跟我学算法- tensorflow VGG模型进行测试

我们使用的VGG模型是别人已经训练好的一个19层的参数所做的一个模型

第一步：定义卷积分部操作函数

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

# 进行卷积操作
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)
# 进行池化操作
def _pool_layer(input):
    return tf.nn.max_pool(input, ksize=(1, 2, 2, 1), strides=(1, 2, 2, 1),
                          padding='SAME')
# 进行去均值的操作
def preprocess(image, mean_pixel):
    return image - mean_pixel

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

def imread(path):
    return scipy.misc.imread(path).astype(np.float)
def imsave(path, img):
    img = np.clip(img, 0, 255).astype(np.uint8)
    scipy.misc.imsave(path, img)

第二步：定义卷积操作函数

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'
    )
    # 载入数据
    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))
            # 重构reshape
            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)
        # 用来存放对应的处理结果
        net[name] = current

    assert len(net) == len(layers)

    return net, mean_pixel, layers

第三步： 构造文件路径

# 返回当前的路径
cwd = os.getcwd()
# 别人已经训练好的模型
VGG_PATH =cwd + '/data/imagenet-vgg-verydeep-19.mat'
IMG_PATH = cwd + '/data/cat.jpg'
input_image = imread(IMG_PATH)
shape = (1, input_image.shape[0], input_image.shape[1], input_image.shape[2])

第四步：训练模型，输出特征图像

with tf.Session as sess:
    image = tf.placeholder('float', shape=shape)
    #训练模型
    nets, mean_pixel, all_layers = net(VGG_PATH, image)
    # 去除均值
    input_image_pre = np.array([preprocess(input_image, mean_pixel)])
    layers = all_layers
    for i, layer in enumerate(layers):
        # 输出模型的单个特征
        features = nets[layer].eval(feed_dict={image:input_image})
        print(" Type of 'features' is ", type(features))
        print(" Shape of 'features' is %s" % (features.shape,))
    # 画卷积特征图
        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()