这篇文章主要阐述加载已经训练好的Imagenet VGG-19网络对图像猫进行识别，并且可视化VGG网络卷积层的特征图像。
下载Imagenet VGG-19
/matconvnet/models/beta16/imagenet-vgg-verydeep-19.mat
加载Imagenet VGG-19
完整代码如下：
import scipy.ioimport numpy as npimport osimport scipy.miscimport matplotlib.pyplot as pltimport tensorflow as tfdef _conv_layer(input, weights, bias):conv = tf.nn.conv2d(input, tf.constant(weights), strides=(1,1,1,1), padding='SAME')return tf.nn.bias_add(conv, bias)def _pool_layer(input):return tf.nn.max_pool(input, ksize=(1,2,2,1), strides=(1,2,2,1), padding='SAME')def preprocess(image, mean_pixel):return image - mean_pixeldef unprocess(image, mean_piexl):return image + mean_piexldef imread(path):return scipy.misc.imread(path).astype(np.float)def imsave(path, img):img = np.clip(img, 0, 255).astype(np.int8)scipy.misc.imsave(path, img)print('functions for vgg ready')def net(data_path, input_image):layers = ('conv1_1', 'relu1_1', 'conv1_2', 'relu1_2', 'pool1','conv2_1', 'relu2_1', 'conv2_2', 'relu2_2', 'pool2','conv3_1', 'relu3_1', 'conv3_2', 'relu3_2','conv3_3', 'relu3_3', 'conv3_4', 'relu3_4', 'pool3','conv4_1', 'relu4_1', 'conv4_2', 'relu4_2','conv4_3', 'relu4_3', 'conv4_4', 'relu4_4', 'pool4','conv5_1', 'relu5_1', 'conv5_2', 'relu5_2','conv5_3', 'relu5_3', 'conv5_4', 'relu5_4')data = scipy.io.loadmat(data_path)mean = data['normalization'][0][0][0]mean_pixel = np.mean(mean, axis=(0,1))weights = data['layers'][0]net = {}current = input_imagefor i, name in enumerate(layers):kind = name[:4]if kind == 'conv':kernels, bias = weights[i][0][0][0][0]kernels = np.transpose(kernels, (1, 0, 2, 3))bias = bias.reshape(-1)current = _conv_layer(current, kernels, bias)elif kind == 'relu':current = tf.nn.relu(current)elif kind == 'pool':current = _pool_layer(current)net[name] = currentassert len(net) == len(layers)return net, mean_pixel, layersprint('network for vgg ready')cwd = os.getcwd()vgg_path = cwd + '/data/imagenet-vgg-verydeep-19.mat'img_path = cwd + '/data/cat.jpeg'input_image = imread(img_path)shape = (1, input_image.shape[0], input_image.shape[1], input_image.shape[2])with tf.Session() as sess:image = tf.placeholder('float', shape=shape)nets, mean_pixel, all_layers = net(vgg_path, image)input_image_pre = np.array([preprocess(input_image, mean_pixel)])layers = all_layersfor i, layer in enumerate(layers):print('[%d/%d] %s' % (i+1, len(layers), layer))features = nets[layer].eval(feed_dict={image: input_image_pre})print('Type of ‘features’ is ', type(features))print('Shape of ‘features’ is ', (features.shape,))if 1:plt.figure(i+1, figsize=(10, 5))plt.matshow(features[0, :, :, 0], cmap=plt.cm.gray, fignum=i+1)plt.title(''+layer)plt.colorbar()plt.show()
卷积层特征图像显示
vgg-19网络的输入图片如下
各卷积层的特征图像
