Ubuntu18.04+python3.6+pip3入门tensorflow
最近课程要求学习tensorflow，在中文社区跑了一下入门例程，在这总结一下踩的一些坑
学习过程如下：
根据官方入门手册我进行的是最简单的二进制安装cpu版本
pip install https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.5.0-cp27-none-linux_x86_64.whl
然后就是测试例程：
运行 TensorFlow
打开一个 python 终端:
$ python
>>> import tensorflow as tf
>>> hello = tf.constant('Hello, TensorFlow!')
>>> sess = tf.Session()
>>> print sess.run(hello)
Hello, TensorFlow!
>>> a = tf.constant(10)
>>> b = tf.constant(32)
>>> print sess.run(a+b)
42
>>>
这些都十分顺利，一点问题都没有。
接下来就是：
面向机器学习初学者的 MNIST 初级教程
这是整个新手入门最坑的地方。。。
先来看入门手册开篇：
TensorFlow Python API 依赖 Python 2.7 版本.
在 Linux 和 Mac 下最简单的安装方式, 是使用 pip 安装.
这是官方手册原话，我照搬过来的。
按他手册走，接着开始mnist数据集入门：
MNIST数据集的官网是Yann LeCun’s website。在这里，我们提供了一份python源代码用于自动下载和安装这个数据集。你可以下载这份代码，然后用下面的代码导入到你的项目里面，也可以直接复制粘贴到你的代码文件里面。
直接按连接是打不开的，googlesource不能直接访问。
源码我也贴在这：
input_data.py
# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Functions for downloading and reading MNIST data (deprecated).
This module and all its submodules are deprecated.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import gzip
import os
import numpy
from six.moves import urllib
from six.moves import xrange # pylint: disable=redefined-builtin
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated
_Datasets = collections.namedtuple('_Datasets', ['train', 'validation', 'test'])
# CVDF mirror of http://yann.lecun.com/exdb/mnist/
DEFAULT_SOURCE_URL = 'https://storage.googleapis.com/cvdf-datasets/mnist/'
def _read32(bytestream):
dt = numpy.dtype(numpy.uint32).newbyteorder('>')
return numpy.frombuffer(bytestream.read(4), dtype=dt)[0]
@deprecated(None, 'Please use tf.data to implement this functionality.')
def _extract_images(f):
"""Extract the images into a 4D uint8 numpy array [index, y, x, depth].
Args:
f: A file object that can be passed into a gzip reader.
Returns:
data: A 4D uint8 numpy array [index, y, x, depth].
Raises:
ValueError: If the bytestream does not start with 2051.
"""
print('Extracting', f.name)
with gzip.GzipFile(fileobj=f) as bytestream:
magic = _read32(bytestream)
if magic != 2051:
raise ValueError('Invalid magic number %d in MNIST image file: %s' %
(magic, f.name))
num_images = _read32(bytestream)
rows = _read32(bytestream)
cols = _read32(bytestream)
buf = bytestream.read(rows * cols * num_images)
data = numpy.frombuffer(buf, dtype=numpy.uint8)
data = data.reshape(num_images, rows, cols, 1)
return data
@deprecated(None, 'Please use tf.one_hot on tensors.')
def _dense_to_one_hot(labels_dense, num_classes):
"""Convert class labels from scalars to one-hot vectors."""
num_labels = labels_dense.shape[0]
index_offset = numpy.arange(num_labels) * num_classes
labels_one_hot = numpy.zeros((num_labels, num_classes))
labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1
return labels_one_hot
@deprecated(None, 'Please use tf.data to implement this functionality.')
def _extract_labels(f, one_hot=False, num_classes=10):
"""Extract the labels into a 1D uint8 numpy array [index].
Args:
f: A file object that can be passed into a gzip reader.
one_hot: Does one hot encoding for the result.
num_classes: Number of classes for the one hot encoding.
Returns:
labels: a 1D uint8 numpy array.
Raises:
ValueError: If the bystream doesn't start with 2049.
"""
print('Extracting', f.name)
with gzip.GzipFile(fileobj=f) as bytestream:
magic = _read32(bytestream)
if magic != 2049:
raise ValueError('Invalid magic number %d in MNIST label file: %s' %
(magic, f.name))
num_items = _read32(bytestream)
buf = bytestream.read(num_items)
labels = numpy.frombuffer(buf, dtype=numpy.uint8)
if one_hot:
return _dense_to_one_hot(labels, num_classes)
return labels
class _DataSet(object):
"""Container class for a _DataSet (deprecated).
THIS CLASS IS DEPRECATED.
