在MNIST数据集上训练ResNet的test_loss, test_acc结果图

抱歉，我无法在此窗口中提供图片。但是，我可以告诉您如何在MNIST数据集上训练ResNet，并绘制test_loss和test_acc结果图。

首先，您需要下载MNIST数据集并加载它。然后，您需要定义ResNet模型并编译它。接下来，您可以使用fit方法来训练您的模型。以下是一个例子：

import tensorflow as tf
from tensorflow.keras import layers

# Load MNIST dataset
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()

# Normalize pixel values to be between 0 and 1
x_train, x_test = x_train / 255.0, x_test / 255.0

# Define ResNet model
inputs = tf.keras.Input(shape=(28, 28, 1))
x = layers.Conv2D(64, 7, activation='relu', padding='same')(inputs)
x = layers.MaxPooling2D()(x)
residual = x

for _ in range(2):
    x = layers.Conv2D(64, 3, activation='relu', padding='same')(x)
    x = layers.BatchNormalization()(x)
    x = layers.Conv2D(64, 3, activation=None, padding='same')(x)
    x = layers.BatchNormalization()(x)
    x = layers.Add()([x, residual])
    x = layers.Activation('relu')(x)
    residual = x

x = layers.GlobalAveragePooling2D()(x)
outputs = layers.Dense(10, activation='softmax')(x)

model = tf.keras.Model(inputs=inputs, outputs=outputs)

# Compile model
model.compile(optimizer=tf.keras.optimizers.Adam(),
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

# Train model
history = model.fit(x_train, y_train, epochs=10, validation_data=(x_test, y_test))

# Plot test_loss and test_acc results
import matplotlib.pyplot as plt

plt.plot(history.history['val_loss'], label='Test Loss')
plt.plot(history.history['val_accuracy'], label='Test Accuracy')
plt.legend()
plt.show()

这将训练一个简单的ResNet模型，并绘制test_loss和test_acc结果图。请注意，您可以根据需要更改模型的架构和超参数。