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+
+ <div class="section" id="tensor">
+<h1>Tensor<a class="headerlink" href="#tensor" title="Permalink to this headline">¶</a></h1>
+<p>Each Tensor instance is a multi-dimensional array allocated on a specific
+Device instance. Tensor instances store variables and provide
+linear algebra operations over different types of hardware devices without user
+awareness. Note that users need to make sure the tensor operands are
+allocated on the same device except copy functions.</p>
+<div class="section" id="tensor-implementation">
+<h2>Tensor implementation<a class="headerlink" href="#tensor-implementation" title="Permalink to this headline">¶</a></h2>
+<p>SINGA has three different sets of implmentations of Tensor functions, one for each
+type of Device.</p>
+<ul class="simple">
+<li><p>âtensor_math_cpp.hâ implements operations using Cpp (with CBLAS) for CppGPU devices.</p></li>
+<li><p>âtensor_math_cuda.hâ implements operations using Cuda (with cuBLAS) for CudaGPU devices.</p></li>
+<li><p>âtensor_math_opencl.hâ implements operations using OpenCL for OpenclGPU devices.</p></li>
+</ul>
+</div>
+<div class="section" id="module-singa.tensor">
+<span id="python-api"></span><h2>Python API<a class="headerlink" href="#module-singa.tensor" title="Permalink to this headline">¶</a></h2>
+<p>Example usage:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">singa</span> <span class="k">import</span> <span class="n">tensor</span>
+<span class="kn">from</span> <span class="nn">singa</span> <span class="k">import</span> <span class="n">device</span>
+
+<span class="c1"># create a tensor with shape (2,3), default CppCPU device and float32</span>
+<span class="n">x</span> <span class="o">=</span> <span class="n">tensor</span><span class="o">.</span><span class="n">Tensor</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
+<span class="n">x</span><span class="o">.</span><span class="n">set_value</span><span class="p">(</span><span class="mf">0.4</span><span class="p">)</span>
+
+<span class="c1"># create a tensor from a numpy array</span>
+<span class="n">npy</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+<span class="n">y</span> <span class="o">=</span> <span class="n">tensor</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">npy</span><span class="p">)</span>
+
+<span class="n">y</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span> <span class="c1"># sample values from the uniform distribution</span>
+
+<span class="n">z</span> <span class="o">=</span> <span class="n">tensor</span><span class="o">.</span><span class="n">mult</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span> <span class="c1"># gemm -> z of shape (2, 3)</span>
+
+<span class="n">x</span> <span class="o">+=</span> <span class="n">z</span> <span class="c1"># element-wise addition</span>
+
+<span class="n">dev</span> <span class="o">=</span> <span class="n">device</span><span class="o">.</span><span class="n">get_default_device</span><span class="p">()</span>
+<span class="n">x</span><span class="o">.</span><span class="n">to_device</span><span class="p">(</span><span class="n">dev</span><span class="p">)</span> <span class="c1"># move the data to a gpu device</span>
+
+<span class="n">r</span> <span class="o">=</span> <span class="n">tensor</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+
+<span class="n">s</span> <span class="o">=</span> <span class="n">tensor</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">(</span><span class="n">r</span><span class="p">)</span> <span class="c1"># tensor -> numpy array</span>
+</pre></div>
+</div>
+<p>There are two sets of tensor functions,</p>
+<dl class="simple">
+<dt>Tensor member functions</dt><dd><p>which would change the internal state of the Tensor instance.</p>
+</dd>
+<dt>Tensor module functions</dt><dd><p>which accept Tensor instances as arguments and return Tensor instances.</p>
+</dd>
+</dl>
+<p>Every Tesor instance must be initialized before reading data from it.</p>
+<dl class="class">
+<dt id="singa.tensor.AddBias">
+<em class="property">class </em><code class="descclassname">singa.tensor.</code><code class="descname">AddBias</code><span class="sig-paren">(</span><em>axis=0</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.tensor.AddBias" title="Permalink to this definition">¶</a></dt>
+<dd><p>Add Bias to each row / column of the Tensor, depending on the parameter axis.</p>
+<dl class="method">
+<dt id="singa.tensor.AddBias.backward">
+<code class="descname">backward</code><span class="sig-paren">(</span><em>dy</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.tensor.AddBias.backward" title="Permalink to this definition">¶</a></dt>
+<dd><dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>dy</strong> (<em>CTensor</em>) â data for the dL / dy, L is the loss.</p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a tuple for (db, dx), db is data for dL / db, dx is data
+for dL / dx.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="method">
+<dt id="singa.tensor.AddBias.forward">
+<code class="descname">forward</code><span class="sig-paren">(</span><em>x</em>, <em>b</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.tensor.AddBias.forward" title="Permalink to this definition">¶</a></dt>
+<dd><dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> â matrix.</p></li>
+<li><p><strong>b</strong> â bias to be added.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>the result Tensor</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="class">
+<dt id="singa.tensor.CrossEntropy">
+<em class="property">class </em><code class="descclassname">singa.tensor.</code><code class="descname">CrossEntropy</code><a class="headerlink" href="#singa.tensor.CrossEntropy" title="Permalink to this definition">¶</a></dt>
+<dd><p>Calculte CrossEntropy loss for a batch of training data.</p>
+<dl class="method">
+<dt id="singa.tensor.CrossEntropy.backward">
+<code class="descname">backward</code><span class="sig-paren">(</span><em>dy=1.0</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.tensor.CrossEntropy.backward" title="Permalink to this definition">¶</a></dt>
+<dd><dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>dy</strong> (<em>float</em><em> or </em><em>CTensor</em>) â scalar, accumulate gradient from outside of current network, usually</p></li>
+<li><p><strong>to 1.0</strong> (<em>equal</em>) â </p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>data for the dL /dx, L is the loss, x is the output of current network.