"""
@deprecated(None, 'Please use alternatives such as official/mnist/_DataSet.py'
' from tensorflow/models.')
def __init__(self,
images,
labels,
fake_data=False,
one_hot=False,
dtype=dtypes.float32,
reshape=True,
seed=None):
"""Construct a _DataSet.
one_hot arg is used only if fake_data is true. `dtype` can be either
`uint8` to leave the input as `[0, 255]`, or `float32` to rescale into
`[0, 1]`. Seed arg provides for convenient deterministic testing.
Args:
images: The images
labels: The labels
fake_data: Ignore inages and labels, use fake data.
one_hot: Bool, return the labels as one hot vectors (if True) or ints (if
False).
dtype: Output image dtype. One of [uint8, float32]. `uint8` output has
range [0,255]. float32 output has range [0,1].
reshape: Bool. If True returned images are returned flattened to vectors.
seed: The random seed to use.
"""
seed1, seed2 = random_seed.get_seed(seed)
# If op level seed is not set, use whatever graph level seed is returned
numpy.random.seed(seed1 if seed is None else seed2)
dtype = dtypes.as_dtype(dtype).base_dtype
if dtype not in (dtypes.uint8, dtypes.float32):
raise TypeError('Invalid image dtype %r, expected uint8 or float32' %
dtype)
if fake_data:
self._num_examples = 10000
self.one_hot = one_hot
else:
assert images.shape[0] == labels.shape[0], (
'images.shape: %s labels.shape: %s' % (images.shape, labels.shape))
self._num_examples = images.shape[0]
# Convert shape from [num examples, rows, columns, depth]
# to [num examples, rows*columns] (assuming depth == 1)
if reshape:
assert images.shape[3] == 1
images = images.reshape(images.shape[0],
images.shape[1] * images.shape[2])
if dtype == dtypes.float32:
# Convert from [0, 255] -> [0.0, 1.0].
images = images.astype(numpy.float32)
images = numpy.multiply(images, 1.0 / 255.0)
self._images = images
self._labels = labels
self._epochs_completed = 0
self._index_in_epoch = 0
@property
def images(self):
return self._images
@property
def labels(self):
return self._labels
@property
def num_examples(self):
return self._num_examples
@property
def epochs_completed(self):
return self._epochs_completed
def next_batch(self, batch_size, fake_data=False, shuffle=True):
"""Return the next `batch_size` examples from this data set."""
if fake_data:
fake_image = [1] * 784
if self.one_hot:
fake_label = [1] + [0] * 9
else:
fake_label = 0
return [fake_image for _ in xrange(batch_size)
], [fake_label for _ in xrange(batch_size)]
start = self._index_in_epoch
# Shuffle for the first epoch
if self._epochs_completed == 0 and start == 0 and shuffle:
perm0 = numpy.arange(self._num_examples)
numpy.random.shuffle(perm0)
self._images = self.images[perm0]
self._labels = self.labels[perm0]
# Go to the next epoch
if start + batch_size > self._num_examples:
# Finished epoch
self._epochs_completed += 1
# Get the rest examples in this epoch
rest_num_examples = self._num_examples - start
images_rest_part = self._images[start:self._num_examples]
labels_rest_part = self._labels[start:self._num_examples]
# Shuffle the data
if shuffle:
perm = numpy.arange(self._num_examples)
numpy.random.shuffle(perm)
self._images = self.images[perm]
self._labels = self.labels[perm]
# Start next epoch
start = 0
self._index_in_epoch = batch_size - rest_num_examples
end = self._index_in_epoch
images_new_part = self._images[start:end]
labels_new_part = self._labels[start:end]
return numpy.concatenate((images_rest_part, images_new_part),
axis=0), numpy.concatenate(
(labels_rest_part, labels_new_part), axis=0)
else:
self._index_in_epoch += batch_size
end = self._index_in_epoch
return self._images[start:end], self._labels[start:end]
@deprecated(None, 'Please write your own downloading logic.')
def _maybe_download(filename, work_directory, source_url):
"""Download the data from source url, unless it's already here.
Args:
filename: string, name of the file in the directory.
work_directory: string, path to working directory.
source_url: url to download from if file doesn't exist.
Returns:
Path to resulting file.
