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develop/doc/_sources/design/optimizer.md.txt 0 → 100644
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## Optimizer Design
### The Problem
A PaddlePaddle program, or a block, is a sequence of operators operating variables. A training program needs to do three kinds of works:
1. the forward pass, which computes intermediate results and the cost(s),
1. the backward pass, which derives gradients from intermediate results and costs, and
1. the optimization pass, which update model parameters to optimize the cost(s).
These works rely on three kinds of operators:
1. forward operators,
1. gradient operators, and
1. optimization operators.
It's true that users should be able to create all these operators manually by calling some low-level API, but it would be much more convenient if they could only describe the forward pass and let PaddlePaddle create the backward and optimization operators automatically.
In this design, we propose a high-level API that automatically derives the optimisation pass and operators from the forward pass.
### High-level Python API to describe the training process
1. User write code to describe the network:
```python
images = layer.data("images")
labels = layer.data("labels")
w1 = pd.var("w1")
b1 = pd.var("b1")
hidden = layer.fc(images, w=w1, b=b1)
cost = layer.mse(hidden, labels)
```
The above code snippet will create forward operators in [Block](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/block.md).
2. Users create a certain kind of Optimizer with some argument.
```python
optimizer = AdagradOptimizer(learing_rate=0.001)
```
3. Users use the optimizer to `minimize` a certain `cost` through updating parameters in parameter_list.
```python
opt_op_list = optimizer.minimize(cost, parameter_list=[w1, b1])
```
The above code snippet will create gradient and optimization operators in Block. The return value of `minimize()` is list of optimization operators that will be run by session.
4. Users use Session/Executor to run this opt_op_list as target to do training.
```python
sess.run(target= opt_op_list, ...)
```
#### Optimizer Python interface:
```python
class Optimizer(object):
"""Optimizer Base class.
"""
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.
Args:
parameters_and_grads: a list of (variable, gradient) pair to update.
Returns:
optmization_op_list: a list of optimization operator that will update parameter using gradient.
"""
return None
def minimize(self, loss, parameter_list):
"""Add operations to minimize `loss` by updating `parameter_list`.
This method combines interface `create_backward_pass()` and
`create_optimization_pass()` into one.
"""
params_grads = self.create_backward_pass(loss, parameter_list)
update_ops = self.create_optimization_pass(params_grads)
return update_ops
```
Users can inherit the Optimizer above to create their own Optimizer with some special logic, such as AdagradOptimizer.
develop/doc/_sources/design/python_api.md.txt
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...@@ -214,3 +214,7 @@ def fc_layer(input, size, ...): ...@@ -214,3 +214,7 @@ def fc_layer(input, size, ...):
out.writer = op out.writer = op
return out return out
``` ```
## Optimizer
[Optimizer Design Doc](./optimizer.md)
develop/doc/design/optimizer.html 0 → 100644
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<li>Optimizer Design</li>
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<div class="section" id="optimizer-design">
<span id="optimizer-design"></span><h1>Optimizer Design<a class="headerlink" href="#optimizer-design" title="Permalink to this headline"></a></h1>
<div class="section" id="the-problem">
<span id="the-problem"></span><h2>The Problem<a class="headerlink" href="#the-problem" title="Permalink to this headline"></a></h2>
<p>A PaddlePaddle program, or a block, is a sequence of operators operating variables. A training program needs to do three kinds of works:</p>
<ol class="simple">
<li>the forward pass, which computes intermediate results and the cost(s),</li>
<li>the backward pass, which derives gradients from intermediate results and costs, and</li>
<li>the optimization pass, which update model parameters to optimize the cost(s).</li>
</ol>
<p>These works rely on three kinds of operators:</p>
<ol class="simple">
<li>forward operators,</li>
<li>gradient operators, and</li>
<li>optimization operators.</li>
</ol>
<p>It&#8217;s true that users should be able to create all these operators manually by calling some low-level API, but it would be much more convenient if they could only describe the forward pass and let PaddlePaddle create the backward and optimization operators automatically.</p>
<p>In this design, we propose a high-level API that automatically derives the optimisation pass and operators from the forward pass.</p>
</div>
<div class="section" id="high-level-python-api-to-describe-the-training-process">
<span id="high-level-python-api-to-describe-the-training-process"></span><h2>High-level Python API to describe the training process<a class="headerlink" href="#high-level-python-api-to-describe-the-training-process" title="Permalink to this headline"></a></h2>
