diff --git a/Sources/TensorFlow/Layers/Convolutional.swift b/Sources/TensorFlow/Layers/Convolutional.swift
index 45edbaf4c..88fe23728 100644
--- a/Sources/TensorFlow/Layers/Convolutional.swift
+++ b/Sources/TensorFlow/Layers/Convolutional.swift
@@ -377,7 +377,7 @@ public struct TransposedConv2D<Scalar: TensorFlowFloatingPoint>: Layer {
     ///
     /// - Parameters:
     ///   - filter: A 4-D tensor of shape
-    ///     `[width, height, input channel count, output channel count]`.
+    ///     `[height, width, output channel count, input channel count]`.
     ///   - bias: The bias tensor of shape `[output channel count]`.
     ///   - activation: The element-wise activation function.
     ///   - strides: The strides of the sliding window for spatial dimensions.
@@ -404,12 +404,12 @@ public struct TransposedConv2D<Scalar: TensorFlowFloatingPoint>: Layer {
     @differentiable
     public func callAsFunction(_ input: Tensor<Scalar>) -> Tensor<Scalar> {
         let batchSize = input.shape[0]
-        let w = (input.shape[1] - (1 * paddingIndex)) *
+        let h = (input.shape[1] - (1 * paddingIndex)) *
           strides.0 + (filter.shape[0] * paddingIndex)
-        let h = (input.shape[2] - (1 * paddingIndex)) *
+        let w = (input.shape[2] - (1 * paddingIndex)) *
           strides.1 + (filter.shape[1] * paddingIndex)
         let c = filter.shape[2]
-        let newShape = Tensor<Int32>([Int32(batchSize), Int32(w), Int32(h), Int32(c)])
+        let newShape = Tensor<Int32>([Int32(batchSize), Int32(h), Int32(w), Int32(c)])
         return activation(conv2DBackpropInput(
             input,
             shape: newShape,
diff --git a/Tests/TensorFlowTests/LayerTests.swift b/Tests/TensorFlowTests/LayerTests.swift
index 070d40f36..0f177dce2 100644
--- a/Tests/TensorFlowTests/LayerTests.swift
+++ b/Tests/TensorFlowTests/LayerTests.swift
@@ -302,6 +302,18 @@ final class LayerTests: XCTestCase {
         XCTAssertEqual(grads.1.bias, [4, 4, 4, 4])
     }
 
+    func testTransposedConv2D() {
+        let filter =  Tensor(shape: [4, 2, 1, 1], scalars: (0..<8).map(Float.init))
+        let bias = Tensor<Float>([8])
+        let layer = TransposedConv2D(filter: filter, bias: bias, activation: identity,
+                                  strides: (1, 1), padding: .same)
+        let input = Tensor(shape: [1, 4, 2, 1], scalars: (0..<8).map(Float.init))
+        let output = layer.inferring(from: input)
+        let expected = Tensor<Float>(shape: [1, 4, 2, 1],
+                                     scalars: [8, 12, 12, 28, 24, 64, 48, 112])
+        XCTAssertEqual(output, expected)
+    }  
+
     func testSeparableConv1D() {
         let depthwiseFilter = Tensor(shape: [2, 2, 2], scalars: (0..<8).map(Float.init))
         let pointwiseFilter = Tensor(shape: [1, 4, 1], scalars: (0..<4).map(Float.init))
@@ -1318,6 +1330,7 @@ final class LayerTests: XCTestCase {
         ("testConv2DDilation", testConv2DDilation),
         ("testConv3D", testConv3D),
         ("testConv3DGradient", testConv3DGradient),
+        ("testTransposedConv2D", testTransposedConv2D),
         ("testDepthwiseConv2D", testDepthwiseConv2D),
         ("testDepthwiseConv2DGradient", testDepthwiseConv2DGradient),
         ("testSeparableConv1D", testSeparableConv1D),