From 19c05e5618f0b21d434d737ef0c0d0f404bb83fa Mon Sep 17 00:00:00 2001 From: p4iw2ug7a <1744671285@qq.com> Date: Wed, 27 Apr 2022 23:11:59 +0800 Subject: [PATCH] ADD file via upload --- wssclassy.py | 154 +++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 154 insertions(+) create mode 100644 wssclassy.py diff --git a/wssclassy.py b/wssclassy.py new file mode 100644 index 0000000..062ab44 --- /dev/null +++ b/wssclassy.py @@ -0,0 +1,154 @@ +import tensorflow as tf +import numpy as np +from sklearn import datasets +from matplotlib import pyplot as plt +from tensorflow.python.ops.resource_variable_ops import ResourceVariable +import tkinter as tk + +class evaclassy(): + + def __init__(self): + self.data = datasets.load_iris().data + self.target = datasets.load_iris().target + self.train_loss_list = [] + self.test_acc_list = [] + self.k1=0 + self.b1=0 + self.lr=0.1 #学习率设置 + self.epoch=200 + self.eval_loss_all=0 # 初始化loss_all的值，用于记录每轮四个step生成的4个loss的和 + def main(self): + #------------------------------------------------------------------------# + # ----------------------数据处理-----------------------------# + np.random.seed(116) # 使用用一个种子 保持输入特征与标签对应 + np.random.shuffle(self.data) + np.random.seed(116) + np.random.shuffle(self.target) + tf.random.set_seed(116) + data_train = self.data[:-30] # 使用切片，使前120组数据作为训练集，后30组数据作为验证集 + data_test = self.data[-30:] + target_train = self.target[:-30] + target_test = self.target[-30:] + data_train = tf.cast(data_train, tf.float32) + data_test = tf.cast(data_test, tf.float32) + train_db = tf.data.Dataset.from_tensor_slices((data_train, target_train)).batch(32) + test_db = tf.data.Dataset.from_tensor_slices((data_test, target_test)).batch(32) + k = tf.Variable(tf.random.truncated_normal([4, 3], stddev=0.1, seed=1)) + b = tf.Variable(tf.random.truncated_normal([3], stddev=0.1, seed=1)) + #---------------------------------------------------------------------------------------------# + for self.epoch in range(self.epoch): + for step, (data_train, target_train) in enumerate(train_db): + with tf.GradientTape() as tape: + dat = tf.matmul(data_train, k) + b + dat = tf.nn.softmax(dat) # 使输出结果符合概率分布 + targ = tf.one_hot(target_train, depth=3) # 将标签转化为独热码格式 + loss = tf.reduce_mean(tf.square(targ - dat)) # 使用均方差损失函数mse计算损失函数 + self.eval_loss_all += loss.numpy() + grads = tape.gradient(loss, [k, b]) # 计算loss对各个参数的梯度 + k.assign_sub(self.lr * grads[0]) + b.assign_sub(self.lr * grads[1]) # 更新模型偏置量参数b + print("Epoch: {}, loss: {}".format(self.epoch, self.eval_loss_all / 4)) + self.train_loss_list.append(self.eval_loss_all / 4) # 记录loss_all均值放入列表 + self.eval_loss_all = 0 # 归零，便于记录下一个epoch的loss + total_correct, total_number = 0, 0 + with open('k.txt','w') as f: + f.write(str(k)) + with open('b.txt', 'w') as f: + f.write(str(b)) + self.k1=k + self.b1=b + for data_test, target_test in test_db: + dat = tf.matmul(data_test, k) + b + dat = tf.nn.softmax(dat) + pred = tf.argmax(dat, axis=1) # 返回y中最大值的索引，即鸢尾花的分类标签 + pred = tf.cast(pred, dtype=target_test.dtype) # 转换数据类型 + correct = tf.cast(tf.equal(pred, target_test), dtype=tf.int32) # 根据分类是否正确返回布尔 # 值且转换为int型 + correct = tf.reduce_sum(correct) + total_correct += int(correct) + total_number += data_test.shape[0] + acc = total_correct / total_number # 总正确次数/总预测次数，计算准确率 + self.test_acc_list.append(acc) # 添加准确率数据到列表记录下来 + print("acc: ", acc) + def draw(self): + plt.title('Acc Curve') # 图片标题 + plt.xlabel('迭代次数', fontproperties='SimHei', fontsize=15) + plt.ylabel('准确率', fontproperties='SimHei', fontsize=15) + plt.plot(self.test_acc_list, label="$Accuracy$") + plt.legend() + plt.savefig('准确率图像')#图片保存 + plt.show() + + plt.title('Loss Function Curve') + plt.xlabel('迭代次数', fontproperties='SimHei', fontsize=15) + plt.ylabel('损失率', fontproperties='SimHei', fontsize=15) + plt.plot(self.train_loss_list, label="$Loss$") + plt.legend() + plt.savefig('损失率图像')#图片保存 + plt.show() + def predict(self,data): + y = tf.matmul(data, self.k1) + self.b1 + y = tf.nn.softmax(y) + pred = tf.argmax(y, axis=1) + pred=int(pred) + if pred==0: + print('是山鸢尾花') + return '山鸢尾花' + if pred==1: + print('是变色鸢尾花') + return '是变色鸢尾花' + if pred==2: + print('是维吉尼亚鸢尾花') + return '是维吉尼亚鸢尾花' +#数据处理 +def dataloader(list1): + data_train=[] + data_train.append(list1) + data_train = tf.cast(data_train, tf.float32) + return data_train +#窗口展示 +class draw1(): + def insert_point(self): + var = self.e.get() + c=str(var) + var = list(var) + var=list(map(float,var)) + print(var) + self.v1.set('') + var=dataloader(var) + try: + predic=a.predict(var) + except: + self.t.delete('1.0', 'end') + self.t.insert('insert', '请重新输入') + else: + self.t.delete('1.0', 'end') + self.t.insert('insert', c+predic) + + def insert_end(self): + self.t.delete('1.0', 'end') + def main(self): + window = tk.Tk() + window.title('classy') + window.geometry('500x500') + self.v1 = tk.StringVar() + self.e = tk.Entry(window, show=None,width=200, textvariable=self.v1) + self.e.pack() + self.k = tk.Text(window, height=2,state = 'disabled') + self.k.pack() + self.k.insert('insert', '输入四个范围为1-6的数字不加任何连接符') + self.t = tk.Text(window, height=2) + self.t.pack() + b1 = tk.Button(window, text='insert point', width=15, + height=2, command=self.insert_point) + b1.pack() + self.v1.set('') + b2 = tk.Button(window, text='insert end',width=15,height=2, + command=self.insert_end) + b2.pack() + window.mainloop() +if __name__ == '__main__': + a = evaclassy() + a.main() + # a.draw() #损失 正确函数画图 + b=draw1() + b.main() \ No newline at end of file