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'''
数据集：Mnist
准确率：0.1532.
时间：16.96704387664795.
--------------
tips:实现的非常简单，直接将输入数据输入到一个线性层，然后输出10个0-1之间的数，中间没有用任何的隐藏层，
由于是线性变换，所以效果比较差吧，如果加几个隐藏层，效果会好得多！
'''
import numpy as np
import time
def loadData(fileName):
'''
加载数据
:param fileName:数据路径名
:return: 特征向量矩阵、还有标签矩阵
'''
data_list = [];
label_list = []
with open(fileName, "r") as f:
for line in f.readlines():
curline = line.strip().split(",")
label_list.append(int(curline[0]))
data_list.append([int(feature) / 255 for feature in curline[1:]])
data_matrix = np.array(data_list)
label_matrix = np.array(label_list).reshape(1, -1)
return data_matrix, label_matrix
class Softmax:
def __init__(self, train_data, train_label, iter, learning_rate):
'''
构造函数
:param train_data: 训练数据
:param train_label: 训练数据的标签类别
:param iter: epoch的数目
:param learning_rate: 学习率
'''
self.train_data = train_data
self.train_label = train_label
self.iter = iter
self.learning_rate = learning_rate
self.feature_num = self.train_data.shape[1]
self.input_num = self.train_data.shape[0]
self.w, self.b = self.initialize_params(self.feature_num)
def softmax(self, X):
'''
softmax函数
:param X: 输入数据
:return: 返回含有10个元素的np.array
'''
return np.exp(X) / np.sum(np.exp(X))
def initialize_params(self, feature_dim):
'''
初始化参数w,b
:param feature_dim:实例特征数目
:return: 参数w,b
'''
w = np.random.uniform(0, 1, (feature_dim, 10))
b = 0
return w, b
def train(self):
'''
训练
:return:返回参数w,b
'''
for iter in range(self.iter):
for i in range(self.input_num):
x = train_data[i].reshape(-1, 1)
y = np.zeros((10, 1))
y[train_label[0][i]] = 1
y_ = self.softmax(np.dot(self.w.T, x) + self.b)
self.w -= self.learning_rate * (np.dot((y_ - y), x.T))
self.b -= self.learning_rate * (y_ - y)
return self.w, self.b
def predict(self, digit):
'''
预测单个样本的值
:param digit: 严格样本的特征向量
:return: 返回预测的样本类别
'''
return np.argmax(np.dot(self.w.T, digit) + self.b)
def test(self, test_data, test_label):
'''
测试
:param test_data: 测试集的特征向量
:param test_label: 测试集的标签类别
:return: 准确率
'''
error = 0
for i in range(test_data.shape[0]):
if (self.predict(test_data[i]) != test_label[0][i]):
error += 1
print(f"the prediction is {self.predict(test_data[i])},the true is {test_label[0][i]}.")
accuracy = (test_data.shape[0] - error) / test_data.shape[0]
return accuracy
if __name__ == "__main__":
start = time.time()
print("start load data.")
train_data, train_label = loadData("../MnistData/mnist_train.csv")
test_data, test_label = loadData("../MnistData/mnist_test.csv")
print("finished load data.")
a = Softmax(train_data, train_label, 50, 0.005)
accuracy = a.test(test_data, test_label)
end = time.time()
print(f"the accuracy is {accuracy}.")
print(f"the total time is {end - start}.")
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