+note that this is true for dy = 1.0</p>
+</dd>
+<dt class="field-odd">Return type</dt>
+<dd class="field-odd"><p>dx (CTensor)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="method">
+<dt id="singa.tensor.CrossEntropy.forward">
+<code class="descname">forward</code><span class="sig-paren">(</span><em>x</em>, <em>t</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.tensor.CrossEntropy.forward" title="Permalink to this definition">¶</a></dt>
+<dd><dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<em>CTensor</em>) â 1d or 2d tensor, the prediction data(output) of current network.</p></li>
+<li><p><strong>t</strong> (<em>CTensor</em>) â 1d or 2d tensor, the target data for training.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>scalar.</p>
+</dd>
+<dt class="field-odd">Return type</dt>
+<dd class="field-odd"><p>loss (CTensor)</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="class">
+<dt id="singa.tensor.Dummy">
+<em class="property">class </em><code class="descclassname">singa.tensor.</code><code class="descname">Dummy</code><span class="sig-paren">(</span><em>tensor</em>, <em>name=None</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.tensor.Dummy" title="Permalink to this definition">¶</a></dt>
+<dd><p>Dummy operation whice serves as a placehoder for autograd</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>name</strong> (<em>string</em>) â set it for debug</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="class">
+<dt id="singa.tensor.Matmul">
+<em class="property">class </em><code class="descclassname">singa.tensor.</code><code class="descname">Matmul</code><a class="headerlink" href="#singa.tensor.Matmul" title="Permalink to this definition">¶</a></dt>
+<dd><p>For matrix multiplication</p>
+<dl class="method">
+<dt id="singa.tensor.Matmul.backward">
+<code class="descname">backward</code><span class="sig-paren">(</span><em>dy</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.tensor.Matmul.backward" title="Permalink to this definition">¶</a></dt>
+<dd><dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>dy</strong> (<em>CTensor</em>) â data for the dL / dy, L is the loss</p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a tuple for (dx, dw)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="method">
+<dt id="singa.tensor.Matmul.forward">
+<code class="descname">forward</code><span class="sig-paren">(</span><em>x</em>, <em>w</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.tensor.Matmul.forward" title="Permalink to this definition">¶</a></dt>
+<dd><p>Do forward propgation.</p>
+<p>Store the x(or w) if w(or x) requires gradient.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>x</strong> (<em>CTensor</em>) â matrix</p></li>
+<li><p><strong>w</strong> (<em>CTensor</em>) â matrix</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a CTensor for the result</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="class">
+<dt id="singa.tensor.Operation">
+<em class="property">class </em><code class="descclassname">singa.tensor.</code><code class="descname">Operation</code><a class="headerlink" href="#singa.tensor.Operation" title="Permalink to this definition">¶</a></dt>
+<dd><p>An operation includes the forward and backward function of
+tensor calculation.</p>
+<p>To add a specific operation Xxxx, subclass Operation and implement
+forward() and backward(). Then implement a function xxxx which creates
+a Xxxx instance and calls __call__ to do forward. The autograd engine
+is able to do backward propagation by calling the backward() of Xxxx
+automatically. Notice that the tensors are CTensor. NOT Python Tensor.
+The arguments of forward() and backward() should only include CTensor args;</p>
+<dl class="method">
+<dt id="singa.tensor.Operation.backward">
+<code class="descname">backward</code><span class="sig-paren">(</span><em>*dys</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.tensor.Operation.backward" title="Permalink to this definition">¶</a></dt>
+<dd><p>Backward propagation.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>dys</strong> â input args consisting of only CTensors.</p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>CTensor instance(s)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="method">
+<dt id="singa.tensor.Operation.forward">
+<code class="descname">forward</code><span class="sig-paren">(</span><em>*xs</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.tensor.Operation.forward" title="Permalink to this definition">¶</a></dt>
+<dd><p>Forward propagation.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>xs</strong> â input args consisting of only CTensors.</p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>CTensor instance(s)</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="class">
+<dt id="singa.tensor.ReLU">
+<em class="property">class </em><code class="descclassname">singa.tensor.</code><code class="descname">ReLU</code><a class="headerlink" href="#singa.tensor.ReLU" title="Permalink to this definition">¶</a></dt>
+<dd><dl class="method">
+<dt id="singa.tensor.ReLU.backward">
+<code class="descname">backward</code><span class="sig-paren">(</span><em>dy</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.tensor.ReLU.backward" title="Permalink to this definition">¶</a></dt>
+<dd><dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>dy</strong> (<em>CTensor</em>) â dL / dy</p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>dL / dx = dy if x >= 0; otherwise 0;</p>
+</dd>
+<dt class="field-odd">Return type</dt>
+<dd class="field-odd"><p>dx(CTensor)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="method">
+<dt id="singa.tensor.ReLU.forward">
+<code class="descname">forward</code><span class="sig-paren">(</span><em>x</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.tensor.ReLU.forward" title="Permalink to this definition">¶</a></dt>
+<dd><dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>x</strong> (<em>CTensor</em>) â input tensor</p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a new CTensor whose element y = x if x >= 0; otherwise 0;</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="class">
+<dt id="singa.tensor.SoftMax">
+<em class="property">class </em><code class="descclassname">singa.tensor.</code><code class="descname">SoftMax</code><span class="sig-paren">(</span><em>axis=0</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.tensor.SoftMax" title="Permalink to this definition">¶</a></dt>
+<dd><p>Apply SoftMax for each row of the Tensor or each column of the Tensor
+according to the parameter axis.</p>
+<dl class="method">
+<dt id="singa.tensor.SoftMax.backward">
+<code class="descname">backward</code><span class="sig-paren">(</span><em>dy</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.tensor.SoftMax.backward" title="Permalink to this definition">¶</a></dt>
+<dd><dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>dy</strong> (<em>CTensor</em>) â data for the dL / dy, L is the loss</p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>data for the dL / dx, L is the loss,
+x is the input of current Opertion</p>
+</dd>
+<dt class="field-odd">Return type</dt>
+<dd class="field-odd"><p>dx (Ctensor)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="method">
+<dt id="singa.tensor.SoftMax.forward">
+<code class="descname">forward</code><span class="sig-paren">(</span><em>x</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.tensor.SoftMax.forward" title="Permalink to this definition">¶</a></dt>
+<dd><dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>x</strong> (<em>data</em>) â the input 1d or 2d tensor</p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>the result Tensor</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="class">
+<dt id="singa.tensor.Tensor">
+<em class="property">class </em><code class="descclassname">singa.tensor.</code><code class="descname">Tensor</code><span class="sig-paren">(</span><em>shape=()</em>, <em>device=None</em>, <em>dtype=0</em>, <em>data=None</em>, <em>requires_grad=True</em>, <em>stores_grad=False</em>, <em>creator=None</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.tensor.Tensor" title="Permalink to this definition">¶</a></dt>
+<dd><p>Python Tensor, which wraps a swig converted Tensor from CPP Tensor.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>shape</strong> (<em>tuple<int></em>) â a tuple of integers for the tensor shape. If shape
+is not specified, the created tensor is called a dummy tensor.</p></li>
+<li><p><strong>device</strong> â a swig device. If None, the default host device is used.</p></li>
+<li><p><strong>dtype</strong> â data type. currently, most operations only accept float32.</p></li>
+<li><p><strong>data</strong> â a numpy array or swig tensor.</p></li>
+<li><p><strong>requires_grad</strong> â boolean indicator for computing the gradient.</p></li>
+<li><p><strong>stores_grad</strong> â boolean indicator for storing and returning the gradient.
