TensorFlow 2实现Adam优化器的批次优化代码

下面是使用TensorFlow 2实现Adam优化器的批次优化代码的示例：

import tensorflow as tf

# 定义模型和损失函数
model = tf.keras.Sequential([...])
loss_fn = tf.keras.losses.SparseCategoricalCrossentropy()

# 定义Adam优化器，设置初始学习率和其他参数
optimizer = tf.keras.optimizers.Adam(learning_rate=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-07)

# 定义训练过程
@tf.function
def train_step(images, labels):
    with tf.GradientTape() as tape:
        predictions = model(images, training=True)
        loss = loss_fn(labels, predictions)
    gradients = tape.gradient(loss, model.trainable_variables)
    optimizer.apply_gradients(zip(gradients, model.trainable_variables))

# 加载数据集并进行训练
(train_images, train_labels), (test_images, test_labels) = tf.keras.datasets.mnist.load_data()
train_images = train_images.reshape(train_images.shape[0], 28, 28, 1)
train_images = train_images / 255.0
test_images = test_images.reshape(test_images.shape[0], 28, 28, 1)
test_images = test_images / 255.0

BATCH_SIZE = 32
train_ds = tf.data.Dataset.from_tensor_slices((train_images, train_labels)).shuffle(10000).batch(BATCH_SIZE)
test_ds = tf.data.Dataset.from_tensor_slices((test_images, test_labels)).batch(BATCH_SIZE)

EPOCHS = 10
for epoch in range(EPOCHS):
    for images, labels in train_ds:
        train_step(images, labels)

在上述代码中，我们首先定义了一个模型和损失函数。接着，我们使用`tf.keras.optimizers.Adam()`函数定义了一个Adam优化器，并设置了初始学习率和其他参数。然后，我们定义了一个训练过程，其中使用`tf.GradientTape()`记录了梯度，并使用`optimizer.apply_gradients()`方法对模型参数进行更新。最后，我们加载了MNIST数据集，并使用`tf.data.Dataset`进行批次处理，进行了训练。