使用Tensorflow的连接主义时间分类(CTC)实现

Question

我试图在contrib包(tf.contrib.ctc.ctc_loss)下使用Tensorflow的CTC实现,但没有成功.

• 首先,任何人都知道我在哪里可以阅读一个好的分步教程？Tensorflow的文档在这个主题上非常糟糕.
• 我是否必须向ctc_loss提供交错的空白标签？
• 即使使用长度超过200个时期的火车数据集,我也无法过度使用我的网络.:(
• 如何使用tf.edit_distance计算标签错误率？

这是我的代码:

with graph.as_default():

  max_length = X_train.shape[1]
  frame_size = X_train.shape[2]
  max_target_length = y_train.shape[1]

  # Batch size x time steps x data width
  data = tf.placeholder(tf.float32, [None, max_length, frame_size])
  data_length = tf.placeholder(tf.int32, [None])

  #  Batch size x max_target_length
  target_dense = tf.placeholder(tf.int32, [None, max_target_length])
  target_length = tf.placeholder(tf.int32, [None])

  #  Generating sparse tensor representation of target
  target = ctc_label_dense_to_sparse(target_dense, target_length)

  # Applying LSTM, returning output for each timestep (y_rnn1, 
  # [batch_size, max_time, cell.output_size]) and the final state of shape
  # [batch_size, cell.state_size]
  y_rnn1, h_rnn1 = tf.nn.dynamic_rnn(
    tf.nn.rnn_cell.LSTMCell(num_hidden, state_is_tuple=True, num_proj=num_classes), #  num_proj=num_classes
    data,
    dtype=tf.float32,
    sequence_length=data_length,
  )

  #  For sequence labelling, we want a prediction for each timestamp. 
  #  However, we share the weights for the softmax layer across all timesteps. 
  #  How do we do that? By flattening the first two dimensions of the output tensor. 
  #  This way time steps look the same as examples in the batch to the weight matrix. 
  #  Afterwards, we reshape back to the desired shape


  # Reshaping
  logits = tf.transpose(y_rnn1, perm=(1, 0, 2))

  #  Get the loss by calculating ctc_loss
  #  Also calculates
  #  the gradient.  This class performs the softmax operation for you, so    inputs
  #  should be e.g. linear projections of outputs by an LSTM.
  loss = tf.reduce_mean(tf.contrib.ctc.ctc_loss(logits, target, data_length))

  #  Define our optimizer with learning rate
  optimizer = tf.train.RMSPropOptimizer(learning_rate).minimize(loss)

  #  Decoding using beam search
  decoded, log_probabilities = tf.contrib.ctc.ctc_beam_search_decoder(logits, data_length, beam_width=10, top_paths=1)

谢谢!

更新(2016年6月29日)

谢谢,@ jihyeon-seo!因此,我们输入的RNN类似于[num_batch,max_time_step,num_features].我们使用dynamic_rnn在给定输入的情况下执行循环计算,输出形状张量[num_batch,max_time_step,num_hidden].之后,我们需要在每个tilmestep中使用权重共享进行仿射投影,因此我们要重新设置为[num_batch*max_time_step,num_hidden],乘以形状[num_hidden,num_classes]的权重矩阵,求和偏差重塑,转置(所以我们将[max_time_steps,num_batch,num_classes]用于ctc丢失输入),这个结果将是ctc_loss函数的输入.我做的一切都正确吗？

这是代码:

    cell = tf.nn.rnn_cell.MultiRNNCell([cell] * num_layers, state_is_tuple=True)

    h_rnn1, self.last_state = tf.nn.dynamic_rnn(cell, self.input_data, self.sequence_length, dtype=tf.float32)

    #  Reshaping to share weights accross timesteps
    x_fc1 = tf.reshape(h_rnn1, [-1, num_hidden])

    self._logits = tf.matmul(x_fc1, self._W_fc1) + self._b_fc1

    #  Reshaping
    self._logits = tf.reshape(self._logits, [max_length, -1, num_classes])

    #  Calculating loss
    loss = tf.contrib.ctc.ctc_loss(self._logits, self._targets, self.sequence_length)

    self.cost = tf.reduce_mean(loss)

更新(07/11/2016)

谢谢@Xiv.这是修复错误后的代码:

    cell = tf.nn.rnn_cell.MultiRNNCell([cell] * num_layers, state_is_tuple=True)

    h_rnn1, self.last_state = tf.nn.dynamic_rnn(cell, self.input_data, self.sequence_length, dtype=tf.float32)

    #  Reshaping to share weights accross timesteps
    x_fc1 = tf.reshape(h_rnn1, [-1, num_hidden])

    self._logits = tf.matmul(x_fc1, self._W_fc1) + self._b_fc1

    #  Reshaping
    self._logits = tf.reshape(self._logits, [-1, max_length, num_classes])
    self._logits = tf.transpose(self._logits, (1,0,2))

    #  Calculating loss
    loss = tf.contrib.ctc.ctc_loss(self._logits, self._targets, self.sequence_length)

    self.cost = tf.reduce_mean(loss)

更新(07/25/16)

我发表在我的代码GitHub的一部分,一个话语的工作.随意使用!:)

Answer 1

请参阅此处了解双向LSTM,CTC和编辑距离实现的示例,在TIMIT语料库上训练音素识别模型.如果您在该语料库的训练集上训练,您应该能够在120个左右的时间后将音素错误率降低到20-25%.

Answer 2

我正在尝试做同样的事情。这就是我发现您可能感兴趣的内容。

真的很难找到CTC的教程，但是这个例子很有帮助。

对于空白标签，CTC层假定空白索引为num_classes - 1，因此您需要为空白标签提供其他类。

而且，CTC网络执行softmax层。在您的代码中，RNN层连接到CTC损耗层。RNN层的输出在内部被激活，因此您需要再添加一个不具有激活功能的隐藏层（可能是输出层），然后添加CTC损失层。