具有马氏距离损失h的Keras自定义损失函数

Question

我试图在Keras中使用Mahalanobis距离损失实现一个自定义的损失函数。但是我总是遇到这个讨厌的错误。在

马氏距离（或平方值为[3]的“广义平方插值距离”）也可以定义为具有相同分布的两个随机向量x和y与协方差矩阵S之间的相异度量

d（x，y）=平方[转置（x-y）*逆（S）*（x-y）]

（https://en.wikipedia.org/wiki/Mahalanobis_distance）

n_classes = 4
n_samples=800
X, y = make_classification(n_samples=n_samples, n_features=20, n_informative=4, n_redundant=0, n_classes=n_classes, n_clusters_per_class=2)
y = to_categorical(y)
Xtrainb, testXb, ytrainb, ytestb = train_test_split(X, y, test_size = 0.3, random_state=42)

x_trainb = np.reshape(Xtrainb, (Xtrainb.shape[0], Xtrainb.shape[1], 1))
Xtestb = np.reshape(testXb, (testXb.shape[0], testXb.shape[1], 1))

densesize = 4
input_datab = Input(shape=(Xtrainb.shape[1],1)) 
epochs = 10
batch_size = 32
dropout= 0.1
lr= 0.001

########
def mahalanobis(y_true, y_pred):
    x_minus_mn_with_transpose = K.transpose(y_true - y_pred)
    Covariance = covr1(y_true, y_pred)
    inv_covmat = tf.linalg.inv(Covariance)
    x_minus_mn = y_true - y_pred
    left_term = K.dot(x_minus_mn, inv_covmat)
    D_square = K.dot(left_term, x_minus_mn_with_transpose)
    return D_square 

def covr1(y_true, y_pred):
    #x_mean = K.mean(y_true)
    #y_mean = K.mean(y_pred)
    Cov_numerator = K.sum(((y_true - y_pred)*(y_true - y_pred)))
    Cov_denomerator = len(Xtrainb)-1
    Covariance = (Cov_numerator / Cov_denomerator)
    return Covariance


conv1= Conv1D(filters=80, kernel_size=2, padding='same',   input_dim=Xtrainb.shape[1])(input_datab)
maxpool = MaxPooling1D(pool_size=3, stride=3 )(conv1)
conv2= Conv1D(filters=50, kernel_size=2, padding='same',   input_dim=Xtrainb.shape[1])(maxpool)
maxpool = MaxPooling1D(pool_size=3, stride=3)(conv2)
flatten = Flatten()(maxpool)
dense = Dense(84, activation='relu')(flatten)
dense = Dense(1024, activation='relu')(flatten)
dense = Dense(densesize, activation='softmax')(dense)
model = Model(inputs=[input_datab],outputs=[dense])
model.compile(loss= mahalanobis,  optimizer='adam', metrics=['acc'])
hist = model.fit(x_trainb, ytrainb, validation_data=(Xtestb, ytestb), epochs=epochs, batch_size=batch_size)

ValueError:形状必须至少为2级，但对于输入形状为[]的“loss”或“dense”270“loss/MatrixInverse”（操作：“MatrixInverse”）而言，排名为0。在