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--- incubator/singa/site/trunk/zh/docs/layer.html (added)
+++ incubator/singa/site/trunk/zh/docs/layer.html Fri Jun 8 15:29:00 2018
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+<li class="toctree-l1"><a class="reference internal" href="../downloads.html">ä¸è½½ SINGA</a></li>
+<li class="toctree-l1 current"><a class="reference internal" href="index.html">ææ¡£</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="installation.html">å®è£
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+<li class="toctree-l2"><a class="reference internal" href="software_stack.html">软件æ¶æ</a></li>
+<li class="toctree-l2"><a class="reference internal" href="device.html">设å¤(Device)</a></li>
+<li class="toctree-l2"><a class="reference internal" href="tensor.html">å¼ é(Tensor)</a></li>
+<li class="toctree-l2 current"><a class="current reference internal" href="#">å±(Layer)</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#python-api">Python API</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#singa-layer-engine-cudnn">singa.layer.engine = âcudnnâ</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#class-singa-layer-layer-name-conf-none-kwargs">class singa.layer.Layer(name, conf=None, **kwargs)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#class-singa-layer-dummy-name-input-sample-shape-none">class singa.layer.Dummy(name, input_sample_shape=None)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#class-singa-layer-conv2d-name-nb-kernels-kernel-3-stride-1-border-mode-same-cudnn-prefer-fatest-data-format-nchw-use-bias-true-w-specs-none-b-specs-none-pad-none-input-sample-shape-none">class singa.layer.Conv2D(name, nb_kernels, kernel=3, stride=1, border_mode=âsameâ, cudnn_prefer=âfatestâ, data_format=âNCHWâ, use_bias=True, W_specs=None, b_specs=None, pad=None, input_sample_shape=None)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#class-singa-layer-conv1d-name-nb-kernels-kernel-3-stride-1-border-mode-same-cudnn-prefer-fatest-use-bias-true-w-specs-init-xavier-b-specs-init-constant-value-0-pad-none-input-sample-shape-none">class singa.layer.Conv1D(name, nb_kernels, kernel=3, stride=1, border_mode=âsameâ, cudnn_prefer=âfatestâ, use_bias=True, W_specs={âinitâ: âXavierâ}, b_specs={âinitâ: âConstantâ, âvalueâ: 0}, pad=None, input_sample_shape=None)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#class-singa-layer-pooling2d-name-mode-kernel-3-stride-2-border-mode-same-pad-none-data-format-nchw-input-sample-shape-none">class singa.layer.Pooling2D(name, mode, kernel=3, stride=2, border_mode=âsameâ, pad=None, data_format=âNCHWâ, input_sample_shape=None)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#class-singa-layer-maxpooling2d-name-kernel-3-stride-2-border-mode-same-pad-none-data-format-nchw-input-sample-shape-none">class singa.layer.MaxPooling2D(name, kernel=3, stride=2, border_mode=âsameâ, pad=None, data_format=âNCHWâ, input_sample_shape=None)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#class-singa-layer-avgpooling2d-name-kernel-3-stride-2-border-mode-same-pad-none-data-format-nchw-input-sample-shape-none">class singa.layer.AvgPooling2D(name, kernel=3, stride=2, border_mode=âsameâ, pad=None, data_format=âNCHWâ, input_sample_shape=None)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#class-singa-layer-maxpooling1d-name-kernel-3-stride-2-border-mode-same-pad-none-data-format-nchw-input-sample-shape-none">class singa.layer.MaxPooling1D(name, kernel=3, stride=2, border_mode=âsameâ, pad=None, data_format=âNCHWâ, input_sample_shape=None)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#class-singa-layer-avgpooling1d-name-kernel-3-stride-2-border-mode-same-pad-none-data-format-nchw-input-sample-shape-none">class singa.layer.AvgPooling1D(name, kernel=3, stride=2, border_mode=âsameâ, pad=None, data_format=âNCHWâ, input_sample_shape=None)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#class-singa-layer-batchnormalization-name-momentum-0-9-beta-specs-none-gamma-specs-none-input-sample-shape-none">class singa.layer.BatchNormalization(name, momentum=0.9, beta_specs=None, gamma_specs=None, input_sample_shape=None)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#class-singa-layer-lrn-name-size-5-alpha-1-beta-0-75-mode-cross-channel-k-1-input-sample-shape-none">class singa.layer.LRN(name, size=5, alpha=1, beta=0.75, mode=âcross_channelâ, k=1, input_sample_shape=None)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#class-singa-layer-dense-name-num-output-use-bias-true-w-specs-none-b-specs-none-w-transpose-false-input-sample-shape-none">class singa.layer.Dense(name, num_output, use_bias=True, W_specs=None, b_specs=None, W_transpose=False, input_sample_shape=None)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#class-singa-layer-dropout-name-p-0-5-input-sample-shape-none">class singa.layer.Dropout(name, p=0.5, input_sample_shape=None)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#class-singa-layer-activation-name-mode-relu-input-sample-shape-none">class singa.layer.Activation(name, mode=âreluâ, input_sample_shape=None)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#class-singa-layer-softmax-name-axis-1-input-sample-shape-none">class singa.layer.Softmax(name, axis=1, input_sample_shape=None)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#class-singa-layer-flatten-name-axis-1-input-sample-shape-none">class singa.layer.Flatten(name, axis=1, input_sample_shape=None)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#class-singa-layer-merge-name-input-sample-shape-none">class singa.layer.Merge(name, input_sample_shape=None)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#class-singa-layer-split-name-num-output-input-sample-shape-none">class singa.layer.Split(name, num_output, input_sample_shape=None)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#class-singa-layer-concat-name-axis-input-sample-shapes-none">class singa.layer.Concat(name, axis, input_sample_shapes=None)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#class-singa-layer-slice-name-axis-slice-point-input-sample-shape-none">class singa.layer.Slice(name, axis, slice_point, input_sample_shape=None)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#class-singa-layer-rnn-name-hidden-size-rnn-mode-lstm-dropout-0-0-num-stacks-1-input-mode-linear-bidirectional-false-param-specs-none-input-sample-shape-none">class singa.layer.RNN(name, hidden_size, rnn_mode=âlstmâ, dropout=0.0, num_stacks=1, input_mode=âlinearâ, bidirectional=False, param_specs=None, input_sample_shape=None)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#class-singa-layer-lstm-name-hidden-size-dropout-0-0-num-stacks-1-input-mode-linear-bidirectional-false-param-specs-none-input-sample-shape-none">class singa.layer.LSTM(name, hidden_size, dropout=0.0, num_stacks=1, input_mode=âlinearâ, bidirectional=False, param_specs=None, input_sample_shape=None)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#class-singa-layer-gru-name-hidden-size-dropout-0-0-num-stacks-1-input-mode-linear-bidirectional-false-param-specs-none-input-sample-shape-none">class singa.layer.GRU(name, hidden_size, dropout=0.0, num_stacks=1, input_mode=âlinearâ, bidirectional=False, param_specs=None, input_sample_shape=None)</a></li>
