How to create tensor format for a CNN layer?
I plan to explain to my students how data can be processed using CNNs. For that purpose I would like to be able to explicitly convert a dataset with pixel values of images, like the csv Version of MNIST images, into tensor data that can be processed using a CNN. Is there any way to achieve this with RapidMiner? I tried the "ExamplesToTensor" operator, but the CNN layer complains, that the tensor format is for recurrent networks and cannot be processed by the CNN layer.
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Question has been answered in another thread:
https://community.rapidminer.com/discussion/58052/error-when-using-convolutional-layer-message-new-shape-length-doesnt-match-original-length#latest
In a nutshell:
You
have to specify the input shape manually! The default is set to
"automatic". When switching off automatic mode a number of parameters
can be set specifying how to map the data onto the tensor.
Important:
This approach assumes that the data is stored as a sequence of rows in
the csv and to have a single line per instance. Multi-channel data is
represented as a "sequence" of complete instances per channel in the
same line and indicated by the "depth" parameter of the input shape. Sort by:
1 - 1 of
11
Question has been answered in another thread:
In a nutshell:
You
have to specify the input shape manually! The default is set to
"automatic". When switching off automatic mode a number of parameters
can be set specifying how to map the data onto the tensor.
Important:
This approach assumes that the data is stored as a sequence of rows in
the csv and to have a single line per instance. Multi-channel data is
represented as a "sequence" of complete instances per channel in the
same line and indicated by the "depth" parameter of the input shape.