+Some intermediate tensorsâ gradient can be released
+during the backward propagation. A tensor may require
+grad but not store grad; But if a tensor stores grad
+then it must require grad.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="method">
+<dt id="singa.tensor.Tensor.T">
+<code class="descname">T</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#singa.tensor.Tensor.T" title="Permalink to this definition">¶</a></dt>
+<dd><p>shallow copy, negate the transpose field.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>a new Tensor which shares the underlying data memory (shallow copy)
+but is marked as a transposed version of this tensor.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="method">
+<dt id="singa.tensor.Tensor.add_column">
+<code class="descname">add_column</code><span class="sig-paren">(</span><em>v</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.tensor.Tensor.add_column" title="Permalink to this definition">¶</a></dt>
+<dd><p>Add a tensor to each column of this tensor.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>v</strong> (<a class="reference internal" href="#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â a Tensor to be added as a column to this tensor.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="method">
+<dt id="singa.tensor.Tensor.add_row">
+<code class="descname">add_row</code><span class="sig-paren">(</span><em>v</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.tensor.Tensor.add_row" title="Permalink to this definition">¶</a></dt>
+<dd><p>Add a tensor to each row of this tensor.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>v</strong> (<a class="reference internal" href="#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â a Tensor to be added as a row to this tensor.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="method">
+<dt id="singa.tensor.Tensor.bernoulli">
+<code class="descname">bernoulli</code><span class="sig-paren">(</span><em>p</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.tensor.Tensor.bernoulli" title="Permalink to this definition">¶</a></dt>
+<dd><p>Sample 0/1 for each element according to the given probability.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>p</strong> (<em>float</em>) â with probability p, each element is sample to 1.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="method">
+<dt id="singa.tensor.Tensor.clone">
+<code class="descname">clone</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#singa.tensor.Tensor.clone" title="Permalink to this definition">¶</a></dt>
+<dd><dl class="field-list simple">
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>a new Tensor which does deep copy of this tensor</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="method">
+<dt id="singa.tensor.Tensor.copy">
+<code class="descname">copy</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#singa.tensor.Tensor.copy" title="Permalink to this definition">¶</a></dt>
+<dd><p>shallow copy calls copy constructor of singa::Tensor</p>
+</dd></dl>
+
+<dl class="method">
+<dt id="singa.tensor.Tensor.copy_data">
+<code class="descname">copy_data</code><span class="sig-paren">(</span><em>t</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.tensor.Tensor.copy_data" title="Permalink to this definition">¶</a></dt>
+<dd><p>Copy data from other Tensor instance.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>t</strong> (<a class="reference internal" href="#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â source Tensor.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="method">
+<dt id="singa.tensor.Tensor.copy_from_numpy">
+<code class="descname">copy_from_numpy</code><span class="sig-paren">(</span><em>np_array</em>, <em>offset=0</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.tensor.Tensor.copy_from_numpy" title="Permalink to this definition">¶</a></dt>
+<dd><p>Copy the data from the numpy array.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>np_array</strong> â source numpy array</p></li>
+<li><p><strong>offset</strong> (<em>int</em>) â destination offset</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="method">
+<dt id="singa.tensor.Tensor.deepcopy">
+<code class="descname">deepcopy</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#singa.tensor.Tensor.deepcopy" title="Permalink to this definition">¶</a></dt>
+<dd><p>Same as clone().</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>a new Tensor</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="method">
+<dt id="singa.tensor.Tensor.div_column">
+<code class="descname">div_column</code><span class="sig-paren">(</span><em>v</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.tensor.Tensor.div_column" title="Permalink to this definition">¶</a></dt>
+<dd><p>Divide each column of this tensor by v.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>v</strong> (<a class="reference internal" href="#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â 1d tensor of the same length the column of self.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="method">
+<dt id="singa.tensor.Tensor.div_row">
+<code class="descname">div_row</code><span class="sig-paren">(</span><em>v</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.tensor.Tensor.div_row" title="Permalink to this definition">¶</a></dt>
+<dd><p>Divide each row of this tensor by v.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>v</strong> (<a class="reference internal" href="#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â 1d tensor of the same length the row of self.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="method">
+<dt id="singa.tensor.Tensor.gaussian">
+<code class="descname">gaussian</code><span class="sig-paren">(</span><em>mean</em>, <em>std</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.tensor.Tensor.gaussian" title="Permalink to this definition">¶</a></dt>
+<dd><p>Generate a value for each element following a Gaussian distribution.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>mean</strong> (<em>float</em>) â mean of the distribution</p></li>
+<li><p><strong>std</strong> (<em>float</em>) â standard variance of the distribution</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="method">
+<dt id="singa.tensor.Tensor.is_empty">
+<code class="descname">is_empty</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#singa.tensor.Tensor.is_empty" title="Permalink to this definition">¶</a></dt>
+<dd><dl class="field-list simple">
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>True if the tensor is empty according to its shape</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="method">
+<dt id="singa.tensor.Tensor.is_transpose">
+<code class="descname">is_transpose</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#singa.tensor.Tensor.is_transpose" title="Permalink to this definition">¶</a></dt>
+<dd><dl class="field-list simple">
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>True if the internal data is transposed; otherwise False.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="method">
+<dt id="singa.tensor.Tensor.l1">
+<code class="descname">l1</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#singa.tensor.Tensor.l1" title="Permalink to this definition">¶</a></dt>
+<dd><dl class="field-list simple">
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>the L1 norm.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="method">
+<dt id="singa.tensor.Tensor.l2">
+<code class="descname">l2</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#singa.tensor.Tensor.l2" title="Permalink to this definition">¶</a></dt>
+<dd><dl class="field-list simple">
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>the L2 norm.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="method">
+<dt id="singa.tensor.Tensor.memsize">
+<code class="descname">memsize</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#singa.tensor.Tensor.memsize" title="Permalink to this definition">¶</a></dt>
+<dd><dl class="field-list simple">
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>the number of Bytes allocated for this tensor.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="method">
+<dt id="singa.tensor.Tensor.mult_column">
+<code class="descname">mult_column</code><span class="sig-paren">(</span><em>v</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.tensor.Tensor.mult_column" title="Permalink to this definition">¶</a></dt>
+<dd><p>Multiply each column of this tensor by v element-wisely.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>v</strong> (<a class="reference internal" href="#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â 1d tensor of the same length the column of self.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="method">
+<dt id="singa.tensor.Tensor.mult_row">
+<code class="descname">mult_row</code><span class="sig-paren">(</span><em>v</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.tensor.Tensor.mult_row" title="Permalink to this definition">¶</a></dt>
+<dd><p>Multiply each row of this tensor by v element-wisely.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>v</strong> (<a class="reference internal" href="#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â 1d tensor of the same length the row of self.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="method">
+<dt id="singa.tensor.Tensor.ndim">
+<code class="descname">ndim</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#singa.tensor.Tensor.ndim" title="Permalink to this definition">¶</a></dt>
+<dd><dl class="field-list simple">
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>the number of dimensions of the tensor.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="method">
+<dt id="singa.tensor.Tensor.reset_like">
+<code class="descname">reset_like</code><span class="sig-paren">(</span><em>t</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.tensor.Tensor.reset_like" title="Permalink to this definition">¶</a></dt>
+<dd><p>Reset the shape, dtype and device as the given tensor.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>t</strong> (<a class="reference internal" href="#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â </p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="method">
+<dt id="singa.tensor.Tensor.reshape">
+<code class="descname">reshape</code><span class="sig-paren">(</span><em>shape</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.tensor.Tensor.reshape" title="Permalink to this definition">¶</a></dt>
+<dd><p>Change the tensor shape.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>shape</strong> (<em>list<int></em>) â new shape, which should have the same volumn as
+the original shape.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="method">
+<dt id="singa.tensor.Tensor.set_value">
+<code class="descname">set_value</code><span class="sig-paren">(</span><em>x</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.tensor.Tensor.set_value" title="Permalink to this definition">¶</a></dt>
+<dd><p>Set all elements of the tensor to be the give value.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>x</strong> (<em>float</em>) â </p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="method">
+<dt id="singa.tensor.Tensor.size">
+<code class="descname">size</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#singa.tensor.Tensor.size" title="Permalink to this definition">¶</a></dt>
+<dd><dl class="field-list simple">
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>the number of elements of the tensor.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="method">
+<dt id="singa.tensor.Tensor.to_device">
+<code class="descname">to_device</code><span class="sig-paren">(</span><em>device</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.tensor.Tensor.to_device" title="Permalink to this definition">¶</a></dt>
+<dd><p>Move the tensor data onto a given device.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>device</strong> â a swig Device converted from CudaGPU or CppCPU or OpenclGPU</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="method">
+<dt id="singa.tensor.Tensor.to_host">
+<code class="descname">to_host</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#singa.tensor.Tensor.to_host" title="Permalink to this definition">¶</a></dt>
+<dd><p>Move the tensor data onto the default host CppCPU device.</p>
+</dd></dl>
+
+<dl class="method">
+<dt id="singa.tensor.Tensor.uniform">
+<code class="descname">uniform</code><span class="sig-paren">(</span><em>low</em>, <em>high</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.tensor.Tensor.uniform" title="Permalink to this definition">¶</a></dt>
+<dd><p>Generate a value for each element following a uniform distribution.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>low</strong> (<em>float</em>) â the lower bound</p></li>
+<li><p><strong>high</strong> (<em>float</em>) â the hight bound</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="function">
+<dt id="singa.tensor.abs">
+<code class="descclassname">singa.tensor.</code><code class="descname">abs</code><span class="sig-paren">(</span><em>t</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.tensor.abs" title="Permalink to this definition">¶</a></dt>
+<dd><dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>t</strong> (<a class="reference internal" href="#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â input Tensor</p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a new Tensor whose element y = abs(x), x is an element of t</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="function">
+<dt id="singa.tensor.add">
+<code class="descclassname">singa.tensor.</code><code class="descname">add</code><span class="sig-paren">(</span><em>lhs</em>, <em>rhs</em>, <em>ret=None</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.tensor.add" title="Permalink to this definition">¶</a></dt>
+<dd><p>Elementi-wise addition.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>lhs</strong> (<a class="reference internal" href="#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â </p></li>
+<li><p><strong>rhs</strong> (<a class="reference internal" href="#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â </p></li>
+<li><p><strong>ret</strong> (<a class="reference internal" href="#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a><em>, </em><em>optional</em>) â if not None, the result is stored in it;
+otherwise, a new Tensor would be created for the result.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>the result Tensor</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="function">
+<dt id="singa.tensor.add_column">
+<code class="descclassname">singa.tensor.</code><code class="descname">add_column</code><span class="sig-paren">(</span><em>alpha</em>, <em>v</em>, <em>beta</em>, <em>M</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.tensor.add_column" title="Permalink to this definition">¶</a></dt>
+<dd><p>Add v to each column of M.</p>
+<p>Denote each column of M as m, m = alpha * v + beta * m</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>alpha</strong> (<em>float</em>) â </p></li>
+<li><p><strong>v</strong> (<a class="reference internal" href="#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â </p></li>
+<li><p><strong>beta</strong> (<em>float</em>) â </p></li>
