我想使用 tensorflow 实现线性回归。但我不知道这是怎么回事。如果我只训练一次，预测结果将全部为0。如果我训练更多，损失会增加而不是减少。 谁能帮我？非常感谢!
# Step2
x = tf.placeholder(tf.float64, [None, 14])
y_ = tf.placeholder(tf.float64, [None])
# Step3
feature_size = int(x.shape[1])
label_size = int(1)
w = tf.Variable(tf.zeros([feature_size, label_size], dtype='float64'), name='weight')
b = tf.Variable(tf.zeros([label_size], dtype='float64'), name='bias')
# Step4
y = tf.matmul(x, w) + b
# Step5
loss = tf.reduce_sum(tf.square(y-y_))# + tf.matmul(tf.transpose(w), w)
# Step6
optimizer = tf.train.GradientDescentOptimizer(0.5).minimize(loss)
with tf.Session() as sess:
# Step7
tf.global_variables_initializer().run()
train_loss, _, w_, b_, y_pred = sess.run([loss, optimizer, w , b , y],
{x: X_train.as_matrix(), y_: y_train.as_matrix()})
如果我用下面的代码显示结果:
print("The train_loss is :{0}".format(train_loss))
print("y_pred shape:{1}\n y_pred value{0}".format(y_pred, y_pred.shape))
print("w_:{0}".format(w_))
print("b_:{0}".format(b_))
结果为:
The train_loss is :25366.999902840118
y_pred shape:(151, 1)
y_pred value[[ 0.]
[ 0.]
[ 0.]
[ 0.]
[ 0.]
...
[ 0.]]
w_:[[ -4197.62931207]
[ -5012.08767412]
[-12005.66678623]
[ 16558.73513235]
[ -7305.34601191]
[ -5714.5346788 ]
[ -9633.25591793]
[-12477.03557256]
[ -9630.39349598]
[ -7365.70395179]
[-11168.48902116]
[ -6483.21729379]
[ 2177.84048453]
[ -3059.72968574]]
b_:[ 24045.6024]
但是数据使用libsvm Babyfat_scale数据集:
1.0708 1:-0.482105 2:-0.966102 3:-0.707746 4:0.585492 5:-0.492537 6:-0.514938 7:-0.598475 8:-0.69697 9:-0.411471 10:-0.465839 11:-0.621622 12:-0.287129 13:-0.0791367 14:-0.535714
1.0853 1:-0.743158 2:-1 3:-0.552422 4:0.772021 5:-0.263682 6:-0.497364 7:-0.654384 8:-0.562998 9:-0.426434 10:-0.465839 11:-0.418919 12:-0.435644 13:0.136691 14:-0.142857
如果我尝试使用相同的数据训练 100 次:
for i in range(100):
train_loss, _, w_, b_, y_pred = sess.run([loss, optimizer, w , b , y],
{x: X_train.as_matrix(), y_: y_train.as_matrix()})
损失不降反增!!!为什么？ 请帮我!非常感谢!
最佳答案
您使用哪些数据来训练模型？ 尝试减小梯度下降的步长值 Optimizer = tf.train.GradientDescentOptimizer(0.01).minimize(loss) 并运行 1000 次或更多
关于python - 为什么 tensorflow 线性回归预测全为0？，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/46385974/