Sensor Transformer (SeT)
Adaptation of Vision Transformer (ViT) for Time-Series and Sensor Data in Tensorflow.
Problems/Datasets
Tools
Usage
import argparse
import tensorflow as tf
import transformer
parser = argparse.ArgumentParser()
parser.add_argument("--signal-length", type=int)
parser.add_argument("--segment-size", type=int)
parser.add_argument("--num_channels", type=int)
parser.add_argument("--num_classes", type=int)
args = parser.parse_args()
"""
TF-Data objects, see data.load_data function.
Instances must be of shape x = (batch, signal_length, num_channels)
y = (batch, num_classes)
"""
ds_train, ds_test = ...
model = transformer.SensorTransformer(
signal_length=args.signal_length,
segment_size=args.segment_size,
channels=args.num_channels,
num_classes=args.num_classes,
num_layers=4,
d_model=64,
num_heads=4,
mlp_dim=64,
dropout=0.1,
)
model.compile(
loss=tf.keras.losses.CategoricalCrossentropy(from_logits=True),
optimizer=tf.keras.optimizers.Adam(),
metrics=[tf.keras.metrics.CategoricalAccuracy()],
)
model.fit(ds_train, epochs=50, verbose=1)
model.evaluate(ds_test)Thanks to Phil Wang for open-sourcing Pytorch implementation of ViT
