Number of convolution filters to use.
Number of rows in the convolution kernel.
Number of columns in the convolution kernel.
Initialization method for the weights of the layer. Default is Xavier. You can also pass in corresponding string representations such as 'glorot_uniform' or 'normal', etc. for simple init methods in the factory method.
Activation function to use. Default is null. You can also pass in corresponding string representations such as 'relu' or 'sigmoid', etc. for simple activations in the factory method.
Int array of length 2 corresponding to the step of the convolution in the height and width dimension. Also called strides elsewhere. Default is (1, 1).
The additional zeros added to the height dimension. Default is 0.
The additional zeros added to the width dimension. Default is 0.
Whether to propagate gradient back. Default is true.
Format of input data. Please use DataFormat.NCHW (dimOrdering='th').
An instance of Regularizer, (eg. L1 or L2 regularization), applied to the input 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.
Activation function to use.
Activation function to use. Default is null. You can also pass in corresponding string representations such as 'relu' or 'sigmoid', etc. for simple activations in the factory method.
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.
Whether to include a bias (i.e. make the layer affine rather than linear). Default is true.
Format of input data.
Format of input data. Please use DataFormat.NCHW (dimOrdering='th').
Build graph: some other modules point to current module
Build graph: some other modules point to current module
upstream variables
Variable containing current module
Initialization method for the weights of the layer.
Initialization method for the weights of the layer. Default is Xavier. You can also pass in corresponding string representations such as 'glorot_uniform' or 'normal', etc. for simple init methods in the factory method.
A Single Shape, does not include the batch dimension.
Number of columns in the convolution kernel.
Number of convolution filters to use.
Number of rows in the convolution kernel.
The additional zeros added to the height dimension.
The additional zeros added to the height dimension. Default is 0.
The additional zeros added to the width dimension.
The additional zeros added to the width dimension. Default is 0.
Whether to propagate gradient back.
Whether to propagate gradient back. Default is true.
Int array of length 2 corresponding to the step of the convolution in the height and width dimension.
Int array of length 2 corresponding to the step of the convolution in the height and width dimension. Also called strides elsewhere. Default is (1, 1).
An instance of Regularizer, (eg.
An instance of Regularizer, (eg. L1 or L2 regularization), applied to the input weights matrices. Default is null.
(Since version 0.3.0) please use recommended saveModule(path, overWrite)
Applies a 2D convolution over an input image composed of several input planes. You can also use ShareConv2D as an alias of this layer. Data format currently supported for this layer is DataFormat.NCHW (dimOrdering='th'). The input of this layer should be 4D.
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), e.g. inputShape=Shape(3, 128, 128) for 128x128 RGB pictures.
Remark: This layer is from Torch and wrapped in Keras style.
Numeric type of parameter(e.g. weight, bias). Only support float/double now.