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This change uses tensorflow directly #31
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mmphego
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Feb 29, 2020
| if layer == model.layers[0]: | ||
| input_layer = int(str(layer.input_shape).split(",")[1][1:-1]) | ||
| hidden_layers_nr += 1 | ||
| if type(layer) == Dense: |
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Perhaps, rewrite this as:
Suggested change
| if type(layer) == Dense: | |
| if isinstance(Dense, layer): |
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| if type(layer) == Conv2D: | ||
| layer_types.append("Conv2D") | ||
| elif type(layer) == MaxPooling2D: | ||
| layer_types.append("MaxPooling2D") | ||
| elif type(layer) == Dropout: | ||
| layer_types.append("Dropout") | ||
| elif type(layer) == Flatten: | ||
| layer_types.append("Flatten") | ||
| elif type(layer) == Activation: |
| input_layer = int(str(layer.input_shape).split(",")[1][1:-1]) | ||
| hidden_layers_nr += 1 | ||
| if type(layer) == Dense: | ||
| hidden_layers.append(int(str(layer.output_shape).split(",")[1][1:-1])) |
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This is a bit complex for noobs to read, would be nice to simplify it.
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| if (type(layer) == Conv2D): | ||
| layer_types.append("Conv2D") | ||
| elif (type(layer) == MaxPooling2D): | ||
| layer_types.append("MaxPooling2D") | ||
| elif (type(layer) == Dropout): | ||
| layer_types.append("Dropout") | ||
| elif (type(layer) == Flatten): | ||
| layer_types.append("Flatten") | ||
| elif (type(layer) == Activation): | ||
| layer_types.append("Activation") |
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endolith
referenced
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in endolith/ann-visualizer
Apr 3, 2022
endolith
referenced
this pull request
in endolith/ann-visualizer
Apr 3, 2022
endolith
referenced
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in endolith/ann-visualizer
Apr 3, 2022
Unpythonic C-like syntax, like the semicolons at the end of each line
endolith
referenced
this pull request
in endolith/ann-visualizer
Apr 3, 2022
(but not all the "line too long" ones)
endolith
referenced
this pull request
in endolith/ann-visualizer
Apr 3, 2022
Maybe Keras returned only strings in an earlier version?
endolith
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Apr 3, 2022
These were suggested in RedaOps#31 by @mmphego to support inheritance
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I had trouble using the previous code by TensorFlow and the code could not correctly identify the layers so it didn't work