我已经实现了一个RBF神经网络分类器。我用这个实现来分类MNIST数据集，但它并未学习，总是预测单一类别。如果有人能帮助我找出实现中的问题，我将不胜感激。

我必须指出，由于实现是逐个样本工作的，所以速度相当慢，但我不知道如何使其批量处理。（我对tensorflow和python整体上都是新手）

我的实现如下：

class RBF_NN:    def __init__(self, M, K, L, lr):    #Layer sizes    self.M = M #input layer size - number of features    self.K = K #RBF layer size    self.L = L #output layer size - number of classes    #    x = tf.placeholder(tf.float32,shape=[M])    matrix = tf.reshape(tf.tile(x,multiples=[K]),shape=[K,M])    prototypes_input = tf.placeholder(tf.float32,shape=[K,M])    prototypes = tf.Variable(prototypes_input) # prototypes - representatives of the data    r = tf.reduce_sum(tf.square(prototypes-matrix),1)    s = tf.Variable(tf.random.uniform(shape=[K],maxval=1)) #scaling factors    h = tf.exp(-r/(2*tf.pow(s,2)))    W = tf.Variable(tf.random.uniform(shape=[K,L],maxval=1))    b = tf.Variable(tf.constant(0.1, shape=[L]))    o = tf.matmul(tf.transpose(tf.expand_dims(h,1)),W) + b     pred_class = tf.argmax(o,1)    y = tf.placeholder(shape=[L], dtype=tf.float32)    loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=o, labels=y))    optimizer = tf.train.AdamOptimizer(lr).minimize(loss)    self.x = x    self.prototypes_input = prototypes_input    self.prototypes = prototypes    self.r = r    self.s = s    self.h = h    self.W = W    self.b = b    self.o = o    self.y = y    self.loss = loss    self.optimizer = optimizer    self.pred_class = pred_class    def fit(self,X,y,prototypes,epoch_count,print_step,sess):    for epoch in range(epoch_count):        epoch_loss = 0        for xi,yi in zip(X,y):            iter_loss, _ = sess.run((self.loss,self.optimizer),feed_dict={self.x: xi, self.y: yi, self.prototypes_input:prototypes})            epoch_loss = epoch_loss + iter_loss        epoch_loss = epoch_loss/len(X)        if epoch%print_step == 0:            print("Epoch loss",(epoch+1),":",epoch_loss)    def predict(self,x,sess):    return sess.run((self.pred_class),feed_dict={self.x:x})[0]    def get_prototypes(self,sess):    return sess.run((self.prototypes))

使用方法：

mnist = tf.keras.datasets.mnist(x_train, y_train),(x_test, y_test) = mnist.load_data()y_train = to_one_hot(y_train,10)y_test = to_one_hot(y_test,10)x_train = np.asarray([np.asarray(x).reshape(-1) for x in x_train])x_test = np.asarray([np.asarray(x).reshape(-1) for x in x_test])M = 784K = 1000L = 10lr = 0.01rbfnn = RBF_NN(M,K,L,lr)#Selecting prototypes from the train setidx = np.random.randint(len(x_train), size=K)prototypes = x_train[idx,:]init = tf.global_variables_initializer()sess = tf.InteractiveSession()sess.run(init,feed_dict={rbfnn.prototypes_input:prototypes})rbfnn.fit(x_train,y_train,prototypes,epoch_count=1, print_step=1,sess=sess)y_test_p = []for xi,yi in zip(x_test,y_test):    yp = rbfnn.predict(xi,sess=sess)    y_test_p.append(yp)y_test_t = [np.argmax(yi) for yi in y_test]acc = accuracy_score(y_test_t,y_test_p,)precc = precision_score(y_test_t,y_test_p, average='macro')recall = recall_score(y_test_t,y_test_p, average = 'macro')f1 = f1_score(y_test_t,y_test_p,average='macro')print("Accuracy:",acc)print("Precision:",precc)print("Recall:",recall)print("F1 score:",f1)sess.close()

回答：

实现本身是好的。然而，它似乎对数据非常敏感。如果添加以下几行代码，它将开始正常学习：

x_train = (x_train-x_train.min())/(x_train.max()-x_train.min())x_test = (x_test-x_test.min())/(x_test.max()-x_test.min())

通过这种方式，数据被归一化，使每个特征的区间从0到1。