<ol>
<li><p class="first">User write code to describe the network:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">images</span> <span class="o">=</span> <span class="n">layer</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="s2">&quot;images&quot;</span><span class="p">)</span>
<span class="n">labels</span> <span class="o">=</span> <span class="n">layer</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="s2">&quot;labels&quot;</span><span class="p">)</span>
<span class="n">w1</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="s2">&quot;w1&quot;</span><span class="p">)</span>
<span class="n">b1</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="s2">&quot;b1&quot;</span><span class="p">)</span>
<span class="n">hidden</span> <span class="o">=</span> <span class="n">layer</span><span class="o">.</span><span class="n">fc</span><span class="p">(</span><span class="n">images</span><span class="p">,</span> <span class="n">w</span><span class="o">=</span><span class="n">w1</span><span class="p">,</span> <span class="n">b</span><span class="o">=</span><span class="n">b1</span><span class="p">)</span>
<span class="n">cost</span> <span class="o">=</span> <span class="n">layer</span><span class="o">.</span><span class="n">mse</span><span class="p">(</span><span class="n">hidden</span><span class="p">,</span> <span class="n">labels</span><span class="p">)</span>
</pre></div>
</div>
<p>The above code snippet will create forward operators in <a class="reference external" href="https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/block.md">Block</a>.</p>
</li>
</ol>
<ol>
<li><p class="first">Users create a certain kind of Optimizer with some argument.</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">optimizer</span> <span class="o">=</span> <span class="n">AdagradOptimizer</span><span class="p">(</span><span class="n">learing_rate</span><span class="o">=</span><span class="mf">0.001</span><span class="p">)</span>
</pre></div>
</div>
</li>
<li><p class="first">Users use the optimizer to <code class="docutils literal"><span class="pre">minimize</span></code> a certain <code class="docutils literal"><span class="pre">cost</span></code> through updating parameters in parameter_list.</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">opt_op_list</span> <span class="o">=</span> <span class="n">optimizer</span><span class="o">.</span><span class="n">minimize</span><span class="p">(</span><span class="n">cost</span><span class="p">,</span> <span class="n">parameter_list</span><span class="o">=</span><span class="p">[</span><span class="n">w1</span><span class="p">,</span> <span class="n">b1</span><span class="p">])</span>
</pre></div>
</div>
<p>The above code snippet will create gradient and optimization operators in Block. The return value of <code class="docutils literal"><span class="pre">minimize()</span></code> is list of optimization operators that will be run by session.</p>
</li>
<li><p class="first">Users use Session/Executor to run this opt_op_list as target to do training.</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">target</span><span class="o">=</span> <span class="n">opt_op_list</span><span class="p">,</span> <span class="o">...</span><span class="p">)</span>
</pre></div>
</div>
</li>
</ol>
<div class="section" id="optimizer-python-interface">
<span id="optimizer-python-interface"></span><h3>Optimizer Python interface:<a class="headerlink" href="#optimizer-python-interface" title="Permalink to this headline"></a></h3>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">Optimizer</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Optimizer Base class.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<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>
<span class="sd"> Args:</span>
<span class="sd"> parameters_and_grads: a list of (variable, gradient) pair to update.</span>
<span class="sd"> Returns:</span>
<span class="sd"> optmization_op_list: a list of optimization operator that will update parameter using gradient.</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">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"> `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>
<span class="n">update_ops</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">create_optimization_pass</span><span class="p">(</span><span class="n">params_grads</span><span class="p">)</span>
<span class="k">return</span> <span class="n">update_ops</span>
</pre></div>
</div>
<p>Users can inherit the Optimizer above to create their own Optimizer with some special logic, such as AdagradOptimizer.</p>
</div>
</div>
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...@@ -375,6 +375,10 @@ ...@@ -375,6 +375,10 @@
</div> </div>
</div> </div>
</div> </div>
<div class="section" id="optimizer">
<span id="optimizer"></span><h2>Optimizer<a class="headerlink" href="#optimizer" title="Permalink to this headline"></a></h2>
<p><a class="reference internal" href="optimizer.html"><span class="doc">Optimizer Design Doc</span></a></p>
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</div> </div>
......
