python – 用于从Keras预训练网络中检索图像的三重模型
作者：互联网
我想实现一个图像检索模型.该模型将使用三重损失函数(与facenet或类似架构相同)进行训练.我的想法是使用Keras的预训练分类模型(例如resnet50),并使其成为三重架构.这是我在Keras的模特：
resnet_input = Input(shape=(224,224,3))
resnet_model = ResNet50(weights='imagenet', include_top = False, input_tensor=resnet_input)
net = resnet_model.output
net = Flatten(name='flatten')(net)
net = Dense(512, activation='relu', name='embded')(net)
net = Lambda(l2Norm, output_shape=[512])(net)
base_model = Model(resnet_model.input, net, name='resnet_model')
input_shape=(224,224,3)
input_anchor = Input(shape=input_shape, name='input_anchor')
input_positive = Input(shape=input_shape, name='input_pos')
input_negative = Input(shape=input_shape, name='input_neg')
net_anchor = base_model(input_anchor)
net_positive = base_model(input_positive)
net_negative = base_model(input_negative)
positive_dist = Lambda(euclidean_distance, name='pos_dist')([net_anchor, net_positive])
negative_dist = Lambda(euclidean_distance, name='neg_dist')([net_anchor, net_negative])
stacked_dists = Lambda(
lambda vects: K.stack(vects, axis=1),
name='stacked_dists'
)([positive_dist, negative_dist])
model = Model([input_anchor, input_positive, input_negative], stacked_dists, name='triple_siamese')
def triplet_loss(_, y_pred):
margin = K.constant(1)
return K.mean(K.maximum(K.constant(0), K.square(y_pred[0]) - K.square(y_pred[1]) + margin))
def accuracy(_, y_pred):
return K.mean(y_pred[0] < y_pred[1])
def l2Norm(x):
return K.l2_normalize(x, axis=-1)
def euclidean_distance(vects):
x, y = vects
return K.sqrt(K.maximum(K.sum(K.square(x - y), axis=1, keepdims=True), K.epsilon()))
模型应预测每个图像的特征向量.如果图像来自同一类,则这些向量之间的距离(在这种情况下为欧几里德)应接近于零,如果不是,则接近于1.
我已经尝试了不同的学习步骤,批量大小,损失函数中的不同边距,从原始resnet模型中选择不同的输出层,将不同的层添加到resnet的末尾,仅训练新添加的层与训练整个模型.我也尝试使用没有预训练重量的这个resnet模型,结果仍然相同,大约0.5准确度和1.0损失,无论我做什么. (使用keras.applications.resnet50.preprocess_input以此模型预期的方式预处理输入图像)
我没有进行任何可能导致收敛缓慢的负面挖掘,但在这种情况下(检查函数)的0.5准确度仍然是随机预测.
所以我开始思考也许我想念一些非常重要的东西(这是一个相当困难的架构).所以,如果您在我的实施中发现错误或可疑的话,我会非常高兴.
解决方法:
我没有足够的声誉点来评论,所以我这样写.
我想通过使用您的代码做类似于您正在做的事情.
我是CNN的新手,我不确定我的训练数据应该是什么样子.你愿意分享剩下的代码吗？我会很感激！
编辑：
要回答我自己的问题,这可能对某人有用,这就是我在假期照片(http://lear.inrialpes.fr/%7Ejegou/data.php)上的表现,它有效：
def get_random_image(img_groups, group_names, gid):
gname = group_names[gid]
photos = img_groups[gname]
pid = np.random.choice(np.arange(len(photos)), size=1)[0]
pname = photos[pid]
return gname + pname + ".jpg"
def create_triples(image_dir):
img_groups = {}
for img_file in os.listdir(image_dir):
prefix, suffix = img_file.split(".")
