Note
Two similar implementation exists for conv2d:
signal.conv2d and nnet.conv2d.
The former implements a traditional 2D convolution, while the latter implements the convolutional layers present in convolutional neural networks (where filters are 3D and pool over several input channels).
TODO: Give examples for how to use these things! They are pretty complicated.
This function will build the symbolic graph for convolving a stack of input images with a set of filters. The implementation is modelled after Convolutional Neural Networks (CNN). It is simply a wrapper to the ConvOp but provides a much cleaner interface.
Parameters: |
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Return type: | symbolic 4D tensor |
Returns: | set of feature maps generated by convolutional layer. Tensor is of shape (batch size, nb filters, output row, output col) |
3D “convolution” of multiple filters on a minibatch (does not flip the kernel, moves kernel with a user specified stride)
Parameters: |
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Note : | The order of dimensions do not correspond with the one in conv2d. This is for optimization. |