diff --git a/Sources/TensorFlow/Operators/NN.swift b/Sources/TensorFlow/Operators/NN.swift
index 094226bf5..0b11305b8 100644
--- a/Sources/TensorFlow/Operators/NN.swift
+++ b/Sources/TensorFlow/Operators/NN.swift
@@ -229,12 +229,10 @@ func _vjpConv3D<Scalar: TensorFlowFloatingPoint>(
     let value = conv3D(x, filter: filter, strides: strides,
                        padding: padding)
     return (value, { v in
-        return (
-            conv3DBackpropInput(v, shape: x.shapeTensor, filter: filter,
-                                strides: strides, padding: padding),
-            conv3DBackpropFilter(v, input: x, filterSizes: filter.shapeTensor,
-                                 strides: strides, padding: padding)
-        )
+        (conv3DBackpropInput(v, shape: x.shapeTensor, filter: filter,
+                             strides: strides, padding: padding),
+         conv3DBackpropFilter(v, input: x, filterSizes: filter.shapeTensor,
+                              strides: strides, padding: padding))
     })
 }
 
@@ -268,11 +266,9 @@ func _vjpConv3DBackpropInput<Scalar: TensorFlowFloatingPoint>(
     let value = conv3DBackpropInput(x, shape: shape, filter: filter, strides: strides,
                                     padding: padding)
     return (value, { v in
-        return (
-            conv3DBackpropFilter(x, input: v, filterSizes: shape, strides: strides,
-                                 padding: padding),
-            conv3D(v, filter: filter, strides: strides, padding: padding)
-        )
+        (conv3D(v, filter: filter, strides: strides, padding: padding),
+         conv3DBackpropFilter(x, input: v, filterSizes: filter.shapeTensor, strides: strides,
+                              padding: padding))
     })
 }
 
@@ -287,7 +283,7 @@ func conv3DBackpropFilter<Scalar: TensorFlowFloatingPoint>(
     padding: Padding = .valid
 ) -> Tensor<Scalar> {
     return Raw.conv3DBackpropFilterV2(
-        x,
+        input,
         filterSizes: filterSizes,
         outBackprop: x,
         strides: [Int32(strides.0), Int32(strides.1), Int32(strides.2),
@@ -306,11 +302,9 @@ func _vjpConv3DBackpropFilter<Scalar: TensorFlowFloatingPoint>(
     let value = conv3DBackpropFilter(x, input: input, filterSizes: filterSizes,
                                      strides: strides, padding: padding)
     return (value, { v in
-        return (
-            conv3DBackpropInput(x, shape: filterSizes, filter: v, strides: strides,
-                                  padding: padding),
-            conv3D(input, filter: v, strides: strides, padding: padding)
-        )
+        (conv3D(input, filter: v, strides: strides, padding: padding),
+         conv3DBackpropInput(x, shape: x.shapeTensor, filter: v, strides: strides,
+                             padding: padding))
     })
 }
 
diff --git a/Tests/TensorFlowTests/LayerTests.swift b/Tests/TensorFlowTests/LayerTests.swift
index acfe4bb60..85b8f6837 100644
--- a/Tests/TensorFlowTests/LayerTests.swift
+++ b/Tests/TensorFlowTests/LayerTests.swift
@@ -198,6 +198,55 @@ final class LayerTests: XCTestCase {
         XCTAssertEqual(output, expected)
     }
 
+    func testConv3DGradient() {
+        let filter = Tensor(shape: [1, 4, 4, 1, 1], scalars: (0..<16).map(Float.init))
+        let bias = Tensor<Float>(ones: [2])
+        let layer = Conv3D(filter: filter,
+                           bias: bias,
+                           activation: identity,
+                           strides: (2, 2, 2),
+                           padding: .same)
+        let input = Tensor(shape: [1, 4, 4, 4, 1], scalars: (0..<64).map(Float.init))
+        let grads = gradient(at: input, layer) { $1($0).sum() }
+        // The expected value of the gradient was computed using the following Python code:
+        // ```
+        // import tensorflow as tf
+        // x = tf.reshape(tf.range(64, dtype=tf.float32), [1, 4, 4, 4, 1])
+        // filter = tf.reshape(tf.range(72, dtype=tf.float32), [1, 4, 4, 1, 1])
+        // bias = tf.ones([2])
+        // with tf.GradientTape() as tape:
+        //     tape.watch([x, filter, bias])
+        //     y = tf.math.reduce_sum(tf.nn.conv3d(input=x,
+        //                                         filters=filter,
+        //                                         strides=[1, 2, 2, 2, 1],
+        //                                         padding="SAME") + bias)
+        // print(tape.gradient(y, [x, filter, bias]))
+        // ```
+        XCTAssertEqual(grads.0,
+                       [[[[[10.0], [20.0], [24.0], [12.0]],
+                          [[20.0], [40.0], [48.0], [24.0]],
+                          [[36.0], [72.0], [80.0], [40.0]],
+                          [[18.0], [36.0], [40.0], [20.0]]],
+                         [[[ 0.0], [ 0.0], [ 0.0], [ 0.0]],
+                          [[ 0.0], [ 0.0], [ 0.0], [ 0.0]],
+                          [[ 0.0], [ 0.0], [ 0.0], [ 0.0]],
+                          [[ 0.0], [ 0.0], [ 0.0], [ 0.0]]],
+                         [[[10.0], [20.0], [24.0], [12.0]],
+                          [[20.0], [40.0], [48.0], [24.0]],
+                          [[36.0], [72.0], [80.0], [40.0]],
+                          [[18.0], [36.0], [40.0], [20.0]]],
+                         [[[ 0.0], [ 0.0], [ 0.0], [ 0.0]],
+                          [[ 0.0], [ 0.0], [ 0.0], [ 0.0]],
+                          [[ 0.0], [ 0.0], [ 0.0], [ 0.0]],
+                          [[ 0.0], [ 0.0], [ 0.0], [ 0.0]]]]])
+        XCTAssertEqual(grads.1.filter,
+                       [[[[[ 84.0]], [[168.0]], [[176.0]], [[ 88.0]]],
+                         [[[168.0]], [[336.0]], [[352.0]], [[176.0]]],
+                         [[[200.0]], [[400.0]], [[416.0]], [[208.0]]],
+                         [[[100.0]], [[200.0]], [[208.0]], [[104.0]]]]])
+        XCTAssertEqual(grads.1.bias, [8.0, 8.0])
+    }
+
     func testDepthwiseConv2D() {
         let filter =  Tensor(shape: [2, 2, 2, 2], scalars: (0..<16).map(Float.init))
         let bias = Tensor<Float>([1, 2, 3, 4])
@@ -1237,6 +1286,7 @@ final class LayerTests: XCTestCase {
         ("testConv2DGradient", testConv2DGradient),
         ("testConv2DDilation", testConv2DDilation),
         ("testConv3D", testConv3D),
+        ("testConv3DGradient", testConv3DGradient),
         ("testDepthwiseConv2D", testDepthwiseConv2D),
         ("testDepthwiseConv2DGradient", testDepthwiseConv2DGradient),
         ("testSeparableConv1D", testSeparableConv1D),