gid, pid = prefix[0:4], prefix[4:]
if gid in img_groups.keys():
img_groups[gid].append(pid)
else:
img_groups[gid] = [pid]
pos_triples, neg_triples = [], []
for key in img_groups.keys():
triples = [(key + x[0] + ".jpg", key + x[1] + ".jpg", str(int(key)+3 if int(key)<1495 else int(key)-3)+'01'+'.jpg')
for x in itertools.combinations(img_groups[key], 2)]
pos_triples.extend(triples)
return pos_triples
def triplet_loss(y_true, y_pred):
margin = K.constant(0.2)
return K.mean(K.maximum(K.constant(0), K.square(y_pred[:,0,0]) - K.square(y_pred[:,1,0]) + margin))
def accuracy(y_true, y_pred):
return K.mean(y_pred[:,0,0] < y_pred[:,1,0])
def l2Norm(x):
return K.l2_normalize(x, axis=-1)
def euclidean_distance(vects):
x, y = vects
return K.sqrt(K.maximum(K.sum(K.square(x - y), axis=1, keepdims=True), K.epsilon()))
triples_data = create_triples(IMAGE_DIR)
dim = 1500
h = 299
w= 299
anchor =np.zeros((dim,h,w,3))
positive =np.zeros((dim,h,w,3))
negative =np.zeros((dim,h,w,3))
for n,val in enumerate(triples_data[0:1500]):
image_anchor = plt.imread(os.path.join(IMAGE_DIR, val[0]))
image_anchor = imresize(image_anchor, (h, w))
image_anchor = image_anchor.astype("float32")
#image_anchor = image_anchor/255.
image_anchor = keras.applications.resnet50.preprocess_input(image_anchor, data_format='channels_last')
anchor[n] = image_anchor
image_positive = plt.imread(os.path.join(IMAGE_DIR, val[1]))
image_positive = imresize(image_positive, (h, w))
image_positive = image_positive.astype("float32")
#image_positive = image_positive/255.
image_positive = keras.applications.resnet50.preprocess_input(image_positive, data_format='channels_last')
positive[n] = image_positive
image_negative = plt.imread(os.path.join(IMAGE_DIR, val[2]))
image_negative = imresize(image_negative, (h, w))
image_negative = image_negative.astype("float32")
#image_negative = image_negative/255.
image_negative = keras.applications.resnet50.preprocess_input(image_negative, data_format='channels_last')
negative[n] = image_negative
Y_train = np.random.randint(2, size=(1,2,dim)).T
resnet_input = Input(shape=(h,w,3))
resnet_model = ResNet50(weights='imagenet', include_top = False, input_tensor=resnet_input)
for layer in resnet_model.layers:
layer.trainable = False
net = resnet_model.output
net = Flatten(name='flatten')(net)
net = Dense(128, activation='relu', name='embed')(net)
net = Dense(128, activation='relu', name='embed2')(net)
net = Dense(128, activation='relu', name='embed3')(net)
net = Lambda(l2Norm, output_shape=[128])(net)
base_model = Model(resnet_model.input, net, name='resnet_model')
input_shape=(h,w,3)
input_anchor = Input(shape=input_shape, name='input_anchor')
input_positive = Input(shape=input_shape, name='input_pos')
input_negative = Input(shape=input_shape, name='input_neg')
net_anchor = base_model(input_anchor)
net_positive = base_model(input_positive)
net_negative = base_model(input_negative)
positive_dist = Lambda(euclidean_distance, name='pos_dist')([net_anchor, net_positive])
negative_dist = Lambda(euclidean_distance, name='neg_dist')([net_anchor, net_negative])
stacked_dists = Lambda(
lambda vects: K.stack(vects, axis=1),
name='stacked_dists'
)([positive_dist, negative_dist])
model = Model([input_anchor, input_positive, input_negative], stacked_dists, name='triple_siamese')
model.compile(optimizer="rmsprop", loss=triplet_loss, metrics=[accuracy])
model.fit([anchor, positive, negative], Y_train, epochs=50, batch_size=15, validation_split=0.2)
model.save('triplet_loss_resnet50.h5')
标签：python,tensorflow,keras,conv-neural-network 来源： https://codeday.me/bug/20190828/1749328.html