tenserflow建立网络由于先建立静态的graph，所以没有数据，用placeholder来占位好申请内存。
那么keras的layer类其实是一个方便的直接帮你建立深度网络中的layer的类。
该类继承了object，是个基础的类，后续的诸如input_layer类都会继承与layer
由于model.py中利用这个方法建立网络，所以仔细看一下：他的说明详尽而丰富。
input()这个方法是用来初始化一个keras tensor的，tensor说白了就是个数组。他强大到之通过输入和输出就能建立一个keras模型。shape或者batch shape 必须只能给一个。shape = [None,None,None],会创建一个"htmlcode">
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)
```
"""
tip：我们在model.py中用到了shape这个attribute，
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")
阅读input()里面的句子逻辑：
可以发现，进入if语句的情况是batch_shape不为空，并且tensor为空，此时进入if，用assert判断如果shape不为空，那么久会有错误提示，告诉你要么输入shape 要么输入batch_shape, 还提示你shape不包含batch个数，就是一个batch包含多少张图片。
那么其实如果tensor不空的话，我们可以发现，也会弹出这个提示，但是作者没有写这种题型，感觉有点没有安全感。注意点好了
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.')
如果单纯的按照规定输入shape，举个例子：只将shape输入为None，也就是说tensor的dimension我都不知道，但我知道这是个向量，你看着办吧。
input_gt_class_ids = KL.Input(
shape=[None], name="input_gt_class_ids", dtype=tf.int32)
就会调用Input()函数中的这个判断句式，注意因为shape是个List，所以shape is not None 会返回true。同时有没有输入batch_shape的话，就会用shape的参数去创造一个batch_shape.
if shape is not None and not batch_shape:
batch_shape = (None,) + tuple(shape)
比如如果输入：
shape = (None,) batch_shape = (None,)+shape batch_shape #会得到(None, None)
可以发现，这里要求使用者至少指明你的数据维度，比如图片的话，是三维的，所以shape至少是[None,None,None]，而且我认为shape = [None,1] 与shape = [None]是一样的都会创建一个不知道长度的向量。
以上这篇keras.layer.input()用法说明就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持。