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import tensorflow as tf
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import numpy as np
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from sklearn import datasets
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from matplotlib import pyplot as plt
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from tensorflow.python.ops.resource_variable_ops import ResourceVariable
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import tkinter as tk
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class evaclassy():
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def __init__(self):
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self.data = datasets.load_iris().data
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self.target = datasets.load_iris().target
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self.train_loss_list = []
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self.test_acc_list = []
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self.k1=0
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self.b1=0
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self.lr=0.1 #学习率设置
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self.epoch=200
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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
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def main(self):
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#------------------------------------------------------------------------#
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# ----------------------数据处理-----------------------------#
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np.random.seed(116) # 使用用一个种子 保持输入特征与标签对应
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np.random.shuffle(self.data)
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np.random.seed(116)
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np.random.shuffle(self.target)
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tf.random.set_seed(116)
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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
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data_test = self.data[-30:]
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target_train = self.target[:-30]
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target_test = self.target[-30:]
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data_train = tf.cast(data_train, tf.float32)
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data_test = tf.cast(data_test, tf.float32)
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train_db = tf.data.Dataset.from_tensor_slices((data_train, target_train)).batch(32)
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test_db = tf.data.Dataset.from_tensor_slices((data_test, target_test)).batch(32)
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k = tf.Variable(tf.random.truncated_normal([4, 3], stddev=0.1, seed=1))
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b = tf.Variable(tf.random.truncated_normal([3], stddev=0.1, seed=1))
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#---------------------------------------------------------------------------------------------#
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for self.epoch in range(self.epoch):
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for step, (data_train, target_train) in enumerate(train_db):
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with tf.GradientTape() as tape:
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dat = tf.matmul(data_train, k) + b
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dat = tf.nn.softmax(dat) # 使输出结果符合概率分布
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targ = tf.one_hot(target_train, depth=3) # 将标签转化为独热码格式
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loss = tf.reduce_mean(tf.square(targ - dat)) # 使用均方差损失函数mse计算损失函数
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self.eval_loss_all += loss.numpy()
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grads = tape.gradient(loss, [k, b]) # 计算loss对各个参数的梯度
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k.assign_sub(self.lr * grads[0])
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b.assign_sub(self.lr * grads[1]) # 更新模型偏置量参数b
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print("Epoch: {}, loss: {}".format(self.epoch, self.eval_loss_all / 4))
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self.train_loss_list.append(self.eval_loss_all / 4) # 记录loss_all均值放入列表
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self.eval_loss_all = 0 # 归零，便于记录下一个epoch的loss
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total_correct, total_number = 0, 0
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with open('k.txt','w') as f:
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f.write(str(k))
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with open('b.txt', 'w') as f:
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f.write(str(b))
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self.k1=k
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self.b1=b
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for data_test, target_test in test_db:
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dat = tf.matmul(data_test, k) + b
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dat = tf.nn.softmax(dat)
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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
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pred = tf.cast(pred, dtype=target_test.dtype) # 转换数据类型
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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
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correct = tf.reduce_sum(correct)
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total_correct += int(correct)
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total_number += data_test.shape[0]
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acc = total_correct / total_number # 总正确次数/总预测次数，计算准确率
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self.test_acc_list.append(acc) # 添加准确率数据到列表记录下来
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print("acc: ", acc)
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def draw(self):
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plt.title('Acc Curve') # 图片标题
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plt.xlabel('迭代次数', fontproperties='SimHei', fontsize=15)
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plt.ylabel('准确率', fontproperties='SimHei', fontsize=15)
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plt.plot(self.test_acc_list, label="$Accuracy$")
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plt.legend()
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plt.savefig('准确率图像')#图片保存
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plt.show()
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plt.title('Loss Function Curve')
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plt.xlabel('迭代次数', fontproperties='SimHei', fontsize=15)
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plt.ylabel('损失率', fontproperties='SimHei', fontsize=15)
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plt.plot(self.train_loss_list, label="$Loss$")
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plt.legend()
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plt.savefig('损失率图像')#图片保存
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plt.show()
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def predict(self,data):
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y = tf.matmul(data, self.k1) + self.b1
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y = tf.nn.softmax(y)
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pred = tf.argmax(y, axis=1)
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pred=int(pred)
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if pred==0:
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print('是山鸢尾花')
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return '山鸢尾花'
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if pred==1:
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print('是变色鸢尾花')
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return '是变色鸢尾花'
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if pred==2:
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print('是维吉尼亚鸢尾花')
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return '是维吉尼亚鸢尾花'
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#数据处理
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def dataloader(list1):
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data_train=[]
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data_train.append(list1)
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data_train = tf.cast(data_train, tf.float32)
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return data_train
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#窗口展示
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class draw1():
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def insert_point(self):
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var = self.e.get()
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c=str(var)
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var = list(var)
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var=list(map(float,var))
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print(var)
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self.v1.set('')
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var=dataloader(var)
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try:
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predic=a.predict(var)
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except:
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self.t.delete('1.0', 'end')
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self.t.insert('insert', '请重新输入')
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else:
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self.t.delete('1.0', 'end')
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self.t.insert('insert', c+predic)
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def insert_end(self):
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self.t.delete('1.0', 'end')
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def main(self):
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window = tk.Tk()
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window.title('classy')
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window.geometry('500x500')
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self.v1 = tk.StringVar()
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self.e = tk.Entry(window, show=None,width=200, textvariable=self.v1)
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self.e.pack()
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self.k = tk.Text(window, height=2,state = 'disabled')
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self.k.pack()
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self.k.insert('insert', '输入四个范围为1-6的数字不加任何连接符')
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self.t = tk.Text(window, height=2)
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self.t.pack()
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b1 = tk.Button(window, text='insert point', width=15,
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height=2, command=self.insert_point)
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b1.pack()
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self.v1.set('')
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b2 = tk.Button(window, text='insert end',width=15,height=2,
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command=self.insert_end)
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b2.pack()
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window.mainloop()
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if __name__ == '__main__':
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a = evaclassy()
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a.main()
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# a.draw() #损失 正确函数画图
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b=draw1()
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b.main()
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