Python 不可散列类型：张量流中的“numpy.ndarray”错误
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unhashable type: 'numpy.ndarray' error in tensorflow
提问by madsthaks
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:
这是错误：
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:
这是我输入的两个 numpy 数组的形状：
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.
我不知道是什么原因造成的。我所有的形状都匹配，我不应该有任何问题。如果您需要更多信息，请告诉我。
Edit:From my comment on one of the answers below, it seems as though I had to specify a specific size for my placeholders. Nonewas not satisfactory. When I changed that and re-ran my code, everything worked fine. Still not quite sure why that is.
编辑：从我对以下答案之一的评论来看，似乎我必须为占位符指定特定的大小。None并不令人满意。当我改变它并重新运行我的代码时，一切正常。仍然不太确定为什么会这样。
回答by Andreas Forsl?w
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.
就我而言，问题是将输入参数命名为与占位符变量相同的名称。当然，这会用输入变量替换您的 tensorflow 变量；导致 feed_dict 的键不同。
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:
tensorflow 变量是可散列的，但您的输入参数 (np.ndarray) 不是。因此，不可散列的错误是您尝试将参数作为键而不是 tensorflow 变量传递的结果。一些代码来可视化我想说的话：
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!
希望这可以帮助将来遇到此问题的任何人！
回答by zero
Please carefully check the datatype you feed "x_train/y_train"and the tensor "x/y_label"you defined by 'tf.placeholder(...)'
请仔细检查您提供的数据类型“x_train/y_train”和由“tf.placeholder(...)”定义的张量“x/y_label”
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.
我和你遇到了同样的问题。而原因就是x_train在我的代码是“NP。float64”，但我通过tf.placeholder（）定义为TF。浮动32。日期类型 float64 和 float32 不匹配。
回答by hpaulj
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:
我认为问题在于定义字典。字典键必须是“可散列类型”，例如数字、字符串或元组是常见的。列表或数组不起作用：
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:
我对 tensorflow 的了解不够，不知道这些是什么：
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 xhave a nameattribute?
错误表明它们是 numpy 数组，而不是字符串。是否x有一个name属性？
Or maybe the dictionary should be specified as:
或者字典应该指定为：
{'x': x_train, 'y_label': y_train}
回答by ChaosPredictor
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).
奇怪，我也有这个问题。在我关闭 python shell 并从文件中运行代码后，即使在 shell 中我也没有成功重现它（它只是在没有错误的情况下工作）。
