前一篇文章tensorflow入門應用方法(四)——訓練模型的保存和讀取中提到保存訓練模型和讀取的方法林束。這篇文章主要闡述加載已經(jīng)訓練好的Imagenet VGG-19網(wǎng)絡對圖像貓進行識別刑巧，并且可視化VGG網(wǎng)絡卷積層的特征圖像。

下載Imagenet VGG-19

http://www.vlfeat.org/matconvnet/models/beta16/imagenet-vgg-verydeep-19.mat

加載Imagenet VGG-19

完整代碼如下：

import scipy.io
import numpy as np
import os
import scipy.misc
import matplotlib.pyplot as plt
import tensorflow as tf


def _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_pixel


def unprocess(image, mean_piexl):
    return image + mean_piexl


def 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_image
    for 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] = current
    assert len(net) == len(layers)
    return net, mean_pixel, layers

print('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_layers

    for 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()

卷積層特征圖像顯示

vgg-19網(wǎng)絡的輸入圖片如下

埃及貓

各卷積層的特征圖像

conv1~2	conv3_1~3_4	conv4_1~4_4	conv5_1~5_4
conv1_1	conv3_1	conv4_1	conv5_1
conv1_2	conv3_2	conv4_2	conv5_2
conv2_1	conv3_3	conv4_3	conv5_3
conv2_2	conv3_4	conv4_4	conv5_4