TensorFlow中模型并行和流水线训练的示例代码

import tensorflow as tf

# 定义模型
model = tf.keras.Sequential([
    tf.keras.layers.Dense(64, activation='relu', input_shape=(784,)),
    tf.keras.layers.Dense(64, activation='relu'),
    tf.keras.layers.Dense(10, activation='softmax')
])

# 定义优化器和损失函数
optimizer = tf.keras.optimizers.SGD(learning_rate=0.001)
loss_fn = tf.keras.losses.SparseCategoricalCrossentropy()

# 定义模型并行策略
strategy = tf.distribute.MirroredStrategy()

# 在模型并行策略下编译模型
with strategy.scope():
    model.compile(optimizer=optimizer, loss=loss_fn, metrics=['accuracy'])

# 加载数据集
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()

# 数据预处理
x_train = x_train.reshape(-1, 784) / 255.0
x_test = x_test.reshape(-1, 784) / 255.0

# 在模型并行策略下训练模型
model.fit(x_train, y_train, epochs=10, batch_size=64)

# 在模型并行策略下评估模型
model.evaluate(x_test, y_test)

import tensorflow as tf

# 定义数据处理函数
def preprocess_data(image, label):
    # 数据预处理逻辑
    image = tf.image.resize(image, (224, 224))
    image = tf.cast(image, tf.float32) / 255.0
    return image, label

# 加载数据集
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.cifar10.load_data()

# 创建数据集对象
train_dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train))
test_dataset = tf.data.Dataset.from_tensor_slices((x_test, y_test))

# 数据预处理和批处理
train_dataset = train_dataset.map(preprocess_data).shuffle(10000).batch(64)
test_dataset = test_dataset.map(preprocess_data).batch(64)

# 定义模型
model = tf.keras.applications.ResNet50(weights=None, classes=10)

# 定义优化器和损失函数
optimizer = tf.keras.optimizers.SGD(learning_rate=0.001)
loss_fn = tf.keras.losses.SparseCategoricalCrossentropy()

# 定义模型并行策略
strategy = tf.distribute.MirroredStrategy()

# 在模型并行策略下编译模型
with strategy.scope():
    model.compile(optimizer=optimizer, loss=loss_fn, metrics=['accuracy'])

# 在模型并行策略下训练模型
model.fit(train_dataset, epochs=10)

# 在模型并行策略下评估模型
model.evaluate(test_dataset)