非法指令:运行任何 Tensorflow 程序时出现 4 错误 [英] Illegal Instruction: 4 error when running any Tensorflow program
问题描述
我正在尝试训练 Tensorflow 卷积神经网络，但无论我运行程序的环境如何，我总是遇到一个神秘的错误.
I am trying to train a Tensorflow Convolutional Neural Network, and I am always getting a cryptic error regardless of the environment in which i run the program.
在 Jupyter Notebook 中，内核会死掉.
In Jupyter Notebook, the kernel simply dies.
在终端中，我收到Illegal Instruction: 4"且没有回溯.
In Terminal, I get "Illegal Instruction: 4" with no Traceback.
在 Pycharm 中，我得到:进程已完成，退出代码为 132(被信号 4:SIGILL 中断)".
In Pycharm, I get: "Process finished with exit code 132 (interrupted by signal 4: SIGILL)".
我查看了整个 Internet，但没有发现在这种情况下引发此特定错误的任何实例.如果有人能帮助阐明此错误，我将不胜感激.
I have looked all over the Internet and I have not found any instance in which this particular error was thrown in this situation. I would appreciate it if someone could help shed some light on this error.
我使用 Mac OS X High Sierra 和 python 3.6.2
I am using Mac OS X High Sierra with python 3.6.2
我的代码可以在下面找到，正如我之前所说，没有回溯.
My code can be found below and, as I said earlier, there is no traceback.
import tensorflow as tf
import numpy as np
import pandas as pd
# OS to load files and save checkpoints
import os
image_height = 60
image_width = 1
image1_height = 15
image2_width = 1
model_name = "tensorflowCNN"
train_data = np.asarray(pd.read_csv("/home/student/Desktop/TrainingInput.csv", usecols=[1]))
lis = train_data.tolist()
lis = lis[0:60]
lis = [x[0].strip('[]
,') for x in lis]
nlis = []
for i in lis:
nlis.append(i.split())
for i in range(len(nlis)):
nlis[i] = [float(x) for x in nlis[i] if x != "...,"]
nlis = [np.mean(x) for x in nlis]
train_data = np.asarray(nlis)
train_labels = np.asarray(pd.read_csv("/home/student/Desktop/TrainingInput.csv", usecols=[2]))
mylist = train_labels.tolist()
mylist = mylist[0:60]
mylist = [x[0] for x in mylist]
for i in range(len(mylist)):
if mylist[i] == "GravelTraining":
mylist[i] = 1.0
elif mylist[i] == "WaterTraining":
mylist[i] = 2.0
else:
mylist[i] = 3.0
print(mylist)
train_labels = np.asarray(mylist)
eval_data = np.asarray(pd.read_csv("/home/student/Desktop/TestingInput.csv", usecols=[1]))
List = eval_data.tolist()
List = List[0:15]
eval_data = np.asarray(List)
eval_labels = np.asarray(pd.read_csv("/home/student/Desktop/TestingInput.csv", usecols=[2]))
myList = eval_labels.tolist()
myList = myList[0:15]
eval_labels = np.asarray(myList)
category_names = list(map(str, range(3)))
# TODO: Process mnist data
train_data = np.reshape(train_data, (-1, image_height, image_width, 1))
train_labels = np.reshape(train_labels, (-1, image_height, image_width, 1))
eval_labels = np.reshape(eval_labels, (-1, image1_height, image2_width, 1))
eval_data = np.reshape(eval_data, (-1, image1_height, image2_width, 1))
# TODO: The neural network
class ConvNet:
def __init__(self, image_height, Image_width, num_classes, chan):
self.input_layer = tf.placeholder(dtype=tf.float32, shape=[1, image_height, Image_width, chan], name="inputs")
conv_layer_1 = tf.layers.conv2d(self.input_layer, filters=32, kernel_size=[5, 5], padding="same",
activation=tf.nn.relu)
pooling_layer_1 = tf.layers.max_pooling2d(conv_layer_1, pool_size=[2, 1], strides=1)
conv_layer_2 = tf.layers.conv2d(pooling_layer_1, filters=64, kernel_size=[5, 5], padding="same",
activation=tf.nn.relu)
pooling_layer_2 = tf.layers.max_pooling2d(conv_layer_2, pool_size=[2,1], strides=2)
conv_layer_3 = tf.layers.conv2d(pooling_layer_2, filters=128, kernel_size=[5,5], padding="same",
activation=tf.nn.relu)
