Python Keras代码在Jupyter中不起作用：“；内核似乎已经死了。它将自动重新启动。”；

我正在用Keras为一个简单的基于深度学习的30x30 cat图像分类器编写代码。当我看到我的代码中应该训练模型的部分时，Jupyter停止运行，并给出错误消息“内核似乎已死亡。它将自动重新启动”。我不知道是什么导致了这种情况。如果我查看终端，我会得到一个CUDA错误，内存不足：内存不足；报告的总内存：11520114688。我认为一个简单的分类器不会耗尽我电脑的资源。我有一个RTX2080TI，32gb ram，i9-9900k

我不知道这是不是与软件的兼容性问题。但我确实知道tensorflow gpu正在工作，因为在我的控制台上是这样说的。我使用的代码基本上是Keras书中深入学习的逐字逐句代码。代码在我6岁的笔记本电脑上运行良好，尽管训练速度非常慢

```from keras import layers
```from keras import models
-------------
```model = models.Sequential()
```model.add(layers.Conv2D(32, (3,3), activation='relu', input_shape=(150,150,3)))
```model.add(layers.MaxPooling2D((2,2)))
```model.add(layers.Conv2D(64, (3,3), activation='relu'))
```model.add(layers.MaxPooling2D((2,2)))
```model.add(layers.Conv2D(128, (3,3), activation='relu'))
```model.add(layers.MaxPooling2D((2,2)))
```model.add(layers.Conv2D(128, (3,3), activation='relu'))
```model.add(layers.MaxPooling2D((2,2)))
```model.add(layers.Flatten())
```model.add(layers.Dropout(0.5))
```model.add(layers.Dense(512, activation='relu'))
```model.add(layers.Dense(1,activation='sigmoid'))

---

在上面运行此块之后： 警告：tensorflow:From/home/name/venv/lib/python3.6/site packages/tensorflow/python/framework/op_def_library.py:263:colocate_with（From tensorflow.python.framework.ops）已被弃用，并将在未来版本中删除。 更新说明： 由placer自动处理Colocations。 警告：tensorflow:From/home/name/venv/lib/python3.6/site packages/keras/backend/tensorflow_backend.py:3445:使用keep_prob调用dropout（来自tensorflow.python.ops.nn_ops）已被弃用，并将在未来版本中删除。 更新说明：

请使用

rate

而不是

keep\u prob

。应将速率设置为

Rate=1-keep_prob

。

---

运行此操作后，这是输出：

找到了2000张属于2个类的图片。 找到了1000张属于2类的图片。 警告：tensorflow:From/home/name/venv/lib/python3.6/site packages/tensorflow/python/ops/math_ops.py:3066:to_int32（来自tensorflow.python.ops.math_ops）已被弃用，并将在未来版本中删除。 更新说明： 改用tf.cast。 纪元1/100——然后在这里崩溃，我得到了jupyter错误

从终端附加复制粘贴：

[W 19:44:35.199 NotebookApp] Notebook Desktop/Deep Learning/TF test.ipynb is not trusted
[I 19:44:35.307 NotebookApp] Kernel started: ae9c0530-bdbb-4748-a8b1-4e9a98fad3b8
[I 19:44:35.669 NotebookApp] Adapting to protocol v5.1 for kernel ae9c0530-bdbb-4748-a8b1-4e9a98fad3b8
2019-11-11 19:44:38.840439: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2019-11-11 19:44:38.956151: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-11-11 19:44:38.957106: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x28f53e0 executing computations on platform CUDA. Devices:
2019-11-11 19:44:38.957121: I tensorflow/compiler/xla/service/service.cc:158]   StreamExecutor device (0): GeForce RTX 2080 Ti, Compute Capability 7.5
2019-11-11 19:44:38.984940: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 3600000000 Hz
2019-11-11 19:44:38.985600: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x2953090 executing computations on platform Host. Devices:
2019-11-11 19:44:38.985611: I tensorflow/compiler/xla/service/service.cc:158]   StreamExecutor device (0): <undefined>, <undefined>
2019-11-11 19:44:38.986045: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1433] Found device 0 with properties: 
name: GeForce RTX 2080 Ti major: 7 minor: 5 memoryClockRate(GHz): 1.635
pciBusID: 0000:01:00.0
totalMemory: 10.73GiB freeMemory: 10.18GiB
2019-11-11 19:44:38.986059: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1512] Adding visible gpu devices: 0
2019-11-11 19:44:38.987129: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-11-11 19:44:38.987140: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990]      0 
2019-11-11 19:44:38.987144: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 0:   N 
2019-11-11 19:44:38.987375: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/device:GPU:0 with 9903 MB memory) -> physical GPU (device: 0, name: GeForce RTX 2080 Ti, pci bus id: 0000:01:00.0, compute capability: 7.5)
