Source code for bigdl.chronos.forecaster.tf.tcn_forecaster

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from bigdl.chronos.forecaster.tf.base_forecaster import BaseTF2Forecaster
from bigdl.chronos.model.tf2.TCN_keras import model_creator, TemporalBlock, TemporalConvNet


[docs]class TCNForecaster(BaseTF2Forecaster): """ Example: >>> #The dataset is split into x_train, x_val, x_test, y_train, y_val, y_test >>> forecaster = TCNForecaster(past_seq_len=24, future_seq_len=5, input_feature_num=1, output_feature_num=1, ...) >>> forecaster.fit((x_train, y_train)) >>> forecaster.save({ckpt_name}) >>> forecaster.load({ckpt_name}) """ def __init__(self, past_seq_len, future_seq_len, input_feature_num, output_feature_num, num_channels=[30]*7, kernel_size=3, repo_initialization=True, dropout=0.1, optimizer="Adam", loss="mse", lr=0.001, metrics=["mse"], seed=None, distributed=False, workers_per_node=1, distributed_backend="ray"): """ Build a TCN Forecast Model. TCN Forecast may fall into local optima. Please set repo_initialization to False to alleviate the issue. You can also change a random seed to work around. :param past_seq_len: Specify the history time steps (i.e. lookback). :param future_seq_len: Specify the output time steps (i.e. horizon). :param input_feature_num: Specify the feature dimension. :param output_feature_num: Specify the output dimension. :param num_channels: Specify the convolutional layer filter number in TCN's encoder. This value defaults to [30]*7. :param kernel_size: Specify convolutional layer filter height in TCN's encoder. This value defaults to 3. :param repo_initialization: if to use framework default initialization, True to use paper author's initialization and False to use the framework's default initialization. The value defaults to True. :param dropout: Specify the dropout close possibility (i.e. the close possibility to a neuron). This value defaults to 0.1. :param optimizer: Specify the optimizer used for training. This value defaults to "Adam". :param loss: Str or a tf.keras.losses.Loss instance, specify the loss function used for training. This value defaults to "mse". You can choose from "mse", "mae" and "huber_loss" or any customized loss instance you want to use. :param lr: Specify the learning rate. This value defaults to 0.001. :param metrics: A list contains metrics for evaluating the quality of forecasting. You may only choose from "mse" and "mae" for a distributed forecaster. You may choose from "mse", "mae", "rmse", "r2", "mape", "smape" or a callable function for a non-distributed forecaster. If callable function, it signature should be func(y_true, y_pred), where y_true and y_pred are numpy ndarray. :param seed: int, random seed for training. This value defaults to None. :param distributed: bool, if init the forecaster in a distributed fashion. If True, the internal model will use an Orca Estimator. If False, the internal model will use a pytorch model. The value defaults to False. :param workers_per_node: int, the number of worker you want to use. The value defaults to 1. The param is only effective when distributed is set to True. :param distributed_backend: str, select from "ray" or "horovod". The value defaults to "ray". """ # config setting self.model_config = { "past_seq_len": past_seq_len, "future_seq_len": future_seq_len, "input_feature_num": input_feature_num, "output_feature_num": output_feature_num, "num_channels": num_channels, "kernel_size": kernel_size, "repo_initialization": repo_initialization, "dropout": dropout, "loss": loss, "lr": lr, "optim": optimizer, } # model creator settings self.model_creator = model_creator self.custom_objects_config = {"TemporalBlock": TemporalBlock, "TemporalConvNet": TemporalConvNet} # distributed settings # self.distributed = distributed # self.distributed_backend = distributed_backend # self.workers_per_node = workers_per_node from bigdl.nano.utils.log4Error import invalidInputError if distributed: invalidInputError(False, "We will add distributed support in subsequent releases, " "the feature is currently unavailable, " "Please set distributed=False.") # other settings self.lr = lr self.metrics = metrics self.seed = seed # nano setting # current_num_threads = torch.get_num_threads() # self.num_processes = max(1, current_num_threads//8) # 8 is a magic num # self.use_ipex = False # TCN has worse performance on ipex # self.onnx_available = True # self.quantize_available = True # self.checkpoint_callback = False super().__init__()