Question

I'm use python keras to build a cnn model.

I follow cnn mnist example and modify to my code. This is the example I found

# Read MNIST data  
(X_Train, y_Train), (X_Test, y_Test) = mnist.load_data()  
# Translation of data  
X_Train40 = X_Train.reshape(X_Train.shape[0], 28, 28, 1).astype('float32')  
X_Test40 = X_Test.reshape(X_Test.shape[0], 28, 28, 1).astype('float32')

My data has 30222 rows and 6 columns of csv.

Which is 10074 data each data is 3 * 6 size for one block of information.

For example, the 1 ~ 3row of the matrix is one block of information.

Then I changed the format of my data.

X_Train40 = X_Train.reshape(10074, 3, 6, 1)
X_Test40 = X_Test.reshape(4319, 3, 6, 1)

Then this error occurs.

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-133-4f23172d450a> in <module>()
----> 1 X_Train40 = X_Train.reshape(10074, 3, 6, 1)
      2 X_Test40 = X_Test.reshape(4319, 3, 6, 1)

~\Anaconda3\lib\site-packages\numpy\matrixlib\defmatrix.py in __array_finalize__(self, obj)
    269                 return
    270             elif (ndim > 2):
--> 271                 raise ValueError("shape too large to be a matrix.")
    272         else:
    273             newshape = self.shape

ValueError: shape too large to be a matrix.

Answer 1

只是猜测，但由于数据来自csv文件，因此它被转换为np.matrix，其限制为2维。

内部numpy将尝试保持矩阵的尺寸，因此要重塑为更高的尺寸，您需要将其转换为ndarray，如下所示：

X_Train = np.array(X_Train)
X_Test = np.array(X_Test)
X_Train40 = X_Train.reshape(10074, 3, 6, 1)
X_Test40 = X_Test.reshape(4319, 3, 6, 1)

Answer 2

您知道...我今天遇到这个问题，找不到一个有帮助的答案。

您的问题可能已在此时解决，但是当python抱怨“形状太大而不能成为矩阵”时，要看的一件事是变量的类型，即 它是否是一个numpy .matrix数据类型或numpy.ndarray ？

如果是前者，则您有麻烦。

尝试避免使用numpy.matrix类型，特别是如果您要进行任何线性代数运算或将其堆叠（使用（d / v / h）堆叠等）并坚持使用numpy.ndarray

python shape too large to be a matrix

2 个答案: