def predict(sentence):
checkpoint_dir = 'models'
seq2seqModel.checkpoint.restore(tf.train.latest_checkpoint(checkpoint_dir))
sentence = preprocess_sentence(sentence)
inputs = [
input_word_index.get(i, input_word_index['unk'])
for i in jieba.lcut(sentence)
]
inputs = tf.keras.preprocessing.sequence.pad_sequences(
[inputs], maxlen=max_length_inp, padding='post')
inputs = tf.convert_to_tensor(inputs)
result = ''
hidden = [tf.zeros((1, units))]
enc_out, enc_hidden = seq2seqModel.encoder(inputs, hidden)
dec_hidden = enc_hidden
dec_input = tf.expand_dims([target_word_index['start']], 0)
for t in range(max_length_tar):
predictions, dec_hidden, attention_weights = seq2seqModel.decoder(
dec_input, dec_hidden, enc_out)
predicted_id = tf.argmax(predictions[0]).numpy()
if target_index_word[predicted_id] == 'end':
break
result += target_index_word[predicted_id]
dec_input = tf.expand_dims([predicted_id], 0)
return result
def predict(sentence):
sentence = preprocess_sentence(sentence)
inputs = [input_lang.word_index.get(i, 3) for i in sentence.split(' ')]
inputs = tf.keras.preprocessing.sequence.pad_sequences(
[inputs], maxlen=max_length_input, padding='post')
inputs = tf.convert_to_tensor(inputs)
result = ''
hidden = [tf.zeros((1, units))]
enc_out, enc_hidden = seq2seqModel.encoder(inputs, hidden)
dec_hidden = enc_hidden
dec_input = tf.expand_dims([target_lang.word_index['start']], 0)
for t in range(max_length_tar):
predictions, dec_hidden, attention_weights = seq2seqModel.decoder(
dec_input, dec_hidden, enc_out)
predicted_id = tf.argmax(predictions[0]).numpy()
if target_lang.index_word[predicted_id] == 'end':
break
result += target_lang.index_word[predicted_id] + ' '
dec_input = tf.expand_dims([predicted_id], 0)
return result
def predict(sentence):
checkpoint_dir = gConfig['model_data']
# seq2seqModel.checkpoint.restore(tf.train.latest_checkpoint(checkpoint_dir))
manager = tf.train.CheckpointManager(seq2seqModel.checkpoint,
directory=checkpoint_dir,
max_to_keep=5)
seq2seqModel.checkpoint.restore(manager.latest_checkpoint)
sentence = preprocess_sentence(sentence)
inputs = [input_token.word_index.get(i, 3) for i in sentence.split(' ')]
inputs = tf.keras.preprocessing.sequence.pad_sequences(
[inputs], maxlen=max_length_inp, padding='post')
inputs = tf.convert_to_tensor(inputs)
result = ''
hidden = [tf.zeros((1, units))]
enc_out, enc_hidden = seq2seqModel.encoder(inputs, hidden)
dec_hidden = enc_hidden
dec_input = tf.expand_dims([target_token.word_index['start']], 0)
for t in range(max_length_tar):
predictions, dec_hidden, attention_weights = seq2seqModel.decoder(
dec_input, dec_hidden, enc_out)
predicted_id = tf.argmax(predictions[0]).numpy()
if target_token.index_word[predicted_id] == 'end':
break
val = target_token.index_word[predicted_id]
if isinstance(val, int):
val = str(val)
result += val
dec_input = tf.expand_dims([predicted_id], 0)
return result