[WIP] Add SeparableConv1D Layer #157
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� Conflicts: � api/src/main/kotlin/org/jetbrains/kotlinx/dl/api/inference/keras/ModelSaver.kt
- still have to write tests, and do the 1D variant - this also fixed a bug in WeightLoader that did not load the bias weights correctly for SeparableConv2D.kt
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This is still a work in progress but I would appreciate some feedback already if anyone has time :). Will write some tests and implement the 1D version soon. |
dosier
changed the title
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| * Note: layer attributes cannot be modified after the layer has been called once (except the `trainable` attribute). | ||
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| * TODO: add rank for getting the channel axis? |
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We are supporting at this moment only Channels Last (NWHC)
| * Note: layer attributes cannot be modified after the layer has been called once (except the `trainable` attribute). | ||
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| * TODO: add rank for getting the channel axis? | ||
| * TODO: add docs for params? |
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Of course, we need docs for params
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| * TODO: add rank for getting the channel axis? | ||
| * TODO: add docs for params? | ||
| * TODO: add trainable param? |
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What do you mean here? Please explain or provide an example?
| protected open fun getOutputDepth(numberOfChannels: Long): Long = filtersInternal | ||
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| private fun computeMatricesShapes(numberOfChannels: Long) { | ||
| depthwiseKernelShape = shapeFromDims(*kernelSizeInternal, numberOfChannels, depthMulitplierInternal.toLong()) |
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maybe better to make the function without side-effects, which returns a triple of shapes
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| /** Default SeparableConv2d bias variable name in TensorFlow graph, based on variable's name. */ | ||
| internal fun separableConv2dBiasVarName(name: String) = name + "_" + "separable_conv2d_bias" | ||
| internal fun separableConvBiasVarName(name: String, dim: Int) = name + "_" + "separable_conv2d_bias" |
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dim parameter is not used at this moment.. Are you going to create separate variable names depending on dims? Isn't it?
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So @dosier if you have any questions, ask me. I tried to comment on the existing solution and answer a few questions (and made more:) Great to find any existing NN architecture which uses this layer to attach in comments |
2 participants
Early works for issue:
#125