numpt_array_en (#555)

* numpt_array_en

* Textual Content Check

* fix wrong file location and name

* review on webpage

doc/fluid/user_guides/howto/prepare_data/feeding_data_en.rst

+.. _user_guide_use_numpy_array_as_train_data_en:
+
+#################################
+Take Numpy Array as Training Data
+#################################
+
+PaddlePaddle Fluid supports configuring data layer with :code:`fluid.layers.data()` .
+Then you can use Numpy Array or directly use Python to create C++
+:code:`fluid.LoDTensor` , and then feed it to :code:`fluid.Executor` or :code:`fluid.ParallelExecutor` 
+through :code:`Executor.run(feed=...)` .
+
+Configure Data Layer
+############################
+
+With :code:`fluid.layers.data()` , you can configure data layer in neural network. Details are as follows:
+
+.. code-block:: python
+
+   import paddle.fluid as fluid
+
+   image = fluid.layers.data(name="image", shape=[3, 224, 224])
+   label = fluid.layers.data(name="label", shape=[1], dtype="int64")
+
+   # use image/label as layer input
+   prediction = fluid.layers.fc(input=image, size=1000, act="softmax")
+   loss = fluid.layers.cross_entropy(input=prediction, label=label)
+   ...
+
+In the code above, :code:`image` and :code:`label` are two input data layers created by :code:`fluid.layers.data` . :code:`image` is float data of shape :code:`[3, 224, 224]` ; :code:`label` is the int data of shape :code:`[1]` . Note that:
+
+1. :code:`-1` is represented for the dimension of batch size by default in Fluid. And :code:`-1` is added to the first dimension of :code:`shape` by default. Therefore in the code above, it would be alright to transfer numpy array of :code:`[32, 3, 224, 224]` to :code:`image` . If you want to customize the position of the batch size dimension, please set :code:`fluid.layers.data(append_batch_size=False)` .Please refer to the tutorial in the advanced user guide: :ref:`user_guide_customize_batch_size_rank_en` .
+
+2. Data type of category labels in Fluid is :code:`int64` and the label starts from 0. About the supported data types,please refer to :ref:`user_guide_paddle_support_data_types_en` .
+
+.. _user_guide_feed_data_to_executor_en:
+
+Transfer Train Data to Executor
+################################
+
+Both :code:`Executor.run` and :code:`ParallelExecutor.run` receive a parameter :code:`feed` .
+The parameter is a dict in Python. Its key is the name of data layer,such as :code:`image` in code above. And its value is the corresponding  numpy array.
+
+For example:
+
+.. code-block:: python
+
+   exe = fluid.Executor(fluid.CPUPlace())
+   exe.run(feed={
+      "image": numpy.random.random(size=(32, 3, 224, 224)).astype('float32'),
+      "label": numpy.random.random(size=(32, 1)).astype('int64')
+   })
+
+Advanced Usage
+###############
+
+How to feed Sequence Data
+--------------------------
+
+Sequence data is a unique data type supported by PaddlePaddle Fluid. You can take :code:`LoDTensor` as input data type.
+
+You need to: 
+
+1. Feed all data to be trained in a mini-batch.
+
+2. Get the length of each sequence.
+
+You can use :code:`fluid.create_lod_tensor` to create :code:`LoDTensor` .
+
+To feed sequence information, it is necessary to set the sequence nested depth :code:`lod_level` .
+
+For instance, if the training data are sentences consisting of words, :code:`lod_level=1`; if train data are paragraphs which consists of sentences that consists of words, :code:`lod_level=2` .
+
+For example:
+
+.. code-block:: python
+
+   sentence = fluid.layers.data(name="sentence", dtype="int64", shape=[1], lod_level=1)
+
+   ...
+
+   exe.run(feed={
+     "sentence": create_lod_tensor(
+       data=numpy.array([1, 3, 4, 5, 3, 6, 8], dtype='int64').reshape(-1, 1),
+       lod=[4, 1, 2],
+       place=fluid.CPUPlace()
+     )
+   })
+
+Training data :code:`sentence` contain three samples, the lengths of which are :code:`4, 1, 2` respectively.
+
+They are :code:`data[0:4]`, :code:`data[4:5]` and :code:`data[5:7]` respectively.
+
+How to prepare training data for every device in ParallelExecutor
+-------------------------------------------------------------------
+
+When you feed data to :code:`ParallelExecutor.run(feed=...)` , 
+you can explicitly assign data for every training device (such as GPU).
+
+You need to feed a list to :code:`feed` . Each element of the list is a dict.
+
+The key of the dict is name of data layer and the value of dict is value of data layer.
+
+For example:
+
+.. code-block:: python
+
+   parallel_executor = fluid.ParallelExecutor()
+   parallel_executor.run(
+     feed=[
+        {
+          "image": numpy.random.random(size=(32, 3, 224, 224)).astype('float32'),
+          "label": numpy.random.random(size=(32, 1)).astype('int64')
+        },
+        {
+          "image": numpy.random.random(size=(16, 3, 224, 224)).astype('float32'),
+          "label": numpy.random.random(size=(16, 1)).astype('int64')
+        },
+     ]
+   )
+
+In the code above, GPU0 will train 32 samples and GPU1 will train 16 samples.
+
+.. _user_guide_customize_batch_size_rank_en:
+
+Customize the BatchSize dimension
+------------------------------------
+
+Batch size is the first dimension of data by default in PaddlePaddle Fluid, indicated by :code:`-1` .But in advanced usage, batch_size could be fixed or respresented by other dimension or multiple dimensions, which could be implemented by setting :code:`fluid.layers.data(append_batch_size=False)` .
+
+1. fixed BatchSize dimension
+
+  .. code-block:: python
+
+     image = fluid.layers.data(name="image", shape=[32, 784], append_batch_size=False)
+
+  Here :code:`image` is always a matrix with size of :code:`[32, 784]` .
+
+2. batch size expressed by other dimension
+
+  .. code-block:: python
+
+     sentence = fluid.layers.data(name="sentence",
+                                  shape=[80, -1, 1],
+                                  append_batch_size=False,
+                                  dtype="int64")
+
+  Here the middle dimension of :code:`sentence` is batch size. This type of data layout is applied in fixed-length recurrent neural networks.
+
+.. _user_guide_paddle_support_data_types_en:
+
+Data types supported by Fluid
+-------------------------------
+
+Data types supported by PaddlePaddle Fluid contains:
+
+   * float16: supported by part of operations
+   * float32: major data type of real number
+   * float64: minor data type of real number, supported by most operations
+   * int32: minor data type of labels
+   * int64: major data type of labels
+   * uint64: minor data type of labels
+   * bool:  type of control flow data
+   * int16: minor type of labels
+   * uint8: input data type, used for pixel of picture