@tf.function def train_step(images, labels): with tf.GradientTape() as tape: predictions = model(images, training=True) loss = loss_object(labels, predictions) gradients = tape.gradient(loss, model.trainable_variables) optimizer.apply_gradients(zip(gradients, model.trainable_variables)) train_loss(loss) train_accuracy(labels, predictions)

这段代码定义了一个 TensorFlow 2.0+ 的计算图函数 `train_step`，用来进行一次训练迭代。函数的输入参数是 `images` 和 `labels`，分别表示训练数据的图像和标签。函数的核心是使用 `tf.GradientTape()` 记录前向传播过程中的计算图，计算出损失函数对各个可训练参数的梯度，并用优化器进行参数的更新。同时，函数还记录了训练过程中的损失函数和准确率的度量指标的数值，以便后续的输出和可视化。

相关问题

@tf.function def train_step(images, labels): with tf.GradientTape() as tape: # 计算模型预测结果和损失函数值 predictions = model(images, training=True) loss = loss_object(labels, predictions) # 计算梯度并优化模型参数 gradients = tape.gradient(loss, model.trainable_variables) optimizer.apply_gradients(zip(gradients, model.trainable_variables)) # 更新训练过程中的度量指标 train_loss(loss) train_accuracy(labels, predictions) @tf.function def test_step(images, labels): # 计算模型预测结果和损失函数值 predictions = model(images, training=False) t_loss = loss_object(labels, predictions) # 更新测试过程中的度量指标 test_loss(t_loss) test_accuracy(labels, predictions)代码

这段代码是一个 TensorFlow 的训练和测试过程，主要包含两个函数：train_step 和 test_step。train_step 函数用于训练模型，它接收输入数据 images 和对应标签 labels，使用 tf.GradientTape 记录前向传播过程中的计算图，计算模型预测结果和损失函数值，然后根据梯度更新模型参数，同时更新训练过程中的度量指标，如损失函数和准确率。test_step 函数用于测试模型，在测试过程中，它接收输入数据 images 和对应标签 labels，计算模型预测结果和损失函数值，并更新测试过程中的度量指标，如损失函数和准确率。这两个函数都使用了 TensorFlow 提供的 @tf.function 装饰器，将它们编译成 TensorFlow 的计算图，从而提高执行效率。这段代码还使用了 TensorFlow 提供的优化器和损失函数，如 Adam 优化器和交叉熵损失函数。

import tensorflow as tf from tensorflow.keras.layers import Dense, Flatten, Conv2D, MaxPool2D, Dropoutfrom tensorflow.keras import Model​# 在GPU上运算时，因为cuDNN库本身也有自己的随机数生成器，所以即使tf设置了seed，也不会每次得到相同的结果tf.random.set_seed(100)​mnist = tf.keras.datasets.mnist(X_train, y_train), (X_test, y_test) = mnist.load_data()X_train, X_test = X_train/255.0, X_test/255.0​# 将特征数据集从(N,32,32)转变成(N,32,32,1),因为Conv2D需要(NHWC)四阶张量结构X_train = X_train[..., tf.newaxis] X_test = X_test[..., tf.newaxis]​batch_size = 64# 手动生成mini_batch数据集train_ds = tf.data.Dataset.from_tensor_slices((X_train, y_train)).shuffle(10000).batch(batch_size)test_ds = tf.data.Dataset.from_tensor_slices((X_test, y_test)).batch(batch_size)​class Deep_CNN_Model(Model): def __init__(self): super(Deep_CNN_Model, self).__init__() self.conv1 = Conv2D(32, 5, activation='relu') self.pool1 = MaxPool2D() self.conv2 = Conv2D(64, 5, activation='relu') self.pool2 = MaxPool2D() self.flatten = Flatten() self.d1 = Dense(128, activation='relu') self.dropout = Dropout(0.2) self.d2 = Dense(10, activation='softmax') def call(self, X): # 无需在此处增加training参数状态。只需要在调用Model.call时，传递training参数即可 X = self.conv1(X) X = self.pool1(X) X = self.conv2(X) X = self.pool2(X) X = self.flatten(X) X = self.d1(X) X = self.dropout(X) # 无需在此处设置training状态。只需要在调用Model.call时，传递training参数即可 return self.d2(X)​model = Deep_CNN_Model()loss_object = tf.keras.losses.SparseCategoricalCrossentropy()optimizer = tf.keras.optimizers.Adam()​train_loss = tf.keras.metrics.Mean(name='train_loss')train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='train_accuracy')test_loss = tf.keras.metrics.Mean(name='test_loss')test_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='test_accuracy')​# TODO：定义单批次的训练和预测操作@tf.functiondef train_step(images, labels): ...... @tf.functiondef test_step(images, labels): ...... # TODO：执行完整的训练过程EPOCHS = 10for epoch in range(EPOCHS)补全代码

