Register Forward Hook to Capture Activations

Problem Statement

Use a forward hook to capture intermediate activations from a hidden layer.

Background

module.register_forward_hook(hook_fn) lets you inspect outputs of any layer without modifying the model. The hook function receives (module, input, output).

Your Task

The starter code creates a 3-layer model and an activations dict. Use a forward hook to capture the output of the first layer during a forward pass. Store the captured activation in the provided dict and remember to clean up the hook afterward.

Output Format

Returns a dictionary with "activation_shape", "activation_values", and "final_output".

The model is created as nn.Sequential(nn.Linear(3, 4), nn.ReLU(), nn.Linear(4, 2)), and a forward hook is registered on the first Linear layer to capture its output.
The input [[1.0, 2.0, 3.0]] is passed through the model, and the forward hook captures the output of the first Linear layer, which is a linear transformation of the input: $y = x \cdot W + b$ , where $x$ is the input, $W$ is the weight matrix, and $b$ is the bias.
The captured output is then passed through the ReLU activation function, but since the hook captures the output before the ReLU function, the captured values are not yet activated.
The final output of the model is obtained by passing the output of the ReLU function through the second Linear layer, resulting in the values [-0.3288, 0.7085].

Problem Statement

Use a forward hook to capture intermediate activations from a hidden layer.

Background

module.register_forward_hook(hook_fn) lets you inspect outputs of any layer without modifying the model. The hook function receives (module, input, output).

Your Task

Output Format

Returns a dictionary with "activation_shape", "activation_values", and "final_output".

The model is created as nn.Sequential(nn.Linear(3, 4), nn.ReLU(), nn.Linear(4, 2)), and a forward hook is registered on the first Linear layer to capture its output.
The input [[1.0, 2.0, 3.0]] is passed through the model, and the forward hook captures the output of the first Linear layer, which is a linear transformation of the input: $y = x \cdot W + b$ , where $x$ is the input, $W$ is the weight matrix, and $b$ is the bias.
The captured output is then passed through the ReLU activation function, but since the hook captures the output before the ReLU function, the captured values are not yet activated.
The final output of the model is obtained by passing the output of the ReLU function through the second Linear layer, resulting in the values [-0.3288, 0.7085].

Problem Statement

Background

Your Task

Output Format

Example:

Constraints:

Test Results

Register Forward Hook to Capture Activations

Problem Statement

Background

Your Task

Output Format

Example:

Constraints:

Test Results