<p>我很难理解张量流<em>张量</em>和<em>稀疏张量</em>的含义和用法。</p>
<p>根据文件</p>
<p>张量</p>
<blockquote>
<p>Tensor is a typed multi-dimensional array. For example, you can represent a mini-batch of images as a 4-D array of floating point numbers with dimensions [batch, height, width, channels].</p>
</blockquote>
<p>稀疏张量</p>
<blockquote>
<p>TensorFlow represents a sparse tensor as three separate dense tensors: indices, values, and shape. In Python, the three tensors are collected into a SparseTensor class for ease of use. If you have separate indices, values, and shape tensors, wrap them in a SparseTensor object before passing to the ops below.</p>
</blockquote>
<p>我的理解是张量用于运算、输入和输出。稀疏张量只是张量(稠密)的另一种表示。希望有人能进一步解释这些差异,以及它们的用例。</p>