Comments (1)
Yes, it is possible to use mLSTM
for time series of raster datasets. Below is an example code snippet demonstrating how to achieve this. The code can be modified according to your needs.
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import os
import sys
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
from xLSTM.mlstm import mLSTM
from xLSTM.utils import config, logging, utils
# Simulate raster data
def generate_synthetic_raster_data(num_timesteps, width, height):
return np.random.rand(num_timesteps, width, height)
# Generate synthetic raster data
num_timesteps = 10
width = 5
height = 5
raster_data = generate_synthetic_raster_data(num_timesteps, width, height)
# Normalize the raster data
raster_data = raster_data / np.max(raster_data)
# Convert raster data to PyTorch tensor
raster_data = torch.Tensor(raster_data).unsqueeze(-1) # Add channel dimension
# Define the mLSTM model
class RasterTimeSeriesModel(nn.Module):
def __init__(self, input_dim, hidden_dim, output_dim, num_layers):
super(RasterTimeSeriesModel, self).__init__()
self.mlstm = mLSTM(input_dim, hidden_dim, num_layers)
self.fc = nn.Linear(hidden_dim, output_dim)
def forward(self, x):
h, _ = self.mlstm(x)
out = self.fc(h)
return out
# Model parameters
input_dim = width * height # Flattened raster data
hidden_dim = 128
output_dim = input_dim # Output should match the flattened input size
num_layers = 2 # Number of layers in mLSTM
# Reshape raster data to (batch_size, sequence_length, input_dim)
raster_data = raster_data.view(num_timesteps, -1).unsqueeze(0) # Add batch dimension
model = RasterTimeSeriesModel(input_dim, hidden_dim, output_dim, num_layers)
# Training parameters
criterion = nn.MSELoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)
# Split data into train and test sets
train_data = raster_data[:, :8, :] # Using first 8 timesteps for training
test_data = raster_data[:, 8:, :] # Using the last 2 timesteps for testing
# Training loop
for epoch in range(100): # Number of epochs
model.train()
optimizer.zero_grad()
output = model(train_data)
loss = criterion(output, train_data)
loss.backward()
optimizer.step()
if epoch % 10 == 0:
print(f'Epoch {epoch}, Loss: {loss.item()}')
# Step 4: Evaluate the model
model.eval()
with torch.no_grad():
output = model(test_data)
test_loss = criterion(output, test_data)
print(f'Test Loss: {test_loss.item()}')
This example demonstrates how to use mLSTM for time series of raster datasets. The model is trained on synthetic raster data, and the training and evaluation process is shown. You can replace the synthetic data with your actual raster data.
Feel free to modify the code according to your needs.
from pyxlstm.
Related Issues (20)
- ModuleNotFoundError: No module named 'xLSTM.data' HOT 7
- RuntimeError: input has inconsistent input_size: got 8 expected 16 HOT 2
- RuntimeError: Boolean value of Tensor with more than one value is ambiguous in xLSTM/slstm.py HOT 3
- Unable to find the get_device implementation HOT 2
- Stabilizer state missing from sLSTM HOT 1
- The Equations of sLSTM is wrong? HOT 1
- Make embedding/vocab optional for timeseries data HOT 2
- jhlk
- can this model be used for image classification tasks? HOT 3
- Fix sLSTM and mLSTM to work with 1 layer
- Problem with input gates in sLSTM HOT 1
- ModuleList HOT 2
- unit tests as per README fail HOT 1
- Can't we use both slstm and mlstm blocks in the model.
- NaNs in testing HOT 3
- I am getting nan values in the mlstm output HOT 16
- Hidden layer state output error found when utilizing mlstm
- Timeseries examples HOT 1
- provide examples HOT 1
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from pyxlstm.