diff --git a/doc/fluid/advanced_guide/addon_development/design_idea/fluid_design_idea_en.md b/doc/fluid/advanced_guide/addon_development/design_idea/fluid_design_idea_en.md index 9cb2821a4964ca752d3be03631909a6dd6d9431a..1830096f1e53ab544348cbedb107962307773564 100644 --- a/doc/fluid/advanced_guide/addon_development/design_idea/fluid_design_idea_en.md +++ b/doc/fluid/advanced_guide/addon_development/design_idea/fluid_design_idea_en.md @@ -59,40 +59,41 @@ The blocks contain: The concept of block is the same with that in generic programs. For example, there are three blocks in the following C++ code: ``` cpp -int main(){ //block 0 - int i = 0; - if (i<10){ //block 1 - for (int j=0;j<10;j++){ //block 2 - } - } - return 0; +#include + +int main() { + int x = 5; // block 0 + int y = 4; // block 0 + int out; // block 0 + + if (x < y) { // block 0 + out = 1; // block 1 + } else { + out = 0; // block 2 + } + + std::cout << out << std::endl; + return 0; } ``` Similarly, the following Program contains 3 blocks: ```python -import paddle.fluid as fluid # block 0 - -limit = fluid.layers.fill_constant_batch_size_like( - Input=label, dtype='int64', shape=[1], value=5.0) -cond = fluid.layers.less_than(x=label, y=limit) - -ie = fluid.layers.IfElse(cond) -with ie.true_block(): # block 1 - true_image = ie.input(image) - hidden = fluid.layers.fc(input=true_image, size=100, act='tanh') - prob = fluid.layers.fc(input=hidden, size=10, act='softmax') - ie.output(prob) - -with ie.false_block(): # block 2 - false_image = ie.input(image) - hidden = fluid.layers.fc( - input=false_image, size=200, act='tanh') - prob = fluid.layers.fc(input=hidden, size=10, act='softmax') - ie.output(prob) - -prob = ie() +import paddle.fluid as fluid + +x = fluid.data(name='x', shape=[1], dtype='int64') # block 0 +y = fluid.data(name='y', shape=[1], dtype='int64') # block 0 + +def true_block(): + return fluid.layers.fill_constant(dtype='int64', value=1, shape=[1]) # block 1 + +def false_block(): + return fluid.layers.fill_constant(dtype='int64', value=0, shape=[1]) # block 2 + +condition = fluid.layers.less_than(x, y) # block 0 + +out = fluid.layers.cond(condition, true_block, false_block) # block 0 ``` ### BlockDesc and ProgramDesc @@ -229,8 +230,8 @@ import numpy train_data=numpy.array([[1.0],[2.0],[3.0],[4.0]]).astype('float32') y_true = numpy.array([[2.0],[4.0],[6.0],[8.0]]).astype('float32') # Define the network -x = fluid.layers.data(name="x",shape=[1],dtype='float32') -y = fluid.layers.data(name="y",shape=[1],dtype='float32') +x = fluid.data(name="x",shape=[None, 1],dtype='float32') +y = fluid.data(name="y",shape=[None, 1],dtype='float32') y_predict = fluid.layers.fc(input=x,size=1,act=None) #definition loss function cost = fluid.layers.square_error_cost(input=y_predict,label=y) @@ -299,7 +300,7 @@ As you can see from the output, the entire definition process is transformed int BlockDesc contains defined vars and a series of ops. Take input x as an example. In python code, x is 1D data of data type "float 32": ```python -x = fluid.layers.data(name="x",shape=[1],dtype='float32') +x = fluid.data(name="x",shape=[None, 1],dtype='float32') ``` In BlockDesc, the variable x is described as: ``` @@ -348,7 +349,7 @@ Since there are multiple columns of incoming and outgoing data, fluid defines tr ```python # Start training outs = exe.run( - feed={'x':train_data,'y':y_true}, + feed={'x':train_data,'y':y_true}, fetch_list=[y_predict.name,avg_cost.name]) ``` The above code defines that train_data is to be passed into the x variable, y_true is to be passed into the y variable, and output the predicted value of y and the last round value of cost. diff --git a/doc/fluid/advanced_guide/data_preparing/reader.md b/doc/fluid/advanced_guide/data_preparing/reader.md index bfba87966ea57c4c0d5a166077185b55727b024d..8647dd45bef5be20b41ded78e70e850bc98c2c7d 100644 --- a/doc/fluid/advanced_guide/data_preparing/reader.md +++ b/doc/fluid/advanced_guide/data_preparing/reader.md @@ -193,14 +193,3 @@ def image_reader_creator(image_path, label_path, n): reader = image_reader_creator("/path/to/image_file", "/path/to/label_file", 1024) paddle.train(paddle.batch(reader, 128), {"image":0, "label":1}, ...) ``` - -### How is `paddle.train` implemented - -An example implementation of