tensorflow: ValueError: setting an array element with a sequence

Question

I am playing with the fixed code from this question. I am getting the above error. Googling suggests it might be some kind of dimension mismatch, though my diagnostics does not show any:

with tf.Session() as sess:
    sess.run(init)

    # Fit all training data
    for epoch in range(training_epochs):
        for (_x_, _y_) in getb(train_X, train_Y):
            print("y data raw", _y_.shape )
            _y_ = tf.reshape(_y_, [-1, 1])
            print( "y data ", _y_.get_shape().as_list())
            print("y place holder", yy.get_shape().as_list())

            print("x data", _x_.shape )            
            print("x place holder", xx.get_shape().as_list() )

            sess.run(optimizer, feed_dict={xx: _x_, yy: _y_})

Looking at the dimensions, everything is alright:

y data raw (20,)
y data  [20, 1]
y place holder [20, 1]

x data (20, 10)
x place holder [20, 10]

Error:

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-131-00e0bdc140b2> in <module>()
     16             print("x place holder", xx.get_shape().as_list() )
     17 
---> 18             sess.run(optimizer, feed_dict={xx: _x_, yy: _y_})
     19 
     20 #         # Display logs per epoch step

/usr/local/lib/python3.4/dist-packages/tensorflow/python/client/session.py in run(self, fetches, feed_dict)
    355             e.args = (e.message,)
    356             raise e
--> 357           np_val = np.array(subfeed_val, dtype=subfeed_t.dtype.as_numpy_dtype)
    358           if subfeed_t.op.type == 'Placeholder':
    359             if not subfeed_t.get_shape().is_compatible_with(np_val.shape):

ValueError: setting an array element with a sequence.

Any debugging tips?

mrry · Accepted Answer · 2015-12-22 16:56:11Z

5

This—not very helpful—error is raised when one of the values in the feed_dict argument to tf.Session.run() is a tf.Tensor object (in this case, the result of tf.reshape()).

The values in feed_dict must be numpy arrays, or some value x that can be implicitly converted to a numpy array using numpy.array(x). tf.Tensor objects cannot be implicitly converted, because doing so might require a lot of work: instead you have to call sess.run(t) to convert a tensor t to a numpy array.

As you noticed in your answer, using np.reshape(_y_, [-1, 1]) works, because it produces a numpy array (and because _y_ is a numpy array to begin with). In general, you should always prepare data to be fed using numpy and other pure-Python operations.

answered Dec 22, 2015 at 16:56

mrry

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Dima Lituiev · Accepted Answer · 2015-12-22 07:39:51Z

0

replacing tf reshape with plain numpy one helped:

        _y_ = np.reshape(_y_, [-1, 1])

the actual reason why is still unclear, but it works.

answered Dec 22, 2015 at 7:39

Dima Lituiev

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