训练结果有测试集和训练集的准确率,但是调取测试集的预测值时,全都是nan,我需要得到测试结果实际应用的。请问怎么解决?拿一个二分类为例
以下是前期搭建框架
import numpy as np
import tensorflow as tf
x=tf.placeholder("float", [None,115])
y=tf.placeholder("float", [None,2])
W=tf.Variable(tf.zeros([115,2]))
b=tf.Variable(tf.zeros([2]))
actv= tf.nn.softmax(tf.matmul(x,W)+b)
####网上查到说下面这个loss函数中如果是log(0)的话就会有nan,但那应该连训练结果都没有才对吧?我现在能得到训练结果,但是结果都是nan怎么办?
cost=tf.reduce_mean(-tf.reduce_sum(y*tf.log(actv), reduction_indices=1))
learning_rate=0.01
optm= tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)
pred=tf.equal(tf.argmax(actv, 1), tf.argmax(y, 1))
accr=tf.reduce_mean(tf.cast(pred,"float"))
init=tf.global_variables_initializer()
sess=tf.InteractiveSession()
sess=tf.Session()
sess.run(init)
training_lenth=len(G)####(回测长度)
training_epochs =50 #训练次数
batch_size = len(G) #每次迭代用多少样本(用全套)
##display_step = 5 #展示
print('Down')
训练重点来了,我需要得到result的实际结果
lenth=2
for epoch in range(training_epochs):
avg_cost=0
num_batch=int(len(G)/batch_size)
for i in range((lenth-1),lenth):
batch_xs=np.array(G[i])
batch_ys=np.array(F[i])
sess.run(optm, feed_dict={x: batch_xs, y: batch_ys})
feeds={x:batch_xs, y: batch_ys}
avg_cost += sess.run (cost, feed_dict=feeds)/(num_batch*lenth)
feeds_train = {x: batch_xs, y: batch_ys}
feeds_test = {x: G[i+1], y: F[i+1]}
train_acc = sess.run(accr, feed_dict=feeds_train) #feed_dict 针对place holder占位
test_acc = sess.run(accr,feed_dict=feeds_test)
result=sess.run(actv,feed_dict=feeds_test)
但是实际给我的结果中,test_acc和train_acc都是有的,但是具体分类的概率值都是nan。
result=sess.run(actv,feed_dict=feeds_train)
print (train_acc)#
print(test_acc)
train_acc
a=sess.run([accr,actv],feed_dict=feeds_test)
print(a)
0.930233
0.465116
[0.46511629, array([[ nan, nan],
[ nan, nan],
[ nan, nan],
[ nan, nan],
[ nan, nan],
[ nan, nan],
[ nan, nan],
[ nan, nan],
[ nan, nan],
[ nan, nan],
[ nan, nan],
[ nan, nan],
[ nan, nan],
[ nan, nan],
[ nan, nan],
[ nan, nan],
[ nan, nan],
[ nan, nan],
[ nan, nan],
[ nan, nan],
[ nan, nan],
[ nan, nan],
[ nan, nan],
[ nan, nan],
[ nan, nan],
[ nan, nan],
[ nan, nan],
[ nan, nan],
[ nan, nan],
[ nan, nan],
[ nan, nan],
[ nan, nan],
[ nan, nan],
[ nan, nan],
[ nan, nan],
[ nan, nan],
[ nan, nan],
[ nan, nan],
[ nan, nan],
[ nan, nan],
[ nan, nan],
[ nan, nan],
[ nan, nan]], dtype=float32)]
求大神指教,跪送分