如何获得 w.r.t 的权重梯度到目标神经元？

Question

I have found code online to get the derivative of the total loss with respect to the deep learning weights. I am trying to find the derivative of the weights with respect to the loss of a single class instead of all classes.

I used the following code to get the gradient of an input image with respect to the total loss. If I visualize it, it shows the importance of the pixels for all predictions. But, I would like to compute the derivative of the input image with respect to a particular class (eg "lady_bug"). This should show the importance of the pixels for the prediction of lady_bug. Do you have an idea how I can do that?

from keras.applications.vgg19 import VGG19
import numpy as np
import cv2
from keras import backend as K
import matplotlib.pyplot as plt

from keras.applications.inception_v3 import decode_predictions


def get_model():
    model = VGG19(include_top=True, weights='imagenet')
    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
    return model


def predict(model, images):
    numeric_prediction = model.predict(images)
    categorical_prediction = decode_predictions(numeric_prediction, top=1)
    return [(x[0][1], x[0][2]) for x in categorical_prediction]


def get_test_image():
    # Image
    image_path = "lady_bug.jpg"
    image = cv2.imread(image_path)
    my_image = cv2.resize(image, (224,224))
    my_image = np.expand_dims(my_image, axis=0)
    return my_image


def visualize_sample(sample, file_path):
    plt.figure()
    plt.imshow(sample)
    plt.savefig(file_path, bbox_inches='tight')


def test_input_gradient():
    images = get_test_image()
    model = get_model()

    prediction = predict(model, images)
    print(prediction)

    gradients = K.gradients(model.output, model.input)              #Gradient of output wrt the input of the model (Tensor)
    print(gradients)

    sess = K.get_session()
    evaluated_gradients = sess.run(gradients[0], feed_dict={model.input:
    images})

    visualize_sample((evaluated_gradients[0]*(10**9.5)).clip(0,255), "test.png")


if __name__ == "__main__":
    test_input_gradient()

Output:

[('ladybug', 0.53532666)]
[<tf.Tensor 'gradients/block1_conv1/convolution_grad/Conv2DBackpropInput:0' shape=(?, 224, 224, 3) dtype=float32>]

Answer 1

It seems the code is taking the gradients of the outputs wrt the inputs.
So, this is just taking a single slice from the outputs.

Warning: This considers a regular model output. I have no idea of what you're doing in decode predictions and the following list.

gradients = K.gradients(model.output[:, lady_bug_class], model.input)