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Posted to commits@singa.apache.org by wa...@apache.org on 2018/06/08 15:29:02 UTC

svn commit: r1833193 [8/17] - in /incubator/singa/site/trunk: en/ en/_sources/ en/_sources/develop/ en/_sources/docs/ en/_sources/docs/model_zoo/examples/imagenet/densenet/ en/_sources/docs/model_zoo/examples/imagenet/vgg/ en/_sources/releases/ en/comm...

Modified: incubator/singa/site/trunk/zh/docs.html
URL: http://svn.apache.org/viewvc/incubator/singa/site/trunk/zh/docs.html?rev=1833193&r1=1833192&r2=1833193&view=diff
==============================================================================
--- incubator/singa/site/trunk/zh/docs.html (original)
+++ incubator/singa/site/trunk/zh/docs.html Fri Jun  8 15:29:00 2018
@@ -31,6 +31,16 @@
     <link rel="index" title="Index" href="genindex.html" />
     <link rel="search" title="Search" href="search.html" />
     <link href="_static/style.css" rel="stylesheet" type="text/css">
+    <!--link href="_static/fontawesome-all.min.css" rel="stylesheet" type="text/css"-->
+	<link rel="stylesheet" href="https://use.fontawesome.com/releases/v5.0.13/css/all.css" integrity="sha384-DNOHZ68U8hZfKXOrtjWvjxusGo9WQnrNx2sqG0tfsghAvtVlRW3tvkXWZh58N9jp" crossorigin="anonymous">
+    <style>
+	.fa:hover {
+	    opacity: 0.7;
+	}
+	.fab:hover {
+	    opacity: 0.7;
+	}
+    </style>
 
 
   
@@ -89,6 +99,7 @@
             
               <ul>
 <li class="toctree-l1"><a class="reference internal" href="downloads.html">下载 SINGA</a></li>
+<li class="toctree-l1"><a class="reference internal" href="docs/index.html">文档</a></li>
 </ul>
 <p class="caption"><span class="caption-text">开发</span></p>
 <ul>
@@ -167,6 +178,370 @@
   <div class="section" id="id1">
 <h1>文档<a class="headerlink" href="#id1" title="Permalink to this headline">¶</a></h1>
 <div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="docs/index.html">文档</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="docs/installation.html">安装</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="docs/installation.html#conda">从Conda安装</a></li>
+<li class="toctree-l3"><a class="reference internal" href="docs/installation.html#">从源码安装</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="docs/installation.html#condasinga">由conda创建SINGA</a></li>
+<li class="toctree-l4"><a class="reference internal" href="docs/installation.html#ubuntusinga">使用本地工具在Ubuntu上创建SINGA</a></li>
+<li class="toctree-l4"><a class="reference internal" href="docs/installation.html#windowssinga">在Windows上编译SINGA</a></li>
+<li class="toctree-l4"><a class="reference internal" href="docs/installation.html#">更多编译选择</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="docs/installation.html#use-modules">USE_MODULES</a></li>
+<li class="toctree-l5"><a class="reference internal" href="docs/installation.html#use-python">USE_PYTHON</a></li>
+<li class="toctree-l5"><a class="reference internal" href="docs/installation.html#use-cuda">USE_CUDA</a></li>
+<li class="toctree-l5"><a class="reference internal" href="docs/installation.html#use-opencl">USE_OPENCL</a></li>
+<li class="toctree-l5"><a class="reference internal" href="docs/installation.html#package">PACKAGE</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="docs/installation.html#faq">FAQ</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="docs/software_stack.html">软件架构</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="docs/software_stack.html#">核心</a></li>
+<li class="toctree-l3"><a class="reference internal" href="docs/software_stack.html#">模型</a></li>
+<li class="toctree-l3"><a class="reference internal" href="docs/software_stack.html#">输入输出</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="docs/device.html">设备(Device)</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="docs/device.html#">特定设备</a></li>
+<li class="toctree-l3"><a class="reference internal" href="docs/device.html#python-api">Python API</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="docs/device.html#singa-device-create-cuda-gpus-num">singa.device.create_cuda_gpus(num)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="docs/device.html#singa-device-create-cuda-gpus-on-device-ids">singa.device.create_cuda_gpus_on(device_ids)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="docs/device.html#singa-device-get-default-device">singa.device.get_default_device()</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="docs/tensor.html">张量(Tensor)</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="docs/tensor.html#python-api">PYTHON API</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="docs/tensor.html#class-singa-tensor-tensor-shape-none-device-none-dtype-0">class singa.tensor.Tensor(shape=None, device=None, dtype=0)</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="docs/tensor.html#t">T()</a></li>
+<li class="toctree-l5"><a class="reference internal" href="docs/tensor.html#add-column-v">add_column(v)</a></li>
+<li class="toctree-l5"><a class="reference internal" href="docs/tensor.html#add-row-v">add_row(v)</a></li>
+<li class="toctree-l5"><a class="reference internal" href="docs/tensor.html#bernoulli-p">bernoulli(p)</a></li>
+<li class="toctree-l5"><a class="reference internal" href="docs/tensor.html#clone">clone()</a></li>
+<li class="toctree-l5"><a class="reference internal" href="docs/tensor.html#copy">copy()</a></li>
+<li class="toctree-l5"><a class="reference internal" href="docs/tensor.html#copy-data-t">copy_data(t)</a></li>
+<li class="toctree-l5"><a class="reference internal" href="docs/tensor.html#copy-from-numpy-np-array-offset-0">copy_from_numpy(np_array, offset=0)</a></li>
+<li class="toctree-l5"><a class="reference internal" href="docs/tensor.html#deepcopy">deepcopy()</a></li>
+<li class="toctree-l5"><a class="reference internal" href="docs/tensor.html#div-column-v">div_column(v)</a></li>
+<li class="toctree-l5"><a class="reference internal" href="docs/tensor.html#div-row-v">div_row(v)</a></li>
+<li class="toctree-l5"><a class="reference internal" href="docs/tensor.html#gaussian-mean-std">gaussian(mean, std)</a></li>
+<li class="toctree-l5"><a class="reference internal" href="docs/tensor.html#is-empty">is_empty()</a></li>
+<li class="toctree-l5"><a class="reference internal" href="docs/tensor.html#is-transpose">is_transpose()</a></li>
+<li class="toctree-l5"><a class="reference internal" href="docs/tensor.html#l1">l1()</a></li>
