jupyter notebook's kernel keeps dying when I run the code

Question

I made my first steps in deep learning by following this tutorial https://www.youtube.com/watch?v=wQ8BIBpya2k ,and everything was going well until I needed to train the network in jupyter notebook.I tried almost everything and I always get this error "The kernel appears to have died. It will restart automatically." When I check terminal I can see this

[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

The code that I'm trying to run is fairly simple (even for me who is just starting to get into deep-learning)

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)  

I tried out every idea that I had,and went trough almost all same problems on google,right now this is my last hope so thanks in advance

Answer 1

Which version of tensorflow did you download?

It looks like from the error log that there's some OpenMP library issues, I would try reinstalling Tensorflow to the latest stable version.

I had to update my tensorflow (1.13.1) install to get that code working, here's what I output.

WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/resource_variable_ops.py:435: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Colocations handled automatically by placer.
Epoch 1/3
60000/60000 [==============================] - 6s 94us/sample - loss: 0.2652 - acc: 0.9213
Epoch 2/3
60000/60000 [==============================] - 6s 95us/sample - loss: 0.1103 - acc: 0.9660
Epoch 3/3
60000/60000 [==============================] - 6s 100us/sample - loss: 0.0735 - acc: 0.9765
10000/10000 [==============================] - 0s 35us/sample - loss: 0.0875 - acc: 0.9731
0.08748154099322855
0.9731

Depending on what library manager you are using, try upgrading

For Pip & Python3:

pip3 install tensorflow --upgrade

For Anaconda:

conda update tensorflow

Then run

import tensorflow as tf
print(tf.__version__)

To verify you have the latest available