无法使用标准 32 位 LAPACK 执行计算 [英] computation cannot be performed with standard 32-bit LAPACK
问题描述
我正在使用 VGG 训练我的数据集，如下所示.没有zca白化效果很好，但是加入zca后，出现了一个叫做
I am training my dataset using VGG as below. It worked well without zca whitening, but after adding zca, it cause an error called
无法使用标准 32 位 LAPACK 执行计算"
"computation cannot be performed with standard 32-bit LAPACK"
.如您所见，我尝试将batchsize..etc..的数量训练为1，甚至只训练6张图像，但仍然无法正常工作.我该怎么办?
. As you can see, I tried to train the number of batchsize..etc.. to 1, and even just train with 6 images, but it still din not work. What should I do?
这是我的代码.
import os
from keras.applications.vgg16 import VGG16
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential, Model
from keras.layers import Input, Activation, Dropout, Flatten, Dense
from keras.preprocessing.image import ImageDataGenerator
from keras import optimizers
import numpy as np
import time
from PIL import Image
import csv
import shutil
# 分類するクラス
classes = ['sugi', 'hinoki']
nb_classes = len(classes)
img_width, img_height = 256, 256
# トレーニング用とバリデーション用の画像格納先
train_data_dir = 'dataset/train1'
#validation_data_dir = 'dataset/validation'
# 今回はトレーニング用に200枚、バリデーション用に50枚の画像を用意した。
nb_train_samples = 1998
#nb_validation_samples = 50
batch_size = 32
nb_epoch = 10
gen_tr_batches = 4
folder = './output'
result_dir = 'results'
if not os.path.exists(result_dir):
os.mkdir(result_dir)
train_imagelist = os.listdir(train_data_dir)
def vgg_model_maker():
""" VGG16のモデルをFC層以外使用。FC層のみ作成して結合して用意する """
# VGG16のロード。FC層は不要なので include_top=False
input_tensor = Input(shape=(img_width, img_height, 3))
vgg16 = VGG16(include_top=False, weights='imagenet', input_tensor=input_tensor)
# FC層の作成
top_model = Sequential()
top_model.add(Flatten(input_shape=vgg16.output_shape[1:]))
top_model.add(Dense(256, activation='relu'))
top_model.add(Dropout(0.5))
top_model.add(Dense(nb_classes, activation='softmax'))
# VGG16とFC層を結合してモデルを作成
model = Model(input=vgg16.input, output=top_model(vgg16.output))
return model
def image_generator():
""" ディレクトリ内の画像を読み込んでトレーニングデータとバリデーションデータの作成 """
gen_train = (ImageDataGenerator(rescale=1.0 / 255.).flow_from_directory(train_data_dir,
target_size=(img_width, img_height),
#color_mode='rgb',
batch_size=batch_size,
shuffle=True))
gen_tr_x = np.vstack(next(gen_train)[0] for _ in range(gen_tr_batches))
#train_datagen = ImageDataGenerator(
# rescale=1.0 / 255,
# zoom_range=0.2,
# horizontal_flip=True,
# zca_whitening = True)
g = ImageDataGenerator(rescale=1.0 / 255.,
zca_whitening=True)
g.fit(gen_tr_x)
#validation_datagen = ImageDataGenerator(rescale=1.0 / 255)
train_generator = g.flow_from_directory(
train_data_dir,
classes=classes,
class_mode='categorical')
#validation_generator = validation_datagen.flow_from_directory(
# validation_data_dir,
#target_size=(img_width, img_height),
#color_mode='rgb',
#classes=classes,
#class_mode='categorical',
#batch_size=batch_size,
#shuffle=True)
return (train_generator)
# Generator for the network's training generator.
# Actual generator for the network's training.
if __name__ == '__main__':
start = time.time()
for the_file in os.listdir(folder):
file_path = os.path.join(folder, the_file)
try:
if os.path.isfile(file_path):
os.unlink(file_path)
#elif os.path.isdir(file_path): shutil.rmtree(file_path)
except Exception as e:
print(e)
# モデル作成
vgg_model = vgg_model_maker()
# 最後のconv層の直前までの層をfreeze
for layer in vgg_model.layers[:15]:
layer.trainable = False
# 多クラス分類を指定
vgg_model.compile(loss='categorical_crossentropy',
optimizer=optimizers.SGD(lr=1e-3, momentum=0.9),
metrics=['accuracy'])
# 画像のジェネレータ生成
train_generator = image_generator()
# Fine-tuning
history_callback = vgg_model.fit_generator(
train_generator,
samples_per_epoch=nb_train_samples,
nb_epoch=nb_epoch)
#validation_data=validation_generator,
#nb_val_samples=nb_validation_samples)
loss_history = history_callback.history["loss"]
accuracy_history = history_callback.history["acc"]
numpy_loss_history = np.array(loss_history)
numpy_accuracy_history = np.array(accuracy_history)
f = open("result.csv","w")
writer = csv.writer(f)
writer.writerow(["loss","accuracy"])
for j in range(len(numpy_loss_history)):
writer.writerow([numpy_loss_history[j],numpy_accuracy_history[j]])
vgg_model.save_weights(os.path.join(result_dir, 'finetuning.h5'))
process_time = (time.time() - start) / 60
print(u'学習終了。かかった時間は', process_time, u'分です。')
推荐答案
我猜，这不是 tensorflow 问题，而是 numpy 问题，我猜那是因为这里是什么当您尝试将 zca 参数设置为 True 时发生的情况:
My guess is, it's not a tensorflow issue, but rather a numpy issue, and I guess that because here's what's happening when you try to set the zca parameter to True:
if self.zca_whitening:
if self.principal_components is not None:
flatx = np.reshape(x, (-1, np.prod(x.shape[-3:])))
whitex = np.dot(flatx, self.principal_components)
x = np.reshape(whitex, x.shape)
默认情况下，当您安装 numpy 时，它会尝试找到安装在您系统上的低级线性代数库，并使用它.LAPACK 就是其中之一.
By default, when you install numpy, it tries to find a low level linear algebra library installed on your system, and use that. LAPACK is one of them.
numpy 将使用自己的代码.因此，请尝试按照 文档:
numpy will use its own code if no library is available. So try installing your numpy without any of those libraries as suggested in the docs:
BLAS=None LAPACK=None ATLAS=None python setup.py build
如果仍然使用这些库，请尝试此处.
If that still uses the libraries, try the solution given here.
然后，如果上述解决方法解决了您的问题，请尝试编译 64 位 LAPACK 并针对它编译您的 numpy.
Then, if the above workaround solves your problem, try compiling a 64bit LAPACK and compile your numpy against it.
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