contraction_net.contraction_net
Classes
ContractionNet model for detecting contraction intervals from time-series data of individual Z-band positions |
Module Contents
- class contraction_net.contraction_net.ContractionNet(n_filter=64, in_channels=1, out_channels=2, dropout_rate=0.5)[source]
Bases:
torch.nn.ModuleContractionNet model for detecting contraction intervals from time-series data of individual Z-band positions and sarcomere lengths of beating cardiomyocytes.
This neural network is designed to handle noisy data and distinguish between contracting and non-contracting intervals. The network first extracts various features from a single input time-series by two convolutional layers with kernel size 5, followed by a dilated convolution in the third layer to capture broader temporal patterns. Each convolution is followed by instance normalization and ReLU activation. A self-attention layer enhances focus on salient features. The processed signal then undergoes two further convolutions before being outputted through a sigmoid activation function.
- conv1
- in1
- conv2
- bn2
- conv3
- bn3
- attention
- norm1
- dropout_attention
- conv4
- bn4
- dropout_pre_output
- conv_out
- forward(x)[source]
Forward pass through the network.
- Parameters:
x (torch.Tensor) – Input tensor of shape (batch_size, in_channels, sequence_length).
- Returns:
torch.Tensor – Output tensor of shape (batch_size, out_channels, sequence_length) after sigmoid activation.
torch.Tensor – Raw output tensor of shape (batch_size, out_channels, sequence_length).