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Fix derivative of RNN.callAsFunction(_:initialState:).
#660
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Previously, `RNN._vjpcallAsFunction(_:initialState:)` incorrectly used a zero initial state. Now, it uses `initialState` as the initial state. Add `RNN` gradient tests for `SimpleRNNCell`, `LSTMCell`, and `GRUCell`. Todo: verify that gradients are correct using a reference implementation.
marcrasi
approved these changes
Feb 3, 2020
marcrasi
reviewed
Feb 3, 2020
| @@ -1190,22 +1189,62 @@ final class LayerTests: XCTestCase { | |||
| [ 0.074910110, 0.021107012, -0.049724963, -0.069670826], | |||
| [ 0.078670055, 0.022462710, -0.051899005, -0.075331904]], | |||
| accuracy: 1e-6) | |||
| let (𝛁lstm, _) = pullback(.init(inputs.map { LSTMCell<Float>.State(cell: $0, hidden: $0) })) | |||
| // TODO: Verify that LSTM gradients are correct using a reference implementation. | |||
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I'm interested in doing this. Should we do something like check some TF python code into this repository that reproduces the gradients?
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Previously,
RNN._vjpcallAsFunction(_:initialState:)incorrectly used a zeroinitial state. Now, it uses
initialStateas the initial state.Add
RNNgradient tests forSimpleRNNCell,LSTMCell, andGRUCell.Todo: verify that gradients are correct using a reference implementation.