From ad5dbb3960072776db1112b1729282d4acb7c7d7 Mon Sep 17 00:00:00 2001 From: Travis CI Date: Wed, 11 Oct 2017 02:57:51 +0000 Subject: [PATCH] Deploy to GitHub Pages: 696874ac6ee1b2b284d9817988aa4c99f74c0c76 --- develop/doc/_sources/design/optimizer.md.txt | 105 ++++++ develop/doc/_sources/design/python_api.md.txt | 4 + develop/doc/design/optimizer.html | 340 +++++++++++++++++ develop/doc/design/python_api.html | 4 + develop/doc/objects.inv | Bin 2834 -> 2844 bytes develop/doc/searchindex.js | 2 +- .../doc_cn/_sources/design/optimizer.md.txt | 105 ++++++ .../doc_cn/_sources/design/python_api.md.txt | 4 + develop/doc_cn/design/optimizer.html | 355 ++++++++++++++++++ develop/doc_cn/design/python_api.html | 4 + develop/doc_cn/objects.inv | Bin 3410 -> 3421 bytes develop/doc_cn/searchindex.js | 2 +- 12 files changed, 923 insertions(+), 2 deletions(-) create mode 100644 develop/doc/_sources/design/optimizer.md.txt create mode 100644 develop/doc/design/optimizer.html create mode 100644 develop/doc_cn/_sources/design/optimizer.md.txt create mode 100644 develop/doc_cn/design/optimizer.html diff --git a/develop/doc/_sources/design/optimizer.md.txt b/develop/doc/_sources/design/optimizer.md.txt new file mode 100644 index 0000000000..17440fae50 --- /dev/null +++ b/develop/doc/_sources/design/optimizer.md.txt @@ -0,0 +1,105 @@ +## 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. diff --git a/develop/doc/_sources/design/python_api.md.txt b/develop/doc/_sources/design/python_api.md.txt index c4665e44fc..56ae1d925a 100644 --- a/develop/doc/_sources/design/python_api.md.txt +++ b/develop/doc/_sources/design/python_api.md.txt @@ -214,3 +214,7 @@ def fc_layer(input, size, ...): out.writer = op return out ``` + +## Optimizer + +[Optimizer Design Doc](./optimizer.md) diff --git a/develop/doc/design/optimizer.html b/develop/doc/design/optimizer.html new file mode 100644 index 0000000000..821375b37c --- /dev/null +++ b/develop/doc/design/optimizer.html @@ -0,0 +1,340 @@ + + + + + + + + + + + Optimizer Design — PaddlePaddle documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+
+ Fork me on Github +
+ + English + + + + +
+ +
+
+ + + + +
+
+ + + +
+
+
+
+
+ +
+ + + + +
+ + + + + + + + + + + +
+
    + +
  • Optimizer Design
    • +
+
+ +
+
+
+
+ +
+

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),
    2. +
  2. the backward pass, which derives gradients from intermediate results and costs, and
    3. +
  3. the optimization pass, which update model parameters to optimize the cost(s).
    4. +
+

These works rely on three kinds of operators:

+
    +
  1. forward operators,
    2. +
  2. gradient operators, and
    3. +
  3. optimization operators.
    4. +
+

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:

    +
    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.

    +
    2. +
+
    +

  1. Users create a certain kind of Optimizer with some argument.

    +
    optimizer = AdagradOptimizer(learing_rate=0.001)
+
    +
    +
    2. +

  2. Users use the optimizer to minimize a certain cost through updating parameters in parameter_list.

    +
    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.

    +
    3. +

  3. Users use Session/Executor to run this opt_op_list as target to do training.

    +
    sess.run(target= opt_op_list, ...)
+
    +
    +
    4. +
+
+

Optimizer Python interface:

+
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.

+
+
+
+ + +
+
+
+ + +
+ +
+

+ © Copyright 2016, PaddlePaddle developers. + +

+
+ Built with Sphinx using a theme provided by Read the Docs. + +
+ +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/develop/doc/design/python_api.html b/develop/doc/design/python_api.html index 1bbd93e650..3fbcddde61 100644 --- a/develop/doc/design/python_api.html +++ b/develop/doc/design/python_api.html @@ -375,6 +375,10 @@ +
+

Optimizer

+

Optimizer Design Doc

+
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\ No newline at end of file diff --git a/develop/doc_cn/_sources/design/optimizer.md.txt b/develop/doc_cn/_sources/design/optimizer.md.txt new file mode 100644 index 0000000000..17440fae50 --- /dev/null +++ b/develop/doc_cn/_sources/design/optimizer.md.txt @@ -0,0 +1,105 @@ +## 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. diff --git a/develop/doc_cn/_sources/design/python_api.md.txt b/develop/doc_cn/_sources/design/python_api.md.txt index c4665e44fc..56ae1d925a 100644 --- a/develop/doc_cn/_sources/design/python_api.md.txt +++ b/develop/doc_cn/_sources/design/python_api.md.txt @@ -214,3 +214,7 @@ def fc_layer(input, size, ...): out.writer = op return out ``` + +## Optimizer + +[Optimizer Design Doc](./optimizer.md) diff --git a/develop/doc_cn/design/optimizer.html b/develop/doc_cn/design/optimizer.html new file mode 100644 index 0000000000..c041044fdf --- /dev/null +++ b/develop/doc_cn/design/optimizer.html @@ -0,0 +1,355 @@ + + + + + + + + + + + Optimizer Design — PaddlePaddle 文档 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+
+ Fork me on Github +
+ + English + + + + +
+ +
+
+ + + + +
+
+ + + +
+
+
+
+
+ +
+ + + + +
+ + + + + + + + + + + +
+
    + +
  • Optimizer Design
    • +
+
+ +
+
+
+
+ +
+

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),
    2. +
  2. the backward pass, which derives gradients from intermediate results and costs, and
    3. +
  3. the optimization pass, which update model parameters to optimize the cost(s).
    4. +
+

These works rely on three kinds of operators:

+
    +
  1. forward operators,
    2. +
  2. gradient operators, and
    3. +
  3. optimization operators.
    4. +
+

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:

    +
    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.

    +
    2. +
+
    +

  1. Users create a certain kind of Optimizer with some argument.

    +
    optimizer = AdagradOptimizer(learing_rate=0.001)
+
    +
    +
    2. +

  2. Users use the optimizer to minimize a certain cost through updating parameters in parameter_list.

    +
    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.

    +
    3. +

  3. Users use Session/Executor to run this opt_op_list as target to do training.

    +
    sess.run(target= opt_op_list, ...)
+
    +
    +
    4. +
+
+

Optimizer Python interface:

+
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.

+
+
+
+ + +
+
+
+ + +
+ +
+

+ © Copyright 2016, PaddlePaddle developers. + +

+
+ Built with Sphinx using a theme provided by Read the Docs. + +
+ +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/develop/doc_cn/design/python_api.html b/develop/doc_cn/design/python_api.html index 2e0d0592f8..47b75e2552 100644 --- a/develop/doc_cn/design/python_api.html +++ b/develop/doc_cn/design/python_api.html @@ -389,6 +389,10 @@ +
+

Optimizer

+

Optimizer Design Doc

+
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