TensorFlow成本值返回NAN
TensorFlow成本值返回NAN
提问于 2018-05-28 16:19:34
我正在使用Tensorflow创建一个简单的Logistic回归模型。但是成本值总是返回nan。
我的数据集分为x_data和y_data。x_data是图像中的坐标，y_data是1或0，因为我的图像是黑白的。我想找出白色和黑色之间的分界线。
def train(input,iterations):
import tensorflow as tf
tf.set_random_seed(777) # for reproducibility
x_data = []
y_data = []
i_dim = input.shape[0]
j_dim = input.shape[1]
for i in range(i_dim):
for j in range(j_dim):
x_data.append([j,i_dim-i-1])
y_data.append([int(input[i,j])])
# placeholders for a tensor that will be always fed.
X = tf.placeholder(tf.float32, shape=[None, 2])
Y = tf.placeholder(tf.float32, shape=[None, 1])
W = tf.Variable(tf.random_normal([2, 1]), name='weight')
b = tf.Variable(tf.random_normal([1]), name='bias')
# Hypothesis using sigmoid: tf.div(1., 1. + tf.exp(tf.matmul(X, W)))
hypothesis = tf.sigmoid(tf.matmul(X, W) + b)
# cost/loss function
cost = -tf.reduce_mean(Y * tf.log(hypothesis) + (1 - Y) *
tf.log(1 - hypothesis))
train = tf.train.AdamOptimizer(1e-4).minimize(cost)
# Launch graph
with tf.Session() as sess:
# Initialize TensorFlow variables
sess.run(tf.global_variables_initializer())
for step in range(iterations):
cost_val, _ = sess.run([cost, train], feed_dict={X: x_data, Y: y_data})
print(step, cost_val)这是我的日志(0, nan) (1, nan) (2, nan) (3, nan) (4, nan) (5, nan) (6, nan) (7, nan) (8, nan) (9, nan) (10, nan) (11, nan) (12, nan) (13, nan) (14, nan) (15, nan) (16, nan) (17, nan) (18, nan) (19, nan) (20, nan)
诸若此类
回答 1
Stack Overflow用户
回答已采纳
发布于 2018-05-28 17:07:24
当您的假设等于1时，损失的第二部分变成Y* log(0)，因此是nan输出。我建议你在对数中添加一个小常量，它应该会起作用。尝尝这个
cost = -tf.reduce_mean(Y*(tf.log(hypothesis+1e-4))+(1-Y)*(tf.log(1-hypothesis+1e-4)))页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：
https://stackoverflow.com/questions/50561959
复制相关文章
相似问题