Fix minor typos
This is simplified version of previous attempt at
"learning tasks, such as federated training, against user-supplied models\n",
"implemented in TensorFlow.\n",
"\n",
- "This tutorial, and the Federated Learning API, are intended primarly for users\n",
+ "This tutorial, and the Federated Learning API, are intended primarily for users\n",
"who want to plug their own TensorFlow models into TFF, treating the latter\n",
"mostly as a black box. For a more in-depth understanding of TFF and how to\n",
"implement your own federated learning algorithms, see the tutorials on the FC Core API - [Custom Federated Algorithms Part 1](custom_federated_algorithms_1.ipynb) and [Part 2](custom_federated_algorithms_2.ipynb).\n",
"recognize that the server state consists of a `model` (the initial model\n",
"parameters for MNIST that will be distributed to all devices), and\n",
"`optimizer_state` (additional information maintained by the server, such as the\n",
- "number of rounds to use for hypermarameter schedules, etc.).\n",
+ "number of rounds to use for hyperparameter schedules, etc.).\n",
"\n",
"Let's invoke the `initialize` computation to construct the server state."
]
"id": "-gm-yx2Mr_bl"
},
"source": [
- "Next, we define a function that returns a set of local metrics, again using TensorFlow. These are the values (in addition to model updates, which are handled automatically) that are elligible to be aggregated to the server in a federated learning or evaluation process.\n",
+ "Next, we define a function that returns a set of local metrics, again using TensorFlow. These are the values (in addition to model updates, which are handled automatically) that are eligible to be aggregated to the server in a federated learning or evaluation process.\n",
"\n",
"Here, we simply return the average `loss` and `accuracy`, as well as the\n",
"`num_examples`, which we'll need to correctly weight the contributions from\n",
},
"source": [
"As you can see, the abstract methods and properties defined by\n",
- "`tff.learning.Model` correspond to the code snippets in the preceding section\n",
+ "`tff.learning.Model` corresponds to the code snippets in the preceding section\n",
"that introduced the variables and defined the loss and statistics.\n",
"\n",
"Here are a few points worth highlighting:\n",