+<li><p><strong>M</strong> (<a class="reference internal" href="#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â 2d tensor</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>M</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="function">
+<dt id="singa.tensor.add_row">
+<code class="descclassname">singa.tensor.</code><code class="descname">add_row</code><span class="sig-paren">(</span><em>alpha</em>, <em>v</em>, <em>beta</em>, <em>M</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.tensor.add_row" title="Permalink to this definition">¶</a></dt>
+<dd><p>Add v to each row of M.</p>
+<p>Denote each row of M as m, m = alpha * v + beta * m</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>alpha</strong> (<em>float</em>) â </p></li>
+<li><p><strong>v</strong> (<a class="reference internal" href="#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â </p></li>
+<li><p><strong>beta</strong> (<em>float</em>) â </p></li>
+<li><p><strong>M</strong> (<a class="reference internal" href="#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â 2d tensor</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>M</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="function">
+<dt id="singa.tensor.average">
+<code class="descclassname">singa.tensor.</code><code class="descname">average</code><span class="sig-paren">(</span><em>t</em>, <em>axis=None</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.tensor.average" title="Permalink to this definition">¶</a></dt>
+<dd><dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>t</strong> (<a class="reference internal" href="#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â input Tensor</p></li>
+<li><p><strong>axis</strong> (<em>int</em><em>, </em><em>optional</em>) â if None, average all elements; otherwise average
+along the given dimension. 0 for averaging each column; 1 for
+averaging each row.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a float value if axis is None; otherwise, a new Tensor for the result.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="function">
+<dt id="singa.tensor.axpy">
+<code class="descclassname">singa.tensor.</code><code class="descname">axpy</code><span class="sig-paren">(</span><em>alpha</em>, <em>x</em>, <em>y</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.tensor.axpy" title="Permalink to this definition">¶</a></dt>
+<dd><p>Element-wise operation for y += alpha * x.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>alpha</strong> (<em>float</em>) â </p></li>
+<li><p><strong>x</strong> (<a class="reference internal" href="#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â </p></li>
+<li><p><strong>y</strong> (<a class="reference internal" href="#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â </p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>y</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="function">
+<dt id="singa.tensor.bernoulli">
+<code class="descclassname">singa.tensor.</code><code class="descname">bernoulli</code><span class="sig-paren">(</span><em>p</em>, <em>t</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.tensor.bernoulli" title="Permalink to this definition">¶</a></dt>
+<dd><p>Generate a binary value for each element of t.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>p</strong> (<em>float</em>) â each element is 1 with probability p; and 0 with 1 - p</p></li>
+<li><p><strong>t</strong> (<a class="reference internal" href="#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â the results are put into t</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>t</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="function">
+<dt id="singa.tensor.copy_data_to_from">
+<code class="descclassname">singa.tensor.</code><code class="descname">copy_data_to_from</code><span class="sig-paren">(</span><em>dst</em>, <em>src</em>, <em>size</em>, <em>dst_offset=0</em>, <em>src_offset=0</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.tensor.copy_data_to_from" title="Permalink to this definition">¶</a></dt>
+<dd><p>Copy the data between two Tensor instances which could be on different
+devices.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>dst</strong> (<a class="reference internal" href="#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â destination Tensor</p></li>
+<li><p><strong>src</strong> (<a class="reference internal" href="#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â source Tensor</p></li>
+<li><p><strong>size</strong> (<em>int</em>) â number of elements to copy</p></li>
+<li><p><strong>dst_offset</strong> (<em>int</em>) â offset in terms of elements to the start of dst</p></li>
+<li><p><strong>src_offset</strong> (<em>int</em>) â offset in terms of elements to the start of src</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="function">
+<dt id="singa.tensor.copy_from_numpy">
+<code class="descclassname">singa.tensor.</code><code class="descname">copy_from_numpy</code><span class="sig-paren">(</span><em>data</em>, <em>np_array</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.tensor.copy_from_numpy" title="Permalink to this definition">¶</a></dt>
+<dd><p>Copy the data from the numpy array.</p>
+</dd></dl>
+
+<dl class="function">
+<dt id="singa.tensor.ctensor2numpy">
+<code class="descclassname">singa.tensor.</code><code class="descname">ctensor2numpy</code><span class="sig-paren">(</span><em>x</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.tensor.ctensor2numpy" title="Permalink to this definition">¶</a></dt>
+<dd><p>To be used in SoftMax Operation.
+Convert a singa_tensor to numpy_tensor.</p>
+</dd></dl>
+
+<dl class="function">
+<dt id="singa.tensor.div">
+<code class="descclassname">singa.tensor.</code><code class="descname">div</code><span class="sig-paren">(</span><em>lhs</em>, <em>rhs</em>, <em>ret=None</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.tensor.div" title="Permalink to this definition">¶</a></dt>
+<dd><p>Elementi-wise division.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>lhs</strong> (<a class="reference internal" href="#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â </p></li>
+<li><p><strong>rhs</strong> (<a class="reference internal" href="#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â </p></li>
+<li><p><strong>ret</strong> (<a class="reference internal" href="#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a><em>, </em><em>optional</em>) â if not None, the result is stored in it;
+otherwise, a new Tensor would be created for the result.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>the result Tensor</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="function">
+<dt id="singa.tensor.einsum">
+<code class="descclassname">singa.tensor.</code><code class="descname">einsum</code><span class="sig-paren">(</span><em>ops</em>, <em>*args</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.tensor.einsum" title="Permalink to this definition">¶</a></dt>
+<dd><p>function TODO list to finish the function in cpp(just like numpy function):
+1.sum(A,axis = None)
+2.repeat(A,repeats)
+3.transpose(A,axes = None)
+Do the matrix to matrix einsum calculation according to the operands
+Warning : this function could only support two matrixâ einsum calcultion
+:param ops: the string specifies the subscripts for summation such as âki,kj->kijâ</p>
+<blockquote>
+<div><p>Here all the 26 lowercase letter can be used here.</p>
+</div></blockquote>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>arg</strong> (<em>list of array_like</em>) â These are the tensors for the operation,but here only support two tensors.</p>
+</dd>
+</dl>
+<dl class="simple">
+<dt>Returns: Singa.Tensor</dt><dd><p>the output matirx of the einsum calculation</p>
+</dd>
+</dl>