+<li class="toctree-l5"><a class="reference internal" href="docs/tensor.html#l2">l2()</a></li>
+<li class="toctree-l5"><a class="reference internal" href="docs/tensor.html#memsize">memsize()</a></li>
+<li class="toctree-l5"><a class="reference internal" href="docs/tensor.html#mult-column-v">mult_column(v)</a></li>
+<li class="toctree-l5"><a class="reference internal" href="docs/tensor.html#mult-row-v">mult_row(v)</a></li>
+<li class="toctree-l5"><a class="reference internal" href="docs/tensor.html#ndim">ndim()</a></li>
+<li class="toctree-l5"><a class="reference internal" href="docs/tensor.html#reset-like-t">reset_like(t)</a></li>
+<li class="toctree-l5"><a class="reference internal" href="docs/tensor.html#set-value-x">set_value(x)</a></li>
+<li class="toctree-l5"><a class="reference internal" href="docs/tensor.html#size">size()</a></li>
+<li class="toctree-l5"><a class="reference internal" href="docs/tensor.html#to-device-device">to_device(device)</a></li>
+<li class="toctree-l5"><a class="reference internal" href="docs/tensor.html#to-host">to_host()</a></li>
+<li class="toctree-l5"><a class="reference internal" href="docs/tensor.html#uniform-low-high">uniform(low, high)</a></li>
+<li class="toctree-l5"><a class="reference internal" href="docs/tensor.html#singa-tensor-abs-t">singa.tensor.abs(t)</a></li>
+<li class="toctree-l5"><a class="reference internal" href="docs/tensor.html#singa-tensor-add-lhs-rhs-ret-none">singa.tensor.add(lhs, rhs, ret=None)</a></li>
+<li class="toctree-l5"><a class="reference internal" href="docs/tensor.html#singa-tensor-add-column-alpha-v-beta-m">singa.tensor.add_column(alpha, v, beta, M)</a></li>
+<li class="toctree-l5"><a class="reference internal" href="docs/tensor.html#singa-tensor-add-row-alpha-v-beta-m">singa.tensor.add_row(alpha, v, beta, M)</a></li>
+<li class="toctree-l5"><a class="reference internal" href="docs/tensor.html#singa-tensor-average-t-axis-none">singa.tensor.average(t, axis=None)</a></li>
+<li class="toctree-l5"><a class="reference internal" href="docs/tensor.html#singa-tensor-axpy-alpha-x-y">singa.tensor.axpy(alpha, x, y)</a></li>
+<li class="toctree-l5"><a class="reference internal" href="docs/tensor.html#singa-tensor-bernoulli-p-t">singa.tensor.bernoulli(p, t)</a></li>
+<li class="toctree-l5"><a class="reference internal" href="docs/tensor.html#singa-tensor-copy-data-to-from-dst-src-size-dst-offset-0-src-offset-0">singa.tensor.copy_data_to_from(dst, src, size, dst_offset=0, src_offset=0)</a></li>
+<li class="toctree-l5"><a class="reference internal" href="docs/tensor.html#singa-tensor-div-lhs-rhs-ret-none">singa.tensor.div(lhs, rhs, ret=None)</a></li>
+<li class="toctree-l5"><a class="reference internal" href="docs/tensor.html#singa-tensor-eltwise-mult-lhs-rhs-ret-none">singa.tensor.eltwise_mult(lhs, rhs, ret=None)</a></li>
+<li class="toctree-l5"><a class="reference internal" href="docs/tensor.html#singa-tensor-exp-t">singa.tensor.exp(t)</a></li>
+<li class="toctree-l5"><a class="reference internal" href="docs/tensor.html#singa-tensor-from-numpy-np-array">singa.tensor.from_numpy(np_array)</a></li>
+<li class="toctree-l5"><a class="reference internal" href="docs/tensor.html#singa-tensor-gaussian-mean-std-t">singa.tensor.gaussian(mean, std, t)</a></li>
+<li class="toctree-l5"><a class="reference internal" href="docs/tensor.html#singa-tensor-ge-t-x">singa.tensor.ge(t, x)</a></li>
+<li class="toctree-l5"><a class="reference internal" href="docs/tensor.html#singa-tensor-gt-t-x">singa.tensor.gt(t, x)</a></li>
+<li class="toctree-l5"><a class="reference internal" href="docs/tensor.html#singa-tensor-le-t-x">singa.tensor.le(t, x)</a></li>
+<li class="toctree-l5"><a class="reference internal" href="docs/tensor.html#singa-tensor-lt-t-x">singa.tensor.lt(t, x)</a></li>
+<li class="toctree-l5"><a class="reference internal" href="docs/tensor.html#singa-tensor-log-t">singa.tensor.log(t)</a></li>
+<li class="toctree-l5"><a class="reference internal" href="docs/tensor.html#singa-tensor-mult-a-b-c-none-alpha-1-0-beta-0-0">singa.tensor.mult(A, B, C=None, alpha=1.0, beta=0.0)</a></li>
+<li class="toctree-l5"><a class="reference internal" href="docs/tensor.html#singa-tensor-pow-t-x-out-none">singa.tensor.pow(t, x, out=None)</a></li>
+<li class="toctree-l5"><a class="reference internal" href="docs/tensor.html#singa-tensor-relu-t">singa.tensor.relu(t)</a></li>
+<li class="toctree-l5"><a class="reference internal" href="docs/tensor.html#singa-tensor-reshape-t-s">singa.tensor.reshape(t, s)</a></li>
+<li class="toctree-l5"><a class="reference internal" href="docs/tensor.html#singa-tensor-sigmoid-t">singa.tensor.sigmoid(t)</a></li>
+<li class="toctree-l5"><a class="reference internal" href="docs/tensor.html#singa-tensor-sign-t">singa.tensor.sign(t)</a></li>
+<li class="toctree-l5"><a class="reference internal" href="docs/tensor.html#singa-tensor-sizeof-dtype">singa.tensor.sizeof(dtype)</a></li>
+<li class="toctree-l5"><a class="reference internal" href="docs/tensor.html#singa-tensor-softmax-t-out-none">singa.tensor.softmax(t, out=None)</a></li>
+<li class="toctree-l5"><a class="reference internal" href="docs/tensor.html#singa-tensor-sqrt-t">singa.tensor.sqrt(t)</a></li>
+<li class="toctree-l5"><a class="reference internal" href="docs/tensor.html#singa-tensor-square-t">singa.tensor.square(t)</a></li>
+<li class="toctree-l5"><a class="reference internal" href="docs/tensor.html#singa-tensor-sub-lhs-rhs-ret-none">singa.tensor.sub(lhs, rhs, ret=None)</a></li>
+<li class="toctree-l5"><a class="reference internal" href="docs/tensor.html#singa-tensor-sum-t-axis-none">singa.tensor.sum(t, axis=None)</a></li>
+<li class="toctree-l5"><a class="reference internal" href="docs/tensor.html#singa-tensor-sum-columns-m">singa.tensor.sum_columns(M)</a></li>
+<li class="toctree-l5"><a class="reference internal" href="docs/tensor.html#singa-tensor-sum-rows-m">singa.tensor.sum_rows(M)</a></li>
+<li class="toctree-l5"><a class="reference internal" href="docs/tensor.html#singa-tensor-tanh-t">singa.tensor.tanh(t)</a></li>
+<li class="toctree-l5"><a class="reference internal" href="docs/tensor.html#singa-tensor-to-host-t">singa.tensor.to_host(t)</a></li>
+<li class="toctree-l5"><a class="reference internal" href="docs/tensor.html#singa-tensor-to-numpy-t">singa.tensor.to_numpy(t)</a></li>