+</ul>
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+<li class="toctree-l2"><a class="reference internal" href="loss.html">æ失(Loss)</a></li>
+<li class="toctree-l2"><a class="reference internal" href="metric.html">度é(Metric)</a></li>
+<li class="toctree-l2"><a class="reference internal" href="optimizer.html">ä¼åå¨(Optimizer)</a></li>
+<li class="toctree-l2"><a class="reference internal" href="data.html">æ°æ®(Data)</a></li>
+<li class="toctree-l2"><a class="reference internal" href="image_tool.html">å¾åå·¥å
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+<li class="toctree-l2"><a class="reference internal" href="snapshot.html">Snapshot</a></li>
+<li class="toctree-l2"><a class="reference internal" href="utils.html">Utils</a></li>
+<li class="toctree-l2"><a class="reference internal" href="model_zoo/index.html">模ååº</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption"><span class="caption-text">å¼å</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../develop/schedule.html">å¼åæ¶é´è¡¨</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../develop/how-contribute.html">å¦ä½è´¡ç®ç» SINGA</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../develop/contribute-code.html">å¦ä½è´¡ç®ä»£ç </a></li>
+</ul>
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+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../community/source-repository.html">æºä»£ç åº</a></li>
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+ <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+ <div itemprop="articleBody">
+
+ <div class="section" id="layer">
+<span id="layer"></span><h1>å±(Layer)<a class="headerlink" href="#layer" title="Permalink to this headline">¶</a></h1>
+<div class="section" id="python-api">
+<span id="python-api"></span><h2>Python API<a class="headerlink" href="#python-api" title="Permalink to this headline">¶</a></h2>
+<p>Pythonå±å°C++å±å°è£
ææ´ç®åçAPIã</p>
+<p>示ä¾ç¨æ³ï¼</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">singa</span> <span class="kn">import</span> <span class="n">layer</span>
+<span class="kn">from</span> <span class="nn">singa</span> <span class="kn">import</span> <span class="n">tensor</span>
+<span class="kn">from</span> <span class="nn">singa</span> <span class="kn">import</span> <span class="n">device</span>
+
+<span class="n">layer</span><span class="o">.</span><span class="n">engine</span> <span class="o">=</span> <span class="s1">'cudnn'</span> <span class="c1"># to use cudnn layers</span>
+<span class="n">dev</span> <span class="o">=</span> <span class="n">device</span><span class="o">.</span><span class="n">create_cuda_gpu</span><span class="p">()</span>
+
+<span class="c1"># create a convolution layer</span>
+<span class="n">conv</span> <span class="o">=</span> <span class="n">layer</span><span class="o">.</span><span class="n">Conv2D</span><span class="p">(</span><span class="s1">'conv'</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">pad</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">input_sample_shape</span><span class="o">=</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">))</span>
+<span class="n">conv</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 layer data onto a CudaGPU device</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">3</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">),</span> <span class="n">dev</span><span class="p">)</span>
+<span class="n">x</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="n">y</span> <span class="o">=</span> <span class="n">conv</span><span class="o">.</span><span class="n">foward</span><span class="p">(</span><span class="bp">True</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span>
+
+<span class="n">dy</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="n">dy</span><span class="o">.</span><span class="n">reset_like</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
+<span class="n">dy</span><span class="o">.</span><span class="n">set_value</span><span class="p">(</span><span class="mf">0.1</span><span class="p">)</span>
+<span class="c1"># dp is a list of tensors for parameter gradients</span>
+<span class="n">dx</span><span class="p">,</span> <span class="n">dp</span> <span class="o">=</span> <span class="n">conv</span><span class="o">.</span><span class="n">backward</span><span class="p">(</span><span class="n">kTrain</span><span class="p">,</span> <span class="n">dy</span><span class="p">)</span>
+</pre></div>
+</div>
+<hr class="docutils" />
+<div class="section" id="singa-layer-engine-cudnn">
+<span id="singa-layer-engine-cudnn"></span><h3>singa.layer.engine = âcudnnâ<a class="headerlink" href="#singa-layer-engine-cudnn" title="Permalink to this headline">¶</a></h3>
+<p>å¼æï¼engineï¼æ¯å±æ è¯ç¬¦çåç¼ã
+è¿ä¸ªå¼å¯è½æ¯[cudnnâï¼âsingacppâï¼âsingacudaâï¼âsingaclâ]ä¹ä¸ï¼åå«ç¨cudnnåºï¼Cppï¼CudaåOpenCLå®ç°ã ä¾å¦ï¼CudnnConvolutionå±ç±âcudnn_convolutionâæ è¯; âsingacpp_convolutionâç¨äºå·ç§¯å±; æäºå±çå®ç°åªä½¿ç¨tensorå½æ°ï¼å æ¤å®ä»¬å¯¹åºå±è®¾å¤æ¯éæçã 对äºè¿äºå±ï¼å®ä»¬å°å
·æå¤ä¸ªæ è¯ç¬¦ï¼ä¾å¦ï¼singacpp_dropoutï¼singacuda_dropoutåsingacl_dropoutå
¨é½ç¨äºDropoutå±ã æ¤å¤ï¼å®è¿æä¸ä¸ªé¢å¤çæ è¯ç¬¦âsingaâï¼å³âsinga_dropoutâä¹ä»£è¡¨Dropoutå±ã</p>
+<p>å¼ææ¯å¤§å°åææçãæ¯ä¸ªpythonå±å°ä¼ç¨å¼æå±æ§å建æ£ç¡®çå±ã</p>
+</div>
+<hr class="docutils" />
+<div class="section" id="class-singa-layer-layer-name-conf-none-kwargs">
+<span id="class-singa-layer-layer-name-conf-none-kwargs"></span><h3>class singa.layer.Layer(name, conf=None, **kwargs)<a class="headerlink" href="#class-singa-layer-layer-name-conf-none-kwargs" title="Permalink to this headline">¶</a></h3>
+<p>åºç±»ï¼<code class="docutils literal notranslate"><span class="pre">object</span></code></p>
+<p>Pythonå±çåºç±»ã
+å
¸åå°ï¼å±å®ä¾ççå½å¨æå
æ¬ï¼</p>
+<ol class="simple">
+<li>æé å±æ²¡æinput_sample_shapesï¼è½¬å°2;ç¨input_sample_shapesæ建å±ï¼è½¬å°3</li>
+<li>è°ç¨setupæ¥å建åæ°å¹¶è®¾ç½®å
¶ä»å
å段</li>
+<li>è°ç¨ååä¼ ææ访é®å±æå</li>
+<li>è°ç¨ååä¼ æ并è·ååæ°å®ææ´æ°</li>
+</ol>
+<p><strong>åæ°ï¼</strong></p>
+<ul class="simple">
+<li><strong>name (str)</strong> â å±å</li>
+</ul>
+<hr class="docutils" />
+<div class="section" id="setup-in-shapes">
+<span id="setup-in-shapes"></span><h4>setup(in_shapes)<a class="headerlink" href="#setup-in-shapes" title="Permalink to this headline">¶</a></h4>
+<p>è°ç¨C++setupå½æ°å建åæ°å¹¶è®¾ç½®å
æ°æ®ã</p>
+<p><strong>åæ°ï¼</strong></p>
+<ul class="simple">
+<li><strong>in_shapes</strong> â å¦æå±æ¥åå个è¾å
¥tensorï¼åin_shapesæ¯æå®è¾å
¥tensorå½¢ç¶çå个å
ç»; å¦æ该å±æ¥åå¤ä¸ªè¾å
¥tensorï¼ä¾å¦ï¼concatenationå±ï¼ï¼åin_shapesæ¯å
ç»çå
ç»ï¼æ¯ä¸ªå
ç»å¯¹äºä¸ä¸ªè¾å
¥tensor</li>
+</ul>
+</div>
+<hr class="docutils" />
+<div class="section" id="caffe-layer">
+<span id="caffe-layer"></span><h4>caffe_layer()<a class="headerlink" href="#caffe-layer" title="Permalink to this headline">¶</a></h4>
+<p>åºäºcaffeå±çé
ç½®å建ä¸ä¸ªSINGAå±</p>
+</div>
+<hr class="docutils" />
+<div class="section" id="get-output-sample-shape">
+<span id="get-output-sample-shape"></span><h4>get_output_sample_shape()<a class="headerlink" href="#get-output-sample-shape" title="Permalink to this headline">¶</a></h4>
+<p>å¨setup以è·å¾è¾åºæ ·æ¬çå½¢ç¶å被è°ç¨</p>
+<p><strong>è¿åå¼ï¼</strong> å个è¾åºtensorçå
ç»ï¼å¦æ该å±å
·æå¤ä¸ªè¾åºï¼å为å
ç»å表</p>
+</div>
+<hr class="docutils" />
+<div class="section" id="param-names">
+<span id="param-names"></span><h4>param_names()<a class="headerlink" href="#param-names" title="Permalink to this headline">¶</a></h4>
+<p><strong>è¿åå¼ï¼</strong> å符串å表ï¼æ¯ä¸ªå¼ä»£è¡¨ä¸ä¸ªåæ°tensorçå称</p>
+</div>
+<hr class="docutils" />
+<div class="section" id="param-values">
+<span id="param-values"></span><h4>param_values()<a class="headerlink" href="#param-values" title="Permalink to this headline">¶</a></h4>
+<p>è¿ååæ°å¼tensorã
+åæ°tesnorä¸ä½ä¸ºå±æååå¨ãç±äºå±è®¾å¤çæ´æ¹ï¼cpp tensorå¯è½ä¼ç§»å¨å°diff设å¤ä¸ï¼è¿ä¼å¯¼è´ä¸ä¸è´ã</p>
+<p><strong>è¿åå¼ï¼</strong> tensorå表ï¼æ¯ä¸ªåæ°å¯¹åºå表ä¸çä¸ä¸ª</p>
+</div>
+<hr class="docutils" />
+<div class="section" id="forward-flag-x">
+<span id="forward-flag-x"></span><h4>forward(flag, x)<a class="headerlink" href="#forward-flag-x" title="Permalink to this headline">¶</a></h4>
+<p>å½åå±çååä¼ æã</p>
+<p><strong>åæ°ï¼</strong></p>
+<ul class="simple">
+<li><strong>flag</strong> â True (kTrain) for training (kEval); False for evaluating; other values for furture use.</li>
+<li><strong>x (Tensor or list<Tensor>)</strong> â an input tensor if the layer is connected from a single layer; a list of tensors if the layer is connected from multiple layers.</li>
+</ul>
+<p><strong>è¿åå¼ï¼</strong> å¦æ该å±è¢«è¿æ¥å¨ä¸ä¸ªåç¬çå±åè¿åtensorï¼å¦æ被è¿æ¥å°å¤ä¸ªå±ï¼åè¿åä¸ä¸ªtensorå表</p>
+</div>
+<hr class="docutils" />
+<div class="section" id="backward-flag-dy">
+<span id="backward-flag-dy"></span><h4>backward(flag, dy)<a class="headerlink" href="#backward-flag-dy" title="Permalink to this headline">¶</a></h4>
+<p>å½åå±çååä¼ æã</p>
+<p><strong>åæ°ï¼</strong></p>
+<ul class="simple">
+<li><strong>flag (int)</strong> â ä¿ç为以å使ç¨</li>
+<li><strong>dy (Tensor or list<Tensor>)</strong> â ä¸ç®æ æ失ç¸å¯¹åºç梯度tensor</li>
+</ul>
+<p><strong>è¿åå¼ï¼</strong> <dx, <dp1, dp2..>>ï¼dxæ¯è¾å
¥xç梯度ï¼dpiæ¯ç¬¬i个åæ°ç梯度</p>
+</div>
+<hr class="docutils" />
+<div class="section" id="to-device-device">
+<span id="to-device-device"></span><h4>to_device(device)<a class="headerlink" href="#to-device-device" title="Permalink to this headline">¶</a></h4>
+<p>å°å±ç¶ætensor移è³æå®è®¾å¤ã</p>
+<p><strong>åæ°ï¼</strong></p>
+<ul class="simple">
+<li><strong>device</strong> â swig转æ¢ç设å¤ï¼ç±singa.deviceå建</li>
+</ul>
+</div>
+</div>
+<hr class="docutils" />
+<div class="section" id="class-singa-layer-dummy-name-input-sample-shape-none">
+<span id="class-singa-layer-dummy-name-input-sample-shape-none"></span><h3>class singa.layer.Dummy(name, input_sample_shape=None)<a class="headerlink" href="#class-singa-layer-dummy-name-input-sample-shape-none" title="Permalink to this headline">¶</a></h3>
+<p>åºç±»ï¼<code class="docutils literal notranslate"><span class="pre">singa.layer.Layer</span></code></p>
+<p>ä¸ä¸ªèæå±ï¼ä»
ç¨äºåå/ååä¼ éæ°æ®ï¼è¾å
¥/è¾åºæ¯å个tensorï¼ã</p>
+<div class="section" id="forward-flag-x">
+<span id="id1"></span><h4>forward(flag, x)<a class="headerlink" href="#forward-flag-x" title="Permalink to this headline">¶</a></h4>
+<p><strong>è¿åå¼ï¼</strong> è¾å
¥x</p>
+</div>
+<div class="section" id="backward-falg-dy">
+<span id="backward-falg-dy"></span><h4>backward(falg, dy)<a class="headerlink" href="#backward-falg-dy" title="Permalink to this headline">¶</a></h4>
+<p><strong>è¿åå¼ï¼</strong> dyï¼[]</p>
+</div>
+</div>
+<hr class="docutils" />
+<div class="section" id="class-singa-layer-conv2d-name-nb-kernels-kernel-3-stride-1-border-mode-same-cudnn-prefer-fatest-data-format-nchw-use-bias-true-w-specs-none-b-specs-none-pad-none-input-sample-shape-none">
+<span id="class-singa-layer-conv2d-name-nb-kernels-kernel-3-stride-1-border-mode-same-cudnn-prefer-fatest-data-format-nchw-use-bias-true-w-specs-none-b-specs-none-pad-none-input-sample-shape-none"></span><h3>class singa.layer.Conv2D(name, nb_kernels, kernel=3, stride=1, border_mode=âsameâ, cudnn_prefer=âfatestâ, data_format=âNCHWâ, use_bias=True, W_specs=None, b_specs=None, pad=None, input_sample_shape=None)<a class="headerlink" href="#class-singa-layer-conv2d-name-nb-kernels-kernel-3-stride-1-border-mode-same-cudnn-prefer-fatest-data-format-nchw-use-bias-true-w-specs-none-b-specs-none-pad-none-input-sample-shape-none" title="Permalink to this headline">¶</a></h3>
+<p>åºç±»ï¼<code class="docutils literal notranslate"><span class="pre">singa.layer.Layer</span></code></p>
+<p>å建ä¸ä¸ªå±å2Då·ç§¯ã</p>
+<p><strong>åæ°ï¼</strong></p>
+<ul class="simple">
+<li><strong>nb_kernels (int)</strong> â è¾å
¥tensorçééï¼æ ¸ï¼æ°</li>
+<li><strong>kernel</strong> â ä¸ä¸ªæä¸å¯¹æ´åæ°è¡¨ç¤ºæ ¸çé«å宽</li>
+<li><strong>stride</strong> â ä¸ä¸ªæä¸å¯¹æ´åæ°è¡¨ç¤ºæ¥é¿çé«å宽</li>
+<li><strong>border_mode (string)</strong> â å¡«å
模å¼ï¼ä¸åºå大å°åï¼âvalidâ -> å¨é«å宽é¿åº¦ä¸è¡¥0 âsameâ -> å¡«å
æ ¸ä¸åï¼ä¸åæ´ï¼æ°ç®ç0ï¼æ ¸å¿
é¡»æ¯å¥æ°</li>
+<li><strong>cudnn_prefer (string)</strong> â å好çcudnnå·ç§¯ç®æ³ï¼å¯ä»¥æ¯âfatestâ, âautotuneâ, âlimited_workspaceâåâno_workspaceâ</li>
+<li><strong>data_format (string)</strong> â âNCHWâæâNHWCâ</li>
+<li><strong>use_bias (bool)</strong> â TrueæFalse</li>
+<li><strong>pad</strong> â ä¸ä¸ªæä¸å¯¹æ´åæ°è¡¨ç¤ºå¡«å
çé«å宽</li>
+<li><strong>W_specs (dict)</strong> â ç¨äºæå®æéç©éµçè§æ ¼ï¼å段å
æ¬ä»£è¡¨åæ°å称çânameâï¼ä»£è¡¨å¦ä¹ éçä¹æ°çâlr_multï¼ä»£è¡¨æéè¡°åä¹æ°çââdecay_multâï¼ä»£è¡¨åå§åæ¹æ³çâinitâï¼å
¶å¯ä»¥æ¯âgaussianâï¼âuniformâï¼â xavierâï¼ç¸åºçåå§åæ¹æ³ä¸ºâââstdâï¼âmeanâï¼âhighâï¼âlowâãTODOï¼wangweiï¼âclampâ为æ¸å约æï¼value为æ éï¼âregularizerâ为æ£è§åï¼ç®åæ¯æâl2â</li>
+<li><strong>b_specs (dict)</strong> â å移åéçè¶
åæ°ï¼åW_specs类似</li>
+<li><strong>name (string)</strong> â å±å</li>
+<li><strong>input_sample_shape</strong> â ç¨äºè¾å
¥tensorå½¢ç¶çä¸å
ç»ï¼ä¾å¦ï¼ééï¼é«åº¦ï¼å®½åº¦ï¼æï¼é«åº¦ï¼å®½åº¦ï¼ééï¼</li>
+</ul>
+</div>
+<hr class="docutils" />
+<div class="section" id="class-singa-layer-conv1d-name-nb-kernels-kernel-3-stride-1-border-mode-same-cudnn-prefer-fatest-use-bias-true-w-specs-init-xavier-b-specs-init-constant-value-0-pad-none-input-sample-shape-none">
+<span id="class-singa-layer-conv1d-name-nb-kernels-kernel-3-stride-1-border-mode-same-cudnn-prefer-fatest-use-bias-true-w-specs-init-xavier-b-specs-init-constant-value-0-pad-none-input-sample-shape-none"></span><h3>class singa.layer.Conv1D(name, nb_kernels, kernel=3, stride=1, border_mode=âsameâ, cudnn_prefer=âfatestâ, use_bias=True, W_specs={âinitâ: âXavierâ}, b_specs={âinitâ: âConstantâ, âvalueâ: 0}, pad=None, input_sample_shape=None)<a class="headerlink" href="#class-singa-layer-conv1d-name-nb-kernels-kernel-3-stride-1-border-mode-same-cudnn-prefer-fatest-use-bias-true-w-specs-init-xavier-b-specs-init-constant-value-0-pad-none-input-sample-shape-none" title="Permalink to this headline">¶</a></h3>
+<p>åºç±»ï¼<code class="docutils literal notranslate"><span class="pre">singa.layer.Conv2D</span></code></p>
+<p>æ建1Då·ç§¯å±ã
+大é¨ååæ°ä¸Conv2Dçåæ°ç¸åï¼é¤äºæ ¸ï¼æ¥é¿ï¼å¡«å
å¼ï¼è¿æ¯ä¸ä¸ªæ éèä¸æ¯å
ç»ã input_sample_shapeæ¯ä¸ä¸ªå
·æå个è¾å
¥ç¹å¾é¿åº¦å¼çå
ç»ã</p>
+<div class="section" id="get-output-sample-shape">
+<span id="id2"></span><h4>get_output_sample_shape()<a class="headerlink" href="#get-output-sample-shape" title="Permalink to this headline">¶</a></h4>
+</div>
+</div>
+<hr class="docutils" />
+<div class="section" id="class-singa-layer-pooling2d-name-mode-kernel-3-stride-2-border-mode-same-pad-none-data-format-nchw-input-sample-shape-none">
+<span id="class-singa-layer-pooling2d-name-mode-kernel-3-stride-2-border-mode-same-pad-none-data-format-nchw-input-sample-shape-none"></span><h3>class singa.layer.Pooling2D(name, mode, kernel=3, stride=2, border_mode=âsameâ, pad=None, data_format=âNCHWâ, input_sample_shape=None)<a class="headerlink" href="#class-singa-layer-pooling2d-name-mode-kernel-3-stride-2-border-mode-same-pad-none-data-format-nchw-input-sample-shape-none" title="Permalink to this headline">¶</a></h3>
+<p>åºç±»ï¼<code class="docutils literal notranslate"><span class="pre">singa.layer.Layer</span></code></p>
+<p>2Dæ± åå±è¿è¡æ大æå¹³åæ± åã
+ææçåæ°é½ä¸Conv2Dç¸åï¼é¤äºä¸é¢çåæ°ã</p>
+<p><strong>åæ°ï¼</strong></p>
+<ul class="simple">
+<li><strong>mode</strong> â æ± å模å¼ï¼model_pb2.PoolingConf.MAXæmodel_pb2.PoolingConf.AVE</li>
+</ul>
+</div>
+<hr class="docutils" />
+<div class="section" id="class-singa-layer-maxpooling2d-name-kernel-3-stride-2-border-mode-same-pad-none-data-format-nchw-input-sample-shape-none">
+<span id="class-singa-layer-maxpooling2d-name-kernel-3-stride-2-border-mode-same-pad-none-data-format-nchw-input-sample-shape-none"></span><h3>class singa.layer.MaxPooling2D(name, kernel=3, stride=2, border_mode=âsameâ, pad=None, data_format=âNCHWâ, input_sample_shape=None)<a class="headerlink" href="#class-singa-layer-maxpooling2d-name-kernel-3-stride-2-border-mode-same-pad-none-data-format-nchw-input-sample-shape-none" title="Permalink to this headline">¶</a></h3>
+<p>åºç±»: <code class="docutils literal notranslate"><span class="pre">singa.layer.Pooling2D</span></code></p>
+</div>
+<hr class="docutils" />
+<div class="section" id="class-singa-layer-avgpooling2d-name-kernel-3-stride-2-border-mode-same-pad-none-data-format-nchw-input-sample-shape-none">
+<span id="class-singa-layer-avgpooling2d-name-kernel-3-stride-2-border-mode-same-pad-none-data-format-nchw-input-sample-shape-none"></span><h3>class singa.layer.AvgPooling2D(name, kernel=3, stride=2, border_mode=âsameâ, pad=None, data_format=âNCHWâ, input_sample_shape=None)<a class="headerlink" href="#class-singa-layer-avgpooling2d-name-kernel-3-stride-2-border-mode-same-pad-none-data-format-nchw-input-sample-shape-none" title="Permalink to this headline">¶</a></h3>
+<p>åºç±»: <code class="docutils literal notranslate"><span class="pre">singa.layer.Pooling2D</span></code></p>
+</div>
+<hr class="docutils" />
+<div class="section" id="class-singa-layer-maxpooling1d-name-kernel-3-stride-2-border-mode-same-pad-none-data-format-nchw-input-sample-shape-none">
+<span id="class-singa-layer-maxpooling1d-name-kernel-3-stride-2-border-mode-same-pad-none-data-format-nchw-input-sample-shape-none"></span><h3>class singa.layer.MaxPooling1D(name, kernel=3, stride=2, border_mode=âsameâ, pad=None, data_format=âNCHWâ, input_sample_shape=None)<a class="headerlink" href="#class-singa-layer-maxpooling1d-name-kernel-3-stride-2-border-mode-same-pad-none-data-format-nchw-input-sample-shape-none" title="Permalink to this headline">¶</a></h3>
+<p>åºç±»: <code class="docutils literal notranslate"><span class="pre">singa.layer.MaxPooling2D</span></code></p>
+<p>get_output_sample_shape()</p>
+</div>
+<hr class="docutils" />
+<div class="section" id="class-singa-layer-avgpooling1d-name-kernel-3-stride-2-border-mode-same-pad-none-data-format-nchw-input-sample-shape-none">
+<span id="class-singa-layer-avgpooling1d-name-kernel-3-stride-2-border-mode-same-pad-none-data-format-nchw-input-sample-shape-none"></span><h3>class singa.layer.AvgPooling1D(name, kernel=3, stride=2, border_mode=âsameâ, pad=None, data_format=âNCHWâ, input_sample_shape=None)<a class="headerlink" href="#class-singa-layer-avgpooling1d-name-kernel-3-stride-2-border-mode-same-pad-none-data-format-nchw-input-sample-shape-none" title="Permalink to this headline">¶</a></h3>
+<p>åºç±»: <code class="docutils literal notranslate"><span class="pre">singa.layer.AvgPooling2D</span></code></p>
+<p>get_output_sample_shape()</p>
+</div>
+<hr class="docutils" />
+<div class="section" id="class-singa-layer-batchnormalization-name-momentum-0-9-beta-specs-none-gamma-specs-none-input-sample-shape-none">
+<span id="class-singa-layer-batchnormalization-name-momentum-0-9-beta-specs-none-gamma-specs-none-input-sample-shape-none"></span><h3>class singa.layer.BatchNormalization(name, momentum=0.9, beta_specs=None, gamma_specs=None, input_sample_shape=None)<a class="headerlink" href="#class-singa-layer-batchnormalization-name-momentum-0-9-beta-specs-none-gamma-specs-none-input-sample-shape-none" title="Permalink to this headline">¶</a></h3>
+<p>åºç±»ï¼<code class="docutils literal notranslate"><span class="pre">singa.layer.Layer</span></code></p>
+<p>æ¹éæ£ååã</p>
+<p><strong>åæ°ï¼</strong></p>
+<ul class="simple">
+<li><strong>momentum (float)</strong> â ç¨äºè¿è¡çåå¼åæ¹å·®</li>
+<li><strong>beta_specs (dict)</strong> â åå
¸ï¼å
æ¬betaåæ°çå段ï¼ânameâåæ°å称âï¼lr_multâå¦ä¹ éçä¹æ°ï¼âdecay_multâæéè¡°åä¹æ°ï¼âinitâåå§åæ¹æ³ï¼å¯ä»¥æ¯âgaussianâï¼âuniformâåâxavierâï¼âstdâï¼âmeanâï¼âhighâï¼âlowâ表示ç¸åºåå§åæ¹æ³ï¼âclampâ表示梯度约æï¼å¼æ¯æ éï¼âregularizerâç¨äºæ£ååï¼ç®åæ¯æâl2â</li>
+<li><strong>gamma_specs (dict)</strong> â åbeta_specs类似, ä½ç¨äºgammaåæ°.</li>
+<li><strong>name (string)</strong> â å±å</li>
+<li><strong>input_sample_shape (tuple)</strong> â æ´åæ°ï¼è³å°ä¸ä¸ª</li>
+</ul>
+</div>
+<hr class="docutils" />
+<div class="section" id="class-singa-layer-lrn-name-size-5-alpha-1-beta-0-75-mode-cross-channel-k-1-input-sample-shape-none">
+<span id="class-singa-layer-lrn-name-size-5-alpha-1-beta-0-75-mode-cross-channel-k-1-input-sample-shape-none"></span><h3>class singa.layer.LRN(name, size=5, alpha=1, beta=0.75, mode=âcross_channelâ, k=1, input_sample_shape=None)<a class="headerlink" href="#class-singa-layer-lrn-name-size-5-alpha-1-beta-0-75-mode-cross-channel-k-1-input-sample-shape-none" title="Permalink to this headline">¶</a></h3>
+<p>åºç±»ï¼<code class="docutils literal notranslate"><span class="pre">singa.layer.Layer</span></code></p>
+<p>å±é¨ååºå½ä¸åã</p>
+<p><strong>åæ°ï¼</strong></p>
+<ul class="simple">
+<li><strong>size (int)</strong> â ç¨äºå½ä¸åçééæ°.</li>
+<li><strong>mode (string)</strong> â âcross_channelâ</li>
+<li><strong>input_sample_shape (tuple)</strong> â 3ç»´å
ç»ï¼(channel, height, width)</li>
+</ul>
+</div>
+<hr class="docutils" />
+<div class="section" id="class-singa-layer-dense-name-num-output-use-bias-true-w-specs-none-b-specs-none-w-transpose-false-input-sample-shape-none">
+<span id="class-singa-layer-dense-name-num-output-use-bias-true-w-specs-none-b-specs-none-w-transpose-false-input-sample-shape-none"></span><h3>class singa.layer.Dense(name, num_output, use_bias=True, W_specs=None, b_specs=None, W_transpose=False, input_sample_shape=None)<a class="headerlink" href="#class-singa-layer-dense-name-num-output-use-bias-true-w-specs-none-b-specs-none-w-transpose-false-input-sample-shape-none" title="Permalink to this headline">¶</a></h3>
+<p>åºç±»ï¼<code class="docutils literal notranslate"><span class="pre">singa.layer.Layer</span></code></p>
+<p>è¿è¡çº¿æ§ææ¾å°åæ¢ï¼ä¹è¢«å«åå
积æå
¨è¿æ¥å±ã</p>
+<p><strong>åæ°ï¼</strong></p>
+<ul class="simple">
+<li><strong>num_output (int)</strong> â è¾åºç¹å¾é¿åº¦</li>
+<li><strong>use_bias (bool)</strong> â 转æ¢åçç¹å¾åéæ¯å¦å ä¸å移åé</li>
+<li><strong>W_specs (dict)</strong> â å
å«æå¼ç©éµçå段ï¼ânameâåæ°å称âï¼lr_multâå¦ä¹ éçä¹æ°ï¼âdecay_multâæéè¡°åä¹æ°ï¼âinitâåå§åæ¹æ³ï¼å¯ä»¥æ¯âgaussianâï¼âuniformâåâxavierâï¼âstdâï¼âmeanâï¼âhighâï¼âlowâ表示ç¸åºåå§åæ¹æ³ï¼âclampâ表示梯度约æï¼å¼æ¯æ éï¼âregularizerâç¨äºæ£ååï¼ç®åæ¯æâl2â</li>
+<li><strong>b_specs (dict)</strong> â å移åéçå段, åW_specs类似</li>
+<li><strong>W_transpose (bool)</strong> â å¦æ为çï¼è¾åºä¸ºx*W.T+b</li>
+<li><strong>input_sample_shape (tuple)</strong> â è¾å
¥ç¹å¾é¿åº¦</li>
+</ul>
+</div>
+<hr class="docutils" />
+<div class="section" id="class-singa-layer-dropout-name-p-0-5-input-sample-shape-none">
+<span id="class-singa-layer-dropout-name-p-0-5-input-sample-shape-none"></span><h3>class singa.layer.Dropout(name, p=0.5, input_sample_shape=None)<a class="headerlink" href="#class-singa-layer-dropout-name-p-0-5-input-sample-shape-none" title="Permalink to this headline">¶</a></h3>
+<p>åºç±»ï¼<code class="docutils literal notranslate"><span class="pre">singa.layer.Layer</span></code></p>
+<p>Dropoutå±</p>
+<p><strong>åæ°ï¼</strong></p>
+<ul class="simple">
+<li><strong>p (float)</strong> â éæºä¸¢æä¸ä¸ªå
ç´ ï¼å³å°å
¶ä¸è®¾ä¸º0ï¼çæ¦ç</li>
+<li><strong>name (string)</strong> â å±å</li>
+</ul>
+</div>
+<hr class="docutils" />
+<div class="section" id="class-singa-layer-activation-name-mode-relu-input-sample-shape-none">
+<span id="class-singa-layer-activation-name-mode-relu-input-sample-shape-none"></span><h3>class singa.layer.Activation(name, mode=âreluâ, input_sample_shape=None)<a class="headerlink" href="#class-singa-layer-activation-name-mode-relu-input-sample-shape-none" title="Permalink to this headline">¶</a></h3>
+<p>åºç±»ï¼<code class="docutils literal notranslate"><span class="pre">singa.layer.Layer</span></code></p>
+<p>æ¿å±å±</p>
+<p><strong>åæ°ï¼</strong></p>
+<ul class="simple">
+<li><strong>name (string)</strong> â å±å</li>
+<li><strong>mode (string)</strong> â âreluâ, âsigmoidâæ âtanhâ</li>
+<li><strong>input_sample_shape (tuple)</strong> â åä¸ªæ ·æ¬çå½¢ç¶</li>
+</ul>
+</div>
+<hr class="docutils" />
+<div class="section" id="class-singa-layer-softmax-name-axis-1-input-sample-shape-none">
+<span id="class-singa-layer-softmax-name-axis-1-input-sample-shape-none"></span><h3>class singa.layer.Softmax(name, axis=1, input_sample_shape=None)<a class="headerlink" href="#class-singa-layer-softmax-name-axis-1-input-sample-shape-none" title="Permalink to this headline">¶</a></h3>
+<p>åºç±»ï¼<code class="docutils literal notranslate"><span class="pre">singa.layer.Layer</span></code></p>
+<p>éç¨SoftMaxã</p>
+<p><strong>åæ°ï¼</strong></p>
+<ul class="simple">
+<li><strong>axis (int)</strong> â 对[axis, -1)çæ°æ®é个è¿è¡SoftMax</li>
+<li><strong>input_sample_shape (tuple)</strong> â åä¸ªæ ·æ¬çå½¢ç¶</li>
+</ul>
+</div>
+<hr class="docutils" />
+<div class="section" id="class-singa-layer-flatten-name-axis-1-input-sample-shape-none">
+<span id="class-singa-layer-flatten-name-axis-1-input-sample-shape-none"></span><h3>class singa.layer.Flatten(name, axis=1, input_sample_shape=None)<a class="headerlink" href="#class-singa-layer-flatten-name-axis-1-input-sample-shape-none" title="Permalink to this headline">¶</a></h3>
+<p>åºç±»ï¼<code class="docutils literal notranslate"><span class="pre">singa.layer.Layer</span></code></p>
+<p>å°è¾å
¥tensoréå¡ä¸ºä¸ä¸ªç©éµã</p>
+<p><strong>åæ°ï¼</strong></p>
+<ul class="simple">
+<li><strong>axis (int)</strong> â æ ¹æ®æå®ç»´åº¦å°è¾å
¥éå¡ä¸ºç©éµï¼[0,axis)ä½ä¸ºè¡ï¼[axis, -1)ä½ä¸ºå</li>
+<li><strong>input_sample_shape (tuple)</strong> â åä¸ªæ ·æ¬çå½¢ç¶</li>
+</ul>
+</div>
+<hr class="docutils" />
+<div class="section" id="class-singa-layer-merge-name-input-sample-shape-none">
+<span id="class-singa-layer-merge-name-input-sample-shape-none"></span><h3>class singa.layer.Merge(name, input_sample_shape=None)<a class="headerlink" href="#class-singa-layer-merge-name-input-sample-shape-none" title="Permalink to this headline">¶</a></h3>
