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from data import ROOT

class SchemaBuilder(object):
    def __init__(self, prefix=''):
        self.last_key = ''
        self.prefix = prefix
        self.results = {}
    
    def parse(self, obj, my_key='Root'):
        if obj is None: return 'Any'

rjpower

import numpy as np

from keras.models import Sequential
from keras.layers.core import Dense, Activation, Dropout, TimeDistributedDense
from keras.layers.recurrent import LSTM

text = open('/home/russellp/w/data/citation-graph/abstracts.txt', 'r').read()
char_to_idx = { ch: i for (i, ch) in enumerate(sorted(list(set(text)))) }
idx_to_char = { i: ch for (ch, i) in char_to_idx.items() }
vocab_size = len(char_to_idx)

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echo 'deb http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64 /' > /etc/apt/sources.list.d/cuda.list
echo 'deb http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1404/x86_64 /' > /etc/apt/sources.list.d/nvidia-ml.list

apt-get update
apt-get install --force-yes -y nvidia-352
apt-get install --force-yes -y cuda-7-5 cuda-command-line-tools-7-5 cuda-core-7-5 cuda-cublas-7-5 cuda-cublas-dev-7-5 cuda-cudart-7-5 cuda-cudart-dev-7-5 cuda-cufft-7-5 cuda-cufft-dev-7-5 cuda-curand-7-5 cuda-curand-dev-7-5 cuda-cusolver-7-5 cuda-cusolver-dev-7-5 cuda-cusparse-7-5 cuda-cusparse-dev-7-5 cuda-documentation-7-5 cuda-driver-dev-7-5 cuda-gdb-src-7-5 cuda-ld-conf-7-5 cuda-license-7-5 cuda-minimal-build-7-5 cuda-misc-headers-7-5 cuda-npp-7-5 cuda-npp-dev-7-5 cuda-nvrtc-7-5 cuda-nvrtc-dev-7-5 cuda-runtime-7-5 cuda-samples-7-5 cuda-toolkit-7-5 cuda-visual-tools-7-5
apt-get install -y build-essential python-dev python-virtualenv python-pip
apt-get install -y libatlas-dev liblapack-dev
ap

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class BOW(Embedding):
    '''
    Bag-of-words embedding layer.
    
    The default embedding layer outputs the concatenation of [W_t * one_hot(input)];
    this layer simply sums over the resulting output.
    '''    
    def get_output_shape_for(self, input_shape):
        return (input_shape[0], self.output_dim)

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