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develop/doc_cn/_sources/design/optimizer.md.txt 0 → 100644
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## Optimizer Design
### The Problem
A PaddlePaddle program, or a block, is a sequence of operators operating variables. A training program needs to do three kinds of works:
1. the forward pass, which computes intermediate results and the cost(s),
1. the backward pass, which derives gradients from intermediate results and costs, and
1. the optimization pass, which update model parameters to optimize the cost(s).
These works rely on three kinds of operators:
1. forward operators,
1. gradient operators, and
1. optimization operators.
It's true that users should be able to create all these operators manually by calling some low-level API, but it would be much more convenient if they could only describe the forward pass and let PaddlePaddle create the backward and optimization operators automatically.
In this design, we propose a high-level API that automatically derives the optimisation pass and operators from the forward pass.
### High-level Python API to describe the training process
1. User write code to describe the network:
```python
images = layer.data("images")
labels = layer.data("labels")
w1 = pd.var("w1")
b1 = pd.var("b1")
hidden = layer.fc(images, w=w1, b=b1)
cost = layer.mse(hidden, labels)
```
The above code snippet will create forward operators in [Block](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/block.md).
2. Users create a certain kind of Optimizer with some argument.
```python
optimizer = AdagradOptimizer(learing_rate=0.001)
```
3. Users use the optimizer to `minimize` a certain `cost` through updating parameters in parameter_list.
```python
opt_op_list = optimizer.minimize(cost, parameter_list=[w1, b1])
```
The above code snippet will create gradient and optimization operators in Block. The return value of `minimize()` is list of optimization operators that will be run by session.
4. Users use Session/Executor to run this opt_op_list as target to do training.
```python
sess.run(target= opt_op_list, ...)
```
#### Optimizer Python interface:
```python
class Optimizer(object):
"""Optimizer Base class.
"""
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.
Args:
parameters_and_grads: a list of (variable, gradient) pair to update.
Returns:
optmization_op_list: a list of optimization operator that will update parameter using gradient.
"""
return None
def minimize(self, loss, parameter_list):
"""Add operations to minimize `loss` by updating `parameter_list`.
This method combines interface `create_backward_pass()` and
`create_optimization_pass()` into one.
"""
params_grads = self.create_backward_pass(loss, parameter_list)
update_ops = self.create_optimization_pass(params_grads)
return update_ops
```
Users can inherit the Optimizer above to create their own Optimizer with some special logic, such as AdagradOptimizer.
develop/doc_cn/_sources/design/python_api.md.txt
浏览文件 @ ad5dbb39
...@@ -214,3 +214,7 @@ def fc_layer(input, size, ...): ...@@ -214,3 +214,7 @@ def fc_layer(input, size, ...):
out.writer = op out.writer = op
return out return out
``` ```
## Optimizer
[Optimizer Design Doc](./optimizer.md)
develop/doc_cn/design/optimizer.html 0 → 100644
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<li>Optimizer Design</li>
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<div class="section" id="optimizer-design">
<span id="optimizer-design"></span><h1>Optimizer Design<a class="headerlink" href="#optimizer-design" title="永久链接至标题"></a></h1>
<div class="section" id="the-problem">
<span id="the-problem"></span><h2>The Problem<a class="headerlink" href="#the-problem" title="永久链接至标题"></a></h2>
<p>A PaddlePaddle program, or a block, is a sequence of operators operating variables. A training program needs to do three kinds of works:</p>
<ol class="simple">
<li>the forward pass, which computes intermediate results and the cost(s),</li>
<li>the backward pass, which derives gradients from intermediate results and costs, and</li>
<li>the optimization pass, which update model parameters to optimize the cost(s).</li>
</ol>
<p>These works rely on three kinds of operators:</p>
<ol class="simple">
<li>forward operators,</li>
<li>gradient operators, and</li>
<li>optimization operators.</li>
</ol>
<p>It&#8217;s true that users should be able to create all these operators manually by calling some low-level API, but it would be much more convenient if they could only describe the forward pass and let PaddlePaddle create the backward and optimization operators automatically.</p>
<p>In this design, we propose a high-level API that automatically derives the optimisation pass and operators from the forward pass.</p>
</div>
<div class="section" id="high-level-python-api-to-describe-the-training-process">