[I 19:44:43.456 NotebookApp] Saving file at /Desktop/Deep Learning/TF test.ipynb
[I 19:44:43.565 NotebookApp] Starting buffering for ae9c0530-bdbb-4748-a8b1-4e9a98fad3b8:a4ca004462174782820be082f11422c7
[W 19:44:46.645 NotebookApp] Notebook Desktop/Deep Learning/5-2 Cat,Dog.ipynb is not trusted
[I 19:44:46.838 NotebookApp] Kernel started: 38279d87-721e-44c2-a40e-48cd4c7ba1c4
[I 19:44:47.142 NotebookApp] Adapting to protocol v5.1 for kernel 38279d87-721e-44c2-a40e-48cd4c7ba1c4
2019-11-11 19:45:18.007748: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2019-11-11 19:45:18.095869: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-11-11 19:45:18.096371: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x37ab830 executing computations on platform CUDA. Devices:
2019-11-11 19:45:18.096384: I tensorflow/compiler/xla/service/service.cc:158]   StreamExecutor device (0): GeForce RTX 2080 Ti, Compute Capability 7.5
2019-11-11 19:45:18.116837: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 3600000000 Hz
2019-11-11 19:45:18.117825: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x2b14f90 executing computations on platform Host. Devices:
2019-11-11 19:45:18.117837: I tensorflow/compiler/xla/service/service.cc:158]   StreamExecutor device (0): <undefined>, <undefined>
2019-11-11 19:45:18.118228: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1433] Found device 0 with properties: 
name: GeForce RTX 2080 Ti major: 7 minor: 5 memoryClockRate(GHz): 1.635
pciBusID: 0000:01:00.0
totalMemory: 10.73GiB freeMemory: 10.03GiB
2019-11-11 19:45:18.118258: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1512] Adding visible gpu devices: 0
2019-11-11 19:45:18.118879: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-11-11 19:45:18.118886: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990]      0 
2019-11-11 19:45:18.118907: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 0:   N 
2019-11-11 19:45:18.119131: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9756 MB memory) -> physical GPU (device: 0, name: GeForce RTX 2080 Ti, pci bus id: 0000:01:00.0, compute capability: 7.5)
2019-11-11 19:45:19.008213: I tensorflow/stream_executor/dso_loader.cc:152] successfully opened CUDA library libcublas.so.10.0 locally
[I 19:46:32.074 NotebookApp] Kernel interrupted: 38279d87-721e-44c2-a40e-48cd4c7ba1c4
[I 19:46:36.231 NotebookApp] Saving file at /Desktop/Deep Learning/5-2 Cat,Dog.ipynb
[W 19:46:36.232 NotebookApp] Notebook Desktop/Deep Learning/5-2 Cat,Dog.ipynb is not trusted
[I 19:46:36.348 NotebookApp] Starting buffering for 38279d87-721e-44c2-a40e-48cd4c7ba1c4:de4e1e737fef45e0a9393696f2ee53b4
[I 19:46:39.645 NotebookApp] Kernel started: 6192e4fe-6840-45b0-9f26-ffec2efcd443
[I 19:46:39.902 NotebookApp] Adapting to protocol v5.1 for kernel 6192e4fe-6840-45b0-9f26-ffec2efcd443
2019-11-11 19:46:53.523436: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2019-11-11 19:46:53.634301: W tensorflow/compiler/xla/service/platform_util.cc:240] unable to create StreamExecutor for CUDA:0: failed initializing StreamExecutor for CUDA device ordinal 0: Internal: failed call to cuDevicePrimaryCtxRetain: CUDA_ERROR_OUT_OF_MEMORY: out of memory; total memory reported: 11520114688
2019-11-11 19:46:53.634372: F tensorflow/stream_executor/lib/statusor.cc:34] Attempting to fetch value instead of handling error Internal: no supported devices found for platform CUDA
[I 19:46:54.645 NotebookApp] KernelRestarter: restarting kernel (1/5), keep random ports
WARNING:root:kernel 6192e4fe-6840-45b0-9f26-ffec2efcd443 restarted
