deep learning - GradCam for keras tensorflow 2

Question

I'm working on GradCam from my previous version of the code that works. However, after i updated tensorflow to version 2, the code is no longer work. My previous code is the following

def find_target_layer(model):
    """
    This function finds the final convolutional layer in the network by 
    looping over the layers of the network in reverse order

    Raises
    ------
    ValueError
        Report error if 4D layer is not found.

    Returns
    -------
    TYPE
        DESCRIPTION.

    """
    for layer in reversed(model.layers):
        if len(layer.output_shape) == 4:
            return layer.name
    
    raise ValueError("Could not find 4D layer.  Cannot apply GradCAM")

def grad_cam(input_model, image, class_idx, layer_name, H=224, W=224):
    """
    This function apply GradCAM method for visualizing input saliency.

    Parameters
    ----------
    input_model : Keras Model Object
        Trained Model
    image : 4D Numpy Array (1, H, W, 3)
        Preprocessed input for a given model
    class_idx : Integer
        Class index
    layer_name : String
        Layer name of keras model to apply GradCAM
    H : Integer, optional
        Image height. The default is 224.
    W : Integer, optional
        Image width. The default is 224.

    Returns
    -------
    cam : 2D Numpy Array (H, W)
        Heatmap.


    """
    
    target_layer = find_target_layer(input_model)

    y_c = input_model.output[0, class_idx]
    conv_output = input_model.get_layer(target_layer).output
    grads = K.gradients(y_c, conv_output)[0]

    gradient_function = K.function([input_model.input], [conv_output, grads])

    output, grads_val = gradient_function([image])
    output, grads_val = output[0, :], grads_val[0, :, :, :]

    weights = np.mean(grads_val, axis=(0, 1))
    cam = np.dot(output, weights)

    # Process CAM
    cam = cv2.resize(cam, (W, H), cv2.INTER_LINEAR)
    cam = np.maximum(cam, 0)
    cam = cam / cam.max()
    return cam

I debugged line by line using densenet121 but it failed at the line grads = K.gradients(y_c, conv_output)[0]. The error message is

    TypeError: Cannot convert a symbolic Keras input/output to a numpy array. 
This error may indicate that you're trying to pass a symbolic value to a NumPy call, 
which is not supported. Or, you may be trying to pass Keras symbolic inputs/outputs 
to a TF API that does not register dispatching, preventing Keras from automatically 
converting the API call to a lambda layer in the Functional Model.

May I have your suggestions how to fix this? I'm aware there are several tutorials work on GradCam, however, I still would like to learn more how to fix this code to gain more understanding.

question from:https://stackoverflow.com/questions/65921392/gradcam-for-keras-tensorflow-2

Answer 1

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