A channel is one component along a tensor’s channel or depth axis. In image data, channels can represent colors; in convolution outputs, they represent learned filters or features. section 2.2.8 section 8.1.1
Selecting the top-class channel
For a classifier output, find the highest-scoring class and use its corresponding activation as the loss: `top_pred_index = ops.argmax(preds[0])`; `top_class_channel = preds[:, top_pred_index]`. Gradients of this value identify what supports that prediction. section 10.3.3
Keep these channel meanings separate:
- An RGB input has three channels—red, green, and blue—while a grayscale input has one. section 8.1.1
- A convolution layer can produce an arbitrary number of output channels; each channel is a spatial response map for one filter. section 8.1.1
- For top-class interpretation, treat the selected class activation as the target whose gradient is computed. section 10.3.3
Listing 10.22 Computing the top class gradients with Jax
import jax def loss_fn(last_conv_layer_output): #1 preds = classifier_model(last_conv_layer_output) top_pred_index = ops.argmax(preds[0]) top_class_channel = preds[:, top_pred_index] return top_class_channel[0] #2 grad_fn = jax.grad(loss_fn) #3 def get_top_class_gradients(img_array): last_conv_layer_output = last_conv_layer_model(img_array) grads = grad_fn(last_conv_layer_output) #4 return grads, last_conv_layer_output grads, last_conv_layer_output = get_top_class_gradients(img_array) grads = ops.convert_to_numpy(grads) last_conv_layer_output = ops.convert_to_numpy(last_conv_layer_output)
The listing shows the complete JAX path from selecting an activation channel to computing its gradients. section 10.3.3