[I 19:48:39.650 NotebookApp] Saving file at /Desktop/Deep Learning/5-2 cats,dogs w regularization.ipynb
2019-11-11 19:49:31.150869: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2019-11-11 19:49:31.223933: W tensorflow/compiler/xla/service/platform_util.cc:240] unable to create StreamExecutor for CUDA:0: failed initializing StreamExecutor for CUDA device ordinal 0: Internal: failed call to cuDevicePrimaryCtxRetain: CUDA_ERROR_OUT_OF_MEMORY: out of memory; total memory reported: 11520114688
2019-11-11 19:49:31.224025: F tensorflow/stream_executor/lib/statusor.cc:34] Attempting to fetch value instead of handling error Internal: no supported devices found for platform CUDA
[I 19:49:33.653 NotebookApp] KernelRestarter: restarting kernel (1/5), keep random ports
WARNING:root:kernel 6192e4fe-6840-45b0-9f26-ffec2efcd443 restarted
2019-11-11 19:49:51.505573: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2019-11-11 19:49:51.579785: W tensorflow/compiler/xla/service/platform_util.cc:240] unable to create StreamExecutor for CUDA:0: failed initializing StreamExecutor for CUDA device ordinal 0: Internal: failed call to cuDevicePrimaryCtxRetain: CUDA_ERROR_OUT_OF_MEMORY: out of memory; total memory reported: 11520114688
2019-11-11 19:49:51.579900: F tensorflow/stream_executor/lib/statusor.cc:34] Attempting to fetch value instead of handling error Internal: no supported devices found for platform CUDA
[I 19:49:51.665 NotebookApp] KernelRestarter: restarting kernel (1/5), keep random ports
WARNING:root:kernel 6192e4fe-6840-45b0-9f26-ffec2efcd443 restarted
[I 19:50:39.647 NotebookApp] Saving file at /Desktop/Deep Learning/5-2 cats,dogs w regularization.ipynb
[E 19:58:52.375 NotebookApp] nbconvert failed: No template_file specified!
    Traceback (most recent call last):
      File "/home/name/venv/lib/python3.6/site-packages/notebook/nbconvert/handlers.py", line 130, in get
        resources=resource_dict
      File "/home/name/venv/lib/python3.6/site-packages/nbconvert/exporters/templateexporter.py", line 315, in from_notebook_node
        output = self.template.render(nb=nb_copy, resources=resources)
      File "/home/name/venv/lib/python3.6/site-packages/nbconvert/exporters/templateexporter.py", line 113, in template
        self._template_cached = self._load_template()
      File "/home/name/venv/lib/python3.6/site-packages/nbconvert/exporters/templateexporter.py", line 278, in _load_template
        raise ValueError("No template_file specified!")
    ValueError: No template_file specified!
[W 19:58:52.376 NotebookApp] 500 GET /nbconvert/custom/Desktop/Deep%20Learning/5-2%20cats%2Cdogs%20w%20regularization.ipynb?download=true (127.0.0.1): nbconvert failed: No template_file specified!
[E 19:58:52.379 NotebookApp] {
      "Host": "localhost:8888",
      "User-Agent": "Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:70.0) Gecko/20100101 Firefox/70.0",
      "Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8",
      "Accept-Language": "en-US,en;q=0.5",
      "Accept-Encoding": "gzip, deflate",
      "Connection": "keep-alive",
      "Referer": "http://localhost:8888/notebooks/Desktop/Deep%20Learning/5-2%20cats%2Cdogs%20w%20regularization.ipynb",
      "Cookie": "_xsrf=2|79acfc45|e59d0dfc567c5357dd03be1910afbf02|1572817491; username-localhost-8888=\"2|1:0|10:1573523050|23:username-localhost-8888|44:N2U5M2FmY2QzYTE1NDBhZDg4NGY3N2U2MWU4MjYwOTU=|9895ebebc839d5dc7ba5a25aa7fb16775d32c546b63da08b9acd1c060bac0dde\"",
      "Upgrade-Insecure-Requests": "1"

[W 19:44:35.199 NotebookApp]笔记本电脑桌面/深度学习/TF测试。ipynb不受信任
[I 19:44:35.307 NotebookApp]内核已启动：ae9c0530-bdbb-4748-a8b1-4e9a98fad3b8
