神经网络----代码实现鸢尾花分类

运行环境：python3.7+tensorflow2.0

1.首先加载鸢尾花数据集，读入输入特征以及标签

直接采用load函数加载：

x_data = datasets.load_iris().data  # .data返回iris数据集所有输入特征
y_data = datasets.load_iris().target  # .target返回iris数据集所有标签

2.为保证准确性，对数据集进行打乱

 使用相同的seed，保证输入特征和标签一一对应

np.random.seed(116) 
np.random.shuffle(x_data)
np.random.seed(116)
np.random.shuffle(y_data)
tf.random.set_seed(116)

3. 将打乱后的数据集分割为训练集和测试集，训练集为前120行，测试集为后30行

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

4.对x的数据类型进行，否则后面矩阵相乘时会因数据类型不一致报错，利用from_tensor_slices函数使输入特征和标签值一一对应。（把数据集分批次，每个批次batch组数据，这里分为32组）

x_train = tf.cast(x_train, tf.float32)
x_test = tf.cast(x_test, tf.float32)

train_db = tf.data.Dataset.from_tensor_slices((x_train, y_train)).batch(32)
test_db = tf.data.Dataset.from_tensor_slices((x_test, y_test)).batch(32)

5.定义神经网络可训练参数

w1 = tf.Variable(tf.random.truncated_normal([4, 3], stddev=0.1, seed=1))
b1 = tf.Variable(tf.random.truncated_normal([3], stddev=0.1, seed=1))

6.训练函数

for epoch in range(epoch): 
    for step, (x_train, y_train) in enumerate(train_db): 
        with tf.GradientTape() as tape:  
            y = tf.matmul(x_train, w1) + b1  
            y = tf.nn.softmax(y) 
            y_ = tf.one_hot(y_train, depth=3)  
            loss = tf.reduce_mean(tf.square(y_ - y))  
            loss_all += loss.numpy()  
        grads = tape.gradient(loss, [w1, b1])

        w1.assign_sub(lr * grads[0])
        b1.assign_sub(lr * grads[1]) 

    print("Epoch {}, loss: {}".format(epoch, loss_all/4))
    train_loss_results.append(loss_all / 4) 
    loss_all = 0  

7.测试函数

 total_correct, total_number = 0, 0
    for x_test, y_test in test_db:
        y = tf.matmul(x_test, w1) + b1
        y = tf.nn.softmax(y)
        pred = tf.argmax(y, axis=1)  
        pred = tf.cast(pred, dtype=y_test.dtype)
        correct = tf.cast(tf.equal(pred, y_test), dtype=tf.int32)
        correct = tf.reduce_sum(correct)
        total_correct += int(correct)
        total_number += x_test.shape[0]
    acc = total_correct / total_number
    test_acc.append(acc)
    print("Test_acc:", acc)
    print("--------------------------")

8.运行结果：

准确度曲线： 

损失函数曲线：

 

源码：