paddle.train is: - -```python -def train(batch_reader, mapping, batch_size, total_pass): - for pass_idx in range(total_pass): - for mini_batch in batch_reader(): # this loop will never end in online learning. - do_forward_backward(mini_batch, mapping) -``` diff --git a/doc/fluid/beginners_guide/coding_practice/single_node.rst b/doc/fluid/beginners_guide/coding_practice/single_node.rst index a514cf48143c0ce0e4d927606236f66d9422b44e..d63379fc6fdc949441d8961004472429b6ca5d0b 100644 --- a/doc/fluid/beginners_guide/coding_practice/single_node.rst +++ b/doc/fluid/beginners_guide/coding_practice/single_node.rst @@ -60,7 +60,7 @@ 执行单卡训练可以使用 :code:`fluid.Executor()` 中的 :code:`run()` 方法，运行训练\ :code:`fluid.Program` 即可。在运行的时候，用户可以通过 :code:`run(feed=...)`\ -参数传入数据；用户可以通过 :code:`run(fetch=...)` 获取持久的数据。例如:\ +参数传入数据；用户可以通过 :code:`run(fetch=...)` 获取输出数据。例如:\ .. code-block:: python diff --git a/doc/fluid/beginners_guide/coding_practice/single_node_en.rst b/doc/fluid/beginners_guide/coding_practice/single_node_en.rst index 1558e9611a1c34118224a627170cdcf65ec35e40..3e69c281c55e1b5367a9795a83d4f8825e3d607e 100644 --- a/doc/fluid/beginners_guide/coding_practice/single_node_en.rst +++ b/doc/fluid/beginners_guide/coding_practice/single_node_en.rst @@ -51,7 +51,7 @@ Single-card Training ##################### Single-card training can be performed through calling :code:`run()` of :code:`fluid.Executor()` to run training :code:`fluid.Program` . -In the runtime, feed data with :code:`run(feed=...)` and get persistable data with :code:`run(fetch=...)` . For example: +In the runtime, users can feed data with :code:`run(feed=...)` and get output data with :code:`run(fetch=...)` . For example: .. code-block:: python @@ -81,17 +81,12 @@ In the runtime, feed data with :code:`run(feed=...)` and get persistable data wi loss_data, = exe.run(train_program, feed={"X": x}, fetch_list=[loss.name]) - # Or - # compiled_prog = fluid.CompiledProgram(train_program) - # loss_data, = exe.run(compiled_prog, - # feed={"X": x}, - # fetch_list=[loss.name]) - -Notes: + # Or use CompiledProgram: + compiled_prog = fluid.CompiledProgram(train_program) + loss_data, = exe.run(compiled_prog, + feed={"X": x}, + fetch_list=[loss.name]) -1. About data type supported by feed, please refer to the article :ref:`user_guide_feed_data_to_executor_en`. -2. The return value of :code:`Executor.run` is the variable value of :code:`fetch_list=[...]` .The fetched Variable must be persistable. :code:`fetch_list` can be fed with either Variable list or name list of variables . :code:`Executor.run` returns Fetch result list. -3. If the fetched data contain sequence information, you can set :code:`exe.run(return_numpy=False, ...)` to directly get :code:`fluid.LoDTensor` . You can directly access the information in :code:`fluid.LoDTensor` . Multi-card Training ####################### diff --git a/doc/fluid/beginners_guide/coding_practice/test_while_training.rst b/doc/fluid/beginners_guide/coding_practice/test_while_training.rst index 73f969ecba433d3d14892ac0ce7067e474d0e99d..612d8b36a25188dc1cf18427e1df99d667037302 100644 --- a/doc/fluid/beginners_guide/coding_practice/test_while_training.rst +++ b/doc/fluid/beginners_guide/coding_practice/test_while_training.rst @@ -79,7 +79,7 @@ PaddlePaddle Fluid中使用 :code:`fluid.unique_name` 包来随机初始化用 test_program = fluid.Program() with fluid.unique_name.guard(): - with fluid.program_gurad(test_program, fluid.Program()): + with fluid.program_guard(test_program, fluid.Program()): test_loss = network(is_test=True) # fluid.default_main_program() is the train program diff --git a/doc/fluid/beginners_guide/coding_practice/test_while_training_en.rst b/doc/fluid/beginners_guide/coding_practice/test_while_training_en.rst index 22ff4c08bde0aad740fe273c915dd1fed6d7319c..a8bebd78c89f51273dfc3f342b84490c4908c65e 100644 --- a/doc/fluid/beginners_guide/coding_practice/test_while_training_en.rst +++ b/doc/fluid/beginners_guide/coding_practice/test_while_training_en.rst @@ -70,7 +70,7 @@ For example: test_program = fluid.Program() with fluid.unique_name.guard(): - with fluid.program_gurad(test_program, fluid.Program()): + with fluid.program_guard(test_program, fluid.Program()): test_loss = network(is_test=True) # fluid.default_main_program() is the train program