非法指令：运行任何 Tensorflow 程序时出现 4 错误

Question

我正在尝试训练一个 Tensorflow 卷积神经网络，无论我运行程序的环境如何，我总是遇到一个神秘的错误。

在 Jupyter Notebook 中，内核会死掉。

在终端中，我得到“非法指令：4”而没有回溯。

在 Pycharm 中，我得到：“进程已完成，退出代码为 132（被信号 4：SIGILL 中断）”。

我查看了整个互联网，但没有发现在这种情况下抛出此特定错误的任何实例。如果有人可以帮助阐明此错误，我将不胜感激。

我正在使用 Mac OS X High Sierra 和 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。

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('[]\n,') 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。

Answer 1

好的，如果你有 2.66 GHz 版本，在我看来它是 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.

除非你的 CPU 是 Sandy Bridge 或更新的（所以 AVX 指令）

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

降级版本见。
非法指令（核心转储）
tensorflow 在 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，请检查机器包含什么.