小编Roc*_*ngu的帖子

有没有办法将密集张量转换为稀疏张量？显然,Tensorflow的Estimator.fit不接受SparseTensors作为标签.我想将SparseTensors传递给Tensorflow的Estimator.fit的一个原因是能够使用tensorflow ctc_loss.这是代码:

import dataset_utils
import tensorflow as tf
import numpy as np

from tensorflow.contrib import grid_rnn, learn, layers, framework

def grid_rnn_fn(features, labels, mode):
    input_layer = tf.reshape(features["x"], [-1, 48, 1596])
    indices = tf.where(tf.not_equal(labels, tf.constant(0, dtype=tf.int32)))
    values = tf.gather_nd(labels, indices)
    sparse_labels = tf.SparseTensor(indices, values, dense_shape=tf.shape(labels, 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 …