nlp_architect.models.temporal_convolutional_network.TCN

class nlp_architect.models.temporal_convolutional_network.TCN(max_len, n_features_in, hidden_sizes, kernel_size=7, dropout=0.2)[source]

This class defines core TCN architecture. This is only the base class, training strategy is not implemented.

__init__(max_len, n_features_in, hidden_sizes, kernel_size=7, dropout=0.2)[source]
To use this class,
  1. Inherit this class
  2. Define the training losses in build_train_graph()
  3. Define the training strategy in run()
  4. After the inherited class object is initialized, call build_train_graph followed by run
Parameters:
  • max_len – Maximum length of sequence
  • n_features_in – Number of input features (dimensions)
  • hidden_sizes – Number of hidden sizes in each layer of TCN (same for all layers)
  • kernel_size – Kernel size of convolution filter (same for all layers)
  • dropout – Dropout, fraction of activations to drop

Methods

__init__(max_len, n_features_in, hidden_sizes) To use this class,
build_network_graph(x[, last_timepoint]) Given the input placeholder x, build the entire TCN graph :param x: Input placeholder :param last_timepoint: Whether or not to select only the last timepoint to output
build_train_graph(*args, **kwargs) Placeholder for defining training losses and metrics
calculate_receptive_field() Returns:
run(*args, **kwargs) Placeholder for defining training strategy
build_network_graph(x, last_timepoint=False)[source]

Given the input placeholder x, build the entire TCN graph :param x: Input placeholder :param last_timepoint: Whether or not to select only the last timepoint to output

Returns:output of the TCN
build_train_graph(*args, **kwargs)[source]

Placeholder for defining training losses and metrics

calculate_receptive_field()[source]

Returns:

run(*args, **kwargs)[source]

Placeholder for defining training strategy