<span id="high-level-python-api-to-describe-the-training-process"></span><h2>High-level Python API to describe the training process<a class="headerlink" href="#high-level-python-api-to-describe-the-training-process" title="永久链接至标题"></a></h2>
<ol>
<li><p class="first">User write code to describe the network:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">images</span> <span class="o">=</span> <span class="n">layer</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="s2">&quot;images&quot;</span><span class="p">)</span>
<span class="n">labels</span> <span class="o">=</span> <span class="n">layer</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="s2">&quot;labels&quot;</span><span class="p">)</span>
<span class="n">w1</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="s2">&quot;w1&quot;</span><span class="p">)</span>
<span class="n">b1</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="s2">&quot;b1&quot;</span><span class="p">)</span>
<span class="n">hidden</span> <span class="o">=</span> <span class="n">layer</span><span class="o">.</span><span class="n">fc</span><span class="p">(</span><span class="n">images</span><span class="p">,</span> <span class="n">w</span><span class="o">=</span><span class="n">w1</span><span class="p">,</span> <span class="n">b</span><span class="o">=</span><span class="n">b1</span><span class="p">)</span>
<span class="n">cost</span> <span class="o">=</span> <span class="n">layer</span><span class="o">.</span><span class="n">mse</span><span class="p">(</span><span class="n">hidden</span><span class="p">,</span> <span class="n">labels</span><span class="p">)</span>
</pre></div>
</div>
<p>The above code snippet will create forward operators in <a class="reference external" href="https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/block.md">Block</a>.</p>
</li>
</ol>
<ol>
<li><p class="first">Users create a certain kind of Optimizer with some argument.</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">optimizer</span> <span class="o">=</span> <span class="n">AdagradOptimizer</span><span class="p">(</span><span class="n">learing_rate</span><span class="o">=</span><span class="mf">0.001</span><span class="p">)</span>
</pre></div>
</div>
</li>
<li><p class="first">Users use the optimizer to <code class="docutils literal"><span class="pre">minimize</span></code> a certain <code class="docutils literal"><span class="pre">cost</span></code> through updating parameters in parameter_list.</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">opt_op_list</span> <span class="o">=</span> <span class="n">optimizer</span><span class="o">.</span><span class="n">minimize</span><span class="p">(</span><span class="n">cost</span><span class="p">,</span> <span class="n">parameter_list</span><span class="o">=</span><span class="p">[</span><span class="n">w1</span><span class="p">,</span> <span class="n">b1</span><span class="p">])</span>
</pre></div>
</div>
<p>The above code snippet will create gradient and optimization operators in Block. The return value of <code class="docutils literal"><span class="pre">minimize()</span></code> is list of optimization operators that will be run by session.</p>
</li>
<li><p class="first">Users use Session/Executor to run this opt_op_list as target to do training.</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">target</span><span class="o">=</span> <span class="n">opt_op_list</span><span class="p">,</span> <span class="o">...</span><span class="p">)</span>
</pre></div>
</div>
</li>
</ol>
<div class="section" id="optimizer-python-interface">
<span id="optimizer-python-interface"></span><h3>Optimizer Python interface:<a class="headerlink" href="#optimizer-python-interface" title="永久链接至标题"></a></h3>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">Optimizer</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Optimizer Base class.</span>
<span class="sd"> &quot;&quot;&quot;</span>
<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>
<span class="sd"> Args:</span>
<span class="sd"> parameters_and_grads: a list of (variable, gradient) pair to update.</span>
<span class="sd"> Returns:</span>
<span class="sd"> optmization_op_list: a list of optimization operator that will update parameter using gradient.</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">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"> `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>
<span class="n">update_ops</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">create_optimization_pass</span><span class="p">(</span><span class="n">params_grads</span><span class="p">)</span>
<span class="k">return</span> <span class="n">update_ops</span>
</pre></div>
</div>
<p>Users can inherit the Optimizer above to create their own Optimizer with some special logic, such as AdagradOptimizer.</p>
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<span id="optimizer"></span><h2>Optimizer<a class="headerlink" href="#optimizer" title="永久链接至标题"></a></h2>
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