吴裕雄--天生自然神经网络与深度学习实战Python+Keras+TensorFlow:深度学习的线性代数基础
作者:互联网
import numpy as np #构建一个含有一个常数12的0维张量 x = np.array(12) print(x) #ndim表示张量的维度 print(x.ndim)
x1 = np.array([11,12,13]) print(x1) print(x1.ndim)
x2 = np.array([[11,12,13],[14,15,16]]) print(x2) print(x2.ndim)
W1 = np.array([[1,2],[3,4]]) W2 = np.array([[5,6],[7,8]]) print("W2 - W1 = {0}".format(W2-W1))
def matrix_multiply(x, y): #确保第一个向量的列数等于第二个向量的行数 assert x.shape[1] == y.shape[0] #一个m*d维的二维张量与一个d*n的二维张量做乘机后,得到m*n的二维张量 z = np.zeros((x.shape[0], y.shape[1])) for i in range(x.shape[0]): #循环第一个向量的每一行 for j in range(y.shape[1]): #循环第二个向量的每一列 addSum = 0 for k in range(x.shape[1]): addSum += x[i][k]*y[k][j] z[i][j] = addSum return z x = np.array([[0.1, 0.3], [0.2,0.4]]) y = np.array([[1],[2]]) z = matrix_multiply(x, y) print(z)
z = np.dot(x,y) print(z)
def naive_relu(x): assert len(x.shape) == 2 x = x.copy() #确保操作不改变输入的x for i in range(x.shape[0]): for j in range(x.shape[1]): x[i][j] = max(x[i][j], 0) return x x = np.array([[1, -1], [-2, 1]]) print(naive_relu(x))
x = np.array([ [ [1,2], [3,4] ], [ [5,6], [7,8] ], [ [9, 10], [11, 12] ] ]) print(x.ndim)
from keras.datasets import mnist (train_images, train_labels),(test_images, test_labels) = mnist.load_data() print(train_images.shape)
my_slice = train_images[10:100] print(my_slice.shape)
标签:ndim,12,Keras,Python,np,shape,深度,print,array 来源: https://www.cnblogs.com/tszr/p/12195500.html