采用 pip 方式安装 TensorFlow

安装 TensorFlow（pip方式）

安装 python3 和 pip3

首先，需要安装 python 和 pip：

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$ sudo apt-get install python-pip python-dev $ sudo apt-get install python3-pip

这时会有 python 2.7 和 3.5 共存，默认版本为 2.7，为了默认使用 python 3.5 及 pip3，可以在 .bash_profile 中增加如下语句进行重命名：

1 2	alias python=/usr/bin/python3 alias pip=/usr/bin/pip3

用如下命令验证版本：

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$ python --version Python 3.5.2 $ pip --version pip 8.1.1 from /usr/lib/python3/dist-packages (python 3.5)

取消重命名，只需要执行 unalias：

1 2	$ unalias python $ unalias pip

然后开始安装 tensorflow CPU 版本，此时安装版本为0.12.1：

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$ pip install tensorflow Collecting tensorflow Downloading tensorflow-0.12.1-cp35-cp35m-manylinux1_x86_64.whl (43.1MB) 100% |████████████████████████████████| 43.1MB 23kB/s Collecting wheel>=0.26 (from tensorflow) Downloading wheel-0.29.0-py2.py3-none-any.whl (66kB) 100% |████████████████████████████████| 71kB 1.3MB/s Collecting six>=1.10.0 (from tensorflow) Downloading six-1.10.0-py2.py3-none-any.whl Collecting numpy>=1.11.0 (from tensorflow) Downloading numpy-1.12.0-cp35-cp35m-manylinux1_x86_64.whl (16.8MB) 100% |████████████████████████████████| 16.8MB 60kB/s Collecting protobuf>=3.1.0 (from tensorflow) Downloading protobuf-3.2.0-cp35-cp35m-manylinux1_x86_64.whl (5.6MB) 100% |████████████████████████████████| 5.6MB 136kB/s Collecting setuptools (from protobuf>=3.1.0->tensorflow) Downloading setuptools-34.1.1-py2.py3-none-any.whl (389kB) 100% |████████████████████████████████| 399kB 308kB/s Collecting packaging>=16.8 (from setuptools->protobuf>=3.1.0->tensorflow) Downloading packaging-16.8-py2.py3-none-any.whl Collecting appdirs>=1.4.0 (from setuptools->protobuf>=3.1.0->tensorflow) Downloading appdirs-1.4.0-py2.py3-none-any.whl Collecting pyparsing (from packaging>=16.8->setuptools->protobuf>=3.1.0->tensorflow) Downloading pyparsing-2.1.10-py2.py3-none-any.whl (56kB) 100% |████████████████████████████████| 61kB 702kB/s Installing collected packages: wheel, six, numpy, pyparsing, packaging, appdirs, setuptools, protobuf, tensorflow Successfully installed appdirs numpy packaging protobuf pyparsing setuptools-20.7.0 six-1.10.0 tensorflow wheel-0.29.0 You are using pip version 8.1.1, however version 9.0.1 is available. You should consider upgrading via the 'pip install --upgrade pip' command.

“2.20 更新”Google 于2017年2月16日（北京时间）凌晨2点在美国加利福尼亚州山景城举办了首届 TensorFlow 开发者峰会。Google 现场宣布全球领先的深度学习开源框架 TensorFlow 正式对外发布V1.0版本，并保证 Google 的本次发布版本的 API 接口满足生产环境稳定性要求。升级到1.0很简单，执行以下命令即可：

1	$ pip install --upgrade tensorflow

确认安装路径：

1 2	$ python -c 'import os; import inspect; import tensorflow; print(os.path.dirname(inspect.getfile(tensorflow)))' /home/{username}/.local/lib/python3.5/site-packages/tensorflow

