算法小白的第一次尝试---tensorflow2.0实战iris

import tensorflow as tf
import sklearn.datasets as datasets
import numpy as np

## 1.加载数据集
x_data = datasets.load_iris().data #加载features
y_data = datasets.load_iris().target #加载label

# 随机切分数据，此处需设置相同的随机种子
np.random.seed(168)
np.random.shuffle(x_data)
np.random.seed(168)
np.random.shuffle(y_data)

x_train = x_data[:-30]
y_train = y_data[:-30]
x_test = x_data[-30:]
y_test = y_data[-30:]

# 原始x_train为float64,不转化后期矩阵运算报错
x_train = tf.cast(x_train,tf.float32)
x_test = tf.cast(x_test,tf.float32)

### 1.3 featur和label拼接
train_data = tf.data.Dataset.from_tensor_slices((x_train,y_train)).batch(32)
test_data = tf.data.Dataset.from_tensor_slices((x_test,y_test)).batch(32)

# 2.模型训练
### 2.1 定义神经元连接权重和偏置项
w = tf.Variable(tf.random.truncated_normal([4,3],stddev=0.1,dtype = tf.float32))
b = tf.Variable(tf.random.truncated_normal([3],stddev=0.1,dtype=tf.float32))

lr = 0.1 #学习率
epochs = 500 #迭代次数
classes = 3 #总的类别数
train_loss_result = []
accuracy_result = []
loss_all = 0
for epoch in range(epochs):
    for x_train,y_train in train_data:
        with tf.GradientTape() as tape:
            y = tf.matmul(x_train,w) + b
            y = tf.nn.softmax(y) #通过softmax转化为概率模型
            y_= tf.one_hot(y_train,depth =classes) #label转化为独热编码
            loss = tf.reduce_mean(tf.square(y - y_))
            loss_all += loss.numpy()
        grads = tape.gradient(loss,[w,b]) #求取梯度
        w.assign_sub(lr * grads[0])
        b.assign_sub(lr * grads[1])
    train_loss_result.append(loss_all / 4) #4批次的均值作为当前轮次的loss 
    loss_all = 0 
    
    #测试数据
    total_correct ,total_num =0,0
    for x_test,y_test in test_data:
        y = tf.matmul(x_test,w) + b
        y = tf.nn.softmax(y)
        pred = tf.argmax(y,axis=1)
        pred = tf.cast(pred,dtype = tf.int32)
        # 计算准确率
        correct = tf.cast(tf.equal(pred,y_test),dtype = tf.int32) #boolean型数据转化为0，1，方便使用tf.reduce_sum
        correct = tf.reduce_sum(correct)
        #所以batch的correct累加
        total_correct += correct
        total_num += x_test.shape[0]
    
    accuracy = total_correct / total_num 
    accuracy_result.append(accuracy)