python - tensorflow 'feed_dict' : using same symbol for key-value pair got 'TypeError: Cannot interpret feed_dict key as Tensor'

Question

我正在玩构建线性回归的 Tensorflow 示例，我的代码如下:

import numpy as np
import tensorflow as tf

train_X = np.asarray([3.3,4.4,5.5,6.71,6.93,4.168,9.779,6.182,7.59,2.167,7.042,10.791,5.313,7.997,5.654,9.27,3.1])
train_Y = np.asarray([1.7,2.76,2.09,3.19,1.694,1.573,3.366,2.596,2.53,1.221,2.827,3.465,1.65,2.904,2.42,2.94,1.3])

n_samples = train_X.shape[0]
batch_size = 100

total_epochs = 50

X = tf.placeholder('float')
y = tf.placeholder('float')

W = tf.Variable(np.random.randn(), name="weights")
b = tf.Variable(np.random.randn(), name="bias")

y_pred = tf.add(tf.mul(X, W), b)

cost = tf.reduce_sum(tf.pow(y_pred-y, 2))/(2*n_samples) #L2 loss
optimizer = tf.train.AdamOptimizer().minimize(cost) #Gradient 

init = tf.initialize_all_variables()

with tf.Session() as sess:
    sess.run(init)
    print("Initia values for W and b: ", W.eval(), b.eval())
    for _ in range(total_epochs):
        sess.run(optimizer, feed_dict={X: x, y: y})
    print("Value for W and b after GD: ", W.eval(), b.eval())

但是，运行上面的命令会给我这个错误:

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-11-185d8e05cbcd> in <module>()
     28     for _ in range(total_epochs):
     29         for (x, y) in zip(train_X, train_Y):
---> 30             sess.run(optimizer, feed_dict={X: x, y: y})
     31         print("Value for W and b after GD: ", W.eval(), b.eval())

/home/ubuntu/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.pyc in run(self, fetches, feed_dict, options, run_metadata)
    338     try:
    339       result = self._run(None, fetches, feed_dict, options_ptr,
--> 340                          run_metadata_ptr)
    341       if run_metadata:
    342         proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)

/home/ubuntu/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.pyc in _run(self, handle, fetches, feed_dict, options, run_metadata)
    540           except Exception as e:
    541             raise TypeError('Cannot interpret feed_dict key as Tensor: '
--> 542                             + e.args[0])
    543 
    544           if isinstance(subfeed_val, ops.Tensor):

TypeError: Cannot interpret feed_dict key as Tensor: Can not convert a float64 into a Tensor.

深入挖掘后，我意识到错误在这里:

feed_dict={X: x, y: y} 

我使用的键值对是相同的(“y”和“y”)。如果我将其更改为 Y:y，并相应地修改其余部分:

Y = tf.placeholder('float')
cost = tf.reduce_sum(tf.pow(y_pred-Y, 2))/(2*n_samples) #L2 loss
sess.run(optimizer, feed_dict={X: x, Y: y})

代码将完美运行。

我很想知道为什么我不能对 feed_dict 中的键值对使用相同的符号？左边(关键)的'y'不应该指的是上面成本函数中的'y'吗？

Answer 1

feed_dict 参数是一个需要张量作为键的字典。在您更正的示例中，X 和 Y 是那些张量。

但是，如果您使用X 或Y 作为另一个变量的名称，您将覆盖初始张量和X 或Y 将不再对应于图表中的张量。 Tensorflow 无法理解您引用图形中的节点，因为它们已被覆盖。

简而言之，您试图对两个不同的变量使用相同的名称，这是不可能的。