为什么在Python中使用张量流的ANFIS模型中精度、召回率为零

Question

我已经用张量流构建了 AFIS 模型来解决分类问题。对于每个时期，我的精确度和召回率都为零。我正在使用 guassian 隶属函数，但是当我打印 sigma 时，它给出 0。下面使用的训练

## settings
n = X_train.shape[1] # no of input features
m = 2*n # number of fuzzy rules

learning_rate = 0.01
epochs = 1000


################################ train
X_train_t = tf.placeholder(tf.float32, shape=[None, n]) # Train input
y_train_t = tf.placeholder(tf.float32, shape=None)  # Train output

mu = tf.get_variable(name="mu", shape=[m * n], initializer=tf.random_normal_initializer(0, 1))  # mean of Gaussian MFS
sigma = tf.get_variable(name="sigma", shape = [m * n], initializer=tf.random_normal_initializer(0, 1))  # std_dev of Gaussian MFS
w = tf.get_variable(name="w", shape= [1, m], initializer=tf.random_normal_initializer(0, 1))

rula = tf.reduce_prod(tf.reshape(tf.exp( -0.5* ((tf.tile(X_train_t, (1, m))- mu)**2) / (sigma**2)),
               (-1, m, n)), axis=2)  #activations
Y_train_t = tf.reduce_sum(rula*w,axis=1) / tf.clip_by_value(tf.reduce_sum(rula,axis=1), 1e-8, 1e8)


#loss = tf.losses.log_loss(y_train, Y_train)  # loss function
loss = tf.losses.sigmoid_cross_entropy(y_train_t, Y_train_t)  # loss function
#loss = tf.sqrt(tf.losses.mean_squared_error(y_train, Y_train))

optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(loss)  # optimizer

################################ test
X_test_t = tf.placeholder(tf.float32, shape=[None, n]) # Test input
y_test_t = tf.placeholder(tf.float32, shape=None)  # Train output
rula_test = tf.reduce_prod(tf.reshape(tf.exp( -0.5* ((tf.tile(X_test_t, (1, m))- mu)**2) / (sigma**2)),
               (-1, m, n)), axis=2)  # rule activation
Y_test_t = tf.reduce_sum(rula_test*w,axis=1) / tf.clip_by_value(tf.reduce_sum(rula_test,axis=1), 1e-8, 1e8)
loss_test = tf.losses.sigmoid_cross_entropy(y_test_t, Y_test_t)  # loss function

################################ start session
x_axis = []
tr_loss, te_loss = [],[]

tr_prec, te_prec = [], []
tr_rec, te_rec = [], []
init=tf.global_variables_initializer()
with tf.Session() as sess:
    sess.run(init)

    for e in range(epochs):
        Y_train, loss_tr, _ = sess.run([Y_train_t, loss, optimizer], feed_dict={X_train_t: X_train, y_train_t: y_train})
        Y_test, loss_te    = sess.run([Y_test_t, loss_test], feed_dict={X_test_t: X_test, y_test_t: y_test})
        
        if (e+1) % 10 == 0:
            x_axis.append(e+1)

            tr_loss.append(loss_tr)
            te_loss.append(loss_te)

            Y_train = np.where(Y_train > 0, 1, 0)
            Y_test = np.where(Y_test > 0, 1, 0)
            
            prec_tr = precision_score(y_train,Y_train)
            prec_te = precision_score(y_test,Y_test)

            rec_tr = recall_score(y_train,Y_train)
            rec_te = recall_score(y_test,Y_test)

            tr_prec.append(prec_tr)
            te_prec.append(prec_te)
            tr_rec.append(rec_tr)
            te_rec.append(rec_te)

        

代码代码引用自 https://github.com/subhalingamd/ANFIS-diabetes-prediction/blob/main/main.py

我是这个算法的新手。请帮助我哪里出了问题。