+<p>åºç±»ï¼<code class="docutils literal notranslate"><span class="pre">singa.layer.Layer</span></code></p>
+<p>对ææè¾å
¥tensoræ±åã</p>
+<p><strong>åæ°ï¼</strong></p>
+<ul class="simple">
+<li><strong>input_sample_shape</strong> â è¾å
¥æ ·æ¬çå½¢ç¶ãæææ ·æ¬çå½¢ç¶åºè¯¥ä¸è´ã</li>
+</ul>
+<div class="section" id="setup-in-shape">
+<span id="setup-in-shape"></span><h4>setup(in_shape)<a class="headerlink" href="#setup-in-shape" title="Permalink to this headline">¶</a></h4>
+</div>
+<div class="section" id="get-output-sample-shape">
+<span id="id3"></span><h4>get_output_sample_shape()<a class="headerlink" href="#get-output-sample-shape" title="Permalink to this headline">¶</a></h4>
+</div>
+<div class="section" id="forward-flag-inputs">
+<span id="forward-flag-inputs"></span><h4>forward(flag, inputs)<a class="headerlink" href="#forward-flag-inputs" title="Permalink to this headline">¶</a></h4>
+<p>éè¿æ±åå并ææè¾å
¥tensorã
+TODO(wangwei) å
ç´ çº§å«çå并æä½ã</p>
+<p><strong>è¿åå¼ï¼</strong> å个tensorï¼å
å«ææè¾å
¥tensorçå</p>
+</div>
+<div class="section" id="backward-flag-grad">
+<span id="backward-flag-grad"></span><h4>backward(flag, grad)<a class="headerlink" href="#backward-flag-grad" title="Permalink to this headline">¶</a></h4>
+<p>å¤å¶æ¯ä¸ªè¾å
¥å±ç梯度tensorã</p>
+<p><strong>åæ°ï¼</strong></p>
+<ul class="simple">
+<li><strong>grad</strong> - 梯度tensor</li>
+</ul>
+<p><strong>è¿åå¼ï¼</strong> tensorå表ï¼æ¯ä¸ªè¾å
¥å±å¯¹åºå
¶ä¸ä¸ä¸ª</p>
+</div>
+</div>
+<hr class="docutils" />
+<div class="section" id="class-singa-layer-split-name-num-output-input-sample-shape-none">
+<span id="class-singa-layer-split-name-num-output-input-sample-shape-none"></span><h3>class singa.layer.Split(name, num_output, input_sample_shape=None)<a class="headerlink" href="#class-singa-layer-split-name-num-output-input-sample-shape-none" title="Permalink to this headline">¶</a></h3>
+<p>åºç±»ï¼<code class="docutils literal notranslate"><span class="pre">singa.layer.Layer</span></code></p>
+<p>çæè¾å
¥tensorçå¤ä¸ªå¯æ¬ã</p>
+<p><strong>åæ°ï¼</strong></p>
+<ul class="simple">
+<li><strong>num_output (int)</strong> â å¾
çæçè¾åºtensoræ°ç®</li>
+<li><strong>input_sample_shape()</strong> â å
å«ä¸ä¸ªæ´åæ°ï¼ä»£è¡¨è¾å
¥æ ·æ¬ç¹å¾å¤§å°</li>
+</ul>
+<div class="section" id="setup-in-shape">
+<span id="id4"></span><h4>setup(in_shape)<a class="headerlink" href="#setup-in-shape" title="Permalink to this headline">¶</a></h4>
+</div>
+<div class="section" id="get-output-sample-shape">
+<span id="id5"></span><h4>get_output_sample_shape()<a class="headerlink" href="#get-output-sample-shape" title="Permalink to this headline">¶</a></h4>
+</div>
+<div class="section" id="forward">
+<span id="forward"></span><h4>forward()<a class="headerlink" href="#forward" title="Permalink to this headline">¶</a></h4>
+<p>çæè¾å
¥tensorçå¤ä¸ªå¯æ¬ã</p>
+<p><strong>åæ°ï¼</strong></p>
+<ul class="simple">
+<li><strong>flag</strong> â 没æç¨å°</li>
+<li><strong>input</strong> â å个è¾å
¥tensor</li>
+</ul>
+<p><strong>è¿åå¼ï¼</strong> è¾åºtensorå表ï¼æ¯ä¸ªå¯¹åºè¾å
¥çä¸ä¸ªæ·è´</p>
+</div>
+<div class="section" id="backward">
+<span id="backward"></span><h4>backward()<a class="headerlink" href="#backward" title="Permalink to this headline">¶</a></h4>
+<p>对ææè¾å
¥tensoræ±åå¾å°å个è¾åºtensorã</p>
+<p><strong>åæ°ï¼</strong></p>
+<ul class="simple">
+<li><strong>grad</strong> - 梯度tensor</li>
+</ul>
+<p><strong>è¿åå¼ï¼</strong> ä¸ä¸ªtensorï¼ä»£è¡¨ææè¾å
¥æ¢¯åº¦tensorçæ±å</p>
+</div>
+</div>
+<hr class="docutils" />
+<div class="section" id="class-singa-layer-concat-name-axis-input-sample-shapes-none">
+<span id="class-singa-layer-concat-name-axis-input-sample-shapes-none"></span><h3>class singa.layer.Concat(name, axis, input_sample_shapes=None)<a class="headerlink" href="#class-singa-layer-concat-name-axis-input-sample-shapes-none" title="Permalink to this headline">¶</a></h3>
+<p>åºç±»ï¼<code class="docutils literal notranslate"><span class="pre">singa.layer.Layer</span></code></p>
+<p>å°tensorç«ç´(axis=0)ææ°´å¹³(axis=1)æ¼æ¥ãç®åä»
æ¯æ2ç»´tensorã</p>
+<p><strong>åæ°ï¼</strong></p>
+<ul class="simple">
+<li><strong>axis (int)</strong> â 0表示æ¼æ¥è¡; 1表示æ¼æ¥å;</li>
+<li><strong>input_sample_shapes</strong> â æ ·æ¬å½¢ç¶çå
ç»å表ï¼æ¯ä¸ªå¯¹åºä¸ä¸ªè¾å
¥æ ·æ¬çtensor</li>
+</ul>
+<div class="section" id="forward-flag-inputs">
+<span id="id6"></span><h4>forward(flag, inputs)<a class="headerlink" href="#forward-flag-inputs" title="Permalink to this headline">¶</a></h4>
+<p>æ¼æ¥ææè¾å
¥tensorã</p>
+<p><strong>åæ°ï¼</strong></p>
+<ul class="simple">
+<li><strong>flag</strong> â åLayer::forward()</li>
+<li><strong>input</strong> â tensorå表</li>
+</ul>
+<p><strong>è¿åå¼ï¼</strong> ä¸ä¸ªæ¼æ¥åçtensor</p>
+</div>
+<div class="section" id="backward-flag-dy">
+<span id="id7"></span><h4>backward(flag, dy)<a class="headerlink" href="#backward-flag-dy" title="Permalink to this headline">¶</a></h4>
+<p><strong>åæ°ï¼</strong></p>
+<ul class="simple">
+<li><strong>flag</strong> â same as Layer::backward()</li>
+<li><strong>dy (Tensor)</strong> â the gradient tensors of y w.r.t objective loss</li>
+</ul>
+<p><strong>è¿åå¼ï¼</strong> å
ç»(dx, []), dxæ¯tensorå表ï¼å¯¹åºè¾å
¥ç梯度ï¼[]æ¯ç©ºå表</p>
+</div>
+</div>
+<hr class="docutils" />
+<div class="section" id="class-singa-layer-slice-name-axis-slice-point-input-sample-shape-none">
+<span id="class-singa-layer-slice-name-axis-slice-point-input-sample-shape-none"></span><h3>class singa.layer.Slice(name, axis, slice_point, input_sample_shape=None)<a class="headerlink" href="#class-singa-layer-slice-name-axis-slice-point-input-sample-shape-none" title="Permalink to this headline">¶</a></h3>
+<p>åºç±»ï¼<code class="docutils literal notranslate"><span class="pre">singa.layer.Layer</span></code></p>
+<p>å°è¾å
¥tensor沿ç«ç´(axis=0)ææ°´å¹³(axis=1)åæå¤ä¸ªåtensorã</p>
+<p><strong>åæ°ï¼</strong></p>
+<ul class="simple">
+<li><strong>axis (int)</strong> â 0代表åå²è¡; 1代表åå²å;</li>
+<li><strong>slice_point (list)</strong> â 沿çè½´åå²çä½ç½®ï¼n-1个åå²ç¹å¯¹åºn个åtensorï¼</li>
+<li><strong>input_sample_shape</strong> â è¾å
¥æ ·æ¬tensorçå½¢ç¶</li>
+</ul>
+<div class="section" id="get-output-sample-shape">
+<span id="id8"></span><h4>get_output_sample_shape()<a class="headerlink" href="#get-output-sample-shape" title="Permalink to this headline">¶</a></h4>
+</div>
+<div class="section" id="forward-flag-x">
+<span id="id9"></span><h4>forward(flag, x)<a class="headerlink" href="#forward-flag-x" title="Permalink to this headline">¶</a></h4>
+<p>沿ç»å®è½´åå²è¾å
¥tensorã</p>
+<p><strong>åæ°ï¼</strong></p>
+<ul class="simple">
+<li><strong>flag</strong> â åLayer::forward()</li>
+<li><strong>x</strong> â å个è¾å
¥tensor</li>
+</ul>
+<p><strong>è¿åå¼ï¼</strong> è¾åºtensorå表</p>
+</div>
+<div class="section" id="backward-flag-grads">
+<span id="backward-flag-grads"></span><h4>backward(flag, grads)<a class="headerlink" href="#backward-flag-grads" title="Permalink to this headline">¶</a></h4>
+<p>æ¼æ¥ææ梯度tensor以çæä¸ä¸ªè¾åºtensorã</p>
+<p><strong>åæ°ï¼</strong></p>
+<ul class="simple">
+<li><strong>flag</strong> â åLayer::backward()</li>
+<li><strong>grads</strong> â tensorå表ï¼æ¯ä¸ªå¯¹åºä¸ä¸ªåå²ç梯度tensor
+<strong>è¿åå¼ï¼</strong> å
ç»(dx, []), dxæ¯ä¸ä¸ªtensorï¼å¯¹åºåå§è¾å
¥ç梯度ï¼[]æ¯ç©ºå表</li>
+</ul>
+</div>
+</div>
+<hr class="docutils" />
+<div class="section" id="class-singa-layer-rnn-name-hidden-size-rnn-mode-lstm-dropout-0-0-num-stacks-1-input-mode-linear-bidirectional-false-param-specs-none-input-sample-shape-none">
+<span id="class-singa-layer-rnn-name-hidden-size-rnn-mode-lstm-dropout-0-0-num-stacks-1-input-mode-linear-bidirectional-false-param-specs-none-input-sample-shape-none"></span><h3>class singa.layer.RNN(name, hidden_size, rnn_mode=âlstmâ, dropout=0.0, num_stacks=1, input_mode=âlinearâ, bidirectional=False, param_specs=None, input_sample_shape=None)<a class="headerlink" href="#class-singa-layer-rnn-name-hidden-size-rnn-mode-lstm-dropout-0-0-num-stacks-1-input-mode-linear-bidirectional-false-param-specs-none-input-sample-shape-none" title="Permalink to this headline">¶</a></h3>
+<p>åºç±»ï¼<code class="docutils literal notranslate"><span class="pre">singa.layer.Layer</span></code></p>
+<p>éå½å±å
å«4个åå
ï¼å³lstm, gru, tanhåreluã</p>
+<p><strong>åæ°ï¼</strong></p>
+<ul class="simple">
+<li><strong>hidden_size</strong> â éå«å±ç¹å¾å¤§å°ï¼åææå±çå æ ã</li>
+<li><strong>rnn_mode</strong> â å³å®äºRNNåå
ï¼å¯ä»¥æ¯âlstmâ, âgruâ, âtanhâå âreluâã对äºæ¯ç§æ¨¡å¼ï¼å¯ä»¥åècudnnæåã</li>
+<li><strong>num_stacks</strong> â rnnå±çå æ æ°éãè¿ä¸åäºéè¦å±å¼çåºåé¿åº¦ã</li>
+<li><strong>input_mode</strong> â âlinearâï¼éè¿çº¿æ§åæ¢å°è¾å
¥ç¹å¾x转æ¢æ大å°ä¸ºhidden_sizeçç¹å¾åéï¼âskipâï¼ä»
è¦æ±è¾å
¥ç¹å¾å¤§å°çäºhidden_sizeã</li>
+<li><strong>bidirection</strong> â 对äºååRNN为çã</li>
+<li><strong>param_specs</strong> â RNNåæ°çåå§åé
ç½®ã</li>
+<li><strong>input_sample_shape</strong> â å
å«ä¸ä¸ªæ´åæ°ï¼ä»£è¡¨è¾å
¥æ ·æ¬çç¹å¾å¤§å°ã</li>
+</ul>
+<div class="section" id="forward-flag-inputs">
+<span id="id10"></span><h4>forward(flag, inputs)<a class="headerlink" href="#forward-flag-inputs" title="Permalink to this headline">¶</a></h4>
+<p><strong>åæ°ï¼</strong></p>
+<ul class="simple">
+<li><strong>flag</strong> â True(kTrain) 代表è®ç»ï¼False(kEval)代表éªè¯; å
¶ä»å¼ç¨ä½ä»¥å使ç¨ã</li>
+<li><strong><x1, x2,..xn, hx, cx></strong> â å
¶ä¸ï¼xiæ¯è¾å
¥(inputs,)第i个ä½ç½®çtensorï¼å®çå½¢ç¶æ¯ (batch_size, input_feature_length); xiçbatch_sizeå¿
须大äºxi + 1ç大å°; hxæ¯åå§éèç¶æï¼å½¢ç¶æ¯ï¼num_stacks * bidirectionï¼2ï¼1ï¼batch_sizeï¼hidden_sizeï¼ã cxæ¯ä¸hyç¸åå½¢ç¶çåå§ç»èç¶æå¼ éã cxä»
对lstmææã 对äºå
¶ä»RNNï¼ä¸åå¨cxã hxåcxé½å¯ä»¥æ¯æ²¡æå½¢ç¶åæ°æ®çèæå¼ éã
+è¿åå¼ï¼<y1ï¼y2ï¼â¦ ynï¼hyï¼cy>ï¼å
¶ä¸yiæ¯ç¬¬i个ä½ç½®çè¾åºå¼ éï¼å
¶å½¢ç¶æ¯ï¼batch_sizeï¼hidden_size *ååï¼2ï¼1ï¼ã hyæ¯æç»çéèç¶æå¼ éã cxæ¯æç»çç»èç¶æå¼ éã cxä»
ç¨äºlstmã</li>
+</ul>
+</div>
+<div class="section" id="backward-flag-grad">
+<span id="id11"></span><h4>backward(flag, grad)<a class="headerlink" href="#backward-flag-grad" title="Permalink to this headline">¶</a></h4>
+<p><strong>åæ°ï¼</strong></p>
+<ul class="simple">
+<li><strong>flag</strong> â æªæ¥ä½¿ç¨</li>
+<li><strong><dy1, dy2,..dyn, dhy, dcy></strong> - å
¶ä¸ï¼dyiæ¯(grad,) 第i个ä½ç½®ç梯度ï¼å®çå½¢ç¶æ¯ (batch_size, hidden_size*bidirection?2 (i-th) â 1); dhyæ¯æç»éèç¶æçæ¸åï¼å®çå½¢ç¶æ¯ï¼num_stacks *ååï¼2ï¼1ï¼batch_sizeï¼hidden_sizeï¼ãdcyæ¯æç»åå
ç¶æç梯度ã cxä»
对lstmææï¼å
¶ä»RNNä¸åå¨cxã dhyådcyé½å¯ä»¥æ¯æ²¡æå½¢ç¶åæ°æ®çèætensorã</li>
+</ul>
+<p><strong>è¿åå¼ï¼</strong> <dx1ï¼dx2ï¼â¦ dxnï¼dhxï¼dcx>ï¼å
¶ä¸dxiæ¯ç¬¬i个è¾å
¥çæ¢¯åº¦å¼ éï¼å®çå½¢ç¶æ¯ï¼batch_sizeï¼input_feature_lengthï¼ã dhxæ¯åå§éèç¶æç梯度ã dcxæ¯åå§åå
ç¶æç梯度ï¼ä»
对lstmææã</p>
+</div>
+</div>
+<hr class="docutils" />
+<div class="section" id="class-singa-layer-lstm-name-hidden-size-dropout-0-0-num-stacks-1-input-mode-linear-bidirectional-false-param-specs-none-input-sample-shape-none">
+<span id="class-singa-layer-lstm-name-hidden-size-dropout-0-0-num-stacks-1-input-mode-linear-bidirectional-false-param-specs-none-input-sample-shape-none"></span><h3>class singa.layer.LSTM(name, hidden_size, dropout=0.0, num_stacks=1, input_mode=âlinearâ, bidirectional=False, param_specs=None, input_sample_shape=None)<a class="headerlink" href="#class-singa-layer-lstm-name-hidden-size-dropout-0-0-num-stacks-1-input-mode-linear-bidirectional-false-param-specs-none-input-sample-shape-none" title="Permalink to this headline">¶</a></h3>
+<p>åºç±»ï¼<code class="docutils literal notranslate"><span class="pre">singa.layer.RNN</span></code></p>
+</div>
+<hr class="docutils" />
+<div class="section" id="class-singa-layer-gru-name-hidden-size-dropout-0-0-num-stacks-1-input-mode-linear-bidirectional-false-param-specs-none-input-sample-shape-none">
+<span id="class-singa-layer-gru-name-hidden-size-dropout-0-0-num-stacks-1-input-mode-linear-bidirectional-false-param-specs-none-input-sample-shape-none"></span><h3>class singa.layer.GRU(name, hidden_size, dropout=0.0, num_stacks=1, input_mode=âlinearâ, bidirectional=False, param_specs=None, input_sample_shape=None)<a class="headerlink" href="#class-singa-layer-gru-name-hidden-size-dropout-0-0-num-stacks-1-input-mode-linear-bidirectional-false-param-specs-none-input-sample-shape-none" title="Permalink to this headline">¶</a></h3>
+<p>åºç±»ï¼<code class="docutils literal notranslate"><span class="pre">singa.layer.RNN</span></code></p>
+<hr class="docutils" />
+<div class="section" id="singa-layer-get-layer-list">
+<span id="singa-layer-get-layer-list"></span><h4>singa.layer.get_layer_list()<a class="headerlink" href="#singa-layer-get-layer-list" title="Permalink to this headline">¶</a></h4>
+<p>è¿åå
å«æææ¯æå¾å±çæ è¯ç¬¦ï¼æ ç¾ï¼çå符串å表</p>
+<hr class="docutils" />
+</div>
+</div>
+</div>
+</div>
+
+
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+
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</a></li>
+<li class="toctree-l2"><a class="reference internal" href="software_stack.html">软件æ¶æ</a></li>
+<li class="toctree-l2"><a class="reference internal" href="device.html">设å¤(Device)</a></li>
