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我已经按照这个来加载和运行一个预训练的 VGG 模型。但是，我试图从隐藏层中提取特征图，并尝试从此处的“提取任意特征图”部分复制结果。我的代码如下：

#!/usr/bin/python

import matplotlib.pyplot as plt
import theano
from scipy import misc
from PIL import Image
import PIL.ImageOps
from keras.models import Sequential
from keras.layers.core import Flatten, Dense, Dropout
from keras.layers.convolutional import Convolution2D, MaxPooling2D, ZeroPadding2D
from keras.optimizers import SGD
import numpy as np
from keras import backend as K

def get_features(model, layer, X_batch):
    get_features = K.function([model.layers[0].input, K.learning_phase()], [model.layers[layer].output,])
    features = get_features([X_batch,0])
    return features

def VGG_16(weights_path=None):
    model = Sequential()
    model.add(ZeroPadding2D((1,1),input_shape=(3,224,224)))
    model.add(Convolution2D(64, 3, 3, activation='relu'))
    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(64, 3, 3, activation='relu'))
    model.add(MaxPooling2D((2,2), strides=(2,2)))

    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(128, 3, 3, activation='relu'))
    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(128, 3, 3, activation='relu'))
    model.add(MaxPooling2D((2,2), strides=(2,2)))

    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(256, 3, 3, activation='relu'))
    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(256, 3, 3, activation='relu'))
    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(256, 3, 3, activation='relu'))
    model.add(MaxPooling2D((2,2), strides=(2,2)))

    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(512, 3, 3, activation='relu'))
    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(512, 3, 3, activation='relu'))
    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(512, 3, 3, activation='relu'))
    model.add(MaxPooling2D((2,2), strides=(2,2)))

    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(512, 3, 3, activation='relu'))
    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(512, 3, 3, activation='relu'))
    model.add(ZeroPadding2D((1,1)))
    model.add(Convolution2D(512, 3, 3, activation='relu'))
    model.add(MaxPooling2D((2,2), strides=(2,2)))

    model.add(Flatten())
    model.add(Dense(4096, activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(4096, activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(1000, activation='softmax'))

    if weights_path:
        model.load_weights("/home/srilatha/Desktop/Research_intern/vgg16_weights.h5")

    return model

if __name__ == "__main__":
    #f="/home/srilatha/Desktop/Research_intern/Data_sets/Data_set_2/FGNET/male/007A23.JPG"
    f="/home/srilatha/Desktop/Research_intern/Data_sets/Cropped_data_set/1/7.JPG"
    image = Image.open(f)
    new_width  = 224
    new_height = 224
    im = image.resize((new_width, new_height), Image.ANTIALIAS)
    im=np.array(im)
    im=np.tile(im[:,:,None],(1,1,3))
    #imRGB = np.repeat(im[:, :, np.newaxis], 3, axis=2)
    print(im)
    #print(type(im))
    im = im.transpose((2,0,1))
    im = np.expand_dims(im, axis=0)


    # Test pretrained model
    model = VGG_16('/home/srilatha/Desktop/Research_intern/vgg16_weights.h5')
    sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
    model.compile(optimizer=sgd, loss='categorical_crossentropy')
    out = model.predict(im)
    #get_feature = theano.function([model.layers[0].input], model.layers[3].get_output(train=False), allow_input_downcast=False)
    #feat = get_feature(im)
    #get_activations = theano.function([model.layers[0].input], model.layers[1].get_output(train=False), allow_input_downcast=True)
    #activations = get_activations(model, 1, im)
    #plt.imshow(activations)
    #plt.imshow(im)
    features=get_features(model,15,im)
    plt.imshow(features[0][13])
    #out = model.predict(im)
    #plt.plot(out.ravel())
    #plt.show()
    print np.argmax(out)

但是，我收到了这个错误：

File "VGG_Keras.py", line 98, in <module>
    plt.imshow(features[0][13])
IndexError: index 13 is out of bounds for axis 0 with size 1

我怎样才能解决这个问题？

4

1 回答 1

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首先，下次请更新您的代码的更清洁版本，以便其他人可以更轻松地帮助您。

其次，修改你的函数来调试：

def get_features(model, layer, X_batch):
    print model.layers[layer]
    print model.layers[layer].output_shape
    get_features = K.function([model.layers[0].input, K.learning_phase()], [model.layers[layer].output,])
    features = get_features([X_batch,0])
    print features.shape
    return features

你会发现这features实际上是一个list：

  1. 的输出K.function是列表，即get_features是 的结果[model.layers[layer].output,]。
  2. get_features[0]因此model.layers[layer].output在形状(1, 256, 56, 56)==>(batch_size, channel, W, H)
  3. get_features[0][0]是批量第一张图片的特征。
  4. 我相信您正在寻找的是get_features[0][0][13].
于 2016-07-16T07:07:32.480 回答