在tensorflow环境下，用resnet18训练mnist

首先，需要准备好MNIST数据集。可以使用TensorFlow内置的`tf.keras.datasets.mnist`模块来下载和加载数据集：

import tensorflow as tf

(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()

然后，需要对数据进行预处理，将像素值缩放到0到1之间，并将标签转换为独热编码。可以使用`tf.keras.utils.to_categorical`函数实现：

x_train = x_train.astype('float32') / 255
x_test = x_test.astype('float32') / 255

y_train = tf.keras.utils.to_categorical(y_train, num_classes=10)
y_test = tf.keras.utils.to_categorical(y_test, num_classes=10)

接下来，需要定义ResNet18模型。可以使用TensorFlow内置的`tf.keras.applications.ResNet18`模块来加载预训练的ResNet18模型，并将其顶层替换为适合MNIST数据集的全连接层：

base_model = tf.keras.applications.ResNet18(
    include_top=False, weights='imagenet', input_shape=(28, 28, 1))

x = base_model.output
x = tf.keras.layers.GlobalAveragePooling2D()(x)
x = tf.keras.layers.Dense(128, activation='relu')(x)
predictions = tf.keras.layers.Dense(10, activation='softmax')(x)

model = tf.keras.models.Model(inputs=base_model.input, outputs=predictions)

最后，需要编译并训练模型：

model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

model.fit(x_train, y_train, batch_size=128, epochs=10, validation_data=(x_test, y_test))

完整代码如下：

import tensorflow as tf

(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()

x_train = x_train.astype('float32') / 255
x_test = x_test.astype('float32') / 255

y_train = tf.keras.utils.to_categorical(y_train, num_classes=10)
y_test = tf.keras.utils.to_categorical(y_test, num_classes=10)

base_model = tf.keras.applications.ResNet18(
    include_top=False, weights='imagenet', input_shape=(28, 28, 1))

x = base_model.output
x = tf.keras.layers.GlobalAveragePooling2D()(x)
x = tf.keras.layers.Dense(128, activation='relu')(x)
predictions = tf.keras.layers.Dense(10, activation='softmax')(x)

model = tf.keras.models.Model(inputs=base_model.input, outputs=predictions)

model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

model.fit(x_train, y_train, batch_size=128, epochs=10, validation_data=(x_test, y_test))