+<li class="toctree-l5"><a class="reference internal" href="docs/tensor.html#singa-tensor-uniform-low-high-t">singa.tensor.uniform(low, high, t)</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="docs/layer.html">层(Layer)</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="docs/layer.html#python-api">Python API</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="docs/layer.html#singa-layer-engine-cudnn">singa.layer.engine = ‘cudnn’</a></li>
+<li class="toctree-l4"><a class="reference internal" href="docs/layer.html#class-singa-layer-layer-name-conf-none-kwargs">class singa.layer.Layer(name, conf=None, **kwargs)</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="docs/layer.html#setup-in-shapes">setup(in_shapes)</a></li>
+<li class="toctree-l5"><a class="reference internal" href="docs/layer.html#caffe-layer">caffe_layer()</a></li>
+<li class="toctree-l5"><a class="reference internal" href="docs/layer.html#get-output-sample-shape">get_output_sample_shape()</a></li>
+<li class="toctree-l5"><a class="reference internal" href="docs/layer.html#param-names">param_names()</a></li>
+<li class="toctree-l5"><a class="reference internal" href="docs/layer.html#param-values">param_values()</a></li>
+<li class="toctree-l5"><a class="reference internal" href="docs/layer.html#forward-flag-x">forward(flag, x)</a></li>
+<li class="toctree-l5"><a class="reference internal" href="docs/layer.html#backward-flag-dy">backward(flag, dy)</a></li>
+<li class="toctree-l5"><a class="reference internal" href="docs/layer.html#to-device-device">to_device(device)</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="docs/layer.html#class-singa-layer-dummy-name-input-sample-shape-none">class singa.layer.Dummy(name, input_sample_shape=None)</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="docs/layer.html#forward-flag-x">forward(flag, x)</a></li>
+<li class="toctree-l5"><a class="reference internal" href="docs/layer.html#backward-falg-dy">backward(falg, dy)</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="docs/layer.html#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=’same’, cudnn_prefer=’fatest’, data_format=’NCHW’, 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="docs/layer.html#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=’same’, cudnn_prefer=’fatest’, use_bias=True, W_specs={‘init’: ‘Xavier’}, b_specs={‘init’: ‘Constant’, ‘value’: 0}, pad=None, input_sample_shape=None)</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="docs/layer.html#get-output-sample-shape">get_output_sample_shape()</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="docs/layer.html#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=’same’, pad=None, data_format=’NCHW’, input_sample_shape=None)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="docs/layer.html#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=’same’, pad=None, data_format=’NCHW’, input_sample_shape=None)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="docs/layer.html#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=’same’, pad=None, data_format=’NCHW’, input_sample_shape=None)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="docs/layer.html#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=’same’, pad=None, data_format=’NCHW’, input_sample_shape=None)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="docs/layer.html#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=’same’, pad=None, data_format=’NCHW’, input_sample_shape=None)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="docs/layer.html#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="docs/layer.html#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=’cross_channel’, k=1, input_sample_shape=None)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="docs/layer.html#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="docs/layer.html#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="docs/layer.html#class-singa-layer-activation-name-mode-relu-input-sample-shape-none">class singa.layer.Activation(name, mode=’relu’, input_sample_shape=None)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="docs/layer.html#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="docs/layer.html#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="docs/layer.html#class-singa-layer-merge-name-input-sample-shape-none">class singa.layer.Merge(name, input_sample_shape=None)</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="docs/layer.html#setup-in-shape">setup(in_shape)</a></li>
+<li class="toctree-l5"><a class="reference internal" href="docs/layer.html#get-output-sample-shape">get_output_sample_shape()</a></li>
+<li class="toctree-l5"><a class="reference internal" href="docs/layer.html#forward-flag-inputs">forward(flag, inputs)</a></li>
+<li class="toctree-l5"><a class="reference internal" href="docs/layer.html#backward-flag-grad">backward(flag, grad)</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="docs/layer.html#class-singa-layer-split-name-num-output-input-sample-shape-none">class singa.layer.Split(name, num_output, input_sample_shape=None)</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="docs/layer.html#setup-in-shape">setup(in_shape)</a></li>
+<li class="toctree-l5"><a class="reference internal" href="docs/layer.html#get-output-sample-shape">get_output_sample_shape()</a></li>
+<li class="toctree-l5"><a class="reference internal" href="docs/layer.html#forward">forward()</a></li>
+<li class="toctree-l5"><a class="reference internal" href="docs/layer.html#backward">backward()</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="docs/layer.html#class-singa-layer-concat-name-axis-input-sample-shapes-none">class singa.layer.Concat(name, axis, input_sample_shapes=None)</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="docs/layer.html#forward-flag-inputs">forward(flag, inputs)</a></li>
+<li class="toctree-l5"><a class="reference internal" href="docs/layer.html#backward-flag-dy">backward(flag, dy)</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="docs/layer.html#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><ul>
+<li class="toctree-l5"><a class="reference internal" href="docs/layer.html#get-output-sample-shape">get_output_sample_shape()</a></li>
+<li class="toctree-l5"><a class="reference internal" href="docs/layer.html#forward-flag-x">forward(flag, x)</a></li>
+<li class="toctree-l5"><a class="reference internal" href="docs/layer.html#backward-flag-grads">backward(flag, grads)</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="docs/layer.html#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=’lstm’, dropout=0.0, num_stacks=1, input_mode=’linear’, bidirectional=False, param_specs=None, input_sample_shape=None)</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="docs/layer.html#forward-flag-inputs">forward(flag, inputs)</a></li>
+<li class="toctree-l5"><a class="reference internal" href="docs/layer.html#backward-flag-grad">backward(flag, grad)</a></li>
+</ul>
+</li>
+<li class="toctree-l4"><a class="reference internal" href="docs/layer.html#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=’linear’, bidirectional=False, param_specs=None, input_sample_shape=None)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="docs/layer.html#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=’linear’, bidirectional=False, param_specs=None, input_sample_shape=None)</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="docs/layer.html#singa-layer-get-layer-list">singa.layer.get_layer_list()</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="docs/net.html">前馈网络</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="docs/net.html#class-singa-net-feedforwardnet-loss-none-metric-none">class singa.net.FeedForwardNet(loss=None, metric=None)</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="docs/net.html#to-device-dev">to_device(dev)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="docs/net.html#add-lyr-src-none">add(lyr, src=None)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="docs/net.html#param-values">param_values()</a></li>
+<li class="toctree-l4"><a class="reference internal" href="docs/net.html#param-specs">param_specs()</a></li>
+<li class="toctree-l4"><a class="reference internal" href="docs/net.html#param-names">param_names()</a></li>
+<li class="toctree-l4"><a class="reference internal" href="docs/net.html#train-x-y">train(x, y)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="docs/net.html#evaluate-x-y">evaluate(x, y)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="docs/net.html#predict-x">predict(x)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="docs/net.html#topo-sort-layers-src-of-layer">topo_sort(layers, src_of_layer)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="docs/net.html#forward-flag-x-output">forward(flag, x, output=[])</a></li>
+<li class="toctree-l4"><a class="reference internal" href="docs/net.html#backward">backward()</a></li>
+<li class="toctree-l4"><a class="reference internal" href="docs/net.html#save-f-buffer-size-10-use-pickle-false">save(f, buffer_size=10, use_pickle=False)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="docs/net.html#load-f-buffer-size-10-use-pickle-false">load(f, buffer_size=10, use_pickle=False)</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="docs/initializer.html">初始化器(Initializer)</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="docs/initializer.html#python-api">Python API</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="docs/initializer.html#singa-initializer-uniform-t-fan-in-0-fan-out-0">singa.initializer.uniform(t, fan_in=0, fan_out=0)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="docs/initializer.html#singa-initializer-gaussian-t-fan-in-0-fan-out-0">singa.initializer.gaussian(t, fan_in=0, fan_out=0)</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="docs/loss.html">损失(Loss)</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="docs/loss.html#class-singa-loss-loss">class singa.loss.Loss</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="docs/loss.html#backward">backward()</a></li>
+<li class="toctree-l4"><a class="reference internal" href="docs/loss.html#evaluate-flag-x-y">evaluate(flag, x, y)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="docs/loss.html#forward-flag-x-y">forward(flag, x, y)</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="docs/loss.html#class-singa-loss-softmaxcrossentropy">class singa.loss.SoftmaxCrossEntropy</a></li>
+<li class="toctree-l3"><a class="reference internal" href="docs/loss.html#class-singa-loss-squarederror">class singa.loss.SquaredError</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="docs/loss.html#backward">backward()</a></li>
+<li class="toctree-l4"><a class="reference internal" href="docs/loss.html#evaluate-flag-x-y">evaluate(flag, x, y)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="docs/loss.html#forward-flag-x-y">forward(flag, x, y)</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="docs/metric.html">度量(Metric)</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="docs/metric.html#class-singa-metric-metric">class singa.metric.Metric</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="docs/metric.html#forward-x-y">forward(x, y)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="docs/metric.html#evaluate-x-y">evaluate(x, y)</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="docs/metric.html#class-singa-metric-accuracy">class singa.metric.Accuracy</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="docs/optimizer.html">优化器(Optimizer)</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="docs/optimizer.html#class-singa-optimizer-optimizer-lr-none-momentum-none-weight-decay-none-regularizer-none-constraint-none">class singa.optimizer.Optimizer(lr=None, momentum=None, weight_decay=None, regularizer=None, constraint=None)</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="docs/optimizer.html#register-name-specs">register(name, specs)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="docs/optimizer.html#apply-regularizer-constraint-epoch-value-grad-name-none-step-1">apply_regularizer_constraint(epoch, value, grad, name=None, step=-1)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="docs/optimizer.html#apply-with-lr-epoch-lr-grad-value-name-none-step-1">apply_with_lr(epoch, lr, grad, value, name=None, step=-1)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="docs/optimizer.html#apply-epoch-grad-value-name-none-step-1">apply(epoch, grad, value, name=None, step=-1)</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="docs/optimizer.html#class-singa-optimizer-sgd-lr-none-momentum-none-weight-decay-none-regularizer-none-constraint-none">class