Kevin Alberts
fb9ce46bd8
- Encoders: sparse, denoising, contractive and variational - Noise: gaussian
19 lines
601 B
Python
19 lines
601 B
Python
import logging
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from typing import Optional
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from models.base_encoder import BaseEncoder
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class BasicAutoEncoder(BaseEncoder):
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# Based on https://medium.com/pytorch/implementing-an-autoencoder-in-pytorch-19baa22647d1
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name = "BasicAutoEncoder"
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def __init__(self, name: Optional[str] = None, input_shape: int = 0):
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self.log = logging.getLogger(self.__class__.__name__)
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# Call superclass to initialize parameters.
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super(BasicAutoEncoder, self).__init__(name, input_shape)
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# Network, optimizer and loss function are the same as defined in the base encoder.
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