[I 19:44:35.669 NotebookApp]适用于内核ae9c0530-bdbb-4748-a8b1-4e9a98fad3b8的协议v5.1
2019-11-11 19:44:38.840439:I tensorflow/core/platform/cpu_feature_guard.cc:141]您的cpu支持该tensorflow二进制文件未编译为使用的指令：AVX2 FMA
2019-11-11 19:44:38.956151:I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:998]从SysFS读取的成功NUMA节点的值为负值（-1），但必须至少有一个NUMA节点，因此返回NUMA节点零
2019-11-1119:44：38.957106：I TysFult/Cyp/XLA／Service / Service。CC：150）XLA服务0x28 F53E0在CUDA平台上执行计算。设备：
2019-11-11 19:44:38.957121:I tensorflow/compiler/xla/service/service.cc:158]StreamExecutor设备（0）：GeForce RTX 2080 Ti，计算能力7.5
2019-11-11 19:44:38.984940:I tensorflow/core/platform/profile_-utils/cpu_-utils.cc:94]cpu频率：3600000000 Hz
2019-11-1119:44：38.985600：I TysFrace/编译器/ XLA/Service / Service。CC：150）XLA服务0x953090在平台主机上执行计算。设备：
2019-11-11 19:44:38.985611:I tensorflow/compiler/xla/service/service.cc:158]流执行器设备（0）：，
2019-11-11 19:44:38.986045:I tensorflow/core/common_runtime/gpu/gpu_device.cc:1433]找到了具有以下属性的设备0：
名称：GeForce RTX 2080 Ti大调：7小调：5内存锁定速率（GHz）：1.635
pciBusID:0000:01:00.0
总内存：10.73GiB自由内存：10.18GiB
2019-11-11 19:44:38.986059:I tensorflow/core/common_runtime/gpu/gpu_device.cc:1512]添加可见gpu设备：0
2019-11-11 19:44:38.987129:I tensorflow/core/common_runtime/gpu/gpu_device.cc:984]设备互连拖缆执行器与强度1边缘矩阵：
2019-11-11 19:44:38.987140:I tensorflow/core/common_runtime/gpu/gpu_device.cc:990]0
2019-11-11 19:44:38.987144:I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003]0:N
2019-11-11 19:44:38.987375:I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115]创建tensorflow设备（/device:gpu:0，9903 MB内存）->物理gpu（设备：0，名称：GeForce RTX 2080 Ti，pci总线id:0000:01:00.0，计算能力：7.5）
[I 19:44:43.456 NotebookApp]将文件保存在/Desktop/Deep Learning/TF test.ipynb
[I 19:44:43.565 NotebookApp]ae9c0530-bdbb-4748-a8b1-4e9a98fad3b8开始缓冲：A4CA00446217482820BE082F1142C7
[W 19:44:46.645 NotebookApp]笔记本电脑/深度学习/5-2猫，狗。ipynb不受信任
[I 19:44:46.838 NotebookApp]内核已启动：38279d87-721e-44c2-a40e-48cd4c7ba1c4
[I 19:44:47.142 NotebookApp]适用于内核38279d87-721e-44c2-a40e-48cd4c7ba1c4的协议v5.1
2019-11-11 19:45:18.007748:I tensorflow/core/platform/cpu_feature_guard.cc:141]您的cpu支持该tensorflow二进制文件未编译为使用的指令：AVX2 FMA
2019-11-11 19:45:18.095869:I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:998]从SysFS读取的成功NUMA节点的值为负值（-1），但必须至少有一个NUMA节点，因此返回NUMA节点零
2019-11-1119:45：18.096371：I TysFult/Cys/XLA/Service / Service。CC：150）XLA服务0x3AB830在CUDA平台上执行计算。设备：
2019-11-11 19:45:18.096384:I tensorflow/compiler/xla/service/service.cc:158]StreamExecutor设备（0）：GeForce RTX 2080 Ti，计算能力7.5
2019-11-11 19:45:18.116837:I tensorflow/core/platform/profile_utils/cpu_utils.cc:94]cpu频率：3600000000 Hz
2019-11-1119:45：18.117825：I TysFrase/编译器/ XLA/Service / Service .CC：150）XLA服务0x2B14F90在平台主机上执行计算。设备：
2019-11-11 19:45:18.117837:I tenso
[W 19:44:35.199 NotebookApp] Notebook Desktop/Deep Learning/TF test.ipynb is not trusted
[I 19:44:35.307 NotebookApp] Kernel started: ae9c0530-bdbb-4748-a8b1-4e9a98fad3b8
[I 19:44:35.669 NotebookApp] Adapting to protocol v5.1 for kernel ae9c0530-bdbb-4748-a8b1-4e9a98fad3b8
2019-11-11 19:44:38.840439: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2019-11-11 19:44:38.956151: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-11-11 19:44:38.957106: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x28f53e0 executing computations on platform CUDA. Devices:
2019-11-11 19:44:38.957121: I tensorflow/compiler/xla/service/service.cc:158]   StreamExecutor device (0): GeForce RTX 2080 Ti, Compute Capability 7.5
2019-11-11 19:44:38.984940: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 3600000000 Hz
2019-11-11 19:44:38.985600: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x2953090 executing computations on platform Host. Devices:
