt3pm9nUGWmbh5Od+z5W5vMSzho5A6zB3yCkVaADw9W+42via\nfmfh7NABgLoGgjhGGCUza3fMSgSmsXCmqrg4C+zXSqU0y2heJQKAujeYBJifs3ASgZb9vJiBdvzZ\n8kMX1vBv/uMX8MjFNQDqXqjVO5YWpUUHHddlzBCUUlSSZwBv4VysCbeJHIFmef1rC2dLophnoLk1\n94btoSLQ7h1OkCTFcUBOgVZy7hQtnOq+cZoWzq5E4GSwURECXgYiy0ZhpBdrq5Qu73n8Ir7/+Ydx\n88LasbdxlhgwMmZapRAVCTy6086+CZQ36726N8Zm361cSDptBZpu4RxHreIQzBypNvcS/jltMtCI\nQKtajOTb0EaVzxVoZAE2z5/Lmz72Xw2PrUCbiYWz5XPUcwRevHsIoFoJuYhoWhw6DqTICLTS9moW\nvUKfPQqjVue5mYk4Ngi0/+snXliZDN9pcHa/eYcTg85Am9PZRpOGednAOnRYFniORBglCKO4dlLb\nBpkCrf31RaHgQYkCTQqoDLR5Wjhdx0qBxif19PNZKtAAlXVFx4QfmlXJQNME2grmQ9F3m0cG2izR\nZ+e6LZG55rtwpNATb1sIoa5roKlEQL3vvcMAUUlzpi7WkKL0PsDfWwqegXbyFk7dwrl6p/hCYrPv\n2bdwUn5XwDLQKq7XS5t9/OxHn57ZM3JWGOZyoqZ77pBlum3+Gf9Mriq5tTfGpRrVj6tLSE5Hj0Gf\nfzgJW03m6Z5uk4Fmgn+ObQZaxCycNgRaG1U+b+FUSjxRIFWupNnA045nhj0H3/GOB/HeJ3am/n3C\ncRRoVVl8i4icAm3GRJMjBcvUK1GgOVluK50LSdJunsxVvQAwCfMk9UbfW/hFxXmiU6B1mDumzUCz\nBU0aOgVah7MOz1GqkFlma2UlAu0flFqBFiV6sq1zhKSycEYluUizgu/JwoCxrH67voVz9pMsp4Sw\nW3YscwZaE46b/3JScB2VdzKJYmsi85uevY7XXWlfZCDTJlmgXpF1bkgWzonKQJMmgUYK1/Lt5fcx\nUqARgXbSbZhyhc/xRcRPfvgJBJYh1f0WCrRFRY5kmHLbt45DoJVYOF/dG2Onwr4JLI4C7SiItNrV\nBppAi45HoNl8b0dKhMzCWTVXmdbC6bL826NJucqMrI/TPsOEEPh7H3/DVL9rfm5bcphKBADgskWJ\nwKJgXi2cgBpDUg5f2Vi/rERA/Z79Z/he/n4wCuOFX0Q8SXQEWoe546HzQ2wPvcoMheOiU6B16KDg\nOhJBqkCbuYVzCgWabgaNYy03p4EUWbGiONEZU7PGg+eHhcEqb+Gke0ZOgZZ+3/2UQJvHChvPpluV\nDDTdULiC92HdwrkEZOeg52ByFFsfh0HPwXMPlreP1oG/fW2JQKpAu3s4QRwXs1DpHlF1HbgGgSao\nRCA++TD/zsJ5snjbzfJSjzLovJ4g0ufFsk32jpMTRTgOgebKfDg5ALy6P64NW3dPmUCj+0PQMrZC\nlwhMoUDjz4E131aBFmMSJei51dm0/J7d5t5G46cotXCWqQGvagXa6VwT/Nxu+xztpWOwniP182QZ\nMK8WTiA/362zcPqekzuv2pzneVVvhEkY10ZdnDV0BFqHueNND2zjj37uw3N7f7rIV3Hi1qFDG3hS\nIIxiBFE8OwWaO30GGm8GHRnZHFKoAV80xxKBv/uNzyAx/o1bOHsgSyUbjKRE4d5YWTjnMQkrU7wt\nO1ZZnbMsCjRAXV/743AmBSJ14BO8Orvouu9ie+jhr24flrdwpvu2ysrsGZNKsn5Hp9jCuSKX7EqB\nJqiqRCBK/23xr1cOTnxMe6+5stVHz5F4dKd9vpsQAr7raFIpSRLc2rVToA2807JwTvcspd+bKgOt\ntQItKxGoi2yQUxIddI8MUwtnWdD/lVSBNm2G2XGRz0Brb+EEgJ0Nf+7PtVkip0CbQ4kAoYw41i2c\njsxdF22IWZ4reTBW99R1C8L4rKDbEx2WHlIK+K7squU7nHl4rlQWzjiZWRYVDyNtC9qGMIp1jgJN\nDByprFhhXLR1zQplGTdcgVZmOyxaOGc/4Gwa/CwjMgXa6rELjibQFv+7DXrOiZCYfCJTd68RQuDx\nS+v4/Cv7qoWzQKDJ2veosnAmJXbQeaNMsdphMZA1xkX6OC3D9cqRz4ma7rnzHe94EO9+7CI2plSK\n9FypSaWDSYSjIMJOTXD7aSvQ+P2hDTFD54htCydHrkSgBYEWRHGtlS+vFLLenEILZxlJdjMtMLqw\nbm9znSVom/waBV4V/HRnLFP+GTDfDDTeel62P0m153uGhXOaEoEgxl6aCdwRaBmW6+nSoUMF/tb7\nHsPXv/7KaW9Ghw6nCtXCmSBoWOlsg6xEYAoFGhFoJe1QgrKM5kiglYH2yziMEcclJQImgTYHFQP/\nvGWwBdqATrdVLBFYlhZOQF1fJ6HGtrVwAsBjlzbwuVt7iJNiYUizhZNNKoVIiXfVwnnSRBZt+iqe\n48sOIpxGQRYXsAzXK8csLJzDnounrm5OvQ2+K3Xm0a3dEYB64sJ1JH7ig0/gG954derPPA5yLb5T\nKNCyXDL7380r0OwsnJSBVnev5PezNvc2nYGmLZzF8/75xy7iX/7Y83js0ob1+84SNM6Zhhim/b1M\nDZyAGuPS2Hb2JQLqz6pnUY8UaK4sLELZIluUiPV4uE0z8qqj2xMdVgL/0QcfP+1N6NDh1KFaOGOE\nM1Sg9RomuHVw9MAuRrqApScGjhCIY6gSgVMg0CZhXGgGBbgCLYAU81GI8V25KhZOOtaraOFcJkXL\noHdSBBpXS9Tvlycur+NXfj/QGUscjRZObj+RKkMtTpJUzTblxk8JUql0CrTFgy4RCDIF2rJloM2i\nROC48D2pWzhf3RsDAHbW64PbT3P87bF7T5tnaaFEoMVzPiODpNXzTls4WynQpstAO5xEpSSHEAKv\nv75l/Z6zhpQCfU9O9QylMdilJSoQINCxn0eJAFB9zvOFbz4XaPPsomfyKIiwP1YE2npHoGks19Ol\nQ4cOHTpUwktLBGaZgbY58OA5YqqBDw0IgyhToPVZBhpNhE+SdJFSwJUCkyhGWgxqBLKq/98dheh7\nzlwyN/ggZnUINPXnKmZR6gy0JVC0DDyn1Lo8a+QUaA3H/PFU9VBm16ZtrVSgGZNKKh8pU7PNG6uc\n87fsILXZKIgxXlIF2pDliJXlWJ0EfNfRFs5X91MCbYGVPzkFWguSQhNoU1g46byyUZ8BeQWabQZa\nOwUaU/pXWDgXAQPPmeqaJDLo8pJZOIHs2Mx6IVbKegJNlwi4Tu6ca/PsklKg5yhLN5VqbfhdiQBh\nrqMsIcRHhBB/LoT4vBDiPy35uS+E+Bfpz/8/IcTN9N8vCCH+byHEvhDiF+e5jR06dOiwKnAdoTLQ\nZkigffNbbuB/+5F3WQ8WOWgbwijRkxremhsl8y0RqELPVbXoZOHMlwhkCrV5hcavIoFG32kVGwqz\nDLTFnJhwnJQCLZ+BVn/MH7+cNQIWMtDSiUVlBhqbFOsSgSRRBNpJt3DKLHemw2LBkQKeIzAOI00A\nLYNilOM4TYWzQt7CqQi0RbbO8ftGm9gKGnNoBdoUJQK2uW/0TBwFUe3zfmoFms5Aq7ZwLgIGnjPV\nNUl5XsuqQAOmyxC2ed8qYo5nF09bIgBk9wMq1eoUaBnmdocWQjgA/lsAXw/gaQDfLoR42njZ9wO4\nmyTJYwB+HsDfT/99BOBnAfzteW1fhw4dOqwaPCkRRknrSvc6DHsu3nijusa+DrpEII5LFGgCcZwg\nTk5e0UEEWpSkBFpJBhoA9Oc0icmHy67GZHyVyQWdgbYEE/KTykDj4/Amu/ilDV/bioolAg0r6TK/\nek4lAnHcTjUyC8jOwrnQ6LsORkGMV/fGWOs5M5+0zhs9V8JNS7FOq22Qlwi8uj+G5whsDxdXdcLv\nD22epVIKCMEUaFMQaGu2CrT0/ngURLXEqDMtgZaeK2FU3cK5COj3nOky0NLreGdJFWg9Z/bXc1Np\nE52jfmozptOp7S3R95y8Aq0j0DTm+XR5O4DPJ0nyhSRJJgD+OYCPGa/5GIBfSv//fwXwASGESJLk\nIEmS34Ui0jp06NChgwU8VynQwhkq0I61PenDPYiSrIUzfbBLAQQlCrCTQM+RuQw0Pgbh+21uCrQ5\n1pufFjSBtoIB6056TiyDhfPjb76OTz7/8Nw/J6eibFAdUhMnUBzA0wSgiuwwSwRU+YhSoZ00x0DW\nqEWdoJ51+J7EKIzwF7f28Nil9VMjoY6DwZQkw6zgMwLt1u4YO+v+Qu9HyciBtmMeTsC3GYLQvWro\n2yrQMgKtbhunLRGgfaBaOEMMvMUkOZSFcxoFWmrhXEoFmpx5/hmQjSGr3runLZzUcj1dRq3vSoyC\nCHuUgda1cGrMc4Z1HcCX2d9fTP+t9DVJkoQA7gO4MMdt6tChQ4eVhSslgjjBJEpatUrNbXtIgRbF\n6eBR6Ae5lALBFKu/s0DPlSoDLS4q0PhqnT+niQz/PG9FFGi8Vn3VsEwKtK998hI+9Z5H5v45/DDb\nXL+Ug1apQKu4DriSliaKySlZOJ+6uoF/+t1vxbsevXiin9vBDr7rYBzE+Pyt/VNrGzwuhr3pbG6z\ngpmBtsj5Z4SmHMUq0H3LkaIVSdjawpm+99EkmkuJgPpdNfZbZAvng+eHeODcsPXvEQlU1wa7qHCl\nmAuB5jSMt3iJAJARaq0tnJ7ULZyeI1amNX4WmCeVWHaUkileU/0BQvwggB8EgAcffNB+yzp06NBh\nBeE5AmGagbYI1sDMwplgFES5lXUplFoOWAACzfh8Lw1OnddEJhfAvgBKwVnA1ZOR1fg+HHR+LFso\n+TyRz/GzINDSHDQz+J9IsaYwZDoGUivQTv6+IYTAB5++fKKf2cEefU/i1f0xXtkd47FL682/sIAY\n9lzEifU0aObwXanzSm/tjnDj3ODUtsUWnhSYoFqNUwVOoLWBrwk0uyk0V6DVbSO/N7a1pztS4GgS\nIU4WVyH7C9/+3FS/t+Yr5dr5YW/GWzR/OFKgNwetUnOJQH7MQn9ve67300WJ/VGIdd9daDXqSWOe\nI90XATzA/n4DwFeqXiOEcAFsAbhj+wFJkvyTJEnemiTJW3d2do65uR06dOiw3HCl1BbORVCgZRbO\nuECgOUIgiIoKsJMAWTjJBmYOCnpztuzlLZynf5xmAfpOq6xAW7ZQ8nmiTYkAADx+mRRoxZ+5Ulbe\nr+jf6R7BSwS6sXwHjr7n4DMv3QcAbRleNgw851St4r7n6Fyw1/bH2FkC2xzdf9o+S6fN7cwy0OyO\nEy0kHk7qLZx8W9oO31wpsDtSQe+L2sLpGYH2tvjedz2Mf/b97zjx1uVZwJmXAi19y6rmWV0i4ObV\nmW3H2kqBFmF/HGKjv7hZiKeBeY4G/y2Ax4UQDwshegC+DcBvGK/5DQDfk/7/NwP4nSQ5xaWXDh06\ndFhieC4vETj9yT6pS8KoWK8uxHQNWLOA72YZaGWDZx7AOg/kLJwLcJxmAfpOJ30sTwLL1MJ5UuCH\n2WYCqjPQSgbwriMqFbO0ck4TSiEEkgSI42Qlz7UO08N3JW4fTABgiRVoDvqnqCCiDLQwinH7YLIU\nFk66R7QtTtIKtJakgrZwWuZB0eeMGiycQLYQ1fbe5jhCB70vqoVzWuxs+Hj7w+dPezOmwrwtnF6D\nhdM3CLS2JKRSpCoLZ5d/lsfc9kaSJKEQ4kcB/BsADoD/MUmSzwgh/gsAf5AkyW8A+B8A/DMhxOeh\nlGffRr8vhPgrAJsAekKIbwLw4SRJPjuv7e3QoUOHZYcnBYJYWThn1cJ5XLiORBDHGAV5S6QUym4K\nnG4LZ1kmhDdnBVq+hXNFCLSGWvVlBqmguvyPDLIlCXx1q4+PPHMFb7lZnAg5UlS+h2usnNPnhvHJ\nZ6B1WGwQwd1zJR443z5raRHw1990TUcbnAaohfP2wQRJohp0Fx10f562RMBp+czy0+KfoeWCCi0w\nHAZRo0rOTe2obUk9VwrspQTaolo4zyIcKawU2m3RZOHMSgQMC2fL86rvObh7MMHeCFjvGjhzmOve\nSJLkNwH8pvFvP8f+fwTgExW/e3Oe29ahQ4cOqwbXEQjCJCXQFmOy70mhFGhBXoHmyMzCedLS/J4r\nMQriShULBZrPLQNNcvJhNUiAbOV8Mc67WeL6uQF6jsTVrcXPAzoptC0REELgH3/XW0p/VmftyRRo\nmYUTUKrWToDWgYMItEcuri2tOvF73nXzVD9fKdAivHTvCABwZXMZLJz1jYRVODkFmnp9FCeNBJpe\nKGirQJMCe+PFtnCeRczNwkklAhXjR7NE4FgKtDBGGCdLcS84SXR0YocOHTqsCDxHIoxjhFFS+WA9\nabiORJhmoPm5EgHotq+Tzs3qORK7RyFe3h2X2vK0Am1OA9FVLhFYxQy0Zx/Yxqf/7tetjFpwFhBi\ndiTwzrpfqXQplAikfwb0CxJdAAAdN0lEQVRx3CnQOuRACx6Ut9ehPaiF83Mv7wEAnliCfUn3iOO0\ncLZB35P41PMP40NP2RWK8Gdi0zPEmVIp5ErJLJzd1H5R4DoC/hwVaFWErKcVaEYGWstN8V0HoyBC\nAmCjU6Dl0O2NDh06dFgReI5EECWYRPHChNN7jkCQtnBusxYlKUWWgXbSJQKuxEv3jvDZr+7ie955\ns/jzORNoq5iBJlc4Aw1YHavtrCBblgjU4Vd/6J2VeYOuoRKhz406C2cHA2RXemxnOfPPFgGUD/pn\nL+9h2HOWooXTbSATqjAtgSaEwM989Gnr1+cV53NUoGkLZ/esWhT0HDmXTMMmBVqhRMCdskQgVaCN\nw7izcBro9kaHDh06rAhcKVQLZ7xACjRJCrRiBlpwahloDu4cTNBzJH7whUcKP9erd3O2cDpSrAzh\npBvNFuS86zBf8HH4cVWHW8Pqdi8a+JdaOLt5YgeGTIHWEWjTgp55f/LiPTx+eWMpmg9drUCbskRg\nzt+xlQJtym3Kt3B2U/tFwX/2DU/NZZE0y5ytyEAzSwTkdMRs31OK1P1RiHW/a+Hk6K6yDh06dFgR\neKxdclGUTa4jEMYlGWhCZaMBJ0+g0UD7W952A1e2irkO9PO5lQiI6VbMFxn0VdyO1TgTOCkbsier\nSgQ6C2eHPLQCbUkbOBcBtA8/85VdfNOz1095a+ygWzinzUCb8/gjVxrUQPJNm8vGM2VXrYVzmfGW\nh+bTHtpcIqB+npUITK9A2xsFCKKks3Aa6Ea6HTp06LAi8KQiq4DFsQZ6jkQYKQsnb4cSAlqBdtKr\n3Gs9F64U+OH3Plr683lnoK1iYyWRGauYgdahCJ6BNs9DTopGumboY1WJQHeudciwNfDQcyVuXlg7\n7U1ZWpByZRzGeOLK4uefAcewcJ5Q7MA0CrS261D8O3QtnKsPGjpWjSFp7ErngrZwti0R8KQmZjsC\nLY9ub3To0KHDioBnES0KOeNIgTCOcRREejUMUITL5JRKBH7gPY/g6565ghvnhqU/p0HuvFo4ad6/\nSrlaJ7Wa32ExQOSV54gcmTZruMaEMlOglTfodji7+N533cQHnrq0UvfVk4bP9t3rloVAM/KebKFj\nB05SgWZr4WxbIuB0BNpZQhaZUX4+vfuxi/iv/uYb8cbrWwAyRVprCycbs69bts6eFXR7o0OHDh1W\nBFx1tihWOje1FoyDODew45aDk1aSPHhhiAcvlJNnwEkq0BbjGM0CXQba2QKNw+d9nxFCwHMEs3Cq\nf4/iBJ0ArQPHubUezq31ml/YoRKcQHtySQg0beGcskRg3uMPfo9sLBGYciHKYZ8xmNO4pcPiQDbE\ngHiOxLe87QH9d1dOaeFki8gdgZbH6ozeO3To0OGMg6vO2uaBzAueo1p8JlGcW83iFs5Fs/15Oj9i\nPvvQEStIoK14C2eHPLRl9wQIU1fKrEQg/TOIugy0Dh1mDVKJX1jr4eK6f8pbYwciB9o+T90TWvRp\npUATAkKgtarX1YtyYqXGFR3K0TYGxCzjsQV3jWz0uxIBju4q69ChQ4cVASeivAUhMlxHYJ/aoVi9\nusPy2haNdJm3Ao0GMatkNcrsMKvznTpUg+Z3JzFZc5kCjSaWUZy0Xk3v0KFDPUhxsizqMyC7B7V9\nnsop7ZJtwcc3Ngq0abaHPqNTn50NyAYLpwki2qYpESB0GWh5dCPdDh06dFgRcNXZoqxCelJibxQC\nyBNSXD1y0iUCTehpAm1OCrQVtHDqAd2CHcsO84E8QcWh50hmt1L/FsZJ66DtDh061IMmzMtFoE1X\nyuNOaZdsC/7+Tap2R4qpxkP0XYa9juQ4C2jrYqAxbdshJx+zdxbOPLrhR4cOHTqsCDw2o1yULCrX\nEdgflxFo7DULRrpoBZo7JwUalQgsyDGaBR7dWccLT+zg9WlobYfVBp3DJ6F09RyhCTtOvM+zvKBD\nh7MITaBdXh4CTZcItGQHTmoRINfC2bCN7nEVaF2BwJmAXoS1PHfdKUsEOOG73inQcuj2RocOHTqs\nCDhp1nYwOS+4joUCbcEmwmQF8edl4VzBDLStgYdf/v/bu/tg2+6yPuDfZ+9zbm5eSEJCIiGB8F4T\nOpiEFEMpDoUMYmUaZECDVKnCUKda6YvTQuvYkZE/7DBQmWIdB2mRWpFGbaljRyhoW0WQoLxKmaaU\nKoMjMCCVvsAEf/3jrHXuvif37rv3uXevtV8+n5k75+y19z7rl5PzW3utZz3P7/neJ489DAaybAnJ\n+dibPDADLVl96RXsmpuuuzzPf9INufPmrxt7KAvbP2ZG91Cdo5cp4ZxM6ljj6YN0Sjh3w+E55IJl\ny/3f3fk0EVDCeTq/DYAtcVoXzjUJzuxPTmWgXXyWANq6ZMv19pVwwlw