CODEWITHSUNDEEP
pythontensorflow
Weimin Wang
Weimin Wang

Reputation: 113

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

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())

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.

After digging deeper I realized the bug was here: 

feed_dict={X: x, y: y}

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})

The codes will run perfectly.

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?

Upvotes: 8

Views: 15861

Answers (3)

shui
shui

Reputation: 57

sometimes you check and edit again and again, but error again.

you can run the code from the top(import tensorflow as tf) to the end again, maybe you can solve the problem

Upvotes: 0

chu
chu

Reputation: 29

change sess.run(optimizer, feed_dict={X: x, y: y}) to sess.run(optimizer, feed_dict={X: train_x, y: train_y}). fedd_dict's values are the actual input you want to feed to the optimizier.

Upvotes: 0

Olivier Moindrot
Olivier Moindrot

Reputation: 28198

The feed_dict argument is a dictionary that needs Tensor as keys. In your corrected example, X and Y are those Tensors.

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.

In a nutshell, you are trying to use the same name for two different variables, which is impossible.

Upvotes: 23

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