https://bindog.github.io/blog/2018/02/10/model-explanation/
推荐这个博客，感觉原理讲的比较清楚。
代码： 代码参考链接：https://github.com/jacobgil/keras-grad-cam 对其中有问题的地方进行了更改。
from keras.applications.vgg16 import ( VGG16, preprocess_input, decode_predictions) from keras.preprocessing import image from keras.layers.core import Lambda from keras.models import Sequential from tensorflow.python.framework import ops import keras.backend as K import tensorflow as tf import numpy as np import keras import sys import cv2 import os from keras.models import Model def target_category_loss(x, category_index, nb_classes): return tf.multiply(x, K.one_hot([category_index], nb_classes)) def target_category_loss_output_shape(input_shape): return input_shape def normalize(x): # utility function to normalize a tensor by its L2 norm return x / (K.sqrt(K.mean(K.square(x))) + 1e-5) def load_image(path): img_path = sys.argv[1] img = image.load_img(img_path, target_size=(224, 224)) x = image.img_to_array(img) x = np.expand_dims(x, axis=0) x = preprocess_input(x) return x def register_gradient(): if "GuidedBackProp" not in ops._gradient_registry._registry: @ops.RegisterGradient("GuidedBackProp") def _GuidedBackProp(op, grad): dtype = op.inputs[0].dtype return grad * tf.cast(grad > 0., dtype) * \ tf.cast(op.inputs[0] > 0., dtype) def compile_saliency_function(model, activation_layer='block5_conv3'): input_img = model.input layer_dict = dict([(layer.name, layer) for layer in model.layers[1:]]) layer_output = layer_dict[activation_layer].output max_output = K.max(layer_output, axis=3) saliency = K.gradients(K.sum(max_output), input_img)[0] return K.function([input_img, K.learning_phase()], [saliency]) def modify_backprop(model, name): g = tf.get_default_graph() with g.gradient_override_map({'Relu': name}): # get layers that have an activation layer_dict = [layer for layer in model.layers[1:] if hasattr(layer, 'activation')] # replace relu activation for layer in layer_dict: if layer.activation == keras.activations.relu: layer.activation = tf.nn.relu # re-instanciate a new model new_model = VGG16(weights='imagenet') return new_model def deprocess_image(x): ''' Same normalization as in: https://github.com/fchollet/keras/blob/master/examples/conv_filter_visualization.py ''' if np.ndim(x) > 3: x = np.squeeze(x) # normalize tensor: center on 0., ensure std is 0.1 x -= x.mean() x /= (x.std() + 1e-5) x *= 0.1 # clip to [0, 1] x += 0.5 x = np.clip(x, 0, 1) # convert to RGB array x *= 255 if K.image_dim_ordering() == 'th': x = x.transpose((1, 2, 0)) x = np.clip(x, 0, 255).astype('uint8') return x def _compute_gradients(tensor, var_list): grads = tf.gradients(tensor, var_list) return [grad if grad is not None else tf.zeros_like(var) for var, grad in zip(var_list, grads)] def grad_cam(input_model, image, category_index, layer_name): nb_classes = 1000 target_layer = lambda x: target_category_loss(x, category_index, nb_classes) x = Lambda(target_layer, output_shape = target_category_loss_output_shape)(input_model.output) model = Model(inputs=input_model.input, outputs=x) model.summary() loss = K.sum(model.output) conv_output = [l for l in model.layers if l.name is layer_name][0].output grads = normalize(_compute_gradients(loss, [conv_output])[0]) gradient_function = K.function([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.ones(output.shape[0 : 2], dtype = np.float32) for i, w in enumerate(weights): cam += w * output[:, :, i] cam = cv2.resize(cam, (224, 224)) cam = np.maximum(cam, 0) heatmap = cam / np.max(cam) #Return to BGR [0..255] from the preprocessed image image = image[0, :] image -= np.min(image) image = np.minimum(image, 255) cam = cv2.applyColorMap(np.uint8(255*heatmap), cv2.COLORMAP_JET) cam = np.float32(cam) + np.float32(image) cam = 255 * cam / np.max(cam) return np.uint8(cam), heatmap """ def grad_cam(input_model, image, category_index, layer_name): model = Sequential() model.add(input_model) nb_classes = 1000 target_layer = lambda x: target_category_loss(x, category_index, nb_classes) model.add(Lambda(target_layer, output_shape = target_category_loss_output_shape)) loss = K.sum(model.layers[-1].output) conv_output = [l for l in model.layers[0].layers if l.name is layer_name][0].output grads = normalize(_compute_gradients(loss, [conv_output])[0]) gradient_function = K.function([model.layers[0].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.ones(output.shape[0 : 2], dtype = np.float32) for i, w in enumerate(weights): cam += w * output[:, :, i] cam = cv2.resize(cam, (224, 224)) cam = np.maximum(cam, 0) heatmap = cam / np.max(cam) #Return to BGR [0..255] from the preprocessed image image = image[0, :] image -= np.min(image) image = np.minimum(image, 255) cam = cv2.applyColorMap(np.uint8(255*heatmap), cv2.COLORMAP_JET) cam = np.float32(cam) + np.float32(image) cam = 255 * cam / np.max(cam) return np.uint8(cam), heatmap """ os.environ["CUDA_VISIBLE_DEVICES"] = "1" preprocessed_input = load_image(sys.argv[1]) model = VGG16(weights='imagenet') predictions = model.predict(preprocessed_input) top_1 = decode_predictions(predictions)[0][0] print('Predicted class:') print('%s (%s) with probability %.2f' % (top_1[1], top_1[0], top_1[2])) predicted_class = np.argmax(predictions) cam, heatmap = grad_cam(model, preprocessed_input, predicted_class, "block5_conv3") cv2.imwrite("gradcam.jpg", cam) register_gradient() guided_model = modify_backprop(model, 'GuidedBackProp') saliency_fn = compile_saliency_function(guided_model) saliency = saliency_fn([preprocessed_input, 0]) gradcam = saliency[0] * heatmap[..., np.newaxis] cv2.imwrite("guided_gradcam.jpg", deprocess_image(gradcam))环境：keras2.1.6 python3 tf1.8
使用：python grad-cam.py <path_to_image>
测试：
原图：
grad-cam图：
guided-gradcam图：
