VGG16 滤波器的可视化

Question

我正在学习 CNN，现在正在研究层的反卷积。我已经开始学习上采样并观察卷积层如何通过从源 Visualization of the filters of VGG16, with the Source code 的过滤器生成特征图来观察世界。我改了输入，代码如下：

import imageio
import numpy as np
import time
from keras.applications import vgg16
from keras import backend as K

import cv2
import matplotlib.pyplot as plt
import matplotlib.image as mpimg

# 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() + K.epsilon())
    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_data_format() == 'channels_first':
        x = x.transpose((1, 2, 0))
    x = np.clip(x, 0, 255).astype('uint8')
    return x

# build the VGG16 network with ImageNet weights
model = vgg16.VGG16(weights='imagenet', include_top=False)
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))) + K.epsilon())


kept_filters = []
for filter_index in range(200):
    # we only scan through the first 200 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
    if K.image_data_format() == 'channels_first':
        loss = K.mean(layer_output[:, filter_index, :, :])
    else:
        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.

    inpImgg = '/home/sanaalamgeer/Downloads/cat.jpeg'
    inpImg = mpimg.imread(inpImgg)
    inpImg = cv2.resize(inpImg, (img_width, img_height))        

    # we start from a gray image with some random noise
    if K.image_data_format() == 'channels_first':
        input_img_data = inpImg.reshape((1, 3, img_width, img_height))
    else:
        input_img_data = inpImg.reshape((1, img_width, img_height, 3))
    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
imageio.imwrite('stitched_filters_%dx%d.png' % (n, n), stitched_filters)

我使用的输入图像是

它应该生成一个将 64 个特征映射嵌入到一个图像中的输出，如 Visualization of the filters of VGG16 所示，但它在每个过滤器生成相同的输入图像，。

我很困惑哪里出了问题或应该在哪里进行更改。

请帮忙

Answer 1

好复杂的代码....

我会这样做：

from keras.applications.vgg16 import preprocess_input
layer_name = 'block5_conv1'

#create a section of the model to output the layer we want
model = vgg16.VGG16(weights='imagenet', include_top=False)
model = Model(model.input, model.get_layer(layer_name).output)

#open and preprocess the cat image
catImage = openTheCatImage(catFile)
catImage = np.expand_dims(catImage,axis=0)
catImage = preprocess_input(catImage)

#get the layer outputs
features = model.predict(catImage)

#plot
for channel in range(features.shape[-1]): #or .shape[1], or up to a limit you like 
    featureMap = features[:,:,:,channel] #or features[:,channel]
    featureMap = deprocess_image(feature_map)[0]

    saveOrPlot(featureMap)

VGG16 滤波器的可视化

Visualization of the filters of VGG16

visualization

python-3.x

keras

deconvolution

convolutional-neural-network