Neural network 什么是非感知器的双重权重和偏差？

上面的权重和偏差应该是什么？

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由于它是一个双重重量的感知器，我尝试了许多值，但最后至少有1个是错误的

不能在其中一个输入中使用操作。假设它只考虑第二个输入而忽略第一个输入，下面可以看到一个分隔两个类的行示例（x=0.5）

为了找到权重，让我们使用方程w1x1+w2x2+b=0。由于第一个输入被忽略，这意味着w1=0。方程现在变成w2x2+b=0或x2=-b/w2=常数。假设决策边界为x=0.5，我们可以尝试设置w2=-1或w2=1。对于这些值，b必须分别为0.5和-0.5。对于上面的示例，我们应该保持值w2=-1和b=0.5

import pandas as pd

# TODO: Set weight1, weight2, and bias
weight1 = -2.5
weight2 = -1.5
bias = 1.0

# DON'T CHANGE ANYTHING BELOW
# Inputs and outputs
test_inputs = [(0, 0), (0, 1), (1, 0), (1, 1)]
correct_outputs = [True, False, True, False]
outputs = []

# Generate and check output
for test_input, correct_output in zip(test_inputs, correct_outputs):
    linear_combination = weight1 * test_input[0] + weight2 * test_input[1] + bias
    output = int(linear_combination >= 0)
    is_correct_string = 'Yes' if output == correct_output else 'No'
    outputs.append([test_input[0], test_input[1], linear_combination, output, is_correct_string])

# Print output
num_wrong = len([output[4] for output in outputs if output[4] == 'No'])
output_frame = pd.DataFrame(outputs, columns=['Input 1', '  Input 2', '  Linear Combination', '  Activation Output', '  Is Correct'])
if not num_wrong:
    print('Nice!  You got it all correct.\n')
else:
    print('You got {} wrong.  Keep trying!\n'.format(num_wrong))
print(output_frame.to_string(index=False))