tensorflow:ValueError:使用序列设置数组元素 [英] tensorflow: ValueError: setting an array element with a sequence

问题描述

我正在使用 这个问题.我收到上述错误.谷歌搜索表明这可能是某种维度不匹配，尽管我的诊断没有显示任何:

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_})

看尺寸，一切正常:

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

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

错误:

---------------------------------------------------------------------------
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.

任何调试技巧?

推荐答案

这个—不是很有帮助—当 feed_dict 参数中的值之一给 tf.Session.run() 是一个tf.Tensor 对象(在这种情况下，tf.reshape()).

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()).

feed_dict 中的值必须是 numpy 数组，或者某些值 x 可以使用 numpy.array(x).tf.Tensor 对象不能隐式转换，因为这样做可能需要大量工作:相反，您必须调用 sess.run(t) 将张量 t 转换为 numpy 数组.

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.

正如您在回答中所注意到的，使用 np.reshape(_y_, [-1, 1]) 有效，因为它生成一个 numpy 数组(并且因为 _y_是一个 numpy 数组开始).通常，您应该始终使用 numpy 和其他纯 Python 操作准备要馈送的数据.

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.

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