2019-11-11 19:44:38.985611: I tensorflow/compiler/xla/service/service.cc:158]   StreamExecutor device (0): <undefined>, <undefined>
2019-11-11 19:44:38.986045: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1433] Found device 0 with properties: 
name: GeForce RTX 2080 Ti major: 7 minor: 5 memoryClockRate(GHz): 1.635
pciBusID: 0000:01:00.0
totalMemory: 10.73GiB freeMemory: 10.18GiB
2019-11-11 19:44:38.986059: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1512] Adding visible gpu devices: 0
2019-11-11 19:44:38.987129: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-11-11 19:44:38.987140: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990]      0 
2019-11-11 19:44:38.987144: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 0:   N 
2019-11-11 19:44:38.987375: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/device:GPU:0 with 9903 MB memory) -> physical GPU (device: 0, name: GeForce RTX 2080 Ti, pci bus id: 0000:01:00.0, compute capability: 7.5)
[I 19:44:43.456 NotebookApp] Saving file at /Desktop/Deep Learning/TF test.ipynb
[I 19:44:43.565 NotebookApp] Starting buffering for ae9c0530-bdbb-4748-a8b1-4e9a98fad3b8:a4ca004462174782820be082f11422c7
[W 19:44:46.645 NotebookApp] Notebook Desktop/Deep Learning/5-2 Cat,Dog.ipynb is not trusted
[I 19:44:46.838 NotebookApp] Kernel started: 38279d87-721e-44c2-a40e-48cd4c7ba1c4
[I 19:44:47.142 NotebookApp] Adapting to protocol v5.1 for kernel 38279d87-721e-44c2-a40e-48cd4c7ba1c4
2019-11-11 19:45:18.007748: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2019-11-11 19:45:18.095869: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-11-11 19:45:18.096371: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x37ab830 executing computations on platform CUDA. Devices:
2019-11-11 19:45:18.096384: I tensorflow/compiler/xla/service/service.cc:158]   StreamExecutor device (0): GeForce RTX 2080 Ti, Compute Capability 7.5
2019-11-11 19:45:18.116837: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 3600000000 Hz
2019-11-11 19:45:18.117825: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x2b14f90 executing computations on platform Host. Devices:
2019-11-11 19:45:18.117837: I tensorflow/compiler/xla/service/service.cc:158]   StreamExecutor device (0): <undefined>, <undefined>
2019-11-11 19:45:18.118228: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1433] Found device 0 with properties: 
name: GeForce RTX 2080 Ti major: 7 minor: 5 memoryClockRate(GHz): 1.635
pciBusID: 0000:01:00.0
totalMemory: 10.73GiB freeMemory: 10.03GiB
2019-11-11 19:45:18.118258: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1512] Adding visible gpu devices: 0
2019-11-11 19:45:18.118879: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-11-11 19:45:18.118886: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990]      0 
2019-11-11 19:45:18.118907: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 0:   N 
2019-11-11 19:45:18.119131: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 9756 MB memory) -> physical GPU (device: 0, name: GeForce RTX 2080 Ti, pci bus id: 0000:01:00.0, compute capability: 7.5)
2019-11-11 19:45:19.008213: I tensorflow/stream_executor/dso_loader.cc:152] successfully opened CUDA library libcublas.so.10.0 locally
[I 19:46:32.074 NotebookApp] Kernel interrupted: 38279d87-721e-44c2-a40e-48cd4c7ba1c4
[I 19:46:36.231 NotebookApp] Saving file at /Desktop/Deep Learning/5-2 Cat,Dog.ipynb
[W 19:46:36.232 NotebookApp] Notebook Desktop/Deep Learning/5-2 Cat,Dog.ipynb is not trusted
[I 19:46:36.348 NotebookApp] Starting buffering for 38279d87-721e-44c2-a40e-48cd4c7ba1c4:de4e1e737fef45e0a9393696f2ee53b4
[I 19:46:39.645 NotebookApp] Kernel started: 6192e4fe-6840-45b0-9f26-ffec2efcd443
