tenserflow建立網絡由于先建立靜態的graph，所以沒有數據，用placeholder來占位好申請內存。
那么keras的layer類其實是一個方便的直接幫你建立深度網絡中的layer的類。
該類繼承了object，是個基礎的類，后續的諸如input_layer類都會繼承與layer
由于model.py中利用這個方法建立網絡，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說白了就是個數組。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],會創建一個?*?*?的三維數組。
下面還舉了個例子，a,b,c都是keras的tensor， `model = Model(input=[a, b], output=c)`
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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：我們在model.py中用到了shape這個attribute，
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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") |
閱讀input()里面的句子邏輯：
可以發現，進入if語句的情況是batch_shape不為空，并且tensor為空，此時進入if，用assert判斷如果shape不為空，那么久會有錯誤提示，告訴你要么輸入shape 要么輸入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。
那么其實如果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
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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)
就會調用Input()函數中的這個判斷句式，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的參數去創造一個batch_shape.
if shape is not None and not batch_shape:
batch_shape = (None,) + tuple(shape)
比如如果輸入：
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shape = (None,)batch_shape = (None,)+shapebatch_shape#會得到(None, None) |
可以發現，這里要求使用者至少指明你的數據維度，比如圖片的話，是三維的，所以shape至少是[None,None,None]，而且我認為shape = [None,1] 與shape = [None]是一樣的都會創建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，也希望大家多多支持服務器之家。
原文鏈接：https://blog.csdn.net/u013249853/article/details/88950943
