Tensorflow2.0 keras版本模型训练使用样本加权和类别加权（可运行代码案例）

import tensorflow as tf
import numpy as np
# 构建模型
def get_uncompiled_model():
    inputs = tf.keras.Input(shape=(32,), name='digits')
    x = tf.keras.layers.Dense(64, activation='relu', name='dense_1')(inputs)
    x = tf.keras.layers.Dense(64, activation='relu', name='dense_2')(x)
    outputs = tf.keras.layers.Dense(10, name='predictions')(x)
    model = tf.keras.Model(inputs=inputs, outputs=outputs)
    return model

def get_compiled_model():
    model = get_uncompiled_model()
    model.compile(optimizer=tf.keras.optimizers.RMSprop(learning_rate=1e-3),
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=['sparse_categorical_accuracy'])
    return model

# 构建数据集
x_train = np.random.random((1000, 32))
y_train = np.random.randint(10, size=(1000, ))

x_val = np.random.random((200, 32))
y_val = np.random.randint(10, size=(200, ))

x_test = np.random.random((200, 32))
y_test = np.random.randint(10, size=(200, ))

# 模型训练(类别5加权）
class_weight = {0:1., 1:1., 2:1., 3:1., 4:1., 5:2., 6:1., 7:1., 8:1., 9:1.}
print('Fit with class weight')
model = get_compiled_model()
model.fit(x_train, y_train, class_weight=class_weight, batch_size=64, epochs=4)

# 模型训练（样本标签为5的样本加权）
sample_weight = np.ones(shape=(len(y_train),))
sample_weight[y_train == 5] = 2.
print('\nFit with sample weight')
model = get_compiled_model()
model.fit(x_train, y_train, sample_weight=sample_weight, batch_size=64, epochs=4)