title: Using Sparse Matrix in Tensorflow for Convolutional Network
body: <p>We developed a convolution network using tensorflow which works well on small datasets. However, we want to scale this as to make use of a very sparse tfidf matrix with dimension of more than 20,000. But it seems like tensorflow is not suitable for working with large dataset. Sure it has SparseTensors but its functionalities seems limited. I've tried all day to search for information on using SparseTensors for convolutional networks but can't find one. Is there a way to use functions like <code>tf.layers.dense</code> and <code>tf.tile</code> with SparseTensors? In general to build a dense layer with SparceTensors. I have these: </p>
  
  <pre><code>X = tf.layers.dense( X, dim, activation=self.activation,
                               name='{0}L{1}'.format(name, l),
                               kernel_initializer=tf.truncated_normal_initializer(stddev=.01),
                               reuse=reuse )
  </code></pre>
  
  <p>and </p>
  
  <pre><code>X = tf.tile( self.Xl, [self.L,1,1] )
  </code></pre>
  
  <p>Where <code>X</code> and <code>Xl</code> are just normal tensor objects, they work well. But when I change them to SparseTensors, It give bunch of errors.</p>