import tensorflow as tf
from tensorflow.keras.layers import Dense, Flatten, Conv2D, MaxPool2D, Dropout
from tensorflow.keras import Model

# 在GPU上运算时，因为cuDNN库本身也有自己的随机数生成器，所以即使tf设置了seed，也不会每次得到相同的结果
tf.random.set_seed(100)

# 加载MNIST数据集并进行预处理
mnist = tf.keras.datasets.mnist
(X_train, y_train), (X_test, y_test) = mnist.load_data()
X_train, X_test = X_train/255.0, X_test/255.0
X_train = X_train[..., tf.newaxis]
X_test = X_test[..., tf.newaxis]

# 定义batch_size并手动生成mini_batch数据集
batch_size = 64
train_ds = tf.data.Dataset.from_tensor_slices((X_train, y_train)).shuffle(10000).batch(batch_size)
test_ds = tf.data.Dataset.from_tensor_slices((X_test, y_test)).batch(batch_size)

# 定义深度CNN模型
class Deep_CNN_Model(Model):
def __init__(self):
super(Deep_CNN_Model, self).__init__()
self.conv1 = Conv2D(32, 5, activation='relu')
self.pool1 = MaxPool2D()
self.conv2 = Conv2D(64, 5, activation='relu')
self.pool2 = MaxPool2D()
self.flatten = Flatten()
self.d1 = Dense(128, activation='relu')
self.dropout = Dropout(0.2)
self.d2 = Dense(10, activation='softmax')

def call(self, X, training=False):
X = self.conv1(X)
X = self.pool1(X)
X = self.conv2(X)
X = self.pool2(X)
X = self.flatten(X)
X = self.d1(X)
X = self.dropout(X, training=training)
return self.d2(X)

# 实例化模型，定义损失函数和优化器
model = Deep_CNN_Model()
loss_object = tf.keras.losses.SparseCategoricalCrossentropy()
optimizer = tf.keras.optimizers.Adam()

# 定义训练和测试过程中的度量指标
train_loss = tf.keras.metrics.Mean(name='train_loss')
train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='train_accuracy')
test_loss = tf.keras.metrics.Mean(name='test_loss')
test_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='test_accuracy')

# 定义单批次的训练和预测操作
@tf.function
def train_step(images, labels):
with tf.GradientTape() as tape:
# 计算模型预测结果和损失函数值
predictions = model(images, training=True)
loss = loss_object(labels, predictions)
# 计算梯度并优化模型参数
gradients = tape.gradient(loss, model.trainable_variables)
optimizer.apply_gradients(zip(gradients, model.trainable_variables))
# 更新训练过程中的度量指标
train_loss(loss)
train_accuracy(labels, predictions)

@tf.function
def test_step(images, labels):
# 计算模型预测结果和损失函数值
predictions = model(images, training=False)
t_loss = loss_object(labels, predictions)
# 更新测试过程中的度量指标
test_loss(t_loss)
test_accuracy(labels, predictions)

# 执行完整的训练过程
EPOCHS = 10
for epoch in range(EPOCHS):
# 在每轮训练开始时，重置度量指标的值
train_loss.reset_states()
train_accuracy.reset_states()
test_loss.reset_states()
test_accuracy.reset_states()

# 遍历训练数据集中的每个mini_batch进行训练
for images, labels in train_ds:
train_step(images, labels)

# 遍历测试数据集中的每个mini_batch进行预测
for test_images, test_labels in test_ds:
test_step(test_images, test_labels)

# 输出每轮训练和测试过程中的度量指标
template = 'Epoch {}, Loss: {}, Accuracy: {}, Test Loss: {}, Test Accuracy: {}'
print(template.format(epoch+1,
train_loss.result(),
train_accuracy.result()*100,
test_loss.result(),
test_accuracy.result()*100))