Dog Language or Barking Classification task using Deep Learning
Sujay Kumar
Data Modelling Analyst
Data Scientist
ML Engineer
Python
PyTorch
TensorFlow
Architecture
The BiLSTM_FCN model consists of the following components:
Fully Convolutional Block: A series of 1D convolutional layers followed by batch normalization and global average pooling to extract spatial features.
BiLSTM Block: A Bidirectional LSTM layer to capture temporal dependencies in the input sequence.
Concatenation: Outputs from the Fully Convolutional Block and the BiLSTM Block are concatenated.
Softmax Classification Layer: A dense layer with softmax activation for classification.
Model Parameters
The model can be customized with the following parameters:
num_classes: Number of output classes.num_conv_layers: Number of convolutional layers in the FCN block.num_filters: Number of filters in each convolutional layer.kernel_size: Size of the convolutional kernel.lstm_units: Number of units in the LSTM layer.Usage
Training the Model
The following code demonstrates how to instantiate and train the BiLSTM_FCN model:
