license: mit
AutoEncoder for Dimensionality Reduction
Model Description
The AutoEncoder presented here is a neural network model based on an encoder-decoder architecture. It is designed to learn efficient representations (encodings) of the input data, typically for dimensionality reduction purposes. The encoder compresses the input into a lower-dimensional latent space, while the decoder reconstructs the input data from the latent representation.
This model is flexible and can be configured with different layer types such as linear layers, LSTMs, GRUs, or RNNs, and can handle bidirectional sequence processing. The model is configured to be used with the Hugging Face Transformers library, allowing for easy download and deployment.
Intended Use
This AutoEncoder is suitable for unsupervised learning tasks where dimensionality reduction or feature learning is desired. Examples include anomaly detection, data compression, and preprocessing for other complex tasks such as feature reduction before classification.
Basic Usage in Python
Here are some simple examples of how to use the AutoEncoder model in Python:
from transformers import AutoConfig, AutoModel
config = AutoConfig.from_pretrained("amaye15/autoencoder", trust_remote_code = True)
# Let's say you want to change the input_dim and latent_dim
config.input_dim = 1024 # New input dimension
config.latent_dim = 64 # New latent dimension
# Similarly, update other parameters as needed
config.layer_types = 'gru' # Change layer types to 'gru'
config.dropout_rate = 0.2 # Update dropout rate
config.num_layers = 4 # Change the number of layers
config.compression_rate = 0.6 # Update compression rate
config.bidirectional = False # Change to unidirectional
### Change Configuration
model = AutoModel.from_config(config, trust_remote_code = True)
# Example input data (batch_size, seq_len, input_dim)
input_data = torch.rand((32, 10, 784)) # Adjust shape according to your needs
# Perform encoding and decoding
with torch.no_grad(): # Assuming inference only
output = model(input_data)
### To-Do
# The `output` is a dictionary with 'encoder_final' and 'decoder_final' keys
# encoded_representation = output['encoder_final']
# reconstructed_data = output['decoder_final']
Training Data
Omitted - to be filled in with details about the training data used for the model.
Training Procedure
Omitted - to be filled in with details about the training procedure, including optimization strategies, loss functions, and regularization techniques.
Performance
Omitted - to be filled in with performance metrics on relevant evaluation datasets or benchmarks.
Limitations
The performance of the AutoEncoder is highly dependent on the architecture configuration and the quality and quantity of the training data. As with any autoencoder, there is no guarantee that the model will learn useful or interpretable features without proper tuning and validation.
Authors
Omitted - to be filled in with the names of the model's creators or maintainers.
Ethical Considerations
When using this model, consider the biases that may be present in the training data, as the model will inevitably learn these biases. Care should be taken to avoid using the model in situations where these biases could lead to unfair or discriminatory outcomes.
Citation
Omitted - to be filled in with citation details if the model is part of a published work or if there is a specific way to cite the use of the model.
The provided Python code is a basic example showing how to instantiate the model, how to create some dummy input data, and how to run data through the model to get the encoded and reconstructed output. Please ensure you have the required dependencies installed and adapt the code according to your specific setup and requirements.