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Merge "Fix LSTM documentation" into qt-dev

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TreeHugger Robot
2019-05-08 20:17:28 +00:00
committed by Android (Google) Code Review
parent 248f72a9a0 e0537f09fb
commit 881e261f0e
3 changed files with 26 additions and 24 deletions
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4
current.txt
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@@ -401,7 +401,7 @@ f7d7cb747dc01a9fdb2d39a80003b4d8df9be733d65f5842198802eb6209db69 android.hardwar
65a021fa89085b62fc96b2b6d3bef2f9103cf4d63379c68bc154fd9eef672852 android.hardware.health@1.0::types 65a021fa89085b62fc96b2b6d3bef2f9103cf4d63379c68bc154fd9eef672852 android.hardware.health@1.0::types
b7ecf29927055ec422ec44bf776223f07d79ad9f92ccf9becf167e62c2607e7a android.hardware.keymaster@4.0::IKeymasterDevice b7ecf29927055ec422ec44bf776223f07d79ad9f92ccf9becf167e62c2607e7a android.hardware.keymaster@4.0::IKeymasterDevice
574e8f1499436fb4075894dcae0b36682427956ecb114f17f1fe22d116a83c6b android.hardware.neuralnetworks@1.0::IPreparedModel 574e8f1499436fb4075894dcae0b36682427956ecb114f17f1fe22d116a83c6b android.hardware.neuralnetworks@1.0::IPreparedModel
e75759b40a1c5f97b463b30aab91954012c9ea9e454dde308db853a56796e5a6 android.hardware.neuralnetworks@1.0::types 1e3576c07006d82ba5bc6ddbf87c101414d361c41afe7a82713750844c488725 android.hardware.neuralnetworks@1.0::types
eb754b58c93e5591613208b4c972811288b0fa16a82430d602f107c91a908b22 android.hardware.neuralnetworks@1.1::types eb754b58c93e5591613208b4c972811288b0fa16a82430d602f107c91a908b22 android.hardware.neuralnetworks@1.1::types
1d4a5776614c08b5d794a5ec5ab04697260cbd4b3441d5935cd53ee71d19da02 android.hardware.radio@1.0::IRadioResponse 1d4a5776614c08b5d794a5ec5ab04697260cbd4b3441d5935cd53ee71d19da02 android.hardware.radio@1.0::IRadioResponse
ed9da80ec0c96991fd03f0a46107815d0e50f764656e49dba4980fa5c31d5bc3 android.hardware.radio@1.0::types ed9da80ec0c96991fd03f0a46107815d0e50f764656e49dba4980fa5c31d5bc3 android.hardware.radio@1.0::types
@@ -515,7 +515,7 @@ b83317b66721241887d2770b5ae95fd5af1e77c5daa7530ecb08fae8892f2b43 android.hardwar
92714960d1a53fc2ec557302b41c7cc93d2636d8364a44bd0f85be0c92927ff8 android.hardware.neuralnetworks@1.2::IExecutionCallback 92714960d1a53fc2ec557302b41c7cc93d2636d8364a44bd0f85be0c92927ff8 android.hardware.neuralnetworks@1.2::IExecutionCallback
36e1064c869965dee533c537cefbe87e54db8bd8cd45be7e0e93e00e8a43863a android.hardware.neuralnetworks@1.2::IPreparedModel 36e1064c869965dee533c537cefbe87e54db8bd8cd45be7e0e93e00e8a43863a android.hardware.neuralnetworks@1.2::IPreparedModel
e1c734d1545e1a4ae749ff1dd9704a8e594c59aea7c8363159dc258e93e0df3b android.hardware.neuralnetworks@1.2::IPreparedModelCallback e1c734d1545e1a4ae749ff1dd9704a8e594c59aea7c8363159dc258e93e0df3b android.hardware.neuralnetworks@1.2::IPreparedModelCallback
e3b6176e3bf235c4e0e4e451b0166e396c7ee176cfe167c9147c3d46d7b34f0c android.hardware.neuralnetworks@1.2::types d18bba0b6c8d2d1da3cfb52b14f556d2e04eb91551d97ee60a3524cf993a3e0e android.hardware.neuralnetworks@1.2::types
cf7a4ba516a638f9b82a249c91fb603042c2d9ca43fd5aad9cf6c0401ed2a5d7 android.hardware.nfc@1.2::INfc cf7a4ba516a638f9b82a249c91fb603042c2d9ca43fd5aad9cf6c0401ed2a5d7 android.hardware.nfc@1.2::INfc
abf98c2ae08bf765db54edc8068e36d52eb558cff6706b6fd7c18c65a1f3fc18 android.hardware.nfc@1.2::types abf98c2ae08bf765db54edc8068e36d52eb558cff6706b6fd7c18c65a1f3fc18 android.hardware.nfc@1.2::types
4cb252dc6372a874aef666b92a6e9529915aa187521a700f0789065c3c702ead android.hardware.power.stats@1.0::IPowerStats 4cb252dc6372a874aef666b92a6e9529915aa187521a700f0789065c3c702ead android.hardware.power.stats@1.0::IPowerStats

