Question

我正在尝试将aX^2+bX+c评估为张量流中的[a,b,c]\*[X*X X 1]。

我尝试了以下代码：

import tensorflow as tf
X = tf.placeholder(tf.float32, name="X")
W = tf.Variable([1,2,1], dtype=tf.float32, name="weights")
W=tf.reshape(W,[1,3])
F = tf.Variable([X*X,X,1.0], dtype=tf.float32, name="Filter")
F=tf.reshape(F,[3,1])
print(W.shape)
print(F.shape)
Y=tf.matmul(W,F)
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    for i in range(10): 
        sess.run(Y, feed_dict={X: i})
    Y=sess.run(Y)
print("Y:",Y)

但是，初始化器并不令人满意：

(1, 3)
(3, 1)
...
tensorflow.python.framework.errors_impl.InvalidArgumentError: You must feed a value for placeholder tensor 'X' with dtype float
     [[{{node X}}]]
During handling of the above exception, another exception occurred:
...
Caused by op 'X', defined at:
  File "sample.py", line 2, in <module>
    X = tf.placeholder(tf.float32, name="X")  
...

是否有其他想法？

Answer 1

您只需要稍微修改一下代码。 tf.Variable的值不应为tf.placeholder，否则在运行sess.run(tf.global_variables_initializer())时会导致初始化错误。您可以使用tf.stack代替它。另外，请记住在运行sess.run(Y)时要提供数据。

import tensorflow as tf

X = tf.placeholder(tf.float32, name="X")
W = tf.Variable([1,2,1], dtype=tf.float32, name="weights")
W = tf.reshape(W,[1,3])
F = tf.stack([X*X,X,1.0])
F = tf.reshape(F,[3,1])
Y = tf.matmul(W,F)
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    for i in range(10):
        Y_val = sess.run(Y, feed_dict={X: i})
        print("Y:",Y_val)

Y: [[1.]]
Y: [[4.]]
Y: [[9.]]
Y: [[16.]]
Y: [[25.]]
Y: [[36.]]
Y: [[49.]]
Y: [[64.]]
Y: [[81.]]
Y: [[100.]]

Answer 2

我认为，即使您仍然可以像这样初始化依赖于占位符的变量，W也会被重复初始化，除非您添加更多代码以仅初始化未初始化的变量。那是更多的努力。

希望我没有错过这种方法的其他低效率之处。

import tensorflow as tf

sess = tf.InteractiveSession()

X = tf.placeholder(tf.float32, name="X")

W = tf.Variable([1, 2, 1], dtype=tf.float32, name="weights")
W = tf.reshape(W, [1, 3])

var = tf.reshape([X*X,X,1],[3,1])
F = tf.get_variable('F', dtype=tf.float32, initializer=var)

init = tf.global_variables_initializer()
Y=tf.matmul(W,F)

for i in range(10):
    sess.run([init], feed_dict={X: i})
    print(sess.run(Y))


[[1.]]
[[4.]]
[[9.]]
[[16.]]
[[25.]]
[[36.]]
[[49.]]
[[64.]]
[[81.]]
[[100.]]

Tensorflow多项式数组

2 个答案: