keras.layer.input()用法说明

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, b], 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)
  ```
  """

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

  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.')

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