查看目录结构：

1	$ tree -d -L 3 ~/.local/lib/python3.5/site-packages/tensorflow

运行 TensorFlow

从命令行运行 TensorFlow

执行命令如下：

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$ python Python 3.5.2 (default, Nov 17 2016, 17:05:23) [GCC 5.4.0 20160609] on linux Type "help", "copyright", "credits" or "license" for more information. >>> import tensorflow as tf >>> hello = tf.constant( 'Hello, TensorFlow!') >>> sess = tf.Session() >>> print(sess.run(hello)) b 'Hello, TensorFlow!' >>> a = tf.constant(10) >>> b = tf.constant(32) >>> print(sess.run(a + b)) 42 >>>

运行 TensorFlow demo

这个 Demo 是用 MNIST 数据集进行手写字符的训练和测试：

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$ python -m tensorflow.models.image.mnist.convolutional Successfully downloaded train-images-idx3-ubyte.gz 9912422 bytes. Successfully downloaded train-labels-idx1-ubyte.gz 28881 bytes. Successfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes. Successfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes. Extracting data/train-images-idx3-ubyte.gz Extracting data/train-labels-idx1-ubyte.gz Extracting data/t10k-images-idx3-ubyte.gz Extracting data/t10k-labels-idx1-ubyte.gz Initialized! Step 0 (epoch 0.00), 7.1 ms Minibatch loss: 8.334, learning rate: 0.010000 Minibatch error: 85.9% Validation error: 84.6% Step 100 (epoch 0.12), 205.8 ms Minibatch loss: 3.250, learning rate: 0.010000 Minibatch error: 6.2% Validation error: 7.6% ... Step 8500 (epoch 9.89), 204.7 ms Minibatch loss: 1.618, learning rate: 0.006302 Minibatch error: 0.0% Validation error: 0.8% Test error: 0.8%

主机是分配了4个核的 E7- 4830 @ 2.13GHz 虚机，每步耗时205 ms左右。

“2.20 更新”在 r1.0 下运行可能会遇到参数错误的问题，如下：

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$ python -m tensorflow.models.image.mnist.convolutional Extracting data/train-images-idx3-ubyte.gz Extracting data/train-labels-idx1-ubyte.gz Extracting data/t10k-images-idx3-ubyte.gz Extracting data/t10k-labels-idx1-ubyte.gz Traceback (most recent call last): File "/usr/lib/python3.5/runpy.py", line 184, in _run_module_as_main "__main__", mod_spec) File "/usr/lib/python3.5/runpy.py", line 85, in _run_code exec(code, run_globals) File "/home/{username}/.local/lib/python3.5/site-packages/tensorflow/models/image/mnist/convolutional.py", line 339, in <module> tf.app.run(main=main, argv=[sys.argv[0]] + unparsed) File "/home/{username}/.local/lib/python3.5/site-packages/tensorflow/python/platform/app.py", line 44, in run _sys.exit(main(_sys.argv[:1] + flags_passthrough)) File "/home/{username}/.local/lib/python3.5/site-packages/tensorflow/models/image/mnist/convolutional.py", line 231, in main logits, train_labels_node)) File "/home/{username}/.local/lib/python3.5/site-packages/tensorflow/python/ops/nn_ops.py", line 1684, in sparse_softmax_cross_entropy_with_logits labels, logits) File "/home/{username}/.local/lib/python3.5/site-packages/tensorflow/python/ops/nn_ops.py", line 1533, in _ensure_xent_args "named arguments (labels=..., logits=..., ...)" % name) ValueError: Only call `sparse_softmax_cross_entropy_with_logits` with named arguments (labels=..., logits=..., ...)

可参考 Fix *_cross_entropy_with_logits calls #864， 将

1	loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits, train_labels_node))

改为

1	loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(labels=train_labels_node, logits=logits))

Reference

TensorFlow Download and Setup

TensorFlow开发者峰会重磅–Google发布TensorFlowV1.0

Isuue with models/tutorials/image/mnist #857

原文链接 大专栏  https://www.dazhuanlan.com/2019/08/16/5d55fe046f530/