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diff --git a/testing/web-platform/tests/webnn/validation_tests/pooling-and-reduction-keep-dims.https.any.js b/testing/web-platform/tests/webnn/validation_tests/pooling-and-reduction-keep-dims.https.any.js
new file mode 100644
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+++ b/testing/web-platform/tests/webnn/validation_tests/pooling-and-reduction-keep-dims.https.any.js
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+// META: title=validation tests for pooling and reduction operators keep dimensions
+// META: global=window,dedicatedworker
+// META: script=../resources/utils.js
+// META: script=../resources/utils_validation.js
+// META: timeout=long
+
+'use strict';
+
+// This is used to reproduce an issue(crbug.com/331841268) of averagePool2d in
+// ResNetV2 50 model.
+//      [input]
+//         |
+//  [globalAveragePool]
+//         |
+//     [conv2d]
+//         |
+//     [reshape]
+//         |
+//     [output]
+promise_test(async t => {
+  const avgPool2dInputShape = [1, 7, 7, 2048];
+  const avgPool2dInput = builder.input(
+      `avgPool2dInput`, {dataType: 'float32', dimensions: avgPool2dInputShape});
+  const avgPool2dOutput =
+      builder.averagePool2d(avgPool2dInput, {layout: 'nhwc'});
+  const conv2dFilterShape = [1001, 1, 1, 2048];
+  const conv2dFilter = builder.constant(
+      {dataType: 'float32', dimensions: conv2dFilterShape},
+      new Float32Array(sizeOfShape(conv2dFilterShape)).fill(1));
+  const conv2dBias = builder.constant(
+      {dataType: 'float32', dimensions: [1001]},
+      new Float32Array(1001).fill(0.01));
+  const conv2dOutput = builder.conv2d(avgPool2dOutput, conv2dFilter, {
+    inputLayout: 'nhwc',
+    filterLayout: 'ohwi',
+    padding: [0, 0, 0, 0],
+    bias: conv2dBias
+  });
+  const newShape = [1, 1001];
+  const reshapeOutput = builder.reshape(conv2dOutput, newShape);
+  assert_equals(reshapeOutput.dataType(), avgPool2dInput.dataType());
+  assert_array_equals(reshapeOutput.shape(), newShape);
+  const graph = await builder.build({reshapeOutput});
+  const result = await context.compute(
+      graph, {
+        'avgPool2dInput':
+            new Float32Array(sizeOfShape(avgPool2dInputShape)).fill(0.1)
+      },
+      {'reshapeOutput': new Float32Array(1001)});
+}, 'Test global average pool operator\'s output shape for ResNetV2 50 model.');
+
+// This is used to reproduce an issue(crbug.com/331841268) of reduceMean in
+// ResNetV2 50 model.
+//      [input]
+//         |
+//    [reduceMean]
+//         |
+//     [conv2d]
+//         |
+//     [reshape]
+//         |
+//     [output]
+promise_test(async t => {
+  const reduceMeanInputShape = [1, 7, 7, 2048];
+  const reduceMeanInput = builder.input(
+      `reduceMeanInput`,
+      {dataType: 'float32', dimensions: reduceMeanInputShape});
+  const reduceMeanOutput =
+      builder.reduceMean(reduceMeanInput, {axes: [1, 2], keepDimensions: true});
+  const conv2dFilterShape = [1001, 1, 1, 2048];
+  const conv2dFilter = builder.constant(
+      {dataType: 'float32', dimensions: conv2dFilterShape},
+      new Float32Array(sizeOfShape(conv2dFilterShape)).fill(1));
+  const conv2dBias = builder.constant(
+      {dataType: 'float32', dimensions: [1001]},
+      new Float32Array(1001).fill(0.01));
+  const conv2dOutput = builder.conv2d(reduceMeanOutput, conv2dFilter, {
+    inputLayout: 'nhwc',
+    filterLayout: 'ohwi',
+    padding: [0, 0, 0, 0],
+    bias: conv2dBias
+  });
+  const newShape = [1, 1001];
+  const reshapeOutput = builder.reshape(conv2dOutput, newShape);
+  assert_equals(reshapeOutput.dataType(), reduceMeanInput.dataType());
+  assert_array_equals(reshapeOutput.shape(), newShape);
+  const graph = await builder.build({reshapeOutput});
+  const result = await context.compute(
+      graph, {
+        'reduceMeanInput':
+            new Float32Array(sizeOfShape(reduceMeanInputShape)).fill(0.1)
+      },
+      {'reshapeOutput': new Float32Array(1001)});
+}, 'Test reduceMean operator\'s output shape for ResNetV2 50 model.');