Initialise weights in tensorflow sequential model | 台灣精品獎-歷屆得獎名單
![Initialise weights in tensorflow sequential model](https://i.imgur.com/DERULla.jpg)
Youcansimplytrythisforlayerinmodel.layers:init_layer_weight=[]#theweightsyourselfinthislayer ...
![Initialise weights in tensorflow sequential model](https://i.imgur.com/DERULla.jpg)
First you need to provide Input() layer, like in code below. This allows layers to actually compute their kernels/weights sizes. Also use //2 instead of /2 in your code for integer division.
After that you may access layers from model.layers list, this list includes all layers except input layer. On each layer you can call .set_weights(...), this method accepts python list of two elements - first element is kernel weights, second is bias weights, both weights should be numpy arrays. For Dense layer type kernel is 2D array with number of rows equal to input size (previous dense output nodes count) and number of columns equal to number of this dense layer nodes/outputs. Bias is 1D array of size equal to number of this dense layer nodes/outputs.
You may also read about .set_weights(...)[1].
In the next code for example I set weights for Layer 2 & 3.
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![](https://i.imgur.com/DERULla.jpg)
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