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+<li class="toctree-l2 current"><a class="current reference internal" href="#">æ失(Loss)</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#class-singa-loss-loss">class singa.loss.Loss</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#backward">backward()</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#evaluate-flag-x-y">evaluate(flag, x, y)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#forward-flag-x-y">forward(flag, x, y)</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="#class-singa-loss-softmaxcrossentropy">class singa.loss.SoftmaxCrossEntropy</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#class-singa-loss-squarederror">class singa.loss.SquaredError</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#backward">backward()</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#evaluate-flag-x-y">evaluate(flag, x, y)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#forward-flag-x-y">forward(flag, x, y)</a></li>
+</ul>
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+
+ <div class="section" id="loss">
+<span id="loss"></span><h1>æ失(Loss)<a class="headerlink" href="#loss" title="Permalink to this headline">¶</a></h1>
+<p>æ失模åå
æ¬ä¸ç»è®ç»æ失å®ä¾ã æäºæ¯ä»C ++å®ç°è½¬æ¢èæ¥çï¼å
¶ä½çé½æ¯ç´æ¥ä½¿ç¨python Tensorå®ç°çã</p>
+<p>示ä¾ç¨æ³ï¼</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">singa</span> <span class="kn">import</span> <span class="n">tensor</span>
+<span class="kn">from</span> <span class="nn">singa</span> <span class="kn">import</span> <span class="n">loss</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">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
+<span class="n">x</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span> <span class="c1"># randomly genearte the prediction activation</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">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</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">int</span><span class="p">))</span> <span class="c1"># set the truth</span>
+
+<span class="n">f</span> <span class="o">=</span> <span class="n">loss</span><span class="o">.</span><span class="n">SoftmaxCrossEntropy</span><span class="p">()</span>
+<span class="n">l</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="bp">True</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"># l is tensor with 3 loss values</span>
+<span class="n">g</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span> <span class="c1"># g is a tensor containing all gradients of x w.r.t l</span>
+</pre></div>
+</div>
+<hr class="docutils" />
+<div class="section" id="class-singa-loss-loss">
+<span id="class-singa-loss-loss"></span><h2>class singa.loss.Loss<a class="headerlink" href="#class-singa-loss-loss" title="Permalink to this headline">¶</a></h2>
+<p>åºç±»ï¼<code class="docutils literal notranslate"><span class="pre">object</span></code></p>
+<p>æ失类çåºç±»</p>
+<p>å°è£
C++æ失类çåç±»å¯ä»¥ä½¿ç¨æ¤åºç±»ç»§æ¿çforwardï¼backwardåevaluateå½æ°ã å
¶ä»åç±»éè¦éåè¿äºå½æ°</p>
+<div class="section" id="backward">
+<span id="backward"></span><h3>backward()<a class="headerlink" href="#backward" title="Permalink to this headline">¶</a></h3>
+<p><strong>è¿åå¼ï¼</strong> ä¸æ失ç¸å¯¹åºç梯度</p>
+</div>
+<hr class="docutils" />
+<div class="section" id="evaluate-flag-x-y">
+<span id="evaluate-flag-x-y"></span><h3>evaluate(flag, x, y)<a class="headerlink" href="#evaluate-flag-x-y" title="Permalink to this headline">¶</a></h3>
+<p><strong>åæ°ï¼</strong></p>
+<ul class="simple">
+<li><strong>flag (int)</strong> â å¿
é¡»æ¯kEval</li>
+<li><strong>x (Tensor)</strong> â é¢æµTensor</li>
+<li><strong>y (Tensor)</strong> â çå®Tensor</li>
+</ul>
+<p><strong>è¿åå¼ï¼</strong> æææ ·æ¬çå¹³åæ失</p>
+</div>
+<hr class="docutils" />
+<div class="section" id="forward-flag-x-y">
+<span id="forward-flag-x-y"></span><h3>forward(flag, x, y)<a class="headerlink" href="#forward-flag-x-y" title="Permalink to this headline">¶</a></h3>
+<p>计ç®æ失å¼</p>
+<p><strong>åæ°ï¼</strong></p>
+<ul class="simple">
+<li><strong>flag</strong> â kTrain/kEvalæå¸å°å¼ãå¦ææ¯kTrain/Trueï¼é£ä¹å¨ä¸ä¸æ¬¡è°ç¨forwardåä¼å
è°ç¨backward计ç®æ¢¯åº¦ã</li>
+<li><strong>x (Tensor)</strong> â é¢æµTensor</li>
+<li><strong>y (Tensor)</strong> â çå®Tensor, x.shape[0]å¿
é¡»åy.shape[0]ç¸å</li>
+</ul>
+<p><strong>è¿åå¼ï¼</strong> tensorï¼æ¯ä¸ªæ ·æ¬å¯¹åºä¸ä¸ªæµ®ç¹åæ失å¼</p>
+</div>
+</div>
+<hr class="docutils" />
+<div class="section" id="class-singa-loss-softmaxcrossentropy">
+<span id="class-singa-loss-softmaxcrossentropy"></span><h2>class singa.loss.SoftmaxCrossEntropy<a class="headerlink" href="#class-singa-loss-softmaxcrossentropy" title="Permalink to this headline">¶</a></h2>
+<p>åºç±»ï¼<code class="docutils literal notranslate"><span class="pre">singa.loss.Loss</span></code></p>
+<p>æ¤æ失å½æ°æ¯SoftMaxå交åçµæ失çç»åãå®éè¿SoftMaxå½æ°è½¬æ¢è¾å
¥ï¼ç¶åæ ¹æ®çå®å¼è®¡ç®äº¤åçµæ失ã 对äºæ¯ä¸ªæ ·æ¬ï¼çå®å¼å¯ä»¥æ¯ä¸ä¸ªæ´æ°ä½ä¸ºæ ç¾ç´¢å¼; æäºè¿å¶æ°ç»ï¼æ示æ ç¾åå¸ã å æ¤ï¼çå®å¼å¯ä»¥æ¯1ç»´æ2ç»´tensorã 对äºä¸æ¹æ ·åï¼æ°æ®/ç¹å¾tensorå¯ä»¥æ¯1ç»´ï¼å¯¹äºåä¸ªæ ·åï¼æ2ç»´ï¼å¯¹äºä¸ç»æ ·æ¬ï¼ã</p>
+</div>
+<hr class="docutils" />
+<div class="section" id="class-singa-loss-squarederror">
+<span id="class-singa-loss-squarederror"></span><h2>class singa.loss.SquaredError<a class="headerlink" href="#class-singa-loss-squarederror" title="Permalink to this headline">¶</a></h2>
+<p>åºç±»ï¼<code class="docutils literal notranslate"><span class="pre">singa.loss.Loss</span></code></p>
+<p>æ¤æ失ç¨æ¥è¡¡éé¢æµå¼åçå®å¼ä¹é´çå¹³æ¹å·®ãå®éè¿Python Tensoræä½å®ç°ã</p>
+<hr class="docutils" />
+<div class="section" id="backward">
+<span id="id1"></span><h3>backward()<a class="headerlink" href="#backward" title="Permalink to this headline">¶</a></h3>
+<p>计ç®ä¸æ失ç¸å¯¹åºåéç梯度ã</p>
+<p><strong>è¿åå¼ï¼</strong> x - y</p>
+</div>
+<hr class="docutils" />
+<div class="section" id="evaluate-flag-x-y">
+<span id="id2"></span><h3>evaluate(flag, x, y)<a class="headerlink" href="#evaluate-flag-x-y" title="Permalink to this headline">¶</a></h3>
+<p>计ç®å¹³å误差ã</p>
+<p><strong>è¿åå¼ï¼</strong> æµ®ç¹åæ°</p>
+</div>
+<hr class="docutils" />
+<div class="section" id="forward-flag-x-y">
+<span id="id3"></span><h3>forward(flag, x, y)<a class="headerlink" href="#forward-flag-x-y" title="Permalink to this headline">¶</a></h3>
+<p>éè¿0.5 * ||x-y||^2计ç®æ失ã</p>
+<p><strong>åæ°ï¼</strong></p>
+<ul class="simple">
+<li><strong>flag (int)</strong> â kTrainækEvalï¼å¦ææ¯kTrainï¼é£ä¹å¨ä¸ä¸æ¬¡è°ç¨forwardåä¼å
è°ç¨backward计ç®æ¢¯åº¦ã</li>
+<li><strong>x (Tensor)</strong> â é¢æµTensor</li>
+<li><strong>y (Tensor)</strong> â çå®Tensor, æ¯ä¸ªæ ·æ¬å¯¹åºä¸ä¸ªæ´åæ°, åå¼ä¸º[0, x.shape[1])ã</li>
+</ul>
+<p><strong>è¿åå¼ï¼</strong> tensorï¼æ¯ä¸ªæ ·æ¬å¯¹åºä¸ä¸ªæ失å¼</p>
+<hr class="docutils" />
+</div>
+</div>
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+
+
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