ModelCompressionRL
FinRL-Library
| ModelCompressionRL | FinRL-Library | |
|---|---|---|
| 2 | 202 | |
| 0 | 2,782 | |
| - | - | |
| 2.6 | 9.8 | |
| over 1 year ago | over 2 years ago | |
| Jupyter Notebook | Jupyter Notebook | |
| - | MIT License |
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ModelCompressionRL
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Requesting help with Custom Layers (Layer Subclassing) - Model fit builds the model again! [Keras]
I saw that the number of filters depends on the height, is the height the same for all images? I guess so since you say that moving the Conv2D to another layer fixes that problem. If my guess is right, the error is that when the model is being build, height is None and you are trying to divide None by a number. To solve this problem you have to get the shape using h = tf.shape(inputs)[1] as 0 is the batch dimension. As for getting the tf.concat as a layer. You can use tf.keras.layers.concatenate, it works the same as tf.concat, but it is a layer. I am using it in a layer that performs to parallel convolutions and then concatenates both convolutions. When I print the summary I only get the name of the layer, not the tf.concat as you mention. Search for the FireLayer class in my code
- Adding new block/inputs to non-sequential network
FinRL-Library
What are some alternatives?
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