1YBOB/empsqY6LfC+8l3D\nTrnkxF5e84JvGHsYS+mPQcuugTZUAO20G5vnGOPepI51XOu7cF4iA20n9KeOi1ZvnCrhXO6Pq6/C\nODGdnNZQACWcAFvjtC6caxKUmj15nA1IzX6Qr1sG2v7ewXhW1kSgjnfCD+uin7H7AyxEtjedpD+M\nTNd47URgeP3NwuNnoK32GLa3TAZanWcGmgDaTuj/RhY9h+z/7pY91+4z0JRvPpCzd4AtMdsBcV2y\nm2Yz4WYz0GY/x9etC+epJgKrORmVgcammwyagTZTwjkzZdYt8A4Mry/hXHbZij7otOo1WGePU+fM\nQJseL4A2nSrh3CWHn78LBn/7z+ll/7b6rDMNBB7I2TvAlljPLpyzGWinTu5mP8jXt4nAan6HpzLQ\n1uu/GxbVT9kh5u7Zmgis2WEDGMFhBtqSn6dnOqaswmyQ41xdOCdVxxpPf56lhHM3nLoJu9jfymEG\n2jGbCFj/7IH8RgC2xP5ppQLrcXU5m4G2KU0ELt6fpmp15RD9r2RdgpywrFMZaKv/G770xN5h+Xcp\n4QRmHK6BtmwG2nSYDLTpaUtrzB/j9JhNBPoyVCWcu2HZRlQnjtlEoD9nl4H2QH4jAFti9mJ2XYIz\ns4G82ZO7dS7hfN5t1+fGqy/JJSdW8xG5jV042S01YAbaj33bnz888Z/d3boF3oHh9eswLtqRsNcf\nP1beRGDm5y/WROB8unC6rN8Fyy4Dsn9YwrncfmSgnZ3fCMCW2F/iTudQZssXZksiZ++ErVsA7erL\nLsqznvDQlf38ZReAhXUzZBD4Mddc9oD9JsvfTQe2z7Ez0AbqwjldojLguE0E+iw3JZy7YdkSzr3j\nNhGwBtpZOXsH2BKzF7NDLO69iL2zZKBN1jiAtmqHa6CtSZATltXHxYc+zpyegTboroE1dNsjHpyn\n/7lrlv48nQwUQFsqA+2YTQR04dwtxy7hXHYNtP0+A21/qfftAiFFgC0xezG7LsGZ2ZPHk3szAbQ1\nbiKwapMl7x7CujnVBWzYv+HZNdBKBhrsvG96/DX5psdfs/T7xshAu2g6P8D1lMc8JA+9/OJj70MX\nzt2w7Dq6fYON5btwHnTAvvxi4aKj/EYAtsR6ZqAdjOnE3uS0oNlpmSQ7FkCbWgONDVeHAbRh/4Z3\nOXMVuHD648fKmwjMlnCeo1Pod91x47H20f83KOHcDdPDJj5LduFc8qZTVeUnX3RbnnjDFcsNcAcI\noAFsif2Zi9l1Cc70nUFPHild2OW1jCZL3j2EddNfEyrhBDZRH9ha9Q282QDaqioDdOHcLaeqGBZt\nInC8Es4k+eYVrge8yZy9A2yJ2YvZ/YEzQ86mz0A7emI3+0G+a5kkfcBQEwE21VidZCdKOIELYDpQ\nGXqfpVu1unOdPSWcO+VUFcOiGWh9F2ufmReKs3eALTF7MXuuUoGh9EG9k0dO7GY/x3ctgKaJAJuu\nn7HDr4F26vtdO24AF06fxbPqoEJ/mDoxnaws6D89LOFUWLYLhsxA48ycvQNsidm7UUOvTXQ2fSbc\n0Tuj0x1ey+jyi/fzvFuvz1Mec/XYQ4FjqSXXYLlQpmdZRxFgGXsDrYFWVdmb1Eozzk914VyP8z5W\nazpZNgOtD6CtbEg7R6gaYEvszWagrU0TgYNxXHQkgDbZ5RLOSeW133HL2MOAYztcA23EJgLKUYDj\n6s9Bhjj/mE5qpRnn02lfwumyfhcs24jqQScP/i5kKF44YpEAW6IPmu1Nam3WBzpcA21fEwHYFpOR\nMtBmDxUCaMBx7Q0YQBsqA00Xzt3QB3/3Fgyg3Xzd5Xnry+7INz7qqlUOa6cIRQJsib5ccp26Ox52\n4TyagWYtI9hYY3WSPT0DbdBdA1ukv3E3xPnHZFIrPVbqwrlb+vtWi1aaVFXueLQlQy6k9bnKAuC8\nTCaVSQ2fFTLPqQy0owG03S3hhE1XA3WwO8pxA7gQ+qDTNmSgPenGB+fOm67NVZeeWNk+WB/f+Oir\nc9ctD8tVl/j/PRYZaABbZG86WavujvuHa3OcfQ0018GwWSajBdBOfb8uZerA5ulPk4Y4hk0nk5Vm\noN3y8Cvzxhf/hZX9fNbLTdddnp+4+9axh7HT1ucqC4DzdmI6Wa8MtO4u7wOaCHRDnK7Rem3AYg6b\nCAwcrC8ZaMAF0GegTbYgAw0YltkMsEX2pqtda2NZe2fJQDtcf0TwDDbOWE0EZq91xc+A4xo2A61y\n0RqdlwHnx2wG2CJ7Ky4VWFZfwnnyaBfOATtgARdWH/fuG5cMZXYNNJmrwHEdZqANcBzZm1b29xyv\nYFusz1UWAOftxLQW7swzhL6E82gGWs2UcAKbZbwMtJkSTgE04Jj6Q9dQGWjrtDYtcH7MZoAtsjed\nHAat1kF/cnrybCWcAmiwcfpZO3QTgdmY2Rod5oANM50O14Xzor1pLj4xPfcLgY2gCyfAFjkoFVif\nK8t+kfGTJ442ERBAg03Vl2AP3URgNgNtiNIrYDuduom3+mPYjz33Cbn85P7K9wMMQwANYIucmE6y\nv0ZBqb7E6+TemddAcxEMm2cyYPnTrNmAu2MHcFxDNhF40o1XrXwfwHDWJ00BgPO2bl04+0XGj5Yv\njHUBDpy/fgH/oY81p3fhdOwAjuewiYBzEGBJMtAAtshDLz+Zyy5an0P7Y6+9L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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "maxpooling_output = MaxPoolingOverTime.predict(convolutional_output)\n", "plt.figure(figsize=(21,11))\n", "plt.plot(maxpooling_output[0,:])\n", "plt.xlabel('coefficient')\n", "plt.ylabel('value')\n", "plt.title('Concatenate')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Highway network\n", "\n", "A Highway network is indicative of its name. It is a network that transforms its input\n", "and also carries part of it intact to the output. It is easily implemented using the functional\n", "API as such (see paper for the equations):" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "__________________________________________________________________________________________________\n", "Layer (type) Output Shape Param # Connected to \n", "==================================================================================================\n", "input_1 (InputLayer) (None, 525) 0 \n", "__________________________________________________________________________________________________\n", "transorm_gate (Dense) (None, 525) 276150 input_1[0][0] \n", "__________________________________________________________________________________________________\n", "dense_1 (Dense) (None, 525) 276150 input_1[0][0] \n", "__________________________________________________________________________________________________\n", "carry_gate (Lambda) (None, 525) 0 transorm_gate[0][0] \n", "__________________________________________________________________________________________________\n", "multiply_1 (Multiply) (None, 525) 0 transorm_gate[0][0] \n", " dense_1[0][0] \n", "__________________________________________________________________________________________________\n", "multiply_2 (Multiply) (None, 525) 0 carry_gate[0][0] \n", " input_1[0][0] \n", "__________________________________________________________________________________________________\n", "add_1 (Add) (None, 525) 0 multiply_1[0][0] \n", " multiply_2[0][0] \n", "==================================================================================================\n", "Total params: 552,300\n", "Trainable params: 552,300\n", "Non-trainable params: 0\n", "__________________________________________________________________________________________________\n" ] }, { "data": { "image/svg+xml": [ "\n", "\n", "G\n", "\n", "\n", "\n", "140495943146240\n", "\n", "input_1: InputLayer\n", "\n", "input:\n", "\n", "output:\n", "\n", "(None, 525)\n", "\n", "(None, 525)\n", "\n", "\n", "\n", "140495943146408\n", "\n", "transorm_gate: Dense\n", "\n", "input:\n", "\n", "output:\n", "\n", "(None, 525)\n", "\n", "(None, 525)\n", "\n", "\n", "\n", "140495943146240->140495943146408\n", "\n", "\n", "\n", "\n", "\n", "140495906222264\n", "\n", "dense_1: Dense\n", "\n", "input:\n", "\n", "output:\n", "\n", "(None, 525)\n", "\n", "(None, 525)\n", "\n", "\n", "\n", "140495943146240->140495906222264\n", "\n", "\n", "\n", "\n", "\n", "140495906213840\n", "\n", "multiply_2: Multiply\n", "\n", "input:\n", "\n", "output:\n", "\n", "[(None, 525), (None, 525)]\n", "\n", "(None, 525)\n", "\n", "\n", "\n", "140495943146240->140495906213840\n", "\n", "\n", "\n", "\n", "\n", "140495944122040\n", "\n", "carry_gate: Lambda\n", "\n", "input:\n", "\n", "output:\n", "\n", "(None, 525)\n", "\n", "(None, 525)\n", "\n", "\n", "\n", "140495943146408->140495944122040\n", "\n", "\n", "\n", "\n", "\n", "140495943109824\n", "\n", "multiply_1: Multiply\n", "\n", "input:\n", "\n", "output:\n", "\n", "[(None, 525), (None, 525)]\n", "\n", "(None, 525)\n", "\n", "\n", "\n", "140495943146408->140495943109824\n", "\n", "\n", "\n", "\n", "\n", "140495906222264->140495943109824\n", "\n", "\n", "\n", "\n", "\n", "140495944122040->140495906213840\n", "\n", "\n", "\n", "\n", "\n", "140495943114880\n", "\n", "add_1: Add\n", "\n", "input:\n", "\n", "output:\n", "\n", "[(None, 525), (None, 525)]\n", "\n", "(None, 525)\n", "\n", "\n", "\n", "140495943109824->140495943114880\n", "\n", "\n", "\n", "\n", "\n", "140495906213840->140495943114880\n", "\n", "\n", "\n", "\n", "" ], "text/plain": [ "" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "inputs = layers.Input(shape=(\n", " maxpooling_output.shape[1],)) # The input must match the output of \n", " # the MaxPoolingOverTime layer\n", " \n", "transform_gate = layers.Dense(maxpooling_output.shape[1],\n", " activation='sigmoid',\n", " name='transorm_gate')(inputs)\n", "\n", "carry_gate = layers.Lambda(lambda x: 1-x,\n", " name='carry_gate')(transform_gate)\n", "\n", "z = layers.Add()([\n", " layers.Multiply()([\n", " transform_gate,\n", " layers.Dense(maxpooling_output.shape[1],\n", " activation='relu')(inputs)\n", " ]),\n", " layers.Multiply()([carry_gate, inputs])\n", "])\n", "\n", "Highway = keras.Model(\n", " inputs=inputs, \n", " outputs=z,\n", " name='Highway'\n", ")\n", "\n", "Highway.summary()\n", "\n", "# Block diagram\n", "SVG(model_to_dot(Highway, show_shapes=True, show_layer_names=True).create(prog='dot', format='svg'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The necessary output example:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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lrVVgkuNce2LNG5bvV5PF7SrCWrZG+u+XmRHWuvJsmuQzwoKRF7W5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\nQBHe/5lL+M9+6kHcNMoHqrL6fAMizCrCcsPyNy9X77BhWEWYywjrKgB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meIZEKFw7mtqg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pBpewM9YoR58E/mHmHOYSdmxAGJugTCCkkY/Z2Z5d81QSYWpco5XbxWSXokySOsXuXgLhDl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\nVreB9Z0MCFNK4dqyRnVBQNin/HPYHGKE2XEKAr/wVfSz1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CNs1iNsvlS6tWnRVBpvnabn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\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K89ijx08SdJa5pTlyY9VIN+dQlrnpHcvRlZnf2KKqkbbiq2eEydpW5ooAjgyDyDFWamHN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MonpZGxBYJNzPUI28VwaP82dD3CjHRcrK9Mkg7bntgw//SwBMJ14Pa3An/wU8Cr32/7P4M0nDUS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\nWmgmUGhi+3WZHmEHvarvMkkjtekRhq50RtjuJfv1vI3X1WPdr0cNWBy7z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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "highway_output = Highway.predict(maxpooling_output)\n", "plt.figure(figsize=(21,11))\n", "plt.plot(maxpooling_output[0])\n", "plt.plot(highway_output[0])\n", "plt.legend(['maxpooling_output', 'highway_output'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Combining Embedding, Convolutional, MaxPool, and Highway\n", "\n", "Before implementing our LSTM layer, we will combine the above layers to a FeatureExtract layer. \n", "We will then distribute that layer across time for all time instants $t-25+1, \\dots t-1, t$ which we will provide to the LSTM layer unrolled for those time instants.