com.intel.analytics.zoo.pipeline.api.keras.layers
The size of output dimension.
Number of Dense layers to use internally. Integer. Default is 4.
An instance of Regularizer, (eg. L1 or L2 regularization), applied to the main weights matrices. Default is null.
An instance of Regularizer, applied to the bias. Default is null.
Whether to include a bias (i.e. make the layer affine rather than linear). Default is true.
A Single Shape, does not include the batch dimension.
An instance of Regularizer, applied to the bias.
An instance of Regularizer, applied to the bias. Default is null.
Whether to include a bias (i.e.
Whether to include a bias (i.e. make the layer affine rather than linear). Default is true.
Build graph: some other modules point to current module
Build graph: some other modules point to current module
upstream variables
Variable containing current module
A Single Shape, does not include the batch dimension.
A Single Shape, does not include the batch dimension.
Number of Dense layers to use internally.
Number of Dense layers to use internally. Integer. Default is 4.
The size of output dimension.
The size of output dimension.
An instance of Regularizer, (eg.
An instance of Regularizer, (eg. L1 or L2 regularization), applied to the main weights matrices. Default is null.
(Since version 0.3.0) please use recommended saveModule(path, overWrite)
A dense maxout layer that takes the element-wise maximum of linear layers. This allows the layer to learn a convex, piecewise linear activation function over the inputs. The input of this layer should be 2D.
When you use this layer as the first layer of a model, you need to provide the argument inputShape (a Single Shape, does not include the batch dimension).
Numeric type of parameter(e.g. weight, bias). Only support float/double now.