When feeding a dictionary to a tensorflow function I get Why do I get TypeError: unhashable type: 'numpy.ndarray'

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I am working on a Tensor Flow Coursera Course and I dont understand why I am getting a type mismatch.

This is the function I am defining:

def one_hot_matrix(labels, C):
    """
    Creates a matrix where the i-th row corresponds to the ith class number and the jth column
                     corresponds to the jth training example. So if example j had a label i. Then entry (i,j) 
                     will be 1. 

Arguments:
labels -- vector containing the labels 
C -- number of classes, the depth of the one hot dimension

Returns: 
one_hot -- one hot matrix
"""

### START CODE HERE ###

# Create a tf.constant equal to C (depth), name it 'C'. (approx. 1 line)
C = tf.constant(C, name="C")
#labels =tf.placeholder(labels, name="labels")

# Use tf.one_hot, be careful with the axis (approx. 1 line)
one_hot_matrix = tf.one_hot(indices=labels, depth=C, axis=0)

# Create the session (approx. 1 line)
sess = tf.Session()

# Run the session (approx. 1 line)
one_hot = sess.run(one_hot_matrix, feed_dict={labels:labels, C:C})

# Close the session (approx. 1 line). See method 1 above.
sess.close()

### END CODE HERE ###

return one_hot

And when running this: 

labels = np.array([1,2,3,0,2,1])
one_hot = one_hot_matrix(labels, C = 4)
print ("one_hot = " + str(one_hot))

I get this type error:

TypeError                                 Traceback (most recent call last)
<ipython-input-113-2b9d0290645f> in <module>()
      1 labels = np.array([1,2,3,0,2,1])
----> 2 one_hot = one_hot_matrix(labels, C = 4)
      3 print ("one_hot = " + str(one_hot))

<ipython-input-112-f9f17c86d0ba> in one_hot_matrix(labels, C)
     28 
     29     # Run the session (approx. 1 line)
---> 30     one_hot = sess.run(one_hot_matrix, feed_dict={labels:labels, C:C})
     31 
     32     # Close the session (approx. 1 line). See method 1 above.

TypeError: unhashable type: 'numpy.ndarray'ter code here

I checked the Tensorflow documentation for tf.one_hot and there shouldn't be a problem with np.arrays.

https://www.tensorflow.org/api_docs/python/tf/one_hot

tensorflow
1 Answers

The labels and C were constants during the graph definition. Therefore, you don't need to feed them again when calling sess.run(). I just slightly changed the line to one_hot = sess.run(one_hot_matrix1) and it should work now.

def one_hot_matrix(labels, C):
    """
    Creates a matrix where the i-th row corresponds to the ith class number and the jth column
                     corresponds to the jth training example. So if example j had a label i. Then entry (i,j) 
                     will be 1. 

    Arguments:
    labels -- vector containing the labels 
    C -- number of classes, the depth of the one hot dimension

    Returns: 
    one_hot -- one hot matrix
    """

    ### START CODE HERE ###

    # Create a tf.constant equal to C (depth), name it 'C'. (approx. 1 line)
    C = tf.constant(C, name="C")
    #labels =tf.placeholder(labels, name="labels")

    # Use tf.one_hot, be careful with the axis (approx. 1 line)
    one_hot_matrix1 = tf.one_hot(indices=labels, depth=C, axis=0)

    # Create the session (approx. 1 line)
    sess = tf.Session()

    # Run the session (approx. 1 line)
    one_hot = sess.run(one_hot_matrix1) #, feed_dict={labels:labels, C:C}

    # Close the session (approx. 1 line). See method 1 above.
    sess.close()

    ### END CODE HERE ###

    return one_hot

Run:

labels = np.array([1,2,3,0,2,1])
one_hot = one_hot_matrix(labels, C = 4)
print ("one_hot = " + str(one_hot))

Output:

one_hot = [[ 0.  0.  0.  1.  0.  0.]
 [ 1.  0.  0.  0.  0.  1.]
 [ 0.  1.  0.  0.  1.  0.]
 [ 0.  0.  1.  0.  0.  0.]]
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