在TensorFlow中，我想建立一個邏輯迴歸模型，代價函數如下：

#使用的資料集截圖如下：

#我的程式碼如下：

train_X = train_data[:, :-1]
train_y = train_data[:, -1:]
feature_num = len(train_X[0])
sample_num = len(train_X)
print("Size of train_X: {}x{}".format(sample_num, feature_num))
print("Size of train_y: {}x{}".format(len(train_y), len(train_y[0])))

X = tf.placeholder(tf.float32)
y = tf.placeholder(tf.float32)

W = tf.Variable(tf.zeros([feature_num, 1]))
b = tf.Variable([-.3])

db = tf.matmul(X, tf.reshape(W, [-1, 1])) + b
hyp = tf.sigmoid(db)

cost0 = y * tf.log(hyp)
cost1 = (1 - y) * tf.log(1 - hyp)
cost = (cost0 + cost1) / -sample_num

loss = tf.reduce_sum(cost)

optimizer = tf.train.GradientDescentOptimizer(0.1)
train = optimizer.minimize(loss)

init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)

print(0, sess.run(W).flatten(), sess.run(b).flatten())
sess.run(train, {X: train_X, y: train_y})
print(1, sess.run(W).flatten(), sess.run(b).flatten())
sess.run(train, {X: train_X, y: train_y})
print(2, sess.run(W).flatten(), sess.run(b).flatten())

運行結果截圖如下：

#可以看到，在迭代兩次之後，得到的W和b都變成了nan，請問是哪裡的問題？