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Correcting calculation of Glorot uniform for Tensors #576

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Dec 7, 2019
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Correcting calculation of Glorot uniform for Tensors #576

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c9d7b63
Correcting calculation of Glorot uniform for Tensors.
BradLarson Dec 6, 2019
d40644c
Restored the GRU test values to what they were before they were adjus…
BradLarson Dec 7, 2019
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2 changes: 1 addition & 1 deletion Sources/TensorFlow/Initializers.swift
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Original file line number Diff line number Diff line change
Expand Up @@ -495,7 +495,7 @@ public extension Tensor where Scalar: TensorFlowFloatingPoint {
/// - shape: The dimensions of the tensor.
init(glorotUniform shape: TensorShape, seed: TensorFlowSeed = Context.local.randomSeed) {
let (fanIn, fanOut) = shape.fans()
let limit = Tensor<Scalar>(6 / Scalar(fanIn + fanOut))
let limit = Tensor<Scalar>(Scalar.sqrt(6 / Scalar(fanIn + fanOut)))
self.init(randomUniform: shape, lowerBound: -limit, upperBound: limit, seed: seed)
}

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6 changes: 3 additions & 3 deletions Tests/TensorFlowTests/InitializerTests.swift
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Expand Up @@ -97,14 +97,14 @@ final class InitializerTests: XCTestCase {
// Constants for testing distribution based initializers.
private let fcShape = TensorShape([200, 100])
private let convShape = TensorShape([25, 25, 20, 20])
private let tolerance = Float(3e-2)

func testDistribution(
_ t: Tensor<Float>,
expectedMean: Float? = nil,
expectedStandardDeviation: Float? = nil,
expectedMin: Float? = nil,
expectedMax: Float? = nil
expectedMax: Float? = nil,
tolerance: Float = 3e-2
) {
if let expectedMean = expectedMean {
let mean = t.mean().scalarized()
Expand Down Expand Up @@ -159,7 +159,7 @@ final class InitializerTests: XCTestCase {
let spatialSize = convShape[0..<2].contiguousSize
let (fanIn, fanOut) = (convShape[2] * spatialSize, convShape[3] * spatialSize)
let stdDev = sqrt(Float(2.0) / Float(fanIn + fanOut))
testDistribution(t, expectedMean: 0, expectedStandardDeviation: stdDev)
testDistribution(t, expectedMean: 0, expectedStandardDeviation: stdDev, tolerance: 1e-4)
}

func testGlorotNormal() {
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36 changes: 19 additions & 17 deletions Tests/TensorFlowTests/LayerTests.swift
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Expand Up @@ -1147,10 +1147,10 @@ final class LayerTests: XCTestCase {
}
assertEqual(
outputs.map { $0.value.squeezingShape(at: 0) }[0],
[[ 0.14798240, 0.14295710, -0.09766942, -0.131820890],
[ 0.15757358, 0.19475500, -0.12810913, -0.112212844],
[ 0.16168950, 0.20306197, -0.13058113, -0.123917180],
[ 0.16325668, 0.20822097, -0.13273866, -0.121018395]],
[[ 0.20775771, 0.20080023, -0.13768704, -0.18534681],
[ 0.22666009, 0.30019346, -0.19720285, -0.14683801],
[ 0.23758979, 0.32101023, -0.20359215, -0.1787096],
[ 0.24337786, 0.3389194, -0.21143384, -0.1675081]],
accuracy: 1e-6)
}
// TODO: Figure out why the following is numerically unstable.
Expand Down Expand Up @@ -1178,17 +1178,17 @@ final class LayerTests: XCTestCase {
}
assertEqual(
outputs.map { $0.cell.squeezingShape(at: 0) }[0],
[[ 0.047114454, 0.013959665, -0.030737250, -0.038524970],
[ 0.069171116, 0.020617897, -0.044740470, -0.058878290],
[ 0.079530790, 0.023841830, -0.051080680, -0.069567055],
[ 0.084416830, 0.025424266, -0.053918116, -0.075140170]],
[[ 0.08981595, 0.027691621, -0.059235442, -0.075101905],
[ 0.12952757, 0.040402323, -0.084273980, -0.116252676],
[ 0.14727503, 0.046511370, -0.094689950, -0.138459030],
[ 0.15532997, 0.049573865, -0.098824400, -0.150242210]],
accuracy: 1e-6)
assertEqual(
outputs.map { $0.hidden.squeezingShape(at: 0) }[0],
[[ 0.024117637, 0.0066833394, -0.015753632, -0.019533360],
[ 0.035230752, 0.0098582430, -0.022934474, -0.029750597],
[ 0.040405065, 0.0113919870, -0.026185552, -0.035087958],
[ 0.042834233, 0.0121438510, -0.027640648, -0.037863784]],
[[ 0.046985064, 0.012670102, -0.031083463, -0.038572006],
[ 0.066482050, 0.018388016, -0.044252350, -0.058907583],
[ 0.074910110, 0.021107012, -0.049724963, -0.069670826],
[ 0.078670055, 0.022462710, -0.051899005, -0.075331904]],
accuracy: 1e-6)
}
}
Expand All @@ -1207,11 +1207,13 @@ final class LayerTests: XCTestCase {
let (outputs, _) = rnn.valueWithPullback(at: inputs) { rnn, inputs in
return rnn(inputs)
}
XCTAssertEqual(outputs.map { $0.hidden },
[[[0.12806869, 0.12806869, 0.12806869, 0.12806869]],
[[0.2007559, 0.2007559, 0.2007559, 0.2007559]],
[[0.23432666, 0.23432666, 0.23432666, 0.23432666]],
[[0.24788898, 0.24788898, 0.24788898, 0.24788898]]])
assertEqual(
outputs.map { $0.hidden }[0],
[[0.1193780, 0.1193780, 0.1193780, 0.1193780],
[0.1887644, 0.1887644, 0.1887644, 0.1887644],
[0.2230835, 0.2230835, 0.2230835, 0.2230835],
[0.2383619, 0.2383619, 0.2383619, 0.2383619]],
accuracy: 1e-5)
}
}

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2 changes: 1 addition & 1 deletion Tests/TensorFlowTests/SequentialTests.swift
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Expand Up @@ -63,7 +63,7 @@ final class SequentialTests: XCTestCase {
}
}
XCTAssertEqual(model.inferring(from: [[0, 0], [0, 1], [1, 0], [1, 1]]),
[[0.5567076], [0.5567076], [0.5567076], [0.5567076]])
[[0.50378805], [0.50378805], [0.50378805], [0.50378805]])
}

static var allTests = [
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