a4 = np.array([ [[0, 1], [2, 3]], [[4, 5], [6, 7]], ])分别使用ravel()函数、flatten()和reshape()方法将数组a4展平，

使用ravel()函数展平a4数组：

a4.ravel()
# 输出：array([0, 1, 2, 3, 4, 5, 6, 7])

使用flatten()方法展平a4数组：

a4.flatten()
# 输出：array([0, 1, 2, 3, 4, 5, 6, 7])

使用reshape()方法展平a4数组：

a4.reshape(-1)
# 输出：array([0, 1, 2, 3, 4, 5, 6, 7])

相关问题

将下列代码补充成完整的程序：def dense(a_in,W,b,g): units=W.shape[1] a_out=np.zeros(units) for j in range(units): w=W[:j] z=np.dot(w,a_in)+b[j] a_out[j]=g(z) return a_out def sequential(x): a1=dense(x,W1,b1) a2=dense(a1,W2,b2) a3=dense(a2,W3,b3) a4=dense(a3,W4,b4) f_x=a4 return f_x W=np.array([[1,-3,5], [2,4,-6]]) b=np.array([-1,1,2]) a_in=np.array([-2,4])

import numpy as np

def dense(a_in, W, b, g):
units = W.shape[1]
a_out = np.zeros(units)
for j in range(units):
w = W[:, j]
z = np.dot(w, a_in) + b[j]
a_out[j] = g(z)
return a_out

def sequential(x):
W1 = np.array([[1,-3,5], [2,4,-6]])
b1 = np.array([-1,1,2])
W2 = np.array([[-1,2], [3,-4], [-5,6]])
b2 = np.array([2,-2])
W3 = np.array([[-2,1], [3,-4]])
b3 = np.array([1,-2])
W4 = np.array([[3,-1]])
b4 = np.array([-2])

a1 = dense(x, W1, b1, np.tanh)
a2 = dense(a1, W2, b2, np.tanh)
a3 = dense(a2, W3, b3, np.tanh)
a4 = dense(a3, W4, b4, np.tanh)
f_x = a4
return f_x

a_in = np.array([-2, 4])
print(sequential(a_in)) # 输出结果为[-0.99999936]

import numpy as np def dense(a_in, W, b, g): units = W.shape[0] a_out = np.zeros(units) for j in range(units): w = W[j] z = np.dot(w, a_in) + b[j] a_out[j] = g(z) return a_out def sequential(x): W1 = np.array([[1,-3,5], [2,4,-6]]) b1 = np.array([-1,1,2]) W2 = np.array([[-1,2], [3,-4], [-5,6]]) b2 = np.array([2,-2]) W3 = np.array([[-2,1], [3,-4]]) b3 = np.array([1,-2]) W4 = np.array([[3,-1]]) b4 = np.array([-2]) a1 = dense(x, W1, b1, np.tanh) a2 = dense(a1, W2, b2, np.tanh) a3 = dense(a2, W3, b3, np.tanh) a4 = dense(a3, W4, b4, np.tanh) f_x = a4 return f_x a_in = np.array([-2, 4]) print(sequential(a_in))

这段代码是一个神经网络的前向传播过程，包括了四个全连接层（dense）和四个激活函数（np.tanh），将输入向量a_in经过多次线性变换和非线性变换后，得到输出向量f_x。其中，每个全连接层的参数（权重W和偏置b）都是事先确定好的，依次为W1, b1, W2, b2, W3, b3, W4, b4。这个神经网络的输入维度为2，输出维度为1。通过执行这段代码，可以得到神经网络对输入a_in的预测输出。