Add usage documentation of C-API.

Author: reyoung

paddle/capi/examples/README.md

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+# C-API Example Usage
+
+* [Model Inference](./model_inference/README.md)

paddle/capi/examples/model_inference/README.md

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+# Use C-API for Model Inference
+
+There are several examples in this directory about how to use Paddle C-API for model inference.
+
+## Convert configuration file to protobuf binary.
+
+Firstly, the user should convert Paddle's model configuration file into a protobuf binary file. In each example directory, there is a file named `convert_protobin.sh`. It will convert `trainer_config.conf` into `trainer_config.bin`.
+
+The `convert_protobin.sh` is very simple, just invoke `dump_config` Python module to dump the binary file. The command line usages are:
+
+```bash
+python -m paddle.utils.dump_config YOUR_CONFIG_FILE 'CONFIG_EXTRA_ARGS' --binary > YOUR_CONFIG_FILE.bin
+```
+
+## Initialize paddle
+
+```c++
+char* argv[] = {"--use_gpu=False"};
+paddle_init(1, (char**)argv);
+```
+
+We must initialize global context before we invoke other interfaces in Paddle. The initialize commands just like the `paddle_trainer` command line arguments.  `paddle train --help`,  will show the list of arguments. The most important argument is `use_gpu` or not.
+
+## Load network and parameters
+
+```c
+paddle_gradient_machine machine;
+paddle_gradient_machine_create_for_inference(&machine, config_file_content, content_size));
+paddle_gradient_machine_load_parameter_from_disk(machine, "./some_where_to_params"));
+```
+
+The gradient machine is a Paddle concept, which represents a neural network can be forwarded and backward. We can create a gradient machine fo model inference, and load the parameter files from disk.
+
+Moreover, if we want to inference in multi-thread, we could create a thread local gradient machine which shared the same parameter by using `paddle_gradient_machine_create_shared_param` API. Please reference `multi_thread` as an example.
+
+## Create input
+
+The input of a neural network is an `arguments`. The examples in this directory will show how to construct different types of inputs for prediction. Please look at `dense`, `sparse_binary`, `sequence` for details.
+
+## Get inference
+
+After invoking `paddle_gradient_machine_forward`, we could get the output of the neural network.  The `value` matrix of output arguments will store the neural network output values. If the output is a `SoftmaxActivation`, the `value` matrix are the probabilities of each input samples. The height of output matrix is number of sample. The width is the number of categories.