pooling_layer_3 = tf.layers.max_pooling2d(conv_layer_3, pool_size=[2,1], strides=2)
flattened_pooling = tf.layers.flatten(pooling_layer_1)
dense_layer = tf.layers.dense(flattened_pooling, 60, activation=tf.nn.relu)
dropout = tf.layers.dropout(dense_layer, rate=0.4, training=True)
output_dense_layer = tf.layers.dense(dropout, num_classes)
self.choice = tf.argmax(output_dense_layer, axis=1)
self.probabilities = tf.nn.softmax(output_dense_layer)
self.labels = tf.placeholder(dtype=tf.float32, name="labels")
self.accuracy, self.accuracy_op = tf.metrics.accuracy(self.labels, self.choice)
one_hot_labels = tf.one_hot(indices=tf.cast(self.labels, dtype=tf.int32), depth=num_classes)
self.loss = tf.losses.softmax_cross_entropy(onehot_labels=one_hot_labels, logits=output_dense_layer)
optimizer = tf.train.GradientDescentOptimizer(learning_rate=1e-2)
self.train_operation = optimizer.minimize(loss=self.loss, global_step=tf.train.get_global_step())
# Training process:variables
training_steps = 20000
batch_size = 60
path = "./" + model_name + "-cnn/"
load_checkpoint = False
tf.reset_default_graph()
dataset = tf.data.Dataset.from_tensor_slices((train_data, train_labels))
dataset = dataset.shuffle(buffer_size=train_labels.shape[0])
dataset = dataset.batch(batch_size)
dataset = dataset.repeat()
dataset_iterator = dataset.make_initializable_iterator()
next_element = dataset_iterator.get_next()
cnn = ConvNet(image_height, image_width, 1, 1)
print("milestone1")
saver = tf.train.Saver(max_to_keep=2)
print('milestone2')
if not os.path.exists(path):
os.makedirs(path)
print('milestone3')
with tf.Session() as sess:
# if load_checkpoint:
# print(path)
# checkpoint = tf.train.get_checkpoint_state(path)
# print(checkpoint)
# saver.restore(sess, checkpoint.model_checkpoint_path)
# else:
sess.run(tf.global_variables_initializer())
print('milestone4')
sess.run(tf.local_variables_initializer())
sess.run(dataset_iterator.initializer)
for step in range(training_steps):
current_batch = sess.run(next_element)
batch_inputs = current_batch[0]
batch_labels = current_batch[1]
sess.run((cnn.train_operation, cnn.accuracy_op),
feed_dict={cnn.input_layer: batch_inputs, cnn.labels: batch_labels})
if step % 1 == 0 and step > 0:
current_acc = sess.run(cnn.accuracy)
print("Accuracy at step " + str(step) + ":" + str(current_acc))
saver.save(sess, path + model_name, step)
print("Saving final checkpoint for training session.")
saver.save(sess, path + model_name, step)
提前致谢.
推荐答案
OK如果你有 2.66 GHz 版本，在我看来是 2010 年发布的 Arrendale 架构，在这种情况下，它绝对不可能工作，因为它没有最新的张量流二进制文件所需的 AVX 指令.
OK If you have the 2.66 GHz version that seems to me to be the the Arrendale architecture released 2010 in which case there is absolutely no chance it's going to work since that thing doesn't have AVX instructions as needed by the latest binaries of tensor flow.
除非你的 CPU 是 Sandy Bridge 或更新的(所以 AVX 指令)
Unless your CPU is Sandy Bridge or newer (so AVX instructions)
您的选择是:
1) 获得更新的 CPU
2)安装旧版本的张量流
3)从源代码编译张量流
1) get a newer CPU
2) install an older version of tensor flow
3) compile tensor flow from source
降级版本见.
非法指令(核心转储)张量流
Python中导入tensorflow时的非法指令
编辑
似乎虽然所有 Core(i3、i5、i7)和至强 CPU Sandy Bridge 和更新版本都支持 AVX，但截至 2018 年，Celron 和 Pentium CPU 都不是这样.如果购买硬件，请检查机器包含哪些内容.
EDIT
It seems that although all Core (i3, i5, i7) and Xeon CPU Sandy Bridge and newer support AVX, this is not true of Celron, nor Pentium CPU's to this day, as of 2018. Check what the machine contains if buying hardware.
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