+<p>The best way to understand this function is to try the examples below:
+<a href="#id1"><span class="problematic" id="id2">A_</span></a> = [0,1,2,3,4,5,6,7,8,9,10,11]
+A = <a href="#id3"><span class="problematic" id="id4">A_</span></a>.reshape(4,3)
+B = <a href="#id5"><span class="problematic" id="id6">A_</span></a>.reshape(3,4)</p>
+<p>Here this einsum calculation is the same as normal âmultâ
+Res = einsum(âij,jk->ikâ,A,B)</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="p">[[</span> <span class="mi">20</span> <span class="mi">23</span> <span class="mi">26</span> <span class="mi">29</span><span class="p">]</span>
+<span class="go"> [ 56 68 80 92]</span>
+<span class="go"> [ 92 113 134 155]</span>
+<span class="go"> [128 158 188 218]]</span>
+</pre></div>
+</div>
+<p><a href="#id7"><span class="problematic" id="id8">A_</span></a> = [0,1,2,3,4,5,6,7,8,9,10,11]
+A = <a href="#id9"><span class="problematic" id="id10">A_</span></a>.reshape(4,3)
+B = <a href="#id11"><span class="problematic" id="id12">A_</span></a>.reshape(4,3)</p>
+<p>Here the einsum calculation is the same as normol âeltwise_multâ
+Res = einsum(âki,ki->kiâ,A,B)</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="p">[[</span> <span class="mi">0</span> <span class="mi">1</span> <span class="mi">4</span><span class="p">]</span>
+<span class="go"> [ 9 16 25]</span>
+<span class="go"> [ 36 49 64]</span>
+<span class="go"> [ 81 100 121]]</span>
+</pre></div>
+</div>
+<p>A = [0,1,2,3,4,5,6,7,8,9,10,11]
+A = A.reshape(4,3)</p>
+<p>Res = einsum(âki,kj->kijâ,A,A)
+>>> [[[ 0 0 0]</p>
+<blockquote>
+<div><blockquote>
+<div><p>[ 0 1 2]
+[ 0 2 4]]</p>
+</div></blockquote>
+<dl class="simple">
+<dt>[[ 9 12 15]</dt><dd><p>[ 12 16 20]
+[ 15 20 25]]</p>
+</dd>
+<dt>[[ 36 42 48]</dt><dd><p>[ 42 49 56]
+[ 48 56 64]]</p>
+</dd>
+<dt>[[ 81 90 99]</dt><dd><p>[ 90 100 110]
+[ 99 110 121]]]</p>
+</dd>
+</dl>
+</div></blockquote>
+<p><a href="#id13"><span class="problematic" id="id14">A_</span></a> = [0,1,2,3,4,5,6,7,8,9,10,11]
+A = <a href="#id15"><span class="problematic" id="id16">A_</span></a>.reshape(3,2,2)</p>
+<p>Res = einsum(âkia,kja->kijâ,A,A)
+>>> [[[ 1 3]</p>
+<blockquote>
+<div><blockquote>
+<div><p>[ 3 13]]</p>
+</div></blockquote>
+<dl class="simple">
+<dt>[[ 41 59]</dt><dd><p>[ 59 85]]</p>
+</dd>
+<dt>[[145 179]</dt><dd><p>[179 221]]]</p>
+</dd>
+</dl>
+</div></blockquote>
+</dd></dl>
+
+<dl class="function">
+<dt id="singa.tensor.eltwise_mult">
+<code class="descclassname">singa.tensor.</code><code class="descname">eltwise_mult</code><span class="sig-paren">(</span><em>lhs</em>, <em>rhs</em>, <em>ret=None</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.tensor.eltwise_mult" title="Permalink to this definition">¶</a></dt>
+<dd><p>Elementi-wise multiplication.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>lhs</strong> (<a class="reference internal" href="#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â </p></li>
+<li><p><strong>rhs</strong> (<a class="reference internal" href="#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â </p></li>
+<li><p><strong>ret</strong> (<a class="reference internal" href="#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a><em>, </em><em>optional</em>) â if not None, the result is stored in it;
+otherwise, a new Tensor would be created for the result.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>the result Tensor</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="function">
+<dt id="singa.tensor.exp">
+<code class="descclassname">singa.tensor.</code><code class="descname">exp</code><span class="sig-paren">(</span><em>t</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.tensor.exp" title="Permalink to this definition">¶</a></dt>
+<dd><dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>t</strong> (<a class="reference internal" href="#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â input Tensor</p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a new Tensor whose element y = exp(x), x is an element of t</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="function">
+<dt id="singa.tensor.from_numpy">
+<code class="descclassname">singa.tensor.</code><code class="descname">from_numpy</code><span class="sig-paren">(</span><em>np_array</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.tensor.from_numpy" title="Permalink to this definition">¶</a></dt>
+<dd><p>Create a Tensor instance with the shape, dtype and values from the numpy
+array.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>np_array</strong> â the numpy array.</p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>A Tensor instance allocated on the default CppCPU device.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="function">
+<dt id="singa.tensor.gaussian">
+<code class="descclassname">singa.tensor.</code><code class="descname">gaussian</code><span class="sig-paren">(</span><em>mean</em>, <em>std</em>, <em>t</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.tensor.gaussian" title="Permalink to this definition">¶</a></dt>
+<dd><p>Generate values following a Gaussian distribution.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>mean</strong> (<em>float</em>) â the mean of the Gaussian distribution.</p></li>
+<li><p><strong>std</strong> (<em>float</em>) â the standard variance of the Gaussian distribution.</p></li>
+<li><p><strong>t</strong> (<a class="reference internal" href="#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â the results are put into t</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>t</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="function">
+<dt id="singa.tensor.ge">
+<code class="descclassname">singa.tensor.</code><code class="descname">ge</code><span class="sig-paren">(</span><em>t</em>, <em>x</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.tensor.ge" title="Permalink to this definition">¶</a></dt>
+<dd><p>Elementi-wise comparison for t >= x.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>t</strong> (<a class="reference internal" href="#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â left hand side operand</p></li>
+<li><p><strong>x</strong> (<a class="reference internal" href="#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a><em> or </em><em>float</em>) â right hand side operand</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>0.0f,
+or t[i] >= x[i] ? 1.0f:0.0f</p>
+</dd>
+<dt class="field-odd">Return type</dt>
+<dd class="field-odd"><p>a Tensor with each element being t[i] >= x ? 1.0f</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="function">
+<dt id="singa.tensor.gt">
+<code class="descclassname">singa.tensor.</code><code class="descname">gt</code><span class="sig-paren">(</span><em>t</em>, <em>x</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.tensor.gt" title="Permalink to this definition">¶</a></dt>
+<dd><p>Elementi-wise comparison for t > x.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>t</strong> (<a class="reference internal" href="#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â left hand side operand</p></li>
+<li><p><strong>x</strong> (<a class="reference internal" href="#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a><em> or </em><em>float</em>) â right hand side operand</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>0.0f,
+or t[i] > x[i] ? 1.0f:0.0f</p>
+</dd>
+<dt class="field-odd">Return type</dt>
+<dd class="field-odd"><p>a Tensor with each element being t[i] > x ? 1.0f</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="function">
+<dt id="singa.tensor.le">
+<code class="descclassname">singa.tensor.</code><code class="descname">le</code><span class="sig-paren">(</span><em>t</em>, <em>x</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.tensor.le" title="Permalink to this definition">¶</a></dt>
+<dd><p>Elementi-wise comparison for t <= x.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>t</strong> (<a class="reference internal" href="#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â left hand side operand</p></li>
+<li><p><strong>x</strong> (<a class="reference internal" href="#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a><em> or </em><em>float</em>) â right hand side operand</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>0.0f,
+or t[i] <= x[i] ? 1.0f:0.0f</p>
+</dd>
+<dt class="field-odd">Return type</dt>
+<dd class="field-odd"><p>a Tensor with each element being t[i] <= x ? 1.0f</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="function">
+<dt id="singa.tensor.log">
+<code class="descclassname">singa.tensor.</code><code class="descname">log</code><span class="sig-paren">(</span><em>t</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.tensor.log" title="Permalink to this definition">¶</a></dt>
+<dd><dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>t</strong> (<a class="reference internal" href="#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â input Tensor</p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a new Tensor whose element y = log(x), x is an element of t</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="function">
+<dt id="singa.tensor.lt">
+<code class="descclassname">singa.tensor.</code><code class="descname">lt</code><span class="sig-paren">(</span><em>t</em>, <em>x</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.tensor.lt" title="Permalink to this definition">¶</a></dt>
+<dd><p>Elementi-wise comparison for t < x</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>t</strong> (<a class="reference internal" href="#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â left hand side operand</p></li>
+<li><p><strong>x</strong> (<a class="reference internal" href="#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a><em> or </em><em>float</em>) â right hand side operand</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>0.0f,
+or t[i] < x[i] ? 1.0f:0.0f</p>
+</dd>
+<dt class="field-odd">Return type</dt>
+<dd class="field-odd"><p>a Tensor with each element being t[i] < x ? 1.0f</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="function">
+<dt id="singa.tensor.mult">
+<code class="descclassname">singa.tensor.</code><code class="descname">mult</code><span class="sig-paren">(</span><em>A</em>, <em>B</em>, <em>C=None</em>, <em>alpha=1.0</em>, <em>beta=0.0</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.tensor.mult" title="Permalink to this definition">¶</a></dt>
+<dd><p>Do matrix-matrix or matrix-vector multiplication.</p>
+<p>This function returns C = alpha * A * B + beta * C</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>A</strong> (<a class="reference internal" href="#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â 2d Tensor</p></li>
+<li><p><strong>B</strong> (<a class="reference internal" href="#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â If B is a 1d Tensor, GEMV would be invoked for matrix-vector
+multiplication; otherwise GEMM would be invoked.</p></li>
+<li><p><strong>C</strong> (<a class="reference internal" href="#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a><em>, </em><em>optional</em>) â for storing the result; If None, a new Tensor
+would be created.</p></li>
+<li><p><strong>alpha</strong> (<em>float</em>) â </p></li>
+<li><p><strong>beta</strong> (<em>float</em>) â </p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>the result Tensor</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="function">
+<dt id="singa.tensor.pow">
+<code class="descclassname">singa.tensor.</code><code class="descname">pow</code><span class="sig-paren">(</span><em>t</em>, <em>x</em>, <em>out=None</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.tensor.pow" title="Permalink to this definition">¶</a></dt>
+<dd><dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>t</strong> (<a class="reference internal" href="#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â input tensor</p></li>
+<li><p><strong>x</strong> (<em>float</em><em> or </em><a class="reference internal" href="#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â y[i] = t[i]^x if x is a float value; otherwise,
+y[i]= t[i]^x[i] if x is a tensor.</p></li>
+<li><p><strong>out</strong> (<em>None</em><em> or </em><a class="reference internal" href="#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â if None, a new Tensor would be constructed to
+store the result; otherwise, the result is put into out.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>the result tensor.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="function">
+<dt id="singa.tensor.reshape">
+<code class="descclassname">singa.tensor.</code><code class="descname">reshape</code><span class="sig-paren">(</span><em>t</em>, <em>s</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.tensor.reshape" title="Permalink to this definition">¶</a></dt>
+<dd><p>Reshape the input tensor with the given shape.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>t</strong> (<a class="reference internal" href="#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â the tensor to be changed</p></li>
+<li><p><strong>s</strong> (<em>list<int></em>) â the new shape, which should have the same volumn as the
+old shape.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>the new Tensor</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="function">
+<dt id="singa.tensor.sigmoid">
+<code class="descclassname">singa.tensor.</code><code class="descname">sigmoid</code><span class="sig-paren">(</span><em>t</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.tensor.sigmoid" title="Permalink to this definition">¶</a></dt>
+<dd><dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>t</strong> (<a class="reference internal" href="#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â input Tensor</p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a new Tensor whose element y = sigmoid(x); x is an element of t</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="function">
+<dt id="singa.tensor.sign">
+<code class="descclassname">singa.tensor.</code><code class="descname">sign</code><span class="sig-paren">(</span><em>t</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.tensor.sign" title="Permalink to this definition">¶</a></dt>
+<dd><dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>t</strong> (<a class="reference internal" href="#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â input Tensor</p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a new Tensor whose element y = sign(x)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="function">
+<dt id="singa.tensor.sizeof">
+<code class="descclassname">singa.tensor.</code><code class="descname">sizeof</code><span class="sig-paren">(</span><em>dtype</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.tensor.sizeof" title="Permalink to this definition">¶</a></dt>
+<dd><dl class="field-list simple">
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>the number of bytes of the given SINGA data type defined in core.proto</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="function">
+<dt id="singa.tensor.softmax">
+<code class="descclassname">singa.tensor.</code><code class="descname">softmax</code><span class="sig-paren">(</span><em>t</em>, <em>out=None</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.tensor.softmax" title="Permalink to this definition">¶</a></dt>
+<dd><p>Apply SoftMax for each row of the Tensor.
