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utils.py
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utils.py
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import codecs
import json
import numpy as np
class VocabularyLoader(object):
def load_vocab(self, vocab_file, encoding):
with codecs.open(vocab_file, 'r', encoding=encoding) as f:
self.vocab_index_dict = json.load(f)
self.index_vocab_dict = {}
self.vocab_size = 0
for char, index in self.vocab_index_dict.iteritems():
self.index_vocab_dict[index] = char
self.vocab_size += 1
def create_vocab(self, text):
unique_chars = list(set(text))
self.vocab_size = len(unique_chars)
self.vocab_index_dict = {}
self.index_vocab_dict = {}
for i, char in enumerate(unique_chars):
self.vocab_index_dict[char] = i
self.index_vocab_dict[i] = char
def save_vocab(self, vocab_file, encoding):
with codecs.open(vocab_file, 'w', encoding=encoding) as f:
json.dump(self.vocab_index_dict, f, indent=2, sort_keys=True)
class BatchGenerator(object):
def __init__(self, vocab_index_dict, text, batch_size, seq_length):
self.batch_size = batch_size
self.seq_length = seq_length
self.tensor = np.array(list(map(vocab_index_dict.get, text)))
self.create_batches()
self.reset_batch_pointer()
def reset_batch_pointer(self):
self.pointer = 0
def create_batches(self):
self.num_batches = int(self.tensor.size / (self.batch_size * self.seq_length))
# When the data (tesor) is too small, let's give them a better error message
if self.num_batches==0:
assert False, "Not enough data. Make seq_length and batch_size small."
self.tensor = self.tensor[:self.num_batches * self.batch_size * self.seq_length]
xdata = self.tensor
ydata = np.copy(self.tensor)
ydata[:-1] = xdata[1:]
ydata[-1] = xdata[0]
self.x_batches = np.split(xdata.reshape(self.batch_size, -1), self.num_batches, 1)
self.y_batches = np.split(ydata.reshape(self.batch_size, -1), self.num_batches, 1)
def next_batch(self):
x, y = self.x_batches[self.pointer], self.y_batches[self.pointer]
self.pointer += 1
return x, y
# util functions
def batche2string(batch, index_vocab_dict):
return ''.join(list(map(index_vocab_dict.get, batch)))