[I 19:46:39.902 NotebookApp] Adapting to protocol v5.1 for kernel 6192e4fe-6840-45b0-9f26-ffec2efcd443
2019-11-11 19:46:53.523436: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2019-11-11 19:46:53.634301: W tensorflow/compiler/xla/service/platform_util.cc:240] unable to create StreamExecutor for CUDA:0: failed initializing StreamExecutor for CUDA device ordinal 0: Internal: failed call to cuDevicePrimaryCtxRetain: CUDA_ERROR_OUT_OF_MEMORY: out of memory; total memory reported: 11520114688
2019-11-11 19:46:53.634372: F tensorflow/stream_executor/lib/statusor.cc:34] Attempting to fetch value instead of handling error Internal: no supported devices found for platform CUDA
[I 19:46:54.645 NotebookApp] KernelRestarter: restarting kernel (1/5), keep random ports
WARNING:root:kernel 6192e4fe-6840-45b0-9f26-ffec2efcd443 restarted
[I 19:48:39.650 NotebookApp] Saving file at /Desktop/Deep Learning/5-2 cats,dogs w regularization.ipynb
2019-11-11 19:49:31.150869: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2019-11-11 19:49:31.223933: W tensorflow/compiler/xla/service/platform_util.cc:240] unable to create StreamExecutor for CUDA:0: failed initializing StreamExecutor for CUDA device ordinal 0: Internal: failed call to cuDevicePrimaryCtxRetain: CUDA_ERROR_OUT_OF_MEMORY: out of memory; total memory reported: 11520114688
2019-11-11 19:49:31.224025: F tensorflow/stream_executor/lib/statusor.cc:34] Attempting to fetch value instead of handling error Internal: no supported devices found for platform CUDA
[I 19:49:33.653 NotebookApp] KernelRestarter: restarting kernel (1/5), keep random ports
WARNING:root:kernel 6192e4fe-6840-45b0-9f26-ffec2efcd443 restarted
2019-11-11 19:49:51.505573: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2019-11-11 19:49:51.579785: W tensorflow/compiler/xla/service/platform_util.cc:240] unable to create StreamExecutor for CUDA:0: failed initializing StreamExecutor for CUDA device ordinal 0: Internal: failed call to cuDevicePrimaryCtxRetain: CUDA_ERROR_OUT_OF_MEMORY: out of memory; total memory reported: 11520114688
2019-11-11 19:49:51.579900: F tensorflow/stream_executor/lib/statusor.cc:34] Attempting to fetch value instead of handling error Internal: no supported devices found for platform CUDA
[I 19:49:51.665 NotebookApp] KernelRestarter: restarting kernel (1/5), keep random ports
WARNING:root:kernel 6192e4fe-6840-45b0-9f26-ffec2efcd443 restarted
[I 19:50:39.647 NotebookApp] Saving file at /Desktop/Deep Learning/5-2 cats,dogs w regularization.ipynb
[E 19:58:52.375 NotebookApp] nbconvert failed: No template_file specified!
    Traceback (most recent call last):
      File "/home/name/venv/lib/python3.6/site-packages/notebook/nbconvert/handlers.py", line 130, in get
        resources=resource_dict
      File "/home/name/venv/lib/python3.6/site-packages/nbconvert/exporters/templateexporter.py", line 315, in from_notebook_node
        output = self.template.render(nb=nb_copy, resources=resources)
      File "/home/name/venv/lib/python3.6/site-packages/nbconvert/exporters/templateexporter.py", line 113, in template
        self._template_cached = self._load_template()
      File "/home/name/venv/lib/python3.6/site-packages/nbconvert/exporters/templateexporter.py", line 278, in _load_template
        raise ValueError("No template_file specified!")
    ValueError: No template_file specified!
[W 19:58:52.376 NotebookApp] 500 GET /nbconvert/custom/Desktop/Deep%20Learning/5-2%20cats%2Cdogs%20w%20regularization.ipynb?download=true (127.0.0.1): nbconvert failed: No template_file specified!
[E 19:58:52.379 NotebookApp] {
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