我试图在 contrib 包(tf.contrib.ctc.ctc_loss)下使用 Tensorflow 的 CTC 实现,但没有成功。
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 (06/29/2016)
谢谢你,@jihyeon-seo!所以,我们在 RNN 的输入上有类似 [num_batch, max_time_step, num_features] 的东西。我们使用 dynamic_rnn 执行给定输入的循环计算,输出一个形状为 [num_batch, max_time_step, num_hidden] 的张量。之后,我们需要在每个 tilmestep 中使用权重共享进行仿射投影,因此我们必须 reshape 为 [num_batch*max_time_step, num_hidden],乘以形状为 [num_hidden, num_classes] 的权重矩阵,求和偏置撤消reshape, transpose(所以我们将有 [max_time_steps, num_batch, num_classes] 用于 ctc loss 输入),这个结果将是 ctc_loss 函数的输入。我做的一切正确吗?
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 published在我的代码的 GitHub 部分,使用一个话语。随意使用! :)
最佳答案
我正在尝试做同样的事情。
以下是我发现您可能感兴趣的内容。
很难找到 CTC 的教程,但是 this example was helpful 。
而对于空白标签 CTC layer assumes that the blank index is num_classes - 1
,您需要为空白标签提供一个额外的类。
此外,CTC 网络执行 softmax 层。在您的代码中,RNN 层连接到 CTC 损失层。 RNN层的输出是内部激活的,所以你需要再添加一个没有激活功能的隐藏层(可能是输出层),然后添加CTC损失层。
关于tensorflow - 使用 Tensorflow 的 Connectionist 时间分类 (CTC) 实现,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/38059247/