机器学习的奇/偶预测不起作用(成功率达50％) [英] Machine learning odd/even prediction doesn't work (50% success)

问题描述

我是机器学习的新手.我试图创建一个模型来预测数字是否为偶数.

I'm very new to machine learning. I tried to create a model to predict if the number is even.

我使用了此代码 https://machinelearningmastery.com/tutorial- first-neural-network-python-keras/ 我改变了我的需求.

I used this code https://machinelearningmastery.com/tutorial-first-neural-network-python-keras/ which I changed to my needs.

问题是成功率大约为50％，等于随机数.

The problem is that there is circa 50% success which is equal to random.

您知道该怎么做吗?

from keras.models import Sequential
from keras.layers import Dense
import numpy
# fix random seed for reproducibility
seed = 7
numpy.random.seed(seed)

X = list(range(1000))
Y = [1,0]*500
# create model
model = Sequential()
model.add(Dense(12, input_dim=1, init='uniform', activation='relu'))
model.add(Dense(8, init='uniform', activation='relu'))
model.add(Dense(1, init='uniform', activation='sigmoid'))
# Compile model
model.compile(loss='mean_squared_error', optimizer='adam', metrics=['accuracy'])
# Fit the model
model.fit(X, Y, epochs=150, batch_size=10,  verbose=2)
# calculate predictions
predictions = model.predict(X)
# round predictions
rounded = [round(x[0])for x in predictions]
print(rounded)


>>> [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 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1.0]

推荐答案

我认为阅读感知器XOR问题，以了解单个感知器的工作原理及其局限性.

I think it's a good idea for you to read perceptron XOR-problem to understand how a single perceptron works and what is its limitation.

使用一维输入来预测数字是否为偶数分类问题；在分类问题中，训练了神经网络以通过边界将类分离.思考此问题的一种方法是通过将输入数字添加到添加的维度(例如，将映射7映射到[7，7])来将其一维输入映射为二维输入，并查看散点图中奇偶矢量的样子.

Predicting if a number is even is a binary classification problem, with one dimensional input; In classification problem a neural network is trained to separate the classes via a boundary. One way of thinking about this problem is to map its one dimensional input into two dimensional input by adding input number to added dimension (e.g. map 7 to [7, 7]) and see how even and odd vectors look like in a scatter diagram.

如果您在Jupyter笔记本中运行以下代码

If you run the following code in Jupyter notebook

%matplotlib inline
import matplotlib.pyplot as plt

X = list(range(-20, 20))
evens = [x for x in X if x % 2 == 0]
odds = [x for x in X if x % 2 != 0]
data = (evens, odds)
colors = ("green", "blue")
groups = ("Even", "Odd") 

fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
for data, color, group in zip(data, colors, groups):
    x = data
    ax.scatter(x, x, alpha=0.8, c=color, edgecolors='none', s=30, label=group)
plt.title('Even/Odd numbers')
plt.legend(loc=2)
plt.show()

data = (evens, odds)
fig2 = plt.figure()
ax = fig2.add_subplot(1, 1, 1)
for data, color, group in zip(data, colors, groups):
    x = data
    y = [abs(i) if i%2==0 else -abs(i) for i in data]
    ax.scatter(x, y, alpha=0.8, c=color, edgecolors='none', s=30, label=group)
plt.title('Even/Odd numbers (Separatable)')
plt.legend(loc=2)
plt.show()

您将看到类似下图的内容:

You will see something like the following image:

您可以在第一个图中看到，实际上不可能在偶数和奇数向量之间找到边界，但是如果将第二维数映射到其等效负数，则可以在两个类(偶数和奇数)之间绘制边界数字向量)很容易.结果，如果将输入数据转换为二维，并基于偶数或奇数取反第二维值，则神经网络可以学习如何分离偶数和奇数向量类.

You can see in the first figure it's not really possible to come up with a boundary between even and odd vectors, but If you map the second dimension number to its equivalent negative number then drawing a boundary between two classes (even and odd number vectors) is easy. As a result if you transform your input data to two dimensions and negate the second dimension value based on being even or odd then neural network can learn how to separate even and odd vector classes.

您可以尝试使用类似以下代码的方法，您将看到网络将学习并收敛到几乎100％的准确性.

You can try something like the following code, and you will see the network will learn and converge to almost 100% accuracy.

import numpy
from keras.models import Sequential
from keras.layers import Dense

# fix random seed for reproducibility
seed = 7
numpy.random.seed(seed)

X = numpy.array([[x, x if x%2 == 0 else -x] for x in range(1000)])
Y = [1,0]*500

# create model
model = Sequential()
model.add(Dense(12, input_dim=2, init='uniform', activation='relu'))
model.add(Dense(8, init='uniform', activation='relu'))
model.add(Dense(1, init='uniform', activation='sigmoid'))
# Compile model
model.compile(loss='mean_squared_error', optimizer='adam', metrics=['accuracy'])
# Fit the model
model.fit(X, Y, epochs=50, batch_size=10,  verbose=2)
# Calculate predictions
predictions = model.predict(X)

请注意，基于偶数或奇数将数字转换为负空间同样适用于一维，但是使用带有二维向量的散点图更容易演示.

Note that transforming number into negative space based on being even or odd will work for one dimension as well, but it is easier to demonstrate with a scatter diagram with two dimension vectors.

这篇关于机器学习的奇/偶预测不起作用(成功率达50％)的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！