python shape too large to be a matrix

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

Just guessing, but since the data comes from a csv file, it was converted to np.matrix, which have the restriction to be 2-dimensional.

Internally numpy will try to keep the dimensions of the matrix, so to reshape to higher dimensions, you will need to convert it to a ndarray like this:

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)