Added docs to CQAT example.

Change-Id: I5f3fc8baf58e04585f1886151f9de6e47e157353

Author: wwwind

tensorflow_model_optimization/g3doc/guide/combine/cqat_example.ipynb

@@ -253,6 +253,7 @@
         "clustering_params = {\n",
         "  'number_of_clusters': 8,\n",
         "  'cluster_centroids_init': CentroidInitialization.KMEANS_PLUS_PLUS\n",
+        " 'cluster_per_channel': True,\n",
         "}\n",
         "\n",
         "clustered_model = cluster_weights(model, **clustering_params)\n",
@@ -597,7 +598,7 @@
       "source": [
         "## Apply post-training quantization and compare to CQAT model\n",
         "\n",
-        "Next, we use post-training quantization (no fine-tuning) on the clustered model and check its accuracy against the CQAT model. This demonstrates why you would need to use CQAT to improve the quantized model's accuracy.\n",
+        "Next, we use post-training quantization (no fine-tuning) on the clustered model and check its accuracy against the CQAT model. This demonstrates why you would need to use CQAT to improve the quantized model's accuracy. The difference could be not very visible, because the MNIST model is quite small and overparametrized.\n",
         "\n",
         "First, define a generator for the callibration dataset from the first 1000 training images."
       ]