1 import tensorflow as tf
2 from sklearn import datasets
3 import numpy as np
4
5
6 x_train = datasets.load_iris().data
7 y_train = datasets.load_iris().target
8
9
10 np.random.seed(116)
11 np.random.shuffle(x_train)
12 np.random.seed(116)
13 np.random.shuffle(y_train)
14 tf.random.set_seed(116)
15
16
17 model = tf.keras.models.Sequential([
18 tf.keras.layers.Dense(3, activation='softmax',
19 kernel_regularizer=tf.keras.regularizers.l2())
20 ])
21
22 model.compile(optimizer=tf.keras.optimizers.SGD(lr=0.1),
23 loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),
24 metrics=['sparse_categorical_accuracy'])
25
26 model.fit(x_train, y_train, batch_size=32, epochs=500, validation_split=0.2, validation_freq=20)
27
28
29 model.summary()
