x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols) x_test =
x_test.reshape(x_test.shape[0], 1, img_rows, img_cols) from numpy import * l =
zeros((5,4))# Build a 5*4 Zero matrix of for i in range(5):# Assign a value to the matrix for j in range(4): l[i][j] =
i * 5 + j print(l)# Print assigned matrix print(shape(l))# output l Column value of print(l.shape[0])# output l Row value of
print(l.shape[1])# output l Column values for
python Image reading reshape Problems in size
#coding=utf-8 import matplotlib.pyplot as plt import matplotlib.image as
mimage image=mimage.imread('lala.jpg') print image.shape # show a picture
image=image.reshape(1,-1) #-1 Is the automatic inference of dimensions based on the size of the array
# If the image=image.reshape In a row , Respectively R a block , G block ,B a block # t=imgX1[222,:].reshape(3,32,32)
# print('t= ' ,t.shape) # image=np.transpose(t,(1,2,0))
image=image.reshape(1186,1920,3) print(image.shape) plt.imshow(image)
plt.axis('off') plt.show()
 
/ If you convert a floating-point number to an integer , The decimal part is truncated In [7]: arr2 = np.array([1.1, 2.2, 3.3, 4.4, 5.3221])
In [8]: arr2 Out[8]: array([ 1.1 , 2.2 , 3.3 , 4.4 , 5.3221]) // View current data type In
[9]: arr2.dtype Out[9]: dtype('float64') // shifting clause float -> int In [10]:
arr2.astype(np.int32) Out[10]: array([1, 2, 3, 4, 5], dtype=int32) Data type conversion
x_train = x_train.astype('float32') x_test = x_test.astype('float32')
# data normalization (0,1) x_train /= 255 x_test /= 255
 

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