+:param t: the input 1d or 2d tensor
+:type t: Tensor
+:param out: if not None, it is used to store the result
+:type out: Tensor, optional</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>the result Tensor</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="function">
+<dt id="singa.tensor.sqrt">
+<code class="descclassname">singa.tensor.</code><code class="descname">sqrt</code><span class="sig-paren">(</span><em>t</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.tensor.sqrt" title="Permalink to this definition">¶</a></dt>
+<dd><dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>t</strong> (<a class="reference internal" href="#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â input Tensor</p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a new Tensor whose element y = sqrt(x), x is an element of t</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="function">
+<dt id="singa.tensor.square">
+<code class="descclassname">singa.tensor.</code><code class="descname">square</code><span class="sig-paren">(</span><em>t</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.tensor.square" title="Permalink to this definition">¶</a></dt>
+<dd><dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>t</strong> (<a class="reference internal" href="#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â input Tensor</p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a new Tensor whose element y = x * x, x is an element of t</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="function">
+<dt id="singa.tensor.sub">
+<code class="descclassname">singa.tensor.</code><code class="descname">sub</code><span class="sig-paren">(</span><em>lhs</em>, <em>rhs</em>, <em>ret=None</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.tensor.sub" title="Permalink to this definition">¶</a></dt>
+<dd><p>Elementi-wise subtraction.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>lhs</strong> (<a class="reference internal" href="#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â </p></li>
+<li><p><strong>rhs</strong> (<a class="reference internal" href="#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â </p></li>
+<li><p><strong>ret</strong> (<a class="reference internal" href="#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a><em>, </em><em>optional</em>) â if not None, the result is stored in it;
+otherwise, a new Tensor would be created for the result.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>the result Tensor</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="function">
+<dt id="singa.tensor.sum">
+<code class="descclassname">singa.tensor.</code><code class="descname">sum</code><span class="sig-paren">(</span><em>t</em>, <em>axis=None</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.tensor.sum" title="Permalink to this definition">¶</a></dt>
+<dd><p>Sum elements of the input tensor long the given axis.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>t</strong> (<a class="reference internal" href="#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â input Tensor</p></li>
+<li><p><strong>axis</strong> (<em>int</em><em>, </em><em>optional</em>) â if None, the summation is done over all elements;
+if axis is provided, then it is calculated along the given axis,
+e.g. 0 â sum each column; 1 â sum each row.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a float value as the sum of all elements, or a new Tensor</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="function">
+<dt id="singa.tensor.sum_columns">
+<code class="descclassname">singa.tensor.</code><code class="descname">sum_columns</code><span class="sig-paren">(</span><em>M</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.tensor.sum_columns" title="Permalink to this definition">¶</a></dt>
+<dd><p>Sum all columns into a single column.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>M</strong> (<a class="reference internal" href="#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â the input 2d tensor.</p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a new Tensor as the resulted column.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="function">
+<dt id="singa.tensor.sum_rows">
+<code class="descclassname">singa.tensor.</code><code class="descname">sum_rows</code><span class="sig-paren">(</span><em>M</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.tensor.sum_rows" title="Permalink to this definition">¶</a></dt>
+<dd><p>Sum all rows into a single row.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>M</strong> (<a class="reference internal" href="#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â the input 2d tensor.</p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a new Tensor as the resulted row.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="function">
+<dt id="singa.tensor.tanh">
+<code class="descclassname">singa.tensor.</code><code class="descname">tanh</code><span class="sig-paren">(</span><em>t</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.tensor.tanh" title="Permalink to this definition">¶</a></dt>
+<dd><dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>t</strong> (<a class="reference internal" href="#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â input Tensor</p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a new Tensor whose element y = tanh(x), x is an element of t</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="function">
+<dt id="singa.tensor.to_host">
+<code class="descclassname">singa.tensor.</code><code class="descname">to_host</code><span class="sig-paren">(</span><em>t</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.tensor.to_host" title="Permalink to this definition">¶</a></dt>
+<dd><p>Copy the data to a host tensor.</p>
+</dd></dl>
+
+<dl class="function">
+<dt id="singa.tensor.to_numpy">
+<code class="descclassname">singa.tensor.</code><code class="descname">to_numpy</code><span class="sig-paren">(</span><em>t</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.tensor.to_numpy" title="Permalink to this definition">¶</a></dt>
+<dd><p>Copy the tensor into a numpy array.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>t</strong> (<a class="reference internal" href="#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â </p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>a numpy array</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="function">
+<dt id="singa.tensor.uniform">
+<code class="descclassname">singa.tensor.</code><code class="descname">uniform</code><span class="sig-paren">(</span><em>low</em>, <em>high</em>, <em>t</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.tensor.uniform" title="Permalink to this definition">¶</a></dt>
+<dd><p>Generate values following a Uniform distribution.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>low</strong> (<em>float</em>) â the lower bound</p></li>
+<li><p><strong>hight</strong> (<em>float</em>) â the higher bound</p></li>
+<li><p><strong>t</strong> (<a class="reference internal" href="#singa.tensor.Tensor" title="singa.tensor.Tensor"><em>Tensor</em></a>) â the results are put into t</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>t</p>
+</dd>
+</dl>
+</dd></dl>
+
+</div>
+<div class="section" id="cpp-api">
+<h2>CPP API<a class="headerlink" href="#cpp-api" title="Permalink to this headline">¶</a></h2>
+</div>
+</div>
+
+
+ </div>
+
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+ <dl>
+ <dt>Languages</dt>
+ <dd><a href="../../en/index.html">English</a></dd>
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