singa.optimizer.SGD(lr=None, momentum=None, weight_decay=None, regularizer=None, constraint=None)</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="docs/optimizer.html#apply-with-lr-epoch-lr-grad-value-name-step-1">apply_with_lr(epoch, lr, grad, value, name, step=-1)</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="docs/optimizer.html#class-singa-optimizer-nesterov-lr-none-momentum-0-9-weight-decay-none-regularizer-none-constraint-none">class singa.optimizer.Nesterov(lr=None, momentum=0.9, weight_decay=None, regularizer=None, constraint=None)</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="docs/optimizer.html#apply-with-lr-epoch-lr-grad-value-name-step-1">apply_with_lr(epoch, lr, grad, value, name, step=-1)</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="docs/optimizer.html#class-singa-optimizer-rmsprop-rho-0-9-epsilon-1e-08-lr-none-weight-decay-none-regularizer-none-constraint-none">class singa.optimizer.RMSProp(rho=0.9, epsilon=1e-08, lr=None, weight_decay=None, regularizer=None, constraint=None)</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="docs/optimizer.html#apply-with-lr-epoch-lr-grad-value-name-step-1">apply_with_lr(epoch, lr, grad, value, name, step=-1)</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="docs/optimizer.html#class-singa-optimizer-adagrad-epsilon-1e-08-lr-none-weight-decay-none-lr-gen-none-regularizer-none-constraint-none">class singa.optimizer.AdaGrad(epsilon=1e-08, lr=None, weight_decay=None, lr_gen=None, regularizer=None, constraint=None)</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="docs/optimizer.html#apply-with-lr-epoch-lr-grad-value-name-step-1">apply_with_lr(epoch, lr, grad, value, name, step=-1)</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="docs/optimizer.html#class-singa-optimizer-adam-beta-1-0-9-beta-2-0-999-epsilon-1e-08-lr-none-weight-decay-none-regularizer-none-constraint-none">class singa.optimizer.Adam(beta_1=0.9, beta_2=0.999, epsilon=1e-08, lr=None, weight_decay=None, regularizer=None, constraint=None)</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="docs/optimizer.html#apply-with-lr-epoch-lr-grad-value-name-step-1">apply_with_lr(epoch, lr, grad, value, name, step=-1)</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="docs/optimizer.html#class-singa-optimizer-regularizer">class singa.optimizer.Regularizer</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="docs/optimizer.html#apply-epoch-value-grad-step-1">apply(epoch, value, grad, step=-1)</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="docs/optimizer.html#class-singa-optimizer-cppregularizer-conf">class singa.optimizer.CppRegularizer(conf)</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="docs/optimizer.html#apply-epoch-value-grad-step-1">apply(epoch, value, grad, step=-1)</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="docs/optimizer.html#class-singa-optimizer-l2regularizer-coefficient">class singa.optimizer.L2Regularizer(coefficient)</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="docs/optimizer.html#apply-epoch-value-grad-step-1">apply(epoch, value, grad, step=-1)</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="docs/optimizer.html#class-singa-optimizer-constraint">class singa.optimizer.Constraint</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="docs/optimizer.html#apply-epoch-value-grad-step-1">apply(epoch, value, grad, step=-1)</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="docs/optimizer.html#class-singa-optimizer-cppconstraint-conf">class singa.optimizer.CppConstraint(conf)</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="docs/optimizer.html#apply-epoch-value-grad-step-1">apply(epoch, value, grad, step=-1)</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="docs/optimizer.html#class-singa-optimizer-l2constraint-threshold-none">class singa.optimizer.L2Constraint(threshold=None)</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="docs/optimizer.html#apply-epoch-value-grad-step-1">apply(epoch, value, grad, step=-1)</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="docs/data.html">数据(Data)</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="docs/data.html#class-singa-data-imagebatchiter-img-list-file-batch-size-image-transform-shuffle-true-delimiter-image-folder-none-capacity-10">class singa.data.ImageBatchIter(img_list_file, batch_size, image_transform, shuffle=True, delimiter=’ ‘, image_folder=None, capacity=10)</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="docs/image_tool.html">图像工具</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="docs/image_tool.html#class-singa-image-tool-imagetool">class singa.image_tool.ImageTool</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="docs/image_tool.html#color-cast-offset-20-inplace-true">color_cast(offset=20, inplace=True)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="docs/image_tool.html#crop3-patch-num-case-1-inplace-true">crop3(patch, num_case=1, inplace=True)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="docs/image_tool.html#crop5-patch-num-case-1-inplace-true">crop5(patch, num_case=1, inplace=True)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="docs/image_tool.html#crop8-patch-num-case-1-inplace-true">crop8(patch, num_case=1, inplace=True)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="docs/image_tool.html#enhance-scale-0-2-inplace-true">enhance(scale=0.2, inplace=True)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="docs/image_tool.html#flip-num-case-1-inplace-true">flip(num_case=1, inplace=True)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="docs/image_tool.html#num-augmentation">num_augmentation()</a></li>
