unhashable type: 'numpy.ndarray' error in tensorflow

Question

data = pd.read_excel("/Users/madhavthaker/Downloads/Reduced_Car_Data.xlsx")

train = np.random.rand(len(data)) < 0.8

data_train = data[train]
data_test = data[~train]


x_train = data_train.ix[:,0:3].values
y_train = data_train.ix[:,-1].values
x_test = data_test.ix[:,0:3].values
y_test = data_test.ix[:,-1].values

y_label = tf.placeholder(shape=[None,1], dtype=tf.float32, name='y_label')
x = tf.placeholder(shape=[None,3], dtype=tf.float32, name='x')
W = tf.Variable(tf.random_normal([3,1]), name='weights')
b = tf.Variable(tf.random_normal([1]), name='bias')
y = tf.matmul(x,W)  + b

init = tf.global_variables_initializer()

with tf.Session() as sess:
    sess.run(init)
    summary_op = tf.summary.merge_all()
    #Fit all training data
    for epoch in range(1000):
        sess.run(train, feed_dict={x: x_train, y_label: y_train})

        # Display logs per epoch step
        if (epoch+1) % display_step == 0:
            c = sess.run(loss, feed_dict={x: x_train, y_label:y_train})
            print("Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(c), \
                "W=", sess.run(W), "b=", sess.run(b))

    print("Optimization Finished!")
    training_cost = sess.run(loss, feed_dict={x: x_train, y_label: y_train})
    print("Training cost=", training_cost, "W=", sess.run(W), "b=", sess.run(b), '\n')

Here is the error:

x---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-37-50102cbac823> in <module>()
      6     #Fit all training data
      7     for epoch in range(1000):
----> 8         sess.run(train, feed_dict={x: x_train, y_label: y_train})
      9 
     10         # Display logs per epoch step

TypeError: unhashable type: 'numpy.ndarray'

Here are the shapes of both of the numpy arrays that I am inputting:

y_train.shape = (78,)
x_train.shape = (78, 3)

I have no idea what is causing this. All of my shapes match up and I shouldn't have any issues. Let me know if you need any more information.

Edit: From my comment on one of the answers below, it seems as though I had to specify a specific size for my placeholders. None was not satisfactory. When I changed that and re-ran my code, everything worked fine. Still not quite sure why that is.

Sneak

Answer 1

In my case, the problem was naming the input parameter the same as the placeholder variable. This, of course, replaces your tensorflow variable with the input variable; resulting in a different key for the feed_dict.

A tensorflow variable is hashable, but your input parameter (np.ndarray) isn't. The unhashable error is therefore a result of you trying to pass your parameter as the key instead of a tensorflow variable. Some code to visualize what I'm trying to say:

a = tf.placeholder(dtype=tf.float32, shape=[1,2,3])
b = tf.identity(a)

with tf.Session() as sess:
    your_var = np.ones((1,2,3))
    a = your_var
    sess.run(b, feed_dict={a: a})

Hopes this helps anyone stumbling upon this problem in the future!

Rorqual

Answer 2

Please carefully check the datatype you feed "x_train/y_train" and the tensor "x/y_label" you defined by 'tf.placeholder(...)'

I have met the same problem with you. And the reason is x_train in my code is "np.float64", but what I defined by tf.placeholder() is tf.float32. The date type float64 and float32 is mismatching.

Coontie

This was the case for my code. Changed numpy type to np.float32 to match Tensorflow type of tf.float32 – Remote

Answer 3

I think problem is in defining the dictionary. A dictionary key has to be a 'hashable type', e.g. a number, a string or a tuple are common. A list or an array don't work:

In [256]: {'x':np.array([1,2,3])}
Out[256]: {'x': array([1, 2, 3])}
In [257]: x=np.array([1,2,3])
In [258]: {x:np.array([1,2,3])}
...
TypeError: unhashable type: 'numpy.ndarray'

I don't know enough of tensorflow to know what these are:

y_label = tf.placeholder(shape=[None,1], dtype=tf.float32, name='y_label')
x = tf.placeholder(shape=[None,3], dtype=tf.float32, name='x')

The error indicates that they are are numpy arrays, not strings. Does x have a name attribute?

Or maybe the dictionary should be specified as:

{'x': x_train, 'y_label': y_train}

Kolva

this was pretty interesting. It seems as though I had to specific a specific size for my placeholders. None was not satisfactory. When I changed that and re-ran my code, everything worked fine. Still not quite sure why that is. – Sneak

Answer 4

Strange, I had this issue too. After I close python shell and run the code from a file I didn't succeed to reproduce it even in the shell (it just works w/o an error).

Photolysis