Deploy to GitHub Pages: dd0008d5

develop/doc/_sources/design/optimizer.md.txt

@@ -65,20 +65,6 @@ class Optimizer(object):
     def __init__(self):
         pass
 
-    def create_backward_pass(self, loss, parameter_list=None):
-        """
-        create and add gradient Operators in BlockDesc to Compute gradients of `loss`
-        for parameters in parameter_list
-
-        Args:
-          loss: an variable generated by cost function.
-          parameter_list: parameters that need to compute gradient and update to optimize the lost.
-
-        Returns:
-          list of (parameters, gradients) pair.
-        """
-        return None
-
     def create_optimization_pass(self, parameters_and_grads):
         """Add optimization operators to update gradients to variables.
 
@@ -93,7 +79,7 @@ class Optimizer(object):
     def minimize(self, loss, parameter_list):
         """Add operations to minimize `loss` by updating `parameter_list`.
 
-        This method combines interface `create_backward_pass()` and
+        This method combines interface `append_backward_ops()` and
         `create_optimization_pass()` into one.
         """
         params_grads = self.create_backward_pass(loss, parameter_list)

develop/doc/design/optimizer.html

@@ -243,20 +243,6 @@
     <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="k">pass</span>
 
-    <span class="k">def</span> <span class="nf">create_backward_pass</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">loss</span><span class="p">,</span> <span class="n">parameter_list</span><span class="o">=</span><span class="bp">None</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        create and add gradient Operators in BlockDesc to Compute gradients of `loss`</span>
-<span class="sd">        for parameters in parameter_list</span>
-
-<span class="sd">        Args:</span>
-<span class="sd">          loss: an variable generated by cost function.</span>
-<span class="sd">          parameter_list: parameters that need to compute gradient and update to optimize the lost.</span>
-
-<span class="sd">        Returns:</span>
-<span class="sd">          list of (parameters, gradients) pair.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="bp">None</span>
-
     <span class="k">def</span> <span class="nf">create_optimization_pass</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">parameters_and_grads</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot;Add optimization operators to update gradients to variables.</span>
 
@@ -271,7 +257,7 @@
     <span class="k">def</span> <span class="nf">minimize</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">loss</span><span class="p">,</span> <span class="n">parameter_list</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot;Add operations to minimize `loss` by updating `parameter_list`.</span>
 
-<span class="sd">        This method combines interface `create_backward_pass()` and</span>
+<span class="sd">        This method combines interface `append_backward_ops()` and</span>
 <span class="sd">        `create_optimization_pass()` into one.</span>
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="n">params_grads</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">create_backward_pass</span><span class="p">(</span><span class="n">loss</span><span class="p">,</span> <span class="n">parameter_list</span><span class="p">)</span>

develop/doc_cn/_sources/design/optimizer.md.txt

@@ -65,20 +65,6 @@ class Optimizer(object):
     def __init__(self):
         pass
 
-    def create_backward_pass(self, loss, parameter_list=None):
-        """
-        create and add gradient Operators in BlockDesc to Compute gradients of `loss`
-        for parameters in parameter_list
-
-        Args:
-          loss: an variable generated by cost function.
-          parameter_list: parameters that need to compute gradient and update to optimize the lost.
-
-        Returns:
-          list of (parameters, gradients) pair.
-        """
-        return None
-
     def create_optimization_pass(self, parameters_and_grads):
         """Add optimization operators to update gradients to variables.
 
@@ -93,7 +79,7 @@ class Optimizer(object):
     def minimize(self, loss, parameter_list):
         """Add operations to minimize `loss` by updating `parameter_list`.
 
-        This method combines interface `create_backward_pass()` and
+        This method combines interface `append_backward_ops()` and
         `create_optimization_pass()` into one.
         """
         params_grads = self.create_backward_pass(loss, parameter_list)

develop/doc_cn/design/optimizer.html

@@ -257,20 +257,6 @@
     <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="k">pass</span>
 
-    <span class="k">def</span> <span class="nf">create_backward_pass</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">loss</span><span class="p">,</span> <span class="n">parameter_list</span><span class="o">=</span><span class="bp">None</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        create and add gradient Operators in BlockDesc to Compute gradients of `loss`</span>
-<span class="sd">        for parameters in parameter_list</span>
-
-<span class="sd">        Args:</span>
-<span class="sd">          loss: an variable generated by cost function.</span>
-<span class="sd">          parameter_list: parameters that need to compute gradient and update to optimize the lost.</span>
-
-<span class="sd">        Returns:</span>
-<span class="sd">          list of (parameters, gradients) pair.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="bp">None</span>
-
     <span class="k">def</span> <span class="nf">create_optimization_pass</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">parameters_and_grads</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot;Add optimization operators to update gradients to variables.</span>
 
@@ -285,7 +271,7 @@
     <span class="k">def</span> <span class="nf">minimize</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">loss</span><span class="p">,</span> <span class="n">parameter_list</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot;Add operations to minimize `loss` by updating `parameter_list`.</span>
 
-<span class="sd">        This method combines interface `create_backward_pass()` and</span>
+<span class="sd">        This method combines interface `append_backward_ops()` and</span>
 <span class="sd">        `create_optimization_pass()` into one.</span>
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="n">params_grads</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">create_backward_pass</span><span class="p">(</span><span class="n">loss</span><span class="p">,</span> <span class="n">parameter_list</span><span class="p">)</span>