When to use those similar layers in Keras?

General Discussion
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#1

Hey I just find that those keras.layers are quite similar, there must be some reasons to define them respectively, just do not get it when and how to use them:

Group 1

Resizing
ZeroPadding2D
UpSampling2D 
sp.ndimage.zoom 
Conv2DTranspose
Cropping2D

Group 2

Reshape
RepeatVector

Thanks

#3

Hi @ChrisXY_Zhao

Welcome to the TensorFlow Forum!

There are some differences in each of these Keras layers and its usages. Please find the below definition and usability of thee API layers:

  1. Resizing layer: - A preprocessing layer which resizes images to a specified size.
    For ex - In the cats and dogs images classification, you may need to resize all of the images to the same size so that the model can learn features that are independent of the image size.

  2. ZeroPadding2D layer: This layer pads the 2D input with zeros to be in a consistent size before training a model by adding rows and columns of zeros at the top, bottom, left and right side of an image tensor.
    For ex - In handwritten digits image classification, need to pad all of the images to the same size so that the model can learn features that are independent of the position of the digits in the image.

  3. UpSampling2D layers: This layer upsample images to a specific size by repeating the rows and columns of the data by size[0] and size[1] respectively.
    For ex - to increase the resolution of low-resolution images before training a model to classify them.

Likewise, you can find other api layers definition and how to use them by checking the below linked URLs.
sp.ndimage.zoom,
Conv2DTranspose,
Cropping2D,
Reshape and
RepeatVector

You can check the example code or the Used in Notebooks section to further understand these APIs by clicking the given links. Thank you.

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