\n", "\n" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "__________________________________________________________________________________________________\n", "Layer (type) Output Shape Param # Connected to \n", "==================================================================================================\n", "Embeddings_input (InputLayer) (None, 21) 0 \n", "__________________________________________________________________________________________________\n", "Embeddings (Sequential) (None, 21, 15) 780 Embeddings_input[0][0] \n", "__________________________________________________________________________________________________\n", "Convolutional (Model) [(None, 21, 25), (No 34650 Embeddings[1][0] \n", "__________________________________________________________________________________________________\n", "MaxPoolingOverTime (Model) (None, 525) 0 Convolutional[1][0] \n", " Convolutional[1][1] \n", " Convolutional[1][2] \n", " Convolutional[1][3] \n", " Convolutional[1][4] \n", " Convolutional[1][5] \n", "__________________________________________________________________________________________________\n", "Highway (Model) (None, 525) 552300 MaxPoolingOverTime[1][0] \n", "==================================================================================================\n", "Total params: 587,730\n", "Trainable params: 587,730\n", "Non-trainable params: 0\n", "__________________________________________________________________________________________________\n" ] }, { "data": { "image/svg+xml": [ "\n", "\n", "G\n", "\n", "\n", "\n", "140498816003824\n", "\n", "Embeddings_input: InputLayer\n", "\n", "input:\n", "\n", "output:\n", "\n", "(None, 21)\n", "\n", "(None, 21)\n", "\n", "\n", "\n", "140498816004048\n", "\n", "Embeddings: Sequential\n", "\n", "input:\n", "\n", "output:\n", "\n", "(None, 21)\n", "\n", "(None, 21, 15)\n", "\n", "\n", "\n", "140498816003824->140498816004048\n", "\n", "\n", "\n", "\n", "\n", "140496709026480\n", "\n", "Convolutional: Model\n", "\n", "input:\n", "\n", "output:\n", "\n", "(None, 21, 15)\n", "\n", "[(None, 21, 25), (None, 20, 50), (None, 19, 75), (None, 18, 100), (None, 17, 125), (None, 16, 150)]\n", "\n", "\n", "\n", "140498816004048->140496709026480\n", "\n", "\n", "\n", "\n", "\n", "140495952899880\n", "\n", "MaxPoolingOverTime: Model\n", "\n", "input:\n", "\n", "output:\n", "\n", "[(None, 21, 25), (None, 20, 50), (None, 19, 75), (None, 18, 100), (None, 17, 125), (None, 16, 150)]\n", "\n", "(None, 525)\n", "\n", "\n", "\n", "140496709026480->140495952899880\n", "\n", "\n", "\n", "\n", "\n", "140495943115272\n", "\n", "Highway: Model\n", "\n", "input:\n", "\n", "output:\n", "\n", "(None, 525)\n", "\n", "(None, 525)\n", "\n", "\n", "\n", "140495952899880->140495943115272\n", "\n", "\n", "\n", "\n", "" ], "text/plain": [ "" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "inputs = Embeddings.inputs\n", "x = Embeddings(inputs=inputs)\n", "x = Convolutional(inputs=x)\n", "x = MaxPoolingOverTime(inputs=x)\n", "x = Highway(inputs=x)\n", "\n", "FeatureExtract = keras.Model(inputs=inputs, outputs=x)\n", "FeatureExtract.summary()\n", "\n", "# Block diagram\n", "SVG(model_to_dot(FeatureExtract, show_shapes=True).create(prog='dot', format='svg'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`FeatureExtract` does process one word at a time but at training time we process sequences of words of length `seq_len=25`. We are going to assign a `FeatureExtract` at each timestep with the `TimeDistributed` layer, and finally add 2 LSTM layers with `hidden_units = 300` hidden units and a time distributed `Dense` layer. In the paper there is a `softmax` activation after the last fully connected layer. We will ommit it however and defer its calculation to `K.categorical_crossentropy(target, pred, from_logits=True)`." