centos7安装matplotlib sklearn pandas

sklearn pandas

系统环境
1.安装pandas

测试
出现问题

2. 安装sklearn
3. 配置离线fashion_mnist数据集

能连上google的时候是这样
不能科学上网的,我们就下载数据集

4. 安装matplotlib
5. 测试

系统环境

1 2	CentOS Linux release 7.7.1908 (Core) Linux version 3.10.0-1062.1.1.el7.x86_64 ([email protected]) (gcc version 4.8.5 20150623 (Red Hat 4.8.5-39) (GCC) ) #1 SMP Fri Sep 13 22:55:44 UTC 2019

1.安装pandas

在已经安装的环境

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# 激活该环境
conda activate tensorflow2_env

# 安装pandas

测试

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>>> import pandas
>>> pandas.__version__
'1.0.3'

出现问题

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# 1
ImportError: libSM.so.6: cannot open shared object file: No such file or directory

yum whatprovides libSM.so.6
yum install libSM-1.2.2-2.el7.x86_64 --setopt=protected_multilib=false

# 2
ImportError: libXrender.so.1: cannot open shared object file: No such file or directory

yum whatprovides libXrender.so.1
yum isntall libXrender

# 3
ImportError: libXext.so.6: cannot open shared object file: No such file or directory
>>> exit()
yum install libXext

2. 安装sklearn

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conda install sklearn
# conda没有
pip install sklearn

3. 配置离线fashion_mnist数据集

能连上google的时候是这样

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from tensorflow import keras
fashion_mnist = keras.datasets.fashion_mnist
(x_train_all, y_train_all), (x_test, y_test) = fashion_mnist.load_data()

不能科学上网的,我们就下载数据集

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http://www.obs.sixmillions.cn/fashion_mnist/t10k-images-idx3-ubyte.gz
http://www.obs.sixmillions.cn/fashion_mnist/t10k-labels-idx1-ubyte.gz
http://www.obs.sixmillions.cn/fashion_mnist/train-images-idx3-ubyte.gz
http://www.obs.sixmillions.cn/fashion_mnist/train-labels-idx1-ubyte.gz

我下载到了 /opt/packages/keras_datasets/fashion_mnist/ 文件夹

编辑当前python路径里面的fashion_mnist.py 文件
因为我是用anaconda安装的环境,所以我去anaconda环境中找这个文件

编辑这个文件
本来是这样的

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from __future__ import print_function

import gzip
import os

import numpy as np

from tensorflow.python.keras.utils.data_utils import get_file
from tensorflow.python.util.tf_export import keras_export

@keras_export('keras.datasets.fashion_mnist.load_data')
def load_data():
"""Loads the Fashion-MNIST dataset.

Returns:
Tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`.

License:
The copyright for Fashion-MNIST is held by Zalando SE.
Fashion-MNIST is licensed under the [MIT license](
https://github.com/zalandoresearch/fashion-mnist/blob/master/LICENSE).

"""
dirname = os.path.join('datasets', 'fashion-mnist')
base = 'https://storage.googleapis.com/tensorflow/tf-keras-datasets/'
files = [
'train-labels-idx1-ubyte.gz', 'train-images-idx3-ubyte.gz',
't10k-labels-idx1-ubyte.gz', 't10k-images-idx3-ubyte.gz'
]

paths = []
for fname in files:
paths.append(get_file(fname, origin=base + fname, cache_subdir=dirname))

with gzip.open(paths[0], 'rb') as lbpath:
y_train = np.frombuffer(lbpath.read(), np.uint8, offset=8)

with gzip.open(paths[1], 'rb') as imgpath:
x_train = np.frombuffer(
imgpath.read(), np.uint8, offset=16).reshape(len(y_train), 28, 28)

with gzip.open(paths[2], 'rb') as lbpath:
y_test = np.frombuffer(lbpath.read(), np.uint8, offset=8)

with gzip.open(paths[3], 'rb') as imgpath:
x_test = np.frombuffer(
imgpath.read(), np.uint8, offset=16).reshape(len(y_test), 28, 28)

return (x_train, y_train), (x_test, y_test)

将数据源那个地方换成我们的

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编辑后

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import gzip
import os

import numpy as np

from tensorflow.python.keras.utils.data_utils import get_file
from tensorflow.python.util.tf_export import keras_export

@keras_export('keras.datasets.fashion_mnist.load_data')
def load_data():
"""Loads the Fashion-MNIST dataset.

Returns:
Tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`.

License:
The copyright for Fashion-MNIST is held by Zalando SE.
Fashion-MNIST is licensed under the [MIT license](
https://github.com/zalandoresearch/fashion-mnist/blob/master/LICENSE).

"""
dirname = os.path.join('datasets', 'fashion-mnist')
#base = 'https://storage.googleapis.com/tensorflow/tf-keras-datasets/'
base = '/opt/packages/keras_datasets/fashion_mnist/'
files = [
'train-labels-idx1-ubyte.gz', 'train-images-idx3-ubyte.gz',
't10k-labels-idx1-ubyte.gz', 't10k-images-idx3-ubyte.gz'
]

paths = []
for fname in files:
#paths.append(get_file(fname, origin=base + fname, cache_subdir=dirname))
paths.append(base + fname)

with gzip.open(paths[0], 'rb') as lbpath:
y_train = np.frombuffer(lbpath.read(), np.uint8, offset=8)

with gzip.open(paths[1], 'rb') as imgpath:
x_train = np.frombuffer(
imgpath.read(), np.uint8, offset=16).reshape(len(y_train), 28, 28)

with gzip.open(paths[2], 'rb') as lbpath:
y_test = np.frombuffer(lbpath.read(), np.uint8, offset=8)

with gzip.open(paths[3], 'rb') as imgpath:
x_test = np.frombuffer(
imgpath.read(), np.uint8, offset=16).reshape(len(y_test), 28, 28)

return (x_train, y_train), (x_test, y_test)

注意路径后面有个斜杠

4. 安装matplotlib

1	conda install matplotlib

5. 测试

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import matplotlib as mpl
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
import sklearn
import pandas as pd
import os
import sys
import time
import tensorflow as tf
#import cv2
from tensorflow import keras
print(tf.__version__)
#print(cv2.__version__)

print(sys.version_info)
for moudule in mpl, np, pd, sklearn, tf, keras:
print(moudule.__name__, moudule.__version__)

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fashion_mnist = keras.datasets.fashion_mnist
# 训练集测试集分开
(x_train_all, y_train_all), (x_test, y_test) = fashion_mnist.load_data()
# 训练集拆分为验证集(前5000)和训练(后)
x_valid, x_train = x_train_all[:5000], x_train_all[5000:]
y_valid, y_train = y_train_all[:5000], y_train_all[5000:]

print(x_valid.shape, y_valid.shape)
print(x_train.shape, y_train.shape)
print(x_test.shape, y_test.shape)

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def show_single_image(img_arr):
plt.imshow(img_arr, cmap='binary')
plt.show()

show_single_image(x_train[0])