用LSTM網絡重複使用speechT例子

從例子https://github.com/timediv/speechT，我試圖適應與LSTM網絡一起使用，但失敗請幫忙。我嘗試了許多組合，但我總是有錯誤，即輸入必須是序列或其他。爲了語音識別的目的，我需要實現LSTM網絡，並且在嘗試了幾個星期後，我仍然陷入了編碼問題。任何人都可以幫助我提供使用LSTM網絡的例子，樣本會很好。用LSTM網絡重複使用speechT例子

class InputBatchLoader(BaseInputLoader): 
    def __init__(self, input_size, batch_size, data_generator_creator, max_steps=None): 

    super().__init__(input_size) 
    self.batch_size = batch_size 
    self.data_generator_creator = data_generator_creator 
    self.steps_left = max_steps 

    with tf.device("/cpu:0"): 


with tf.device("/cpu:0"): 
     # Define input and label placeholders 
     self.inputs = tf.placeholder(tf.float32, [batch_size, None, input_size], name='inputs') 

     self.sequence_lengths = tf.placeholder(tf.int32, [batch_size], name='sequence_lengths') 
     self.labels = tf.sparse_placeholder(tf.int32, name='labels') 

     # Queue for inputs and labels 
     self.queue = tf.FIFOQueue(dtypes=[tf.float32, tf.int32, tf.string], 
          capacity=100) 

     # queues do not support sparse tensors yet, we need to serialize... 
     serialized_labels = tf.serialize_many_sparse(self.labels) 

     self.enqueue_op = self.queue.enqueue([self.inputs, 
             self.sequence_lengths, 
             serialized_labels]) 

class Wav2LetterLSTMModel(SpeechModel): #Add Sep 14, 2017 to create LSTM model 

    def __init__(self, input_loader: BaseInputLoader, input_size: int, num_classes: int): 
super().__init__(input_loader, input_size, num_classes) 

    def _create_network(self, num_classes): 

cellsize = 256 
num_layers = 3 

inputs = self.inputs 
lstm_cell = rnn.BasicLSTMCell(cellsize, forget_bias=1.0) 
outputs, states = tf.nn.dynamic_rnn(lstm_cell, inputs, dtype=tf.float32) 

return tf.transpose(outputs, (1, 0, 2)) 

def create_default_model(flags, input_size: int, speech_input: BaseInputLoader) -> SpeechModel: 
    model = Wav2LetterLSTMModel(input_loader=speech_input, 
         input_size=input_size, 
         num_classes=speecht.vocabulary.SIZE + 1) #Add Sep 14, 2017, to use LSTM model 

    # TODO how can we restore only selected variables so we do not need to always create the full network? 
    if flags.command == 'train': 
    model.add_training_ops(learning_rate=flags.learning_rate, 

learning_rate_decay_factor=flags.learning_rate_decay_factor, 
         max_gradient_norm=flags.max_gradient_norm, 
         momentum=flags.momentum) 
model.add_decoding_ops() 
elif flags.command == 'export': 
model.add_training_ops() 
model.add_decoding_ops() 
    else: 
model.add_training_ops() 
model.add_decoding_ops(language_model=flags.language_model, 
         lm_weight=flags.lm_weight, 
         word_count_weight=flags.word_count_weight, 
         valid_word_count_weight=flags.valid_word_count_weight) 

    model.finalize(log_dir=flags.log_dir, 
      run_name=flags.run_name, 
      run_type=flags.run_type) 

    return model

來源

2017-09-23 Jirayu Kaewprateep

其實，我們如何養活self.inputs = tf.placeholder（tf.float32，[batch_size時，無，input_size]，名稱= '輸入'）到輸出，states = tf.nn.dynamic_rnn（lstm_cell，inputs，dtype = tf.float32） –

最後我用這個

XT = tf.transpose(inputs, [1, 0, 2]) 
XR = tf.reshape(XT, [-1, self.input_size]) 
X_split = tf.split(XR, cellsize, 0) 

lstm = rnn.BasicLSTMCell(cellsize, forget_bias=1.0, state_is_tuple=True) 
outputs, _states = rnn.static_rnn(lstm, X_split, dtype=tf.float32)

來源

2017-09-24 18:26:54

用LSTM網絡重複使用speechT例子

回答

相關問題