我正在尝试使用FacenetModel实现三重态损失模型。我使用了Coursera作业中提供的Facenet实现。
每当我编译模型时,都会出现此错误:
ValueError:没有为“ FaceRecoModel”提供数据。需要每个键中的数据:['FaceRecoModel','FaceRecoModel','FaceRecoModel']
我的代码:
def batch_generator(batch_size = 64):
while True:
pos = positiveImg[np.random.choice(len(positiveImg), batch_size)]
neg = negativeImg[np.random.choice(len(negativeImg), batch_size)]
anc = anchorsImg[np.random.choice(len(anchorsImg), batch_size)]
x_data = {'inp1': anc,
'inp2': pos,
'inp3': neg
}
y_data = {'y1': np.zeros((64,0)),
'y2': np.zeros((64,0)),
'y3': np.zeros((64,0))}
yield (x_data, y_data)
def triplet_loss(y_true, y_pred):
anchor, positive, negative = y_pred[0], y_pred[1], y_pred[2]
pos_dist = tf.reduce_sum(tf.square(tf.subtract(anchor, positive)), axis=-1)
neg_dist = tf.reduce_sum(tf.square(tf.subtract(anchor, negative)), axis=-1)
basic_loss = tf.add(tf.subtract(pos_dist, neg_dist), 0.2)
loss = tf.reduce_sum(tf.maximum(basic_loss, 0.0))
return loss
def getModels():
FRmodel = keras.models.load_model('FR.h5', custom_objects={'triplet_loss': triplet_loss})
inp1 = Input((3, 96, 96), name= 'inp1')
inp2 = Input((3, 96, 96), name= 'inp2')
inp3 = Input((3, 96, 96), name= 'inp3')
pred1 = FRmodel(inp1)
pred2 = FRmodel(inp2)
pred3 = FRmodel(inp3)
inputs = [inp1, inp2, inp3]
outputs = [pred1, pred2, pred3]
model = keras.models.Model(inputs=[inp1, inp2, inp3], outputs= [pred1, pred2, pred3])
return FRmodel, model
generator = batch_generator(64)
FRmodel, my_model = getModels()
my_model.compile(optimizer = 'adam', loss = triplet_loss, metrics = ['accuracy'])
my_model.fit_generator(generator, epochs=5,steps_per_epoch=30)
预训练的Facenet模型摘要:
FRmodel.summary():https://codeshare.io/arxmev
my_model.summary():https://codeshare.io/arx3N6
参考方案
在Coursera的论坛上找到了解决方案。这有点棘手。我必须使用Lambda在角膜层包装中添加三重损失的欧氏距离。根据文档:
将任意表达式包装为Layer对象。
新的实现:
`
def triplet_loss_v2(y_true, y_pred):
positive, negative = y_pred[:,0,0], y_pred[:,1,0]
margin = K.constant(0.2)
loss = K.mean(K.maximum(K.constant(0), positive - negative + margin))
return loss # shape = [1]
def euclidean_distance(vects):
x, y = vects # shape = [batch_size, 2, 1]
dist = K.sqrt(K.maximum(K.sum(K.square(x - y), axis=1, keepdims=True), K.epsilon()))
return dist # shape = [batch_size, 1]
FRmodel = faceRecoModel(input_shape=(3, 96, 96))
load_weights_from_FaceNet(FRmodel)
for layer in FRmodel.layers[0: 80]:
layer.trainable = False
input_shape=(3, 96, 96)
anchor = Input(shape=input_shape, name = 'anchor')
anchorPositive = Input(shape=input_shape, name = 'anchorPositive')
anchorNegative = Input(shape=input_shape, name = 'anchorNegative')
anchorCode = FRmodel(anchor)
anchorPosCode = FRmodel(anchorPositive)
anchorNegCode = FRmodel(anchorNegative)
positive_dist = Lambda(euclidean_distance, name='pos_dist')([anchorCode, anchorPosCode])
negative_dist = Lambda(euclidean_distance, name='neg_dist')([anchorCode, anchorNegCode])
stacked_dists = Lambda(lambda vects: K.stack(vects, axis=1), name='stacked_dists')([positive_dist, negative_dist]) # shape = [batch_size, 2, 1]
tripletModel = Model([anchor, anchorPositive, anchorNegative], stacked_dists, name='triple_siamese')
tripletModel.compile(optimizer = 'adadelta', loss = triplet_loss_v2, metrics = None)
gen = batch_generator(64)
tripletModel.fit_generator(gen, epochs=1,steps_per_epoch=5)`
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