Convolution 2D transpose layer
This card is a wrapper of this Keras class.
Note: the backend for building and training neural networks is based on Keras. The documentation of this card is a variant of the documentation of its corresponding class.
Inputs
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Filters — Integer
The dimensionality of the output space (i.e. the number of output filters in the convolution).
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Kernel size — List of Integer
A list of 2 integers, specifying the height and width of the 2D convolution window.
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Strides — List of Integer
A list of 2 integers, specifying the strides of the convolution along the height and width. Specifying any value different than “1” is incompatible with specifying any Dilation rate different than “1”.
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Padding — String
The padding algorithm.
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Output padding — List of Integer
A list of 2 integers, specifying the amount of padding along the height and width of the output tensor. The amount of output padding along a given dimension must be lower than the stride along that same dimension. If it is not set, the output shape is inferred.
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Data format — String
The ordering of the dimensions in the inputs.
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Dilation rate — List of Integer
A list of 2 integers, specifying the dilation rate to use for dilated convolution. Specifying any value different than “1” is incompatible with specifying any Strides different than “1”.
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Activation — String
Activation function to use.
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Use bias — Boolean
Whether the layer uses a bias vector.
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Kernel initializer — NeuralNetworkInitializer
Initializer for the kernel weights matrix. If not specified, then Glorot uniform initializer is used.
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Bias initializer — NeuralNetworkInitializer
Initializer for the bias vector. If not specified, then Zeros initializer is used.
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Kernel regularizer — NeuralNetworkRegularizer
Regularizer function applied to the kernel weights matrix.
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Bias regularizer — NeuralNetworkRegularizer
Regularizer function applied to the bias vector.
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Activity regularizer — NeuralNetworkRegularizer
Regularizer function applied to the output of the layer (its “activation”).
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Kernel constraint — NeuralNetworkConstraint
Constraint function applied to the kernel weights matrix.
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Bias constraint — NeuralNetworkConstraint
Constraint function applied to the bias vector.
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Input — NeuralNetworkTensor
Input of this layer.
Outputs
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Layer instance — NeuralNetworkLayer
Instance of this layer. It can be wrapped using a Bidirectional or a TimeDistributed wrapper.
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Output — NeuralNetworkTensor
Output of this layer.