def generateName(start_char='M'):
input = Variable(inputVector(start_char))
hidden1, hidden2, hidden3 = rnn.initHidden()
if uses_cuda:
input = input.cuda()
hidden1 = hidden1.cuda()
hidden2 = hidden2.cuda()
hidden3 = hidden3.cuda()
final_text = start_char
for i in range(length_lim):
output, hidden1, hidden2, hidden3 = rnn(input[0], hidden1, hidden2,
hidden3)
topvec, topidx = output.data.topk(1)
topidx = topidx[0][0]
if topidx == characterSetSize() - 1:
break
else:
letter = characterSet()[topidx]
final_text += letter
input = Variable(inputVector(letter))
if uses_cuda:
input = input.cuda()
return final_text
def targetVector(line):
# Target has to be a longTensor of indices for NLLLoss
target_vec = []
for char_idx in range(1,len(line)):
char_cur = line[char_idx]
target_vec.append(all_chars.find(char_cur))
# Add an EOF as the final target
target_vec.append(characterSetSize()-1)
return torch.LongTensor(target_vec)
def generateName(category, start_char='A'):
category_vec = Variable(class2Vector(category))
input = Variable(inputVector(start_char))
hidden = rnn.initHidden()
final_name = start_char
for i in range(length_lim):
output, hidden = rnn(category_vec, input[0], hidden)
topvec, topidx = output.data.topk(1)
topidx = topidx[0][0]
if topidx == characterSetSize() - 1:
break
else:
letter = characterSet()[topidx]
final_name += letter
input = Variable(inputVector(letter))
return final_name
import torch
import random
import string
from torch.autograd import Variable
from textUtils import class2Vector, inputVector
from processData import loadNames, characterSetSize, characterSet
from categoryRNN import categoryRNN
length_lim = 15
all_names = loadNames()
all_types = all_names.keys()
save_file = 'names-model-checkpoint.dat'
rnn = categoryRNN(characterSetSize(), 128, characterSetSize(), len(all_types))
rnn.eval()
if os.path.isfile(save_file):
rnn.load_state_dict(torch.load(save_file))
# Names start with a capital letter
def generateName(category, start_char='A'):
category_vec = Variable(class2Vector(category))
input = Variable(inputVector(start_char))
hidden = rnn.initHidden()
final_name = start_char
for i in range(length_lim):
output, hidden = rnn(category_vec, input[0], hidden)
from torch.autograd import Variable
from textUtils import inputVector
from processData import loadShakespeare, characterSetSize, characterSet
from vanillaLargeRNN import vanillaLargeRNN
uses_cuda = True
length_lim = 150
all_names = loadShakespeare()
save_file = 'shakespeare-model-checkpoint.dat'
if uses_cuda:
save_file = 'shakespeare-model-checkpoint-cuda.dat'
rnn = vanillaLargeRNN(characterSetSize(), 256, characterSetSize())
rnn.eval()
if uses_cuda:
rnn = rnn.cuda()
if os.path.isfile(save_file):
rnn.load_state_dict(torch.load(save_file))
# Paragraphs start with a capital letter
def generateName(start_char='M'):
input = Variable(inputVector(start_char))
hidden1, hidden2, hidden3 = rnn.initHidden()
if uses_cuda:
input = input.cuda()
def inputVector(line):
input_vec = torch.zeros(len(line), 1, characterSetSize())
for char_idx in range(len(line)):
char_cur = line[char_idx]
input_vec[char_idx][0][all_chars.find(char_cur)] = 1
return input_vec