在使用Adam优化时，需要注意参数的初始化和学习率的设置。对于逻辑回归模型，我们可以采用以下方式对Adam优化进行适配：

import tensorflow as tf

# 定义逻辑回归模型
class LogisticRegression(tf.keras.Model):
    def __init__(self, input_size, num_classes):
        super(LogisticRegression, self).__init__()
        self.linear = tf.keras.layers.Dense(num_classes, input_shape=(input_size,), activation='softmax')

    def call(self, x):
        return self.linear(x)

# 定义优化器
optimizer = tf.keras.optimizers.Adam(learning_rate=0.01)

# 定义模型
model = LogisticRegression(input_size=2, num_classes=2)

# 定义损失函数
loss_object = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)

# 定义评价指标
train_loss = tf.keras.metrics.Mean(name='train_loss')
train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='train_accuracy')

# 定义训练步骤
@tf.function
def train_step(x, y):
    with tf.GradientTape() as tape:
        # 预测结果
        predictions = model(x)
        # 计算损失函数值
        loss = loss_object(y, predictions)
    # 计算梯度
    gradients = tape.gradient(loss, model.trainable_variables)
    # 应用梯度更新模型参数
    optimizer.apply_gradients(zip(gradients, model.trainable_variables))
    # 记录损失函数值和评价指标的值
    train_loss(loss)
    train_accuracy(y, predictions)

# 定义数据集
train_ds = tf.data.Dataset.from_tensor_slices((x_train, y_train)).batch(32)

# 训练模型
for epoch in range(num_epochs):
    # 重置评价指标的状态
    train_loss.reset_states()
    train_accuracy.reset_states()
    # 遍历数据集并训练模型
    for x, y in train_ds:
        train_step(x, y)
    # 输出训练结果
    print('Epoch {}/{}, Loss: {}, Accuracy: {}'.format(
        epoch+1,
        num_epochs,
        train_loss.result(),
        train_accuracy.result()
    ))

在这段代码中，我们定义了一个名为LogisticRegression的类，它继承自tf.keras.Model