+<li class="toctree-l4"><a class="reference internal" href="docs/image_tool.html#random-crop-patch-inplace-true">random_crop(patch, inplace=True)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="docs/image_tool.html#resize-by-list-size-list-num-case-1-inplace-true">resize_by_list(size_list, num_case=1, inplace=True)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="docs/image_tool.html#resize-by-range-rng-inplace-true">resize_by_range(rng, inplace=True)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="docs/image_tool.html#rotate-by-list-angle-list-num-case-1-inplace-true">rotate_by_list(angle_list, num_case=1, inplace=True)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="docs/image_tool.html#rotate-by-range-rng-inplace-true">rotate_by_range(rng, inplace=True)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="docs/image_tool.html#singa-image-tool-color-cast-img-offset">singa.image_tool.color_cast(img, offset)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="docs/image_tool.html#singa-image-tool-crop-img-patch-position">singa.image_tool.crop(img, patch, position)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="docs/image_tool.html#singa-image-tool-crop-and-resize-img-patch-position">singa.image_tool.crop_and_resize(img, patch, position)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="docs/image_tool.html#singa-image-tool-enhance-img-scale">singa.image_tool.enhance(img, scale)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="docs/image_tool.html#singa-image-tool-load-img-path-grayscale-false">singa.image_tool.load_img(path, grayscale=False)</a></li>
+<li class="toctree-l4"><a class="reference internal" href="docs/image_tool.html#singa-image-tool-resize-img-small-size">singa.image_tool.resize(img, small_size)</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="docs/snapshot.html">Snapshot</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="docs/snapshot.html#class-singa-snapshot-snapshot-f-mode-buffer-size-10">class singa.snapshot.Snapshot(f, mode, buffer_size=10)</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="docs/snapshot.html#read">read()</a></li>
+<li class="toctree-l4"><a class="reference internal" href="docs/snapshot.html#write-param-name-param-val">write(param_name, param_val)</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="docs/utils.html">Utils</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="docs/utils.html#singa-utils-update-progress-progress-info">singa.utils.update_progress(progress, info)</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="docs/model_zoo/index.html">模型库</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="docs/model_zoo/cifar10/README.html">在Cifar-10上训练CNN</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="docs/model_zoo/cifar10/README.html#">操作说明</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="docs/model_zoo/cifar10/README.html#singa">SINGA安装</a></li>
+<li class="toctree-l5"><a class="reference internal" href="docs/model_zoo/cifar10/README.html#">数据准备</a></li>
+<li class="toctree-l5"><a class="reference internal" href="docs/model_zoo/cifar10/README.html#">训练</a></li>
+<li class="toctree-l5"><a class="reference internal" href="docs/model_zoo/cifar10/README.html#">预测</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="docs/model_zoo/char-rnn/README.html">在文本上训练Char-RNN</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="docs/model_zoo/char-rnn/README.html#">操作说明</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="docs/model_zoo/imagenet/alexnet/README.html">在ImageNet上训练AlexNet</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="docs/model_zoo/imagenet/alexnet/README.html#">操作说明</a><ul>
+<li class="toctree-l5"><a class="reference internal" href="docs/model_zoo/imagenet/alexnet/README.html#singa">编译SINGA</a></li>
+<li class="toctree-l5"><a class="reference internal" href="docs/model_zoo/imagenet/alexnet/README.html#">数据下载</a></li>
+<li class="toctree-l5"><a class="reference internal" href="docs/model_zoo/imagenet/alexnet/README.html#">数据预处理</a></li>
+<li class="toctree-l5"><a class="reference internal" href="docs/model_zoo/imagenet/alexnet/README.html#">训练</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="docs/model_zoo/imagenet/googlenet/README.html">用GoogleNet做图像分类</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="docs/model_zoo/imagenet/googlenet/README.html#">操作说明</a></li>
+<li class="toctree-l4"><a class="reference internal" href="docs/model_zoo/imagenet/googlenet/README.html#">详细信息</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
 </div>
 </div>
 
@@ -248,6 +623,15 @@
 
   </div>
   <a href="http://incubator.apache.org/"> <img src= "_static/apache.png" style="background-color:white;"> </a>
+
+  <a href="https://github.com/apache/incubator-singa" class="fa fa-github" style="padding: 10px; font-size: 20px; width: 30px; text-align: center; text-decoration: none; margin: 5px 2px;"></a>
+  <a href="https://aws.amazon.com/marketplace/seller-profile?id=5bcac385-12c4-4802-aec7-351e09b77b4c" class="fab fa-aws" style="padding: 10px; font-size: 20px; width: 30px; text-align: center; text-decoration: none; margin: 5px 2px;"></a>
+  <a href="https://hub.docker.com/r/nusdbsystem/singa/" class="fab fa-docker" style="padding: 10px; font-size: 20px; width: 30px; text-align: center; text-decoration: none; margin: 5px 2px;"></a> 
+  <a href="https://www.linkedin.com/groups/13550034" class="fa fa-linkedin" style="padding: 10px; font-size: 20px; width: 30px; text-align: center; text-decoration: none; margin: 5px 2px;"></a>
+  <a href="https://twitter.com/ApacheSinga" class="fa fa-twitter" style="padding: 10px; font-size: 20px; width: 30px; text-align: center; text-decoration: none; margin: 5px 2px;"></a>
+  <a href="https://www.facebook.com/Apache-SINGA-347284219056544/" class="fa fa-facebook" style="padding: 10px; font-size: 20px; width: 30px; text-align: center; text-decoration: none; margin: 5px 2px;"></a>
+  <a href="https://www.researchgate.net/project/Apache-SINGA" class="fab fa-researchgate" style="padding: 10px; font-size: 20px; width: 30px; text-align: center; text-decoration: none; margin: 5px 2px;"></a>
+
 </div>
 
  <a href="https://github.com/apache/incubator-singa">

Added: incubator/singa/site/trunk/zh/docs/data.html
URL: http://svn.apache.org/viewvc/incubator/singa/site/trunk/zh/docs/data.html?rev=1833193&view=auto
==============================================================================
--- incubator/singa/site/trunk/zh/docs/data.html (added)
+++ incubator/singa/site/trunk/zh/docs/data.html Fri Jun  8 15:29:00 2018
@@ -0,0 +1,360 @@
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+<li class="toctree-l2"><a class="reference internal" href="device.html">设备(Device)</a></li>
+<li class="toctree-l2"><a class="reference internal" href="tensor.html">张量(Tensor)</a></li>
+<li class="toctree-l2"><a class="reference internal" href="layer.html">层(Layer)</a></li>
+<li class="toctree-l2"><a class="reference internal" href="net.html">前馈网络</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="loss.html">损失(Loss)</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="optimizer.html">优化器(Optimizer)</a></li>
