我设法通过使用tf.dynnamic_partition而不是tf.gather解决了这个问题。我这样替换了上面的代码：
# Flatten batch elements to rank-2 tensor where 1st max_length rows belong to first batch element and so forthall_timesteps = tf.reshape(raw_output, [-1, n_dim]) # (batch_size*max_length, n_dim)# Indices to last element of each sequence.# Index to first element is the sequence order number times max sequence length.# Index to last element is the index to first element plus sequence length.row_inds = tf.range(0, batch_size) * max_length + (seq_len - 1)# Creating a vector of 0s and 1s that will specify what timesteps to choose.partitions = tf.reduce_sum(tf.one_hot(row_inds, tf.shape(all_timesteps)[0], dtype='int32'), 0)# Selecting the elements we want to choose.last_timesteps = tf.dynamic_partition(all_timesteps, partitions, 2) # (batch_size, n_dim)last_timesteps = last_timesteps[1]