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", "SequenceFeatureExtract (Time (None, 35, 525) 587730 \n", "_________________________________________________________________\n", "RNN1 (LSTM) (None, 35, 300) 991200 \n", "_________________________________________________________________\n", "dropout_1 (Dropout) (None, 35, 300) 0 \n", "_________________________________________________________________\n", "RNN2 (LSTM) (None, 35, 300) 721200 \n", "_________________________________________________________________\n", "dropout_2 (Dropout) (None, 35, 300) 0 \n", "_________________________________________________________________\n", "time_distributed_1 (TimeDist (None, 35, 9999) 3009699 \n", "=================================================================\n", "Total params: 5,309,829\n", "Trainable params: 5,309,829\n", "Non-trainable params: 0\n", "_________________________________________________________________\n" ] }, { "data": { "image/svg+xml": [ "\n", "\n", "G\n", "\n", "\n", "\n", "140495950515672\n", "\n", "SequenceFeatureExtract_input: InputLayer\n", "\n", "input:\n", "\n", "output:\n", "\n", "(None, 35, 21)\n", "\n", "(None, 35, 21)\n", "\n", "\n", "\n", "140495950516008\n", "\n", "SequenceFeatureExtract(model_1): TimeDistributed(Model)\n", "\n", "input:\n", "\n", "output:\n", "\n", "(None, 35, 21)\n", "\n", "(None, 35, 525)\n", "\n", "\n", "\n", "140495950515672->140495950516008\n", "\n", "\n", "\n", "\n", "\n", "140495943551688\n", "\n", "RNN1: LSTM\n", "\n", "input:\n", "\n", "output:\n", "\n", "(None, 35, 525)\n", "\n", "(None, 35, 300)\n", "\n", "\n", "\n", "140495950516008->140495943551688\n", "\n", "\n", "\n", "\n", "\n", "140495943927344\n", "\n", "dropout_1: Dropout\n", "\n", "input:\n", "\n", "output:\n", "\n", "(None, 35, 300)\n", "\n", "(None, 35, 300)\n", "\n", "\n", "\n", "140495943551688->140495943927344\n", "\n", "\n", "\n", "\n", "\n", "140495950515616\n", "\n", "RNN2: LSTM\n", "\n", "input:\n", "\n", "output:\n", "\n", "(None, 35, 300)\n", "\n", "(None, 35, 300)\n", "\n", "\n", "\n", "140495943927344->140495950515616\n", "\n", "\n", "\n", "\n", "\n", "140495950515448\n", "\n", "dropout_2: Dropout\n", "\n", "input:\n", "\n", "output:\n", "\n", "(None, 35, 300)\n", "\n", "(None, 35, 300)\n", "\n", "\n", "\n", "140495950515616->140495950515448\n", "\n", "\n", "\n", "\n", "\n", "140495952271792\n", "\n", "time_distributed_1(dense_2): TimeDistributed(Dense)\n", "\n", "input:\n", "\n", "output:\n", "\n", "(None, 35, 300)\n", "\n", "(None, 35, 9999)\n", "\n", "\n", "\n", "140495950515448->140495952271792\n", "\n", "\n", "\n", "\n", "" ], "text/plain": [ "" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "CharRNN = keras.Sequential()\n", "CharRNN.add(\n", " layers.TimeDistributed(\n", " FeatureExtract,\n", " input_shape=(seq_len,max_word_len), # We need to declare shape\n", " name='SequenceFeatureExtract'\n", " )\n", ")\n", "\n", "# Add the 2 LSTM layers\n", "hidden_units = 300\n", "\n", "CharRNN.add(\n", " layers.LSTM(\n", " hidden_units,\n", " return_sequences=True,\n", " name='RNN1'\n", " )\n", ")\n", "\n", "# Add a dropout of 0.5 to the input->hidden\n", "CharRNN.add(\n", " layers.Dropout(0.5)\n", ")\n", "\n", "CharRNN.add(\n", " layers.LSTM(\n", " hidden_units,\n", " return_sequences=True,\n", " name='RNN2'\n", " )\n", ")\n", "\n", "# Add a dropout of 0.5 to thehidden->softmax\n", "CharRNN.add(\n", " layers.Dropout(0.5)\n", ")\n", "\n", "\n", "CharRNN.add(\n", " layers.TimeDistributed(\n", " layers.Dense(\n", " len(word_idx), \n", " activation='linear' # See the third link in the credits. \n", " # Softmax is calculated in the cross entropy loss\n", " # by tensorflow.\n", " )\n", " )\n", ")\n", "\n", "CharRNN.summary()\n", "\n", "# Block diagram\n", "SVG(model_to_dot(CharRNN, show_shapes=True).create(prog='dot', format='svg'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the paper, SGD is used for training with an initial learning rate of 1.0 (which reduces in half if the loss does not improve after 1 epoch) and gradient clipping value of 5. First we need to define the loss $PPL$:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "import keras.backend as K\n", "\n", "# See the second link in credits for that. Instead of computing a final\n", "# softmax, we defer this to the \n", "def categorical_crossentropy(y_true, y_pred):\n", " return K.categorical_crossentropy(y_true, y_pred,from_logits=True )\n", "\n", "def PPL(y_true, y_pred):\n", " return K.pow(2.0, K.mean(K.categorical_crossentropy(y_true, y_pred,from_logits=True )))\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then the optimizer for the model (norm clipping value of 5. is applied):" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "from keras.optimizers import SGD\n", "\n", "opt = SGD(lr=1.0, clipnorm=5.)