TF Numpy guide: scale benchmark timings.

PiperOrigin-RevId: 324869294

Author: tensorflower-gardener

site/en/guide/tf_numpy.ipynb

@@ -655,13 +655,13 @@
         "print(\"Eager performance\")\n",
         "compute_gradients(model, inputs, labels)\n",
         "print(timeit.timeit(lambda: compute_gradients(model, inputs, labels),\n",
-        "                    number=10)* 1000, \"ms\")\n",
+        "                    number=10)* 100, \"ms\")\n",
         "\n",
         "print(\"\\ntf.function compiled performance\")\n",
         "compiled_compute_gradients = tf.function(compute_gradients)\n",
         "compiled_compute_gradients(model, inputs, labels)  # warmup\n",
         "print(timeit.timeit(lambda: compiled_compute_gradients(model, inputs, labels),\n",
-        "                    number=10) * 1000, \"ms\")"
+        "                    number=10) * 100, \"ms\")"
       ]
     },
     {
@@ -737,12 +737,12 @@
         "\n",
         "print(\"Running vectorized computaton\")\n",
         "print(timeit.timeit(lambda: vectorized_per_example_gradients(inputs, labels),\n",
-        "                    number=10) * 1000, \"ms\")\n",
+        "                    number=10) * 100, \"ms\")\n",
         "\n",
         "print(\"\\nRunning unvectorized computation\")\n",
         "per_example_gradients = unvectorized_per_example_gradients(inputs, labels)\n",
         "print(timeit.timeit(lambda: unvectorized_per_example_gradients(inputs, labels),\n",
-        "                    number=5) * 1000, \"ms\")"
+        "                    number=5) * 200, \"ms\")"
       ]
     },
     {
@@ -892,7 +892,7 @@
         "\n",
         "    _g()  # warmup\n",
         "    t = timeit.timeit(_g, number=number)\n",
-        "    times.append(t * 1000)\n",
+        "    times.append(t * 1000. / number)\n",
         "  return times\n",
         "\n",
         "\n",