def predecir(self, frase):
_, (source_vocab_to_int, target_vocab_to_int), (
source_int_to_vocab,
target_int_to_vocab) = helper.load_preprocess()
load_path = helper.load_params()
tests.test_sentence_to_seq(sentence_to_seq)
translate_sentence = frase
pIngles = translate_sentence
translate_sentence = sentence_to_seq(translate_sentence,
source_vocab_to_int)
loaded_graph = tf.Graph()
with tf.Session(graph=loaded_graph) as sess:
# Load saved model
loader = tf.train.import_meta_graph(load_path + '.meta')
loader.restore(sess, load_path)
input_data = loaded_graph.get_tensor_by_name('input:0')
logits = loaded_graph.get_tensor_by_name('predictions:0')
target_sequence_length = loaded_graph.get_tensor_by_name(
'target_sequence_length:0')
source_sequence_length = loaded_graph.get_tensor_by_name(
'source_sequence_length:0')
keep_prob = loaded_graph.get_tensor_by_name('keep_prob:0')
translate_logits = sess.run(
logits, {
input_data: [translate_sentence] * batch_size,
target_sequence_length:
[len(translate_sentence) * 2] * batch_size,
source_sequence_length:
[len(translate_sentence)] * batch_size,
keep_prob: 1.0
})[0]
"""
print('Input')
print(' Word Ids: {}'.format([i for i in translate_sentence]))
print(' English Words: {}'.format([source_int_to_vocab[i] for i in translate_sentence]))
print('\nPrediction')
print(' Word Ids: {}'.format([i for i in translate_logits]))
print(' Spanish Words: {}'.format(" ".join([target_int_to_vocab[i] for i in translate_logits])))
"""
variableRetornar = format(" ".join(
[target_int_to_vocab[i] for i in translate_logits]))
print('Resultado de ', pIngles)
print(variableRetornar)
miTxt = open("BorderOut\\IA\\respuesta.txt", 'w')
miTxt.write(variableRetornar)
miTxt.close()
return variableRetornar
def run_tests():
import problem_unittests as t
t.test_decoding_layer(decoding_layer)
t.test_decoding_layer_infer(decoding_layer_infer)
t.test_decoding_layer_train(decoding_layer_train)
t.test_encoding_layer(encoding_layer)
t.test_model_inputs(model_inputs)
t.test_process_encoding_input(process_decoder_input)
t.test_sentence_to_seq(sentence_to_seq)
t.test_seq2seq_model(seq2seq_model)
t.test_text_to_ids(text_to_ids)
def run_all_tests():
tests.test_text_to_ids(text_to_ids)
check_tensorflow_gpu()
tests.test_model_inputs(model_inputs)
tests.test_process_encoding_input(process_decoder_input)
from imp import reload
reload(tests)
tests.test_encoding_layer(encoding_layer)
tests.test_decoding_layer_train(decoding_layer_train)
tests.test_decoding_layer_infer(decoding_layer_infer)
tests.test_decoding_layer(decoding_layer)
tests.test_seq2seq_model(seq2seq_model)
tests.test_sentence_to_seq(sentence_to_seq)
"""
# TODO: Implement Function
word_ids = []
for word in sentence.split(' '):
word = word.lower()
if word not in vocab_to_int:
word_ids.append(vocab_to_int['<UNK>'])
else:
word_ids.append(vocab_to_int[word])
return word_ids
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_sentence_to_seq(sentence_to_seq)
# ## 翻译
#
# 将 `translate_sentence` 从英语翻译成法语。
# In[20]:
translate_sentence = 'he saw a old yellow truck .'
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
translate_sentence = sentence_to_seq(translate_sentence, source_vocab_to_int)
loaded_graph = tf.Graph()
with tf.Session(graph=loaded_graph) as sess:
def unit_test(self):
tests.test_sentence_to_seq(self.sentence_to_seq)
def traducir(frase):
# Number of Epochs
epochs = 10
# Batch Size
batch_size = 512
# RNN Size
rnn_size = 128
# Number of Layers
num_layers = 2
# Embedding Size
encoding_embedding_size = 128
decoding_embedding_size = 128
# Learning Rate
learning_rate = 0.001
# Dropout Keep Probability
keep_probability = 0.55
display_step = True
_, (source_vocab_to_int,
target_vocab_to_int), (source_int_to_vocab,
target_int_to_vocab) = helper.load_preprocess()
load_path = helper.load_params()
tests.test_sentence_to_seq(sentence_to_seq)
translate_sentence = frase
pIngles = translate_sentence
translate_sentence = sentence_to_seq(translate_sentence,
source_vocab_to_int)
loaded_graph = tf.Graph()
with tf.Session(graph=loaded_graph) as sess:
# Load saved model
loader = tf.train.import_meta_graph(load_path + '.meta')
loader.restore(sess, load_path)
input_data = loaded_graph.get_tensor_by_name('input:0')
logits = loaded_graph.get_tensor_by_name('predictions:0')
target_sequence_length = loaded_graph.get_tensor_by_name(
'target_sequence_length:0')
source_sequence_length = loaded_graph.get_tensor_by_name(
'source_sequence_length:0')
keep_prob = loaded_graph.get_tensor_by_name('keep_prob:0')
translate_logits = sess.run(
logits, {
input_data: [translate_sentence] * batch_size,
target_sequence_length:
[len(translate_sentence) * 2] * batch_size,
source_sequence_length: [len(translate_sentence)] * batch_size,
keep_prob: 1.0
})[0]
"""
print('Input')
print(' Word Ids: {}'.format([i for i in translate_sentence]))
print(' English Words: {}'.format([source_int_to_vocab[i] for i in translate_sentence]))
print('\nPrediction')
print(' Word Ids: {}'.format([i for i in translate_logits]))
print(' Spanish Words: {}'.format(" ".join([target_int_to_vocab[i] for i in translate_logits])))
"""
variableRetornar = format(" ".join(
[target_int_to_vocab[i] for i in translate_logits]))
return variableRetornar