Question

我正在编写一个神经元网络程序，该代码可以运行，但是我的网络精度始终为1.0。我尝试更改激活功能，但没有任何变化，我的代码有什么问题？

我使用以下功能随机获取数据

#randomly choose data
def random_data(): 
    num = random.randint(0,len(result)-1)

    return result[num]

statue=[[0,0,0]]

以下显示了我的网络的构建

#network definition
def Net(X): 
    global statue
    X1,X2=tf.split(X,[6,3],1)
    X = tf.concat([X1,statue],1)

    We1,We2=tf.split(We['h3'],[1,3],1)
    WW1,WW2 = tf.split(We1,[2,2],0)

    WW1=[[1.],[1.]]
    WW2=[[1.],[1.]]
    We1=tf.concat([WW1,WW2],0)
    We['h3'] = tf.concat([We1,We2],1)
    Wx_plus_b00 = tf.matmul(X, We['h1']) + bi['b1']

    l00 = tf.nn.sigmoid(Wx_plus_b00)

    Wx_plus_b01 = tf.matmul(X, We['h1']) + bi['b1']
    l01 = tf.nn.sigmoid(Wx_plus_b01)

    l0 = tf.concat([l01,l00],1)
    Weights1 = tf.Variable(tf.random_normal([16, 4]))

    biases1 = tf.Variable(tf.zeros([1, 4]) + 0.1)
    Wx_plus_b1 = np.array(tf.matmul(l0, Weights1) + biases1)

    N1act = 2/(1+pow(math.e,-Wx_plus_b1))-1

    l1 = tf.nn.sigmoid(N1act)

    Wx_plus_b3 = tf.matmul(l1, We['h3']) + bi['b3']
    Wx_plus_b31,Wx_plus_b32=tf.split(Wx_plus_b3,[1,3],1)
    statue = Wx_plus_b32
    N3act = 2/(1+pow(math.e,-Wx_plus_b31))-1


    prediction = tf.nn.softsign(N3act)

    return prediction

xs = tf.placeholder(tf.float32, [1, 9])
ys = tf.placeholder(tf.float32, [1, 1])
myNet = Net(xs)
pred = myNet
cost = 
tf.reduce_mean(tf.nn.softmax_cross_entroy_with_logits(labels=ys,logits\
=pred))
optm=tf.train.GradientDescentOptimizer(0.01).minimize(cost)

这是我的准确性函数

#accuracy
accr=tf.reduce_mean(tf.cast(tf.equal(tf.argmax(pred,1), tf.argmax(ys,1)), 
tf.float32)) 
init = tf.global_variables_initializer()

这是我的主要功能

#main function
with tf.Session() as sess:
    sess.run(init)
    loadData('ABCD.txt')
    step = 1 
for i in range(10000):
    batch = random_data()
    x_input0 = np.array(batch[0]).reshape(1,9)
    y_output = np.array(batch[1]).reshape(1,1)
    feeds = {xs: x_input0, ys: y_output}
    sess.run(optm, feed_dict=feeds)      
    sess.run
    step =step + 1

    print('accuracy :',sess.run(accr, feed_dict={xs: x_input0, ys:\ y_output}))

为什么我的代码中Accuarcy的结果始终为1.0？

0 个答案: