当我运行cod时，jupyter笔记本的内核一直在死亡

Question

通过学习本教程，我迈出了深入学习的第一步，一切都很顺利，直到我需要在jupyter笔记本上训练网络。我几乎尝试了所有的方法，但总是出现这样的错误“内核似乎已经死了。它将自动重新启动“当我检查终端时，我可以看到

[I 18:32:24.897 NotebookApp] Adapting to protocol v5.1 for kernel 0d2f57af-46f5-419c-8c8e-9676c14dd9e3 2019-03-09 18:33:12.906756: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA 2019-03-09 18:33:12.907661: I tensorflow/core/common_runtime/process_util.cc:69] Creating new thread pool with default inter op setting: 4. Tune using inter_op_parallelism_threads for best performance. OMP: Error #15: Initializing libiomp5.dylib, but found libiomp5.dylib already initialized. OMP: Hint: This means that multiple copies of the OpenMP runtime have been linked into the program. That is dangerous, since it can degrade performance or cause incorrect results. The best thing to do is to ensure that only a single OpenMP runtime is linked into the process, e.g. by avoiding static linking of the OpenMP runtime in any library. As an unsafe, unsupported, undocumented workaround you can set the environment variable KMP_DUPLICATE_LIB_OK=TRUE to allow the program to continue to execute, but that may cause crashes or silently produce incorrect results. For more information, please see http://www.intel.com/software/products/support/. [I 18:33:13.864 NotebookApp] KernelRestarter: restarting kernel (1/5), keep random ports WARNING:root:kernel 0d2f57af-46f5-419c-8c8e-9676c14dd9e3 restarted

我试图运行的代码相当简单（即使对于刚刚开始深入学习的我来说也是如此）

import tensorflow as tf  

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

x_train = tf.keras.utils.normalize(x_train, axis=1)  
x_test = tf.keras.utils.normalize(x_test, axis=1) 

model = tf.keras.models.Sequential()  
model.add(tf.keras.layers.Flatten())  
model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu))  
model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu))  
model.add(tf.keras.layers.Dense(10, activation=tf.nn.softmax))  

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

model.fit(x_train, y_train, epochs=3)  

val_loss, val_acc = model.evaluate(x_test, y_test)  
print(val_loss)  
print(val_acc)  

我尝试了我的每一个想法，在google上经历了几乎所有相同的问题，现在这是我最后的希望，所以提前感谢