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally compile the model. It is also a good idea to save the untrained model." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "CharRNN.compile(loss=categorical_crossentropy, metrics=['acc', PPL], optimizer=opt)\n", "CharRNN.save('char-rnn-cnn_untrained.hdf5')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can now train the model. First we define several callbacks:\n", "\n", "1. `ReduceLRPlateau` will half the learning rate if it does not improve after one epoch.\n", "2. `ModelCheckpoint` (optional but recommended) will save a checkpoint of the model at each epoch.\n", "3. `TensorBoard` (optional) will use Google's Tensorboard to show training progress.\n", "\n", "Finally, we train the model using the `fit_generator` method with the generator objects defined above.\n", "\n", " Note: You can observe training progress with tensorboard by running `tensorboard --logdir=logs` at the directory this notebook resides." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/25\n", "1267/1267 [==============================] - 101s 79ms/step - loss: 6.2205 - acc: 0.0510 - PPL: 109.0537 - val_loss: 6.5892 - val_acc: 0.0486 - val_PPL: 106.9790\n", "Epoch 2/25\n", "1267/1267 [==============================] - 99s 78ms/step - loss: 6.0385 - acc: 0.0576 - PPL: 88.5834 - val_loss: 6.4777 - val_acc: 0.0505 - val_PPL: 100.3288\n", "Epoch 3/25\n", "1267/1267 [==============================] - 99s 78ms/step - loss: 5.8674 - acc: 0.0691 - PPL: 81.3396 - val_loss: 6.3364 - val_acc: 0.0833 - val_PPL: 92.7788\n", "Epoch 4/25\n", "1267/1267 [==============================] - 99s 78ms/step - loss: 5.8825 - acc: 0.0763 - PPL: 78.2120 - val_loss: 6.3529 - val_acc: 0.0943 - val_PPL: 92.6208\n", "Epoch 5/25\n", "1267/1267 [==============================] - 99s 78ms/step - loss: 5.7617 - acc: 0.0847 - PPL: 72.3175 - val_loss: 6.1295 - val_acc: 0.1016 - val_PPL: 81.4286\n", "Epoch 6/25\n", "1267/1267 [==============================] - 99s 78ms/step - loss: 5.7181 - acc: 0.0904 - PPL: 68.4978 - val_loss: 6.0488 - val_acc: 0.0976 - val_PPL: 77.2231\n", "Epoch 7/25\n", "1267/1267 [==============================] - 99s 78ms/step - loss: 5.6775 - acc: 0.0969 - PPL: 64.7776 - val_loss: 5.9409 - val_acc: 0.1063 - val_PPL: 72.7255\n", "Epoch 8/25\n", "1267/1267 [==============================] - 99s 78ms/step - loss: 5.5066 - acc: 0.0975 - PPL: 61.7341 - val_loss: 5.8394 - val_acc: 0.1203 - val_PPL: 67.7843\n", "Epoch 9/25\n", "1267/1267 [==============================] - 98s 78ms/step - loss: 5.5073 - acc: 0.1008 - PPL: 60.8387 - val_loss: 5.7717 - val_acc: 0.1280 - val_PPL: 64.6741\n", "Epoch 10/25\n", "1267/1267 [==============================] - 98s 77ms/step - loss: 5.4586 - acc: 0.1032 - PPL: 60.2961 - val_loss: 5.7277 - val_acc: 0.1350 - val_PPL: 63.0129\n", "Epoch 11/25\n", "1267/1267 [==============================] - 98s 78ms/step - loss: 5.4499 - acc: 0.1031 - PPL: 63.0168 - val_loss: 5.6674 - val_acc: 0.1446 - val_PPL: 60.2713\n", "Epoch 12/25\n", "1267/1267 [==============================] - 98s 78ms/step - loss: 5.4814 - acc: 0.1055 - PPL: 63.1000 - val_loss: 5.8431 - val_acc: 0.1285 - val_PPL: 68.5861\n", "Epoch 13/25\n", "1267/1267 [==============================] - 98s 77ms/step - loss: 5.4975 - acc: 0.1084 - PPL: 64.7933 - val_loss: 5.6355 - val_acc: 0.1427 - val_PPL: 58.4656\n", "Epoch 14/25\n", "1267/1267 [==============================] - 98s 77ms/step - loss: 5.5273 - acc: 0.1063 - PPL: 70.0334 - val_loss: 5.8094 - val_acc: 0.1262 - val_PPL: 66.5901\n", "Epoch 15/25\n", "1267/1267 [==============================] - 98s 77ms/step - loss: 5.5689 - acc: 0.1098 - PPL: 74.0235 - val_loss: 5.6621 - val_acc: 0.1411 - val_PPL: 60.1453\n", "Epoch 16/25\n", "1267/1267 [==============================] - 98s 77ms/step - loss: 5.5261 - acc: 0.1129 - PPL: 64.9730 - val_loss: 5.6639 - val_acc: 0.1374 - val_PPL: 60.1021\n", "Epoch 17/25\n", "1267/1267 [==============================] - 98s 77ms/step - loss: 5.3318 - acc: 0.1282 - PPL: 51.7135 - val_loss: 5.5873 - val_acc: 0.1571 - val_PPL: 56.5843\n", "Epoch 18/25\n", "1267/1267 [==============================] - 98s 77ms/step - loss: 5.3151 - acc: 0.1196 - PPL: 54.3754 - val_loss: 5.8620 - val_acc: 0.1399 - val_PPL: 69.4699\n", "Epoch 19/25\n", "1267/1267 [==============================] - 98s 77ms/step - loss: 5.3355 - acc: 0.1210 - PPL: 54.4442 - val_loss: 5.7665 - val_acc: 0.1284 - val_PPL: 