tenserflow建立網路由於先建立靜態的graph，所以沒有資料，用placeholder來佔位好申請記憶體。

那麼keras的layer類其實是一個方便的直接幫你建立深度網路中的layer的類。

該類繼承了object，是個基礎的類，後續的諸如input_layer類都會繼承與layer

由於model.py中利用這個方法建立網路，所以仔細看一下：他的說明詳盡而豐富。

input()這個方法是用來初始化一個keras tensor的，tensor說白了就是個陣列。他強大到之通過輸入和輸出就能建立一個keras模型。shape或者batch shape 必須只能給一個。shape = [None,None,None],會建立一個?*?*?的三維陣列。

下面還舉了個例子，a,b,c都是keras的tensor， `model = Model(input=[a,b],output=c)`

def Input(shape=None,batch_shape=None,name=None,dtype=None,sparse=False,tensor=None):
  """`Input()` is used to instantiate a Keras tensor.
  A Keras tensor is a tensor object from the underlying backend
  (Theano,TensorFlow or CNTK),which we augment with certain
  attributes that allow us to build a Keras model
  just by knowing the inputs and outputs of the model.
  For instance,if a,b and c are Keras tensors,it becomes possible to do:
  `model = Model(input=[a,output=c)`
  The added Keras attributes are:
    `_keras_shape`: Integer shape tuple propagated
      via Keras-side shape inference.
    `_keras_history`: Last layer applied to the tensor.
      the entire layer graph is retrievable from that layer,recursively.
  # Arguments
    shape: A shape tuple (integer),not including the batch size.
      For instance,`shape=(32,)` indicates that the expected input
      will be batches of 32-dimensional vectors.
    batch_shape: A shape tuple (integer),including the batch size.
      For instance,`batch_shape=(10,32)` indicates that
      the expected input will be batches of 10 32-dimensional vectors.
      `batch_shape=(None,32)` indicates batches of an arbitrary number
      of 32-dimensional vectors.
    name: An optional name string for the layer.
      Should be unique in a model (do not reuse the same name twice).
      It will be autogenerated if it isn't provided.
    dtype: The data type expected by the input,as a string
      (`float32`,`float64`,`int32`...)
    sparse: A boolean specifying whether the placeholder
      to be created is sparse.
    tensor: Optional existing tensor to wrap into the `Input` layer.
      If set,the layer will not create a placeholder tensor.
  # Returns
    A tensor.
  # Example
  ```python
  # this is a logistic regression in Keras
  x = Input(shape=(32,))
  y = Dense(16,activation='softmax')(x)
  model = Model(x,y)
  ```
  """

tip：我們在model.py中用到了shape這個attribute，

 input_image = KL.Input(
      shape=[None,config.IMAGE_SHAPE[2]],name="input_image")
    input_image_meta = KL.Input(shape=[config.IMAGE_META_SIZE],name="input_image_meta")

閱讀input()裡面的句子邏輯：

可以發現，進入if語句的情況是batch_shape不為空，並且tensor為空，此時進入if，用assert判斷如果shape不為空，那麼久會有錯誤提示，告訴你要麼輸入shape 要麼輸入batch_shape,還提示你shape不包含batch個數，就是一個batch包含多少張圖片。

那麼其實如果tensor不空的話，我們可以發現，也會彈出這個提示，但是作者沒有寫這種題型，感覺有點沒有安全感。注意點好了

  if not batch_shape and tensor is None:
    assert shape is not None,('Please provide to Input either a `shape`'
                  ' or a `batch_shape` argument. Note that '
                  '`shape` does not include the batch '
                  'dimension.')

如果單純的按照規定輸入shape，舉個例子：只將shape輸入為None，也就是說tensor的dimension我都不知道，但我知道這是個向量，你看著辦吧。

input_gt_class_ids = KL.Input(
shape=[None],name="input_gt_class_ids",dtype=tf.int32)

就會呼叫Input()函式中的這個判斷句式，注意因為shape是個List，所以shape is not None 會返回true。同時有沒有輸入batch_shape的話，就會用shape的引數去創造一個batch_shape.

if shape is not None and not batch_shape:
batch_shape = (None,) + tuple(shape)

比如如果輸入：

shape = (None,)
batch_shape = (None,)+shape
batch_shape
#會得到(None,None)

可以發現，這裡要求使用者至少指明你的資料維度，比如圖片的話，是三維的，所以shape至少是[None,None]，而且我認為shape = [None,1] 與shape = [None]是一樣的都會建立一個不知道長度的向量。

以上這篇keras.layer.input()用法說明就是小編分享給大家的全部內容了，希望能給大家一個參考，也希望大家多多支援我們。