talolard

#include "FastLED.h"

#include <Adafruit_NeoPixel.h>
Adafruit_NeoPixel strip = Adafruit_NeoPixel(60, 8, NEO_BRG + NEO_KHZ800);
void setup() {
    strip.begin();
    strip.show(); // Initialize all pixels to 'off'
}

talolard

extern const TProgmemRGBPalette16 autumnleaves FL_PROGMEM = {

        0Xb70c0c, 0Xc71221, 0Xdc0606, 0Xfa250c,
        0Xfc9105, 0Xfcb108, 0X318d03, 0X9dd523,
        0Xfc9105, 0Xfcb108, 0X318d03, 0X9dd523,
        0Xb70c0c, 0Xc71221, 0Xdc0606, 0Xfa250c
};
extern const TProgmemRGBPalette16 desert  FL_PROGMEM = {
        0X000000, 0Xeb6810, 0Xeb8d10, 0Xd35d0e,
        0X8b3d09, 0XFFC233, 0X000000, 0Xeb6810,

talolard

import tensorflow as tf
def conv1d(input_, output_size, width, stride):
    '''
    :param input_: A tensor of embedded tokens with shape [batch_size,max_length,embedding_size]
    :param output_size: The number of feature maps we'd like to calculate
    :param width: The filter width
    :param stride: The stride
    :return: A tensor of the concolved input with shape [batch_size,max_length,output_size]
    '''
    inputSize = input_.get_shape()[-1] # How many channels on the input (The size of our embedding for instance)

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'''
GRU layer implementation orignally taken from https://github.com/ottokart/punctuator2
'''
class GRULayer(object):

    def __init__(self, rng, n_in, n_out, minibatch_size):
        super(GRULayer, self).__init__()
        # Notation from: An Empirical Exploration of Recurrent Network Architectures

        self.n_in = n_in

talolard

from tf.contrib.rnn import GRUCell
cell = GruCell()

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import tensorflow as tf
lstm = tf.contrib.rnn.BasicLSTMCell(lstm_size)

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talolard

talolard

'''
Tools to take a directory of txt files and convert them to TF records
'''
from collections import defaultdict, Counter
import numpy as np
import tensorflow as tf
PAD = "<PAD>"
EOS = "<EOS>"

talolard

class BibPreppy(Preppy):
    '''
        We'll slightly extend to way we right tfrecords to store the id of the book it came from
        '''
    def __init__(self,tokenizer_fn):
        super(BibPreppy,self).__init__(tokenizer_fn)
        self.book_map ={}

    def sequence_to_tf_example(self, sequence, book_id):
        id_list = self.sentance_to_id_list(sequence)