问题描述
我正在使用构建线性回归的 Tensorflow 示例，我的代码如下:
I was playing with Tensorflow examples of building a linear regression, and my codes are below:
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，运行上面的代码会给我这个错误:
However, running the above will give me this error:
---------------------------------------------------------------------------
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，我意识到错误就在这里:
After digging deeper I realized the bug was here:
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:
where the key-value pair am using is the same ('y' and 'y'). And if I changed it to Y:y, and modified the rest accordingly:
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.
很奇怪为什么我不能对 feed_dict 中的键值对使用相同的符号?左边的y"(键)不应该指上面成本函数中的y"吗?
Am quite wondering why I couldn't use the same symbol for the key-value pair in feed_dict? Shouldn't the 'y' on the left (the key) refer to the 'y' in the cost function above?
推荐答案
feed_dict 参数是一个需要 Tensor 作为键的字典.在您更正的示例中，X 和 Y 是那些张量.
The feed_dict argument is a dictionary that needs Tensor as keys. In your corrected example, X and Y are those Tensors.
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 将不再对应于您图表中的张量.Tensorflow 无法理解您引用图中的节点，因为它们已被覆盖.
However, if you use X or Y for the name of another variable, you will overwrite the initial Tensors and X or Y will no longer correspond to the Tensor from your graph. Tensorflow cannot understand that you refer to the nodes from your graph as they have been overwritten.
简而言之，您试图为两个不同的变量使用相同的名称，这是不可能的.
In a nutshell, you are trying to use the same name for two different variables, which is impossible.
这篇关于Tensorflow 'feed_dict':对键值对使用相同的符号得到'TypeError:无法将 feed_dict 键解释为 Tensor'的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！