+<li class="toctree-l2 current"><a class="current reference internal" href="#">数据(Data)</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#class-singa-data-imagebatchiter-img-list-file-batch-size-image-transform-shuffle-true-delimiter-image-folder-none-capacity-10">class singa.data.ImageBatchIter(img_list_file, batch_size, image_transform, shuffle=True, delimiter=’ ‘, image_folder=None, capacity=10)</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="image_tool.html">图像工具</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="model_zoo/index.html">模型库</a></li>
+</ul>
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+</ul>
+<p class="caption"><span class="caption-text">开发</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="../develop/contribute-code.html">如何贡献代码</a></li>
+</ul>
+<p class="caption"><span class="caption-text">社区</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../community/source-repository.html">源代码库</a></li>
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+      <li><a href="../index.html">Docs</a> &raquo;</li>
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+        
+      <li>数据(Data)</li>
+    
+    
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+        
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+           <div itemprop="articleBody">
+            
+  <div class="section" id="data">
+<span id="data"></span><h1>数据(Data)<a class="headerlink" href="#data" title="Permalink to this headline">¶</a></h1>
+<p>这个模块包含加载和预获取批数据的类。</p>
+<p>示例用法：</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">image_tool</span>
+<span class="kn">from</span> <span class="nn">PIL</span> <span class="kn">import</span> <span class="n">Image</span>
+
+<span class="n">tool</span> <span class="o">=</span> <span class="n">image_tool</span><span class="o">.</span><span class="n">ImageTool</span><span class="p">()</span>
+
+<span class="k">def</span> <span class="nf">image_transform</span><span class="p">(</span><span class="n">img_path</span><span class="p">):</span>
+    <span class="k">global</span> <span class="n">tool</span>
+    <span class="k">return</span> <span class="n">tool</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">img_path</span><span class="p">)</span><span class="o">.</span><span class="n">resize_by_range</span><span class="p">(</span>
+        <span class="p">(</span><span class="mi">112</span><span class="p">,</span> <span class="mi">128</span><span class="p">))</span><span class="o">.</span><span class="n">random_crop</span><span class="p">(</span>
+        <span class="p">(</span><span class="mi">96</span><span class="p">,</span> <span class="mi">96</span><span class="p">))</span><span class="o">.</span><span class="n">flip</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">()</span>
+
+<span class="n">data</span> <span class="o">=</span> <span class="n">ImageBatchIter</span><span class="p">(</span><span class="s1">&#39;train.txt&#39;</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span>
+                      <span class="n">image_transform</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span> <span class="n">delimiter</span><span class="o">=</span><span class="s1">&#39;,&#39;</span><span class="p">,</span>
+                      <span class="n">image_folder</span><span class="o">=</span><span class="s1">&#39;images/&#39;</span><span class="p">,</span>
+                      <span class="n">capacity</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
+<span class="n">data</span><span class="o">.</span><span class="n">start</span><span class="p">()</span>
+<span class="c1"># imgs is a numpy array for a batch of images,</span>
+<span class="c1"># shape: batch_size, 3 (RGB), height, width</span>
+<span class="n">imgs</span><span class="p">,</span> <span class="n">labels</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">next</span><span class="p">()</span>
+
+<span class="c1"># convert numpy array back into images</span>
+<span class="k">for</span> <span class="n">idx</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">imgs</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
+    <span class="n">img</span> <span class="o">=</span> <span class="n">Image</span><span class="o">.</span><span class="n">fromarray</span><span class="p">(</span><span class="n">imgs</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">uint8</span><span class="p">)</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span>
+                          <span class="s1">&#39;RGB&#39;</span><span class="p">)</span>
+    <span class="n">img</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s1">&#39;img</span><span class="si">%d</span><span class="s1">.png&#39;</span> <span class="o">%</span> <span class="n">idx</span><span class="p">)</span>
+<span class="n">data</span><span class="o">.</span><span class="n">end</span><span class="p">()</span>
+</pre></div>
+</div>
+<hr class="docutils" />
+<div class="section" id="class-singa-data-imagebatchiter-img-list-file-batch-size-image-transform-shuffle-true-delimiter-image-folder-none-capacity-10">
+<span id="class-singa-data-imagebatchiter-img-list-file-batch-size-image-transform-shuffle-true-delimiter-image-folder-none-capacity-10"></span><h2>class singa.data.ImageBatchIter(img_list_file, batch_size, image_transform, shuffle=True, delimiter=’ ‘, image_folder=None, capacity=10)<a class="headerlink" href="#class-singa-data-imagebatchiter-img-list-file-batch-size-image-transform-shuffle-true-delimiter-image-folder-none-capacity-10" title="Permalink to this headline">¶</a></h2>
+<p>迭代地从数据集中获取批数据。</p>
+<p><strong>参数：</strong></p>
+<ul class="simple">
+<li><strong>img_list_file (str)</strong> – 包含源数据的文件名；每行包含image_path_suffix和标签</li>
+<li><strong>batch_size (int)</strong> – 每个mini-bach包含的样本数目</li>
+<li><strong>image_transform</strong> – 图像增强函数；它接受完整的图像路径并输出一系列增强后的图像</li>
+<li><strong>shuffle (boolean)</strong> – 为真表示对列表做搅乱</li>
+<li><strong>delimiter (char)</strong> – image_path_suffix和标签之间的分割符, 例如空格或逗号</li>
+<li><strong>image_folder (boolean)</strong> – 图片路径的前缀</li>
+<li><strong>capacity (int)</strong> – 内部队列的最大mini-batch数目</li>
+</ul>
+<hr class="docutils" />
+</div>
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+<div class="rst-versions" data-toggle="rst-versions" role="note" aria-label="versions">
+  <span class="rst-current-version" data-toggle="rst-current-version">
+    <span class="fa fa-book"> incubator-singa </span>
+    v: latest
+    <span class="fa fa-caret-down"></span>
+  </span>
+  <div class="rst-other-versions">
+      <dl>
+          <dt>Languages</dt>
+          <dd><a href="../../en/index.html">English</a></dd>
+          <dd><a href="../../zh/index.html">中文</a></dd>
+      </dl>
+      <dl>
+          <dt>Versions</dt>
+          <dd><a href="http://singa.apache.org/v0.3.0/">0.3</a></dd>
+          <dd><a href="http://singa.apache.org/v1.1.0/">1.1</a></dd>
+      </dl>
+
+  </div>
+  <a href="http://incubator.apache.org/"> <img src= "../_static/apache.png" style="background-color:white;"> </a>
+
+  <a href="https://github.com/apache/incubator-singa" class="fa fa-github" style="padding: 10px; font-size: 20px; width: 30px; text-align: center; text-decoration: none; margin: 5px 2px;"></a>
+  <a href="https://aws.amazon.com/marketplace/seller-profile?id=5bcac385-12c4-4802-aec7-351e09b77b4c" class="fab fa-aws" style="padding: 10px; font-size: 20px; width: 30px; text-align: center; text-decoration: none; margin: 5px 2px;"></a>
+  <a href="https://hub.docker.com/r/nusdbsystem/singa/" class="fab fa-docker" style="padding: 10px; font-size: 20px; width: 30px; text-align: center; text-decoration: none; margin: 5px 2px;"></a> 
+  <a href="https://www.linkedin.com/groups/13550034" class="fa fa-linkedin" style="padding: 10px; font-size: 20px; width: 30px; text-align: center; text-decoration: none; margin: 5px 2px;"></a>
+  <a href="https://twitter.com/ApacheSinga" class="fa fa-twitter" style="padding: 10px; font-size: 20px; width: 30px; text-align: center; text-decoration: none; margin: 5px 2px;"></a>
+  <a href="https://www.facebook.com/Apache-SINGA-347284219056544/" class="fa fa-facebook" style="padding: 10px; font-size: 20px; width: 30px; text-align: center; text-decoration: none; margin: 5px 2px;"></a>
+  <a href="https://www.researchgate.net/project/Apache-SINGA" class="fab fa-researchgate" style="padding: 10px; font-size: 20px; width: 30px; text-align: center; text-decoration: none; margin: 5px 2px;"></a>
+
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+
+ <a href="https://github.com/apache/incubator-singa">
+    <img style="position: absolute; top: 0; right: 0; border: 0; z-index: 10000;"
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+        alt="Fork me on GitHub">
+</a>
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+ 
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+
+</body>
+</html>
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Added: incubator/singa/site/trunk/zh/docs/device.html
URL: http://svn.apache.org/viewvc/incubator/singa/site/trunk/zh/docs/device.html?rev=1833193&view=auto
==============================================================================
--- incubator/singa/site/trunk/zh/docs/device.html (added)
+++ incubator/singa/site/trunk/zh/docs/device.html Fri Jun  8 15:29:00 2018
@@ -0,0 +1,368 @@
+
+
+
+<!DOCTYPE html>
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+    <style>
+	.fa:hover {
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+	}
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+  <div class="section" id="device">
+<span id="device"></span><h1>设备(Device)<a class="headerlink" href="#device" title="Permalink to this headline">¶</a></h1>
+<p>设备抽象代表了任何基于内存和计算单元的硬件设备。所有<a class="reference external" href="tensor.html">Tensor操作</a>由寄宿的设备进行调度以执行。Tensor内存也由设备内存管理器进行管理。因此，内存优化和执行都由Device类进行实现。</p>
+<div class="section" id="">
+<span id="id1"></span><h2>特定设备<a class="headerlink" href="#" title="Permalink to this headline">¶</a></h2>
+<p>目前，SINGA有三种Device实现，</p>
+<ol class="simple">
+<li>CudaGPU： 在Nvidia GPU卡上运行Cuda代码</li>
+<li>CppCPU：在CPU上运行Cpp代码</li>
+<li>OpenclGPU： GPU卡上运行OpenCL代码</li>
+</ol>
+</div>
+<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>此脚本包括Device类和它的子类，用户可以调用singa::Device和它的方法。</p>
+<hr class="docutils" />
+<div class="section" id="singa-device-create-cuda-gpus-num">
+<span id="singa-device-create-cuda-gpus-num"></span><h3>singa.device.create_cuda_gpus(num)<a class="headerlink" href="#singa-device-create-cuda-gpus-num" title="Permalink to this headline">¶</a></h3>
+<p>创建一个列表的CudaGPU设备。</p>
+<p><strong>参数：</strong></p>
+<ul class="simple">
+<li><strong>num(int)</strong> - 创建的设备数目</li>
+</ul>
+<p><strong>返回值：</strong> 一个列表CudaGPU设备</p>
+</div>
+<hr class="docutils" />
+<div class="section" id="singa-device-create-cuda-gpus-on-device-ids">
+<span id="singa-device-create-cuda-gpus-on-device-ids"></span><h3>singa.device.create_cuda_gpus_on(device_ids)<a class="headerlink" href="#singa-device-create-cuda-gpus-on-device-ids" title="Permalink to this headline">¶</a></h3>
+<p>创建一个列表的CudaGPU设备。</p>
+<p><strong>参数：</strong></p>
+<ul class="simple">
+<li><strong>device_ids(list)</strong> - 一个列表的显卡ID</li>
+</ul>
+<p><strong>返回值：</strong> 一个列表CudaGPU设备</p>
+</div>
+<hr class="docutils" />
+<div class="section" id="singa-device-get-default-device">
+<span id="singa-device-get-default-device"></span><h3>singa.device.get_default_device()<a class="headerlink" href="#singa-device-get-default-device" title="Permalink to this headline">¶</a></h3>
+<p>获取默认的CppCPU设备。</p>
+<hr class="docutils" />
+<p>下面的代码展示了创建设备的例子：</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">device</span>
+<span class="n">cuda</span> <span class="o">=</span> <span class="n">device</span><span class="o">.</span><span class="n">create_cuda_gpu_on</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>  <span class="c1"># use GPU card of ID 0</span>
+<span class="n">host</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="c1"># get the default host device (a CppCPU)</span>
+<span class="n">ary1</span> <span class="o">=</span> <span class="n">device</span><span class="o">.</span><span class="n">create_cuda_gpus</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>  <span class="c1"># create 2 devices, starting from ID 0</span>
+<span class="n">ary2</span> <span class="o">=</span> <span class="n">device</span><span class="o">.</span><span class="n">create_cuda_gpus</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span><span class="mi">2</span><span class="p">])</span>  <span class="c1"># create 2 devices on ID 0 and 2</span>
+</pre></div>
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