64.4040\n", "Epoch 20/25\n", "1267/1267 [==============================] - 98s 77ms/step - loss: 5.2400 - acc: 0.1362 - PPL: 49.0685 - val_loss: 5.5783 - val_acc: 0.1639 - val_PPL: 55.6973\n", "Epoch 21/25\n", "1267/1267 [==============================] - 98s 77ms/step - loss: 5.2198 - acc: 0.1397 - PPL: 48.4728 - val_loss: 5.5208 - val_acc: 0.1634 - val_PPL: 54.1354\n", "Epoch 22/25\n", "1267/1267 [==============================] - 98s 77ms/step - loss: 5.2313 - acc: 0.1400 - PPL: 48.6355 - val_loss: 5.5302 - val_acc: 0.1630 - val_PPL: 54.3647\n", "Epoch 23/25\n", "1267/1267 [==============================] - 98s 77ms/step - loss: 5.1974 - acc: 0.1397 - PPL: 47.8129 - val_loss: 5.5249 - val_acc: 0.1654 - val_PPL: 54.6090\n", "Epoch 24/25\n", "1267/1267 [==============================] - 98s 77ms/step - loss: 5.1583 - acc: 0.1399 - PPL: 47.0824 - val_loss: 5.5541 - val_acc: 0.1639 - val_PPL: 55.1477\n", "Epoch 25/25\n", "1267/1267 [==============================] - 98s 77ms/step - loss: 5.1568 - acc: 0.1392 - PPL: 47.3577 - val_loss: 5.5370 - val_acc: 0.1648 - val_PPL: 54.6469\n" ] } ], "source": [ "from keras.callbacks import ReduceLROnPlateau, ModelCheckpoint, TensorBoard\n", "from time import strftime\n", "\n", "model_name = 'char-rnn-cnn'\n", "\n", "# Define generators: train_gen for training, val_gen for validation (see above)\n", "train_gen = generator(W_tr)\n", "val_gen = generator(W_vd, batch_size=1)\n", "\n", "# Callbacks (see above.) comment out the ones you don't want to.\n", "callbacks = [\n", " ReduceLROnPlateau(\n", " factor=0.5, # new LR = old LR * 0.5\n", " patience=1, # After one epoch\n", " #min_lr=0.000976562, # Minimum value of learning late\n", " monitor='val_PPL', # Monitor loss \n", " epsilon=1. # Amount by which a plateau is considered\n", " ), \n", " ModelCheckpoint('checkpoints/{}_{{epoch:02d}}-{{val_loss:.2f}}.hdf5'.format(model_name)),\n", " TensorBoard(log_dir='logs/{}/{}'.format(\n", " model_name,\n", " strftime('%d-%m-%y/%H:%M:%S')\n", " ),\n", " write_images=True,\n", " )\n", "]\n", "\n", "history = CharRNN.fit_generator(\n", " train_gen,\n", " epochs=25, #25 epochs for non-arabic languages (from the paper)\n", " steps_per_epoch=len(inputs_tr)//seq_len//batch_size, # number of batches to feed\n", " verbose=1,\n", " validation_data = val_gen,\n", " validation_steps = len(inputs_vd)//seq_len, # We don't use minibatches to validate\n", " shuffle=False,\n", " callbacks=callbacks\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally we evaluate the model. Here is a snapshot of the training process in TensorBoard:\n", "\n", "![training](tensorboard.png)\n", "\n", "Orange lines are with untrainable character embeddings (just a random character LUT) and blue lines\n", "with trainable embeddings.\n", "\n", " Note: There probably is something wrong with the metrics since we have a\n", "perplexity much lower than reported (51.7 vs 92.3 in the paper)." ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Test Loss: 5.487452989824623, Perplexity: 51.72303666000001\n" ] } ], "source": [ "test_gen = generator(W_ts,batch_size=1)\n", "loss, acc, ppl = CharRNN.evaluate_generator(test_gen, steps=len(inputs_ts)//seq_len)\n", "\n", "print(\"Test Loss: {}, Perplexity: {}\".format(loss, ppl))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Credits\n", "\n", "* [Character-Aware Neural Language models, Kim, Y. et al.](https://arxiv.org/abs/1508.06615)\n", "* [How to Implement Perplexity in Keras?](https://stackoverflow.com/questions/44697318/how-to-implement-perplexity-in-keras)\n", "* [icoxfog417's answer about perplexity in Keras](https://github.com/keras-team/keras/issues/2317)\n", "* [byung2685's implementation try](https://github.com/byung2685/Character-Aware-Neural-Language-Models)\n", "\n", "## Final Word\n", "\n", "In the `checkpoints` directory I have a checkpoint for the last training epoch. You can load it in Keras and play with it as you please. \n", "\n", "Please note I wrote this tutorial in order for me to understand how to build such models. If you found it useful or have any questions, criticism, suggestions or other feedback, please [drop me an e-mail](mailto:e.t.chourdakis@qmul.ac.uk). \n", "\n", "I am interested whether I defined perplexity correctly, if someone have any suggestions on that, open an issue or send me an e-mail.\n", "\n", "## Feedback\n", "\n", "This area is for feedback I receive by e-mail." ] } ], "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": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 }