Sets some plt properties.

data is a dict := {'x': [numpy array], 'y': [numpy array]} (on one client) returns x, y, which are both numpy array of length: batch_size

used in split-by-user case

Returns the ids, hierarchies and num_samples for the given clients. Returns info about self.selected_clients if clients=None; Args:

divide list l among g groups each group has either int(len(l)/g) or int(len(l)/g)+1 elements returns a list of groups

Prints weighted averages of the given metrics. Args: metrics: dict with client ids as keys. Each entry is a dict with the met

Pre-processes each batch of features before being fed to the model.

Pre-processes each batch of labels before being fed to the model.

split given line/phrase into list of words Args: line: string representing phrase to be split Return: list of strings, w

Tests self.model on given clients. Tests model on self.selected_clients if clients_to_test=None. Args: clients_to_test:

Converts target into one-hot. Args: size: Size of vector. val: Integer in range [0, size]. Returns: vec: one-hot vec

Returns the number of elements in the given shape Args: shape: TensorShape Return: tot_elems: int

Parses text file in given directory into data for next-character model. Args: txt_dir: path to text file seq_length: length of st

Splits character data into train and test sets. if test_fraction <= 0, returns {} for all_test_examples plays := list of (play, dic

returns one-hot vector with given size and value 1 at given index

Splits the full data by play.

Writes a collection of data files by play & character.

returns bag of words representation of given phrase using given vocab Args: line: string representing phrase to be parsed vocab:

Creates the model for the task. Returns: A 4-tuple consisting of: features: A placeholder for the samples' featur

Gets the names of the metrics. Args: metrics: Dict keyed by client id. Each element is a dict of metrics for that client in t

Returns the size of the given graph in bytes The size of the graph is calculated by summing up the sizes of each trainable variable. The size

returns one-hot representation of given letter

converts given phrase into list of word indices if the phrase has more than max_words words, returns a list containing indices of the fir

We assume all users are always online.

returns dictionary with keys: users, num_samples, user_data raw := bool representing whether to include raw text in all_data if raw is Tr

Writes the given dataframe in path as a csv

parses data in given train and test data directories assumes: - the data in the input directories are .json files with keys 'users'

maps hexadecimal class value (string) to a decimal number returns: - 0 through 9 for classes representing respective numbers - 10 thr

Saves the server model on checkpoints/dataset/model.ckpt.

Selects num_clients clients randomly from possible_clients. Note that within function, num_clients is set to min(num_clie

Instantiates clients based on given train and test data directories. Return: all_clients: list of Client objects.