我已使用此代码成功将Tensor转换为SparseTensor：
def dense_to_sparse(dense_tensor, out_type):
indices = tf.where(tf.not_equal(dense_tensor, tf.constant(0, dense_tensor.dtype)
values = tf.gather_nd(dense_tensor, indices)
shape = tf.shape(dense_tensor, out_type=out_type)
return tf.SparseTensor(indices, values, shape)
我想尝试使用从密集的转换的SparseTensor：
input_layer = tf.placeholder(tf.float32, [None, 1596, 48])
dense_labels = tf.placeholder(tf.int32)
sparse_from_dense = dense_to_sparse(dense_lables, out_type=tf.int64)
cell_fw = grid_rnn.Grid2LSTMCell(num_units=128)
cell_bw = grid_rnn.Grid2LSTMCell(num_units=128)
bidirectional_grid_rnn = tf.nn.bidirectional_dynamic_rnn(cell_fw, cell_bw, input_layer, dtype=tf.float32)
outputs = tf.reshape(bidirectional_grid_rnn[0], [-1, 256])
W = tf.Variable(tf.truncated_normal([256, 80], stddev=0.1, dtype=tf.float32), name='W')
b = tf.Variable(tf.constant(0., dtype=tf.float32, shape=[80], name='b'))
logits = tf.matmul(outputs, W) + b
logits = tf.reshape(logits, [tf.shape(input_layer)[0], -1, 80])
logits = tf.transpose(logits, (1, 0, 2))
loss = tf.nn.ctc_loss(inputs=logits, labels=sparse, sequence_length=320)
不幸的是，当我这样做时，我遇到了这个错误：
Shape must be rank 1 but is rank 0 for 'CTCLoss' (op: 'CTCLoss') with input shapes: [?,?,80], [?,1], [?], [].
如何解决此错误？
答案 0 :(得分：1)
来自Tensorflow文档https://www.tensorflow.org/versions/r0.12/api_docs/python/nn/connectionist_temporal_classification__ctc_#ctc_loss
sequence_length：1-D int32 vector，size [batch_size]。序列长度。
所以你需要传递一个长度为batch_size而不是整数的数组/向量。