So basically, this model takes some time series data as input (let’s say stock prices or weather patterns) and spits out predictions for future values based on that data. But how does it do that? Well, first off, let me explain what we mean by “hidden states”. These are essentially the internal workings of the neural network the stuff that happens behind the scenes before it gives you an output prediction. In this case, the Autoformer model has a bunch of layers (called encoders and decoders) that take in input data and transform it into something else. The hidden states represent what’s going on inside those layers at various points along the way. So when we say “decoder_hidden_states”, for example, we mean the internal workings of the final layer (the one closest to the output prediction) before it spits out its result. Now let me explain how you can access these hidden states and what they might look like. First off, if you want to see all the hidden states at once, you’ll need to set a flag called “output_hidden_states” to True when you initialize your model (or pass it as an argument during inference). This will cause the model to return not just its final output prediction, but also a tuple containing all of the intermediate hidden states. Here’s what that might look like:
# Import necessary libraries
from transformers import AutoformerModel, AutoformerConfig, AutoformerTokenizer
import torch
# Load pretrained model and tokenizer
config = AutoformerConfig() # Initialize configuration object
model = AutoformerModel(config) # Initialize model with the configuration
tokenizer = AutoformerTokenizer.from_pretrained('path/to/your/checkpoint') # Load tokenizer from pretrained checkpoint
# Prepare input data (in this case, a list of time series values)
input_ids = tokenizer(data['time_series'], padding=True, truncation=True, return_tensors='pt').input_ids # Tokenize input data using the tokenizer and convert it to PyTorch tensors
labels = torch.tensor([data['target'] for _ in range(len(input_ids))]) # Create labels tensor with the same length as input_ids
# Set output hidden states flag to True
outputs = model(input_ids, labels=labels, output_hidden_states=True) # Pass input_ids, labels, and output_hidden_states=True to the model to get intermediate hidden states in addition to final output prediction
Now let’s say you want to access the decoder hidden states specifically. You can do that by slicing out the appropriate element from the tuple returned by `model()`. Here’s what that might look like:
# Access decoder_hidden_states (which is a list of tensors)
# The following script shows how to access the decoder hidden states from a tuple returned by a model
# Import necessary libraries
import torch
# Define a model
model = torch.nn.Module()
# Define input embeddings
input_embeddings = torch.tensor([1, 2, 3])
# Define outputs
outputs = (input_embeddings, [4, 5, 6])
# Access decoder_hidden_states (which is a list of tensors)
decoder_hidden_states = outputs[1] # index 0 contains input embeddings, if any
# Print the decoder hidden states
print(decoder_hidden_states)
# Output: [4, 5, 6]
# Explanation:
# - The first line is a comment explaining the purpose of the script.
# - The second line imports the necessary library, in this case, torch.
# - The third line defines a model using the torch.nn.Module() function.
# - The fourth line defines input embeddings using a tensor.
# - The fifth line defines outputs as a tuple containing input embeddings and a list of tensors.
# - The sixth line accesses the decoder hidden states from the tuple using indexing.
# - The seventh line prints the decoder hidden states.
# - The eighth line is a comment explaining the output.
# - The last line is a comment explaining the purpose of the script.
So in this case, `outputs[1]` represents the hidden states at each layer of the decoder. If you want to see what those look like, just print them out:
# Print out the shape and size of the first element (which is a tensor)
print(decoder_hidden_states[0].shape) # should be something like torch.Size([batch_size, sequence_length, hidden_size])
# The above code segment prints out the shape and size of the first element in the decoder_hidden_states list, which represents the hidden states at each layer of the decoder.
# The shape should be in the format of [batch_size, sequence_length, hidden_size], where batch_size is the number of samples in a batch, sequence_length is the length of the input sequence, and hidden_size is the size of the hidden state.
And that’s it! You now have access to all of the internal workings of your Autoformer model including its decoder hidden states.