![]() Shapes are consequences of the model's configuration. In your picture, except for the input layer, which is conceptually different from other layers, you have. ![]() In the image of the neural net below hidden layer1 has 4 units.ĭoes this directly translate to the units attribute of the Layer object? Or does units in Keras equal the shape of every weight in the hidden layer times the number of units? It's a property of each layer, and yes, it's related to the output shape as we will see later. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. ![]() By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service.
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