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neuralnetworks/1.0/types.hal
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@@ -858,20 +858,21 @@ enum OperationType : int32_t {
* elements of the input matrices. * elements of the input matrices.
* *
* The operation has the following independently optional inputs: * The operation has the following independently optional inputs:
* * The cell-to-input weights (\f$W_{ci}\f$), cell-to-forget weights
* (\f$W_{cf}\f$) and cell-to-output weights (\f$W_{co}\f$) either all
* have values or neither of them have values (i.e., all set to null). If
* they have values, the peephole optimization is used.
* * The input-to-input weights (\f$W_{xi}\f$), recurrent-to-input weights * * The input-to-input weights (\f$W_{xi}\f$), recurrent-to-input weights
* (\f$W_{hi}\f$), cell-to-input (\f$W_{ci}\f$) weights, and input gate * (\f$W_{hi}\f$) and input gate bias (\f$b_i\f$) either all have values,
* bias (\f$b_i\f$) either all have values, or none of them have values * or none of them have values. If they have no values, coupling of input
* (i.e., all set to null). If they have no values, coupling of input and * and forget gates (CIFG) is used, in which case the input gate
* forget gates (CIFG) is used, in which case the input gate (\f$i_t\f$) * (\f$i_t\f$) is calculated using the following equation instead.
* is calculated using the following equation instead.
* \f{eqnarray*}{ * \f{eqnarray*}{
* i_t = 1 - f_t * i_t = 1 - f_t
* \f} * \f}
* * The cell-to-forget weights (\f$W_{cf}\f$) and cell-to-output weights * In case peephole optimization is used and CIFG is not used
* (\f$W_{co}\f$) either both have values or neither of them have values. * cell-to-input (\f$W_{ci}\f$) weights must be present. Otherwise, the
* If they have values, the peephole optimization is used. Additionally, * cell-to-input weights must have no value.
* if CIFG is not used, cell-to-input weights (\f$W_{ci}\f$) is also
* required to have values for peephole optimization.
* * The projection weights (\f$W_{proj}\f$) is required only for the * * The projection weights (\f$W_{proj}\f$) is required only for the
* recurrent projection layer, and should otherwise have no value. * recurrent projection layer, and should otherwise have no value.
* * The projection bias (\f$b_{proj}\f$) may (but not required to) have a * * The projection bias (\f$b_{proj}\f$) may (but not required to) have a
@@ -984,8 +985,8 @@ enum OperationType : int32_t {
* Outputs: * Outputs:
* * 0: The scratch buffer. * * 0: The scratch buffer.
* A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape * A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
* [batch_size, num_units * 4] with CIFG, or * [batch_size, num_units * 3] with CIFG, or
* [batch_size, num_units * 3] without CIFG. * [batch_size, num_units * 4] without CIFG.
* * 1: The output state (out) (\f$h_t\f$). * * 1: The output state (out) (\f$h_t\f$).
* A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape * A 2-D tensor of {@link OperandType::TENSOR_FLOAT32}, of shape
* [batch_size, output_size]. * [batch_size, output_size].

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neuralnetworks/1.2/types.hal
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@@ -1177,20 +1177,21 @@ enum OperationType : int32_t {
* https://arxiv.org/pdf/1607.06450.pdf * https://arxiv.org/pdf/1607.06450.pdf
* *
* The operation has the following independently optional inputs: * The operation has the following independently optional inputs:
* * The cell-to-input weights (\f$W_{ci}\f$), cell-to-forget weights
* (\f$W_{cf}\f$) and cell-to-output weights (\f$W_{co}\f$) either all
* have values or neither of them have values (i.e., all set to null). If
* they have values, the peephole optimization is used.
* * The input-to-input weights (\f$W_{xi}\f$), recurrent-to-input weights * * The input-to-input weights (\f$W_{xi}\f$), recurrent-to-input weights
* (\f$W_{hi}\f$), cell-to-input (\f$W_{ci}\f$) weights, and input gate * (\f$W_{hi}\f$) and input gate bias (\f$b_i\f$) either all have values,
* bias (\f$b_i\f$) either all have values, or none of them have values * or none of them have values. If they have no values, coupling of input
* (i.e., all set to null). If they have no values, coupling of input and * and forget gates (CIFG) is used, in which case the input gate
* forget gates (CIFG) is used, in which case the input gate (\f$i_t\f$) * (\f$i_t\f$) is calculated using the following equation instead.
* is calculated using the following equation instead.
* \f{eqnarray*}{ * \f{eqnarray*}{
* i_t = 1 - f_t * i_t = 1 - f_t
* \f} * \f}
* * The cell-to-forget weights (\f$W_{cf}\f$) and cell-to-output weights * In case peephole optimization is used and CIFG is not used
* (\f$W_{co}\f$) either both have values or neither of them have values. * cell-to-input (\f$W_{ci}\f$) weights must be present. Otherwise, the
* If they have values, the peephole optimization is used. Additionally, * cell-to-input weights must have no value.
* if CIFG is not used, cell-to-input weights (\f$W_{ci}\f$) is also
* required to have values for peephole optimization.
* * The projection weights (\f$W_{proj}\f$) is required only for the * * The projection weights (\f$W_{proj}\f$) is required only for the
* recurrent projection layer, and should otherwise have no value. * recurrent projection layer, and should otherwise have no value.
* * The projection bias (\f$b_{proj}\f$) may (but not required to) have a * * The projection bias (\f$b_{proj}\f$) may (but not required to) have a