Keras Convolution 2D Layer

This Node Is Deprecated — This version of the node has been replaced with a new and improved version. The old version is kept for backwards-compatibility, but for all new workflows we suggest to use the version linked below.
Go to Suggested ReplacementKeras Convolution 2D Layer

This layer creates a convolution kernel that is convolved with the layer input over two dimensions. Corresponds to the Keras Convolution 2D Layer.

Options

Name prefix
The name prefix of the layer. The prefix is complemented by an index suffix to obtain a unique layer name. If this option is unchecked, the name prefix is derived from the layer type.
Filters
The dimensionality of the output space (i.e. the number of output filters in the convolution).
Kernel size
A tuple of 2 integers, specifying the height and width of the 2D convolution window.
Strides
A tuple of 2 integers, specifying the strides of the convolution along the height and width. Specifying any stride value != 1 is incompatible with specifying any dilation_rate value != 1.
Padding
Different padding modes to apply to the spatial dimensions (excluding the batch and channel dimensions) of the inputs before the pooling operation. The padding will be done with zeroes. A detailed explanation of the different modes can be found here .
  • Valid: No padding
  • Same: Padding such that the spatial output dimension do not change.
  • Full: Padding with kernel size - 1
Dilation rate
A tuple/list of 2 integers, specifying the dilation rate to use for dilated convolution. Currently, specifying any dilation_rate value != 1 is incompatible with specifying any stride value != 1.
Activation function
The activation function to use.
Use bias?
If checked, a bias vector will be used.
Kernel initializer
Initializer for the kernel weights matrix.
Bias initializer
Initializer for the bias vector.
Kernel regularizer
Regularizer function applied to the kernel weights matrix.
Bias regularizer
Regularizer function applied to the bias vector.
Activation regularizer
Regularizer function applied to the output of the layer (its "activation").
Kernel constraint
Constraint function applied to the kernel matrix.
Bias constraint
Constraint function applied to the bias vector.

Input Ports

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The Keras deep learning network to which to add a Convolution 2D layer.

Output Ports

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The Keras deep learning network with an added Convolution 2D layer.

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