File size: 1,891 Bytes
62d1028 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 | from dataclasses import dataclass
from transformers.configuration_utils import PretrainedConfig
@dataclass
class SymTimeConfig(PretrainedConfig):
"""
Time series encoder configuration for SymTime Model.
Parameters
-----------
num_layers
The number of layers to be used for the encoder.
d_model
The dimension of the model.
d_ff
The dimension of the feedforward network.
num_heads
The number of heads to be used for the attention mechanism.
norm
The normalization to be used for the encoder.
attn_dropout
The dropout rate to be used for the attention mechanism.
dropout
The dropout rate to be used for the encoder.
act
The activation function to be used for the encoder.
pre_norm
Whether to use pre-norm for the encoder.
patch_size
The size of the patch to be used for the input data.
stride
The stride of the patch to be used for the input data.
"""
model_type = "symtime"
def __init__(
self,
num_layers: int = 6,
d_model: int = 512,
d_ff: int = 2048,
num_heads: int = 8,
norm: str = "BatchNorm",
dropout: float = 0.1,
act: str = "gelu",
pre_norm: bool = False,
patch_size: int = 16,
stride: int = 16,
initializer_factor: float = 0.05,
**kwargs,
) -> None:
self.patch_size = patch_size
self.stride = stride
self.num_layers = num_layers
self.d_model = d_model
self.num_heads = num_heads
self.d_ff = d_ff
self.norm = norm
self.dropout = dropout
self.act = act
self.pre_norm = pre_norm
self.initializer_factor = initializer_factor
super().__init__(**kwargs)
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