我是弗朗索瓦·查莱特（Francois Chollet）如何看待世界的博客文章，用于可视化由犯罪现场学习的特征。这是我的代码：
from __future__ import print_function
from scipy.misc import imsave
import numpy as np
import time
from keras import applications
from keras import backend as K
K.set_image_dim_ordering('tf')
from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img
# dimensions of the generated pictures for each filter.
img_width = 128
img_height = 128
# the name of the layer we want to visualize
# (see model definition at keras/applications/vgg16.py)
layer_name = 'block5_conv1'
# util function to convert a tensor into a valid image
def deprocess_image(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)
# build the VGG16 network with ImageNet weights
model = applications.VGG16(include_top=False, weights='imagenet', input_shape=(128,128,3))
print('Model loaded.')
model.summary()
# this is the placeholder for the input images
input_img = model.input
# get the symbolic outputs of each "key" layer (we gave them unique names).
layer_dict = dict([(layer.name, layer) for layer in model.layers[1:]])
def normalize(x):
# utility function to normalize a tensor by its L2 norm
return x / (K.sqrt(K.mean(K.square(x))) + 1e-5)
kept_filters = []
for filter_index in range(0, 20):
# we only scan through the first 50 filters,
# but there are actually 512 of them
print('Processing filter %d' % filter_index)
start_time = time.time()
# we build a loss function that maximizes the activation
# of the nth filter of the layer considered
layer_output = layer_dict[layer_name].output
loss = K.mean(layer_output[:, :, :, filter_index])
# we compute the gradient of the input picture wrt this loss
grads = K.gradients(loss, input_img)[0]
# normalization trick: we normalize the gradient
grads = normalize(grads)
# this function returns the loss and grads given the input picture
iterate = K.function([input_img], [loss, grads])
# step size for gradient ascent
step = 1.
# we start from a gray image with some random noise
img = load_img('para1.jpg') # this is a PIL image
x = img_to_array(img)
x = x.reshape((1,) + x.shape)
input_img_data = x
input_img_data = (input_img_data - 0.5) * 20 + 128
# we run gradient ascent for 20 steps
for i in range(20):
loss_value, grads_value = iterate([input_img_data])
input_img_data += grads_value * step
print('Current loss value:', loss_value)
if loss_value <= 0.:
# some filters get stuck to 0, we can skip them
break
# decode the resulting input image
if loss_value > 0:
img = deprocess_image(input_img_data[0])
kept_filters.append((img, loss_value))
end_time = time.time()
print('Filter %d processed in %ds' % (filter_index, end_time - start_time))
# we will stich the best 64 filters on a 8 x 8 grid.
n = 8
# the filters that have the highest loss are assumed to be better-looking.
# we will only keep the top 64 filters.
kept_filters.sort(key=lambda x: x[1], reverse=True)
kept_filters = kept_filters[:n * n]
# build a black picture with enough space for
# our 8 x 8 filters of size 128 x 128, with a 5px margin in between
margin = 5
width = n * img_width + (n-1) * margin
height = n * img_height + (n-1) * margin
stitched_filters = np.zeros((width, height, 3))
# fill the picture with our saved filters
for i in range(n):
for j in range(n):
img, loss = kept_filters[i * n + j]
stitched_filters[(img_width + margin) * i: (img_width + margin) * i + img_width,
(img_height + margin) * j: (img_height + margin) * j + img_height, :] = img
# save the result to disk
imsave('stitched_filters_%dx%d.png' % (n, n), stitched_filters)
当我运行代码时，我遇到了错误：
File "C:/Users/rajaramans2/codes/untitled8.py", line 94, in <module>
img, loss = kept_filters[i * n + j]
IndexError: list index out of range
请帮助修改。我正在使用尺寸为（128,128）的RGB图像，并试图在vgg16网络的第5块可视化卷积层1。
答案 0 :(得分：0)
在第76行中，keep_filters附加在第42行的循环中。因此，keep_filters的长度最多为20.但是在第94行中，您希望在keep_filters中访问8 * 8 = 64个元素，这些元素不在范围。