Question

我正在使用Tensorflow制作一个简单的Logistic回归模型。但成本价值总是回归纳米。

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。我想找到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)

等等

Answer 1

当您的假设等于1时，损失的第二部分变为Y * log（0），因此为nan输出。我建议你在对数中添加一个小常量，它应该可以工作。试试这个

cost = -tf.reduce_mean(Y*(tf.log(hypothesis+1e-4))+(1-Y)*(tf.log(1-hypothesis+1e-4)))

TensorFlow成本值返回NAN

1 个答案: