問題描述
我正在嘗試運行一個測試程序來檢查我的 Anaconda 環(huán)境是否配置正確.但是，當我運行我的測試程序時，我會在程序設(shè)置圖形時收到此錯誤消息(準確地說是 on_train_end() 回調(diào)):
I'm trying to run a test program to check if my Anaconda environment is configured correctly. However, when I run my test program I get this error message when the program is setting up graph (on_train_end() callback to be precise):
OMP:錯誤 #15:正在初始化 libiomp5.dylib，但找到了 libiomp5.dylib已經(jīng)初始化.OMP:提示 這意味著 OpenMP 運行時的多個副本已經(jīng)存在，因為它會降低性能或?qū)е虏徽_的結(jié)果.最好的辦法是確保只有一個 OpenMP 運行時鏈接到進程中，例如通過避免在任何庫中靜態(tài)鏈接 OpenMP 運行時.作為一種不安全、不受支持、未記錄的解決方法，您可以設(shè)置環(huán)境變量 KMP_DUPLICATE_LIB_OK=TRUE 以允許程序繼續(xù)執(zhí)行，但這可能會導致崩潰或默默地產(chǎn)生不正確的結(jié)果.如需更多信息，請參閱 http://www.intel.com/software/products/support/.
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 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/.
我正在安裝 macOS Mojave 10.14.1 的 MacBook Pro 15" 2015 上運行測試程序.我目前安裝的 Anaconda 發(fā)行版是 https://repo.anaconda.com/archive/Anaconda2-5.3.0-MacOSX-x86_64.sh.
I'm running the test program on my MacBook Pro 15" 2015 where it is installed macOS Mojave 10.14.1. The Anaconda distribution that I have currently installed is https://repo.anaconda.com/archive/Anaconda2-5.3.0-MacOSX-x86_64.sh.
這是測試程序:
#!/usr/bin/env python
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow import keras
Xs = np.array([
[0, 0],
[0, 1],
[1, 1],
[1, 0]
])
Ys = np.array([
[0],
[1],
[0],
[1]
])
class MyCallback(keras.callbacks.Callback):
def __init__(self):
super(MyCallback, self).__init__()
self.stats = []
def on_epoch_end(self, epoch, logs=None):
self.stats.append({
'loss': logs['loss'],
'acc': logs['acc'],
'epoch': epoch
})
def on_train_end(self, logs=None):
loss_x = []
loss_y = []
acc_x = []
acc_y = []
for e in self.stats:
loss_x.append(e['epoch'])
loss_y.append(e['loss'])
acc_x.append(e['epoch'])
acc_y.append(e['acc'])
plt.plot(loss_x, loss_y, 'r', label='Loss')
plt.plot(acc_x, acc_y, 'b', label='Accuracy')
plt.xlabel('Epochs')
plt.ylabel('Loss / Accuracy')
plt.legend(loc='upper left')
plt.show()
with tf.Session() as session:
model = keras.models.Sequential()
model.add(keras.layers.Dense(10, activation=keras.activations.elu, input_dim=2))
model.add(keras.layers.Dense(1, activation=keras.activations.sigmoid))
model.compile(optimizer=keras.optimizers.Adam(lr=0.05),
loss=keras.losses.mean_squared_error,
metrics=['accuracy'])
model.fit(x=Xs, y=Ys, batch_size=4, epochs=50, callbacks=[MyCallback()])
print("Training complete")
loss, acc = model.evaluate(Xs, Ys)
print(f"loss: {loss} - acc: {acc}")
predictions = model.predict(Xs)
print("predictions")
print(predictions)
我已經(jīng)嘗試參考 答案initial">this 相關(guān)問題.因此，在 import 部分之后添加以下代碼行:
I already tried to fix the issue referencing to the answer of this related question. Thus, adding the following lines of code after the import section:
import os
os.environ['KMP_DUPLICATE_LIB_OK'] = 'True'
我得到的是另一個錯誤消息，這是完整的堆棧跟蹤:
What I get is another error message, this is the full stack trace:
2018-12-06 10:18:34.262 python[19319:371282] -[NSApplication _setup:]: unrecognized selector sent to instance 0x7ff2b07a3d00
2018-12-06 10:18:34.266 python[19319:371282] *** Terminating app due to uncaught exception 'NSInvalidArgumentException', reason: '-[NSApplication _setup:]: unrecognized selector sent to instance 0x7ff2b07a3d00'
*** First throw call stack:
(
0 CoreFoundation 0x00007fff2ccf0e65 __exceptionPreprocess + 256
1 libobjc.A.dylib 0x00007fff58d47720 objc_exception_throw + 48
2 CoreFoundation 0x00007fff2cd6e22d -[NSObject(NSObject) __retain_OA] + 0
3 CoreFoundation 0x00007fff2cc92820 ___forwarding___ + 1486
4 CoreFoundation 0x00007fff2cc921c8 _CF_forwarding_prep_0 + 120
5 libtk8.6.dylib 0x0000000b36aeb31d TkpInit + 413
6 libtk8.6.dylib 0x0000000b36a4317e Initialize + 2622
7 _tkinter.cpython-36m-darwin.so 0x0000000b3686ba16 _tkinter_create + 1174
8 python 0x000000010571c088 _PyCFunction_FastCallDict + 200
9 python 0x00000001057f2f4f call_function + 143
10 python 0x00000001057f0abf _PyEval_EvalFrameDefault + 46847
11 python 0x00000001057e4209 _PyEval_EvalCodeWithName + 425
12 python 0x00000001057f3b1c _PyFunction_FastCallDict + 364
13 python 0x000000010569a8b0 _PyObject_FastCallDict + 320
14 python 0x00000001056c1fe8 method_call + 136
15 python 0x00000001056a1efe PyObject_Call + 62
16 python 0x0000000105743385 slot_tp_init + 117
17 python 0x00000001057478c1 type_call + 241
18 python 0x000000010569a821 _PyObject_FastCallDict + 177
19 python 0x00000001056a2a67 _PyObject_FastCallKeywords + 327
20 python 0x00000001057f3048 call_function + 392
21 python 0x00000001057f0b6f _PyEval_EvalFrameDefault + 47023
22 python 0x00000001057f330c fast_function + 188
23 python 0x00000001057f2fac call_function + 236
24 python 0x00000001057f0abf _PyEval_EvalFrameDefault + 46847
25 python 0x00000001057e4209 _PyEval_EvalCodeWithName + 425
26 python 0x00000001057f3b1c _PyFunction_FastCallDict + 364
27 python 0x000000010569a8b0 _PyObject_FastCallDict + 320
28 python 0x00000001056c1fe8 method_call + 136
29 python 0x00000001056a1efe PyObject_Call + 62
30 python 0x00000001057f0cc0 _PyEval_EvalFrameDefault + 47360
31 python 0x00000001057e4209 _PyEval_EvalCodeWithName + 425
32 python 0x00000001057f33ba fast_function + 362
33 python 0x00000001057f2fac call_function + 236
34 python 0x00000001057f0abf _PyEval_EvalFrameDefault + 46847
35 python 0x00000001057f330c fast_function + 188
36 python 0x00000001057f2fac call_function + 236
37 python 0x00000001057f0abf _PyEval_EvalFrameDefault + 46847
38 python 0x00000001057e4209 _PyEval_EvalCodeWithName + 425
39 python 0x00000001057f33ba fast_function + 362
40 python 0x00000001057f2fac call_function + 236
41 python 0x00000001057f0abf _PyEval_EvalFrameDefault + 46847
42 python 0x00000001057e4209 _PyEval_EvalCodeWithName + 425
43 python 0x00000001057f33ba fast_function + 362
44 python 0x00000001057f2fac call_function + 236
45 python 0x00000001057f0b6f _PyEval_EvalFrameDefault + 47023
46 python 0x00000001057e4209 _PyEval_EvalCodeWithName + 425
47 python 0x00000001057f33ba fast_function + 362
48 python 0x00000001057f2fac call_function + 236
49 python 0x00000001057f0abf _PyEval_EvalFrameDefault + 46847
50 python 0x00000001057e4209 _PyEval_EvalCodeWithName + 425
51 python 0x00000001057f33ba fast_function + 362
52 python 0x00000001057f2fac call_function + 236
53 python 0x00000001057f0abf _PyEval_EvalFrameDefault + 46847
54 python 0x00000001057e4209 _PyEval_EvalCodeWithName + 425
55 python 0x00000001057f33ba fast_function + 362
56 python 0x00000001057f2fac call_function + 236
57 python 0x00000001057f0b6f _PyEval_EvalFrameDefault + 47023
58 python 0x00000001057e4209 _PyEval_EvalCodeWithName + 425
59 python 0x00000001057f33ba fast_function + 362
60 python 0x00000001057f2fac call_function + 236
61 python 0x00000001057f0b6f _PyEval_EvalFrameDefault + 47023
62 python 0x00000001057e4209 _PyEval_EvalCodeWithName + 425
63 python 0x000000010583cd4c PyRun_FileExFlags + 252
64 python 0x000000010583c224 PyRun_SimpleFileExFlags + 372
65 python 0x0000000105862d66 Py_Main + 3734
66 python 0x0000000105692929 main + 313
67 libdyld.dylib 0x00007fff59e1608d start + 1
68 ??? 0x0000000000000002 0x0 + 2
)
libc++abi.dylib: terminating with uncaught exception of type NSException
這里是環(huán)境中安裝的相關(guān)依賴的列表(不相關(guān)的依賴為了簡潔省略):
Here is a list of the related dependencies installed in the environment (not related dependencies are omitted for brevity):
Name | Version Build
--------------------|----------------|----------------------
_tflow_select | 2.3.0 | mkl
blas | 1.0 | mkl
intel-openmp | 2019.1 | 144
matplotlib | 3.0.1 | py36h54f8f79_0
mkl | 2018.0.3 | 1
mkl_fft | 1.0.6 | py36hb8a8100_0
mkl_random | 1.0.1 | py36h5d10147_1
numpy | 1.15.4 | py36h6a91979_0
numpy-base | 1.15.4 | py36h8a80b8c_0
tensorboard | 1.12.0 | py36hdc36e2c_0
tensorflow | 1.12.0 | mkl_py36h2b2bbaf_0
tensorflow-base | 1.12.0 | mkl_py36h70e0e9a_0
推薦答案
在大多數(shù)情況下，這樣可以解決問題:
In most cases, this solves the problem:
conda install nomkl
這篇關(guān)于OMP:錯誤 #15:正在初始化 libiomp5.dylib，但發(fā)現(xiàn) libiomp5.dylib 已初始化的文章就介紹到這了，希望我們推薦的答案對大家有所幫助，也希望大家多多支持html5模板網(wǎng)！