FL for image classification tutorial: `metrics` is an OrderedDict
"with summary_writer.as_default():\n",
" for round_num in range(1, NUM_ROUNDS):\n",
" state, metrics = iterative_process.next(state, federated_train_data)\n",
- " for name, value in metrics.train._asdict().items():\n",
+ " for name, value in metrics['train'].items():\n",
" tf.summary.scalar(name, value, step=round_num)"
]
},