"""
if not gfile.Exists(work_directory):
gfile.MakeDirs(work_directory)
filepath = os.path.join(work_directory, filename)
if not gfile.Exists(filepath):
urllib.request.urlretrieve(source_url, filepath)
with gfile.GFile(filepath) as f:
size = f.size()
print('Successfully downloaded', filename, size, 'bytes.')
return filepath
@deprecated(None, 'Please use alternatives such as:'
' tensorflow_datasets.load(\'mnist\')')
def read_data_sets(train_dir,
fake_data=False,
one_hot=False,
dtype=dtypes.float32,
reshape=True,
validation_size=5000,
seed=None,
source_url=DEFAULT_SOURCE_URL):
if fake_data:
def fake():
return _DataSet([], [],
fake_data=True,
one_hot=one_hot,
dtype=dtype,
seed=seed)
train = fake()
validation = fake()
test = fake()
return _Datasets(train=train, validation=validation, test=test)
if not source_url: # empty string check
source_url = DEFAULT_SOURCE_URL
train_images_file = 'train-images-idx3-ubyte.gz'
train_labels_file = 'train-labels-idx1-ubyte.gz'
test_images_file = 't10k-images-idx3-ubyte.gz'
test_labels_file = 't10k-labels-idx1-ubyte.gz'
local_file = _maybe_download(train_images_file, train_dir,
source_url + train_images_file)
with gfile.Open(local_file, 'rb') as f:
train_images = _extract_images(f)
local_file = _maybe_download(train_labels_file, train_dir,
source_url + train_labels_file)
with gfile.Open(local_file, 'rb') as f:
train_labels = _extract_labels(f, one_hot=one_hot)
local_file = _maybe_download(test_images_file, train_dir,
source_url + test_images_file)
with gfile.Open(local_file, 'rb') as f:
test_images = _extract_images(f)
local_file = _maybe_download(test_labels_file, train_dir,
source_url + test_labels_file)
with gfile.Open(local_file, 'rb') as f:
test_labels = _extract_labels(f, one_hot=one_hot)
if not 0 <= validation_size <= len(train_images):
raise ValueError(
'Validation size should be between 0 and {}. Received: {}.'.format(
len(train_images), validation_size))
validation_images = train_images[:validation_size]
validation_labels = train_labels[:validation_size]
train_images = train_images[validation_size:]
train_labels = train_labels[validation_size:]
options = dict(dtype=dtype, reshape=reshape, seed=seed)
train = _DataSet(train_images, train_labels, **options)
validation = _DataSet(validation_images, validation_labels, **options)
test = _DataSet(test_images, test_labels, **options)
return _Datasets(train=train, validation=validation, test=test)
我们先来看看这段代码：
print带括号，完完全全python3的代码。。。就当官方的python3代码是兼容python2.7的，我们接着往下走。
接下来就是官方的训练测试模型，官网都有我就不写了。
在ubuntu的/home目录里创建input_data.py文件，再按要求创建模型文件，我命的名字machinedemo.py，代码如下：
import input_data
mnist = input_data.read_data_sets("MNIST_data", one_hot=True)
import tensorflow as tf
x = tf.placeholder("float", [None, 784])
W = tf.Variable(tf.zeros([784,10]))
b = tf.Variable(tf.zeros([10]))
y = tf.nn.softmax(tf.matmul(x,W) + b)
y_ = tf.placeholder("float", [None,10])
cross_entropy = -tf.reduce_sum(y_*tf.log(y))
train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)
init = tf.initialize_all_variables()
sess = tf.Session()
sess.run(init)
for i in range(1000):
batch_xs, batch_ys = mnist.train.next_batch(100)
sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
print (sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels}))
我是按照python3的要求写的，和官网区别就在于最后的print我加了个（），其余都和官网一模一样。/home目录如下：
接下来运行终端
运行结果一直在报错cannot import name …
网络上查询了各路结果，没有一个是真正能解决问题的，其实就是一个简单的导入包的问题。
期间我也检查了我的各种包文件，pip list如下：
我自己没有发现任何问题。
思来想去我总是觉得问题可能在tensorflow的版本或者官网源码和python版本上，于是我决定推到重来，用本机自带的python3.6试试看。
网上查到python3.6安装tensorflow的教程：
pip3 install tensorflow
安装后pip3 list结果如下：
接下来用python3运行一下machinedemo.py
结果如下：
成功了！
结果符合官网的：这个最终结果值应该大约是91%。
至于python2.7报错的原因，个人认为应该是官网源码调用的包文件tensorflow0.5.0版本可能不支持，源码和教程存在版本的脱节，尤其是开篇的依赖于python2.7,对小白的我简直就是巨坑。
真正的问题原因作为完全机器学习新手来说还需要学习了解，以此文记录机器学习入门历程。
来源：CSDN
作者：bangyumi
链接：https://blog.csdn.net/bangyumi/article/details/103686505