Error messages

Modeling view

Border lost due to stride (${shape}).

{shape}: (Vertical stride, Horizontal stride, Filter).

Cause /

If a block’s stride is greater than 1, information may be lost at the border of the block input.

This means that the convolutional filter cannot be evenly applied at the border of the block input. Therefore some information will be lost.

A horizontal stride of 2 will in this case result in lost information.
Figure 1. A horizontal stride of 2 will in this case result in lost information.

Remedy /

To remove the warning, make sure that:
input_size - offset is evenly divisible by the stride, where:
* input_size is the output size of the previous block
* offset is the kernel’s size.

Example: You’ll get this warning if your block input is 60x60, your kernel is 3x3, and you select a stride of 4. Change the stride to 3 to resolve this warning.

This is just a warning, it does not affect the model in any major way, especially if you get it on the first few blocks.
However, if the amount of border lost is in the same order of magnitude as the corresponding input dimension, it means that a significant part of the image is being lost. Then it is important to fix this warning.

No target data

Cause /

The Target block has not any set Selection , that is, there is no data to train the model with.

Remedy /

Select the Target block and pick a Selection from the dropdown.

Wrong input shape. Expected to see ${shape} dimension(s).

{shape}: Horizontal stride, Vertical stride, Filter

Cause /

You cannot run a model when a block is expecting a different input shape.

Example: For a 10-class classification problem, the last block must have an output size of 10.

Remedy /

Change the output shape in the previous block to match the expected shape of the block.

Example: Change the input Feature in the Input block to an image to match the expected input shape for the following 2D convolution block.

Example: Change the number of nodes to 10 in the last Dense block to match the expected shape of the Target block in a 10-class classification problem.

Expected an input dimension > 1.

Cause /

The input to the Flatten block has only got 1 dimension.

Remedy /

Make sure that the number of input dimensions to the Flatten block is > 1.

${label} is required.

{label}: e.g., Feature in the Input block.

Cause /

A required {label} has not been specified.

Example: The input Feature has not been set in the Input block.

Remedy /

Set the required {label}.

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