import tensorflow as tf

from sklearn import datasets

import numpy as np

x_train=datasets.load_iris().data

y_train=datasets.load_iris().target

np.random.seed(116)

np.random.shuffle(x_train)

np.random.seed(116)

np.random.shuffle(y_train)

tf.random.set_seed(116)

model=tf.keras.models.Sequential([tf.keras.layers.Dense(3,activation='softmax',kernel_regularizer=tf.keras.regularizers.l2())])

model.compile(optimizer=tf.keras.optimizers.SGD(lr=0.1),

              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),#由于末端使用了softmax函数，使输出是概率分布而不是原始输出，故选FALSE

              metrics=['sparse_categorical_accuracy'])

             #由于鸢尾花数据集给的标签是0/1/2，是数值，神经网络前向传播的输出是概率分布，故选择'sparse_categorical_accuracy'

model.fit(x_train,y_train,batch_size=32,epochs=500,validation_split=0.2,validation_freq=40)

model.summary()