[deeplearning-006] tensorflow安装和测试

1. 官网
www.tensorflow.org，需要翻墙。


2.tensorflow的概念抽象
节点：表示数据运算。
边：在节点之间传递多维数据(张量)




3.安装
3.1 文档
https://www.tensorflow.org/install/




3.2 操作系统需要ubuntu 14.04以上


3.3 两种安装版本：cpu和gpu。如果工作机上没有nvida gpu，就安装cpu版本。cpu版本安装简单，因此，即使你有gpu，也建议先安装这个版本。


3.4 推荐使用virtualenv进行安装。推荐使用native pip安装。如果是anancode，推荐使用pip install，而不是conda insall。


3.5 用anacode进行安装


pyenv global anaconda3-4.2.0


conda create -n tensorflow python=3.4


source activate tensorflow #这一步会出错，没有出现期望的结果。
暂时先忽略掉这个安装方式。


3.6 用anacode3-4.2进行安装
mkdir tensorflow-site
cd tensorflow-site
pyenv local anancode3-4.2
pip3 install tensorflow
以后，进入该目录，都要用pyenv设置local的python，然后运行既可


3.7 验证安装
运行一小段代码验证
---------------------
import tensorflow as tf
hello = tf.constant('Hello Tensorflow')
sess = tf.Session()
print(sess.run(hello))
---------------------
运行后会输出b'Hello,Tensorflow'




4.给新手的tf教程
https://www.tensorflow.org/get_started/mnist/beginners
建议都从这里开始。因为这里会解释一些很重要的概念。


MNIST数据集，每幅图是28x28=784个像素，训练集有55000个数据，测试集有10000个数据。minist.train.images是一个tensor，shape是[55000,784]。训练集的标记，是一个[55000, 10]的tensor。


软最大算法，也就是softmax，以线性函数方式实现。公式简单。


用tensorflow的方式实现，如下：






placeholder，不是一个特定的值，而是，存放一些在计算是可能要用到的值。


用交叉熵评估做cost函数。

示例代码如下

# Copyright 2015 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.  # ==============================================================================      """A very simple MNIST classifier.   See extensive documentation at https://www.tensorflow.org/get_started/mnist/beginners """  from __future__ import absolute_import  from __future__ import division  from __future__ import print_function      import argparse  import sys      from tensorflow.examples.tutorials.mnist import input_data      import tensorflow as tf      FLAGS = None          def main(_):    # Import data    mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True)        # Create the model    x = tf.placeholder(tf.float32, [None, 784])    W = tf.Variable(tf.zeros([784, 10]))    b = tf.Variable(tf.zeros([10]))    y = tf.matmul(x, W) + b        # Define loss and optimizer    y_ = tf.placeholder(tf.float32, [None, 10])        # The raw formulation of cross-entropy,    #    #   tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(tf.nn.softmax(y)),    #                                 reduction_indices=[1]))    #    # can be numerically unstable.    #    # So here we use tf.nn.softmax_cross_entropy_with_logits on the raw    # outputs of 'y', and then average across the batch.    cross_entropy = tf.reduce_mean(        tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y))    train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)        sess = tf.InteractiveSession()    tf.global_variables_initializer().run()    # Train    for _ in range(1000):      batch_xs, batch_ys = mnist.train.next_batch(100)      sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})        # Test trained model    correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))    print(sess.run(accuracy, feed_dict={x: mnist.test.images,                                        y_: mnist.test.labels}))      if __name__ == '__main__':    parser = argparse.ArgumentParser()    parser.add_argument('--data_dir', type=str, default='/tmp/tensorflow/mnist/input_data',                        help='Directory for storing input data')    FLAGS, unparsed = parser.parse_known_args()    tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)  -------------------------  注意：下载数据集也需要翻墙。  结果：  ------------  Extracting /tmp/tensorflow/mnist/input_data/train-images-idx3-ubyte.gz  Extracting /tmp/tensorflow/mnist/input_data/train-labels-idx1-ubyte.gz  Extracting /tmp/tensorflow/mnist/input_data/t10k-images-idx3-ubyte.gz  Extracting /tmp/tensorflow/mnist/input_data/t10k-labels-idx1-ubyte.gz  2017-11-06 15:40:53.056574: I tensorflow/core/platform/cpu_feature_guard.cc:137] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA  0.9153  

5.给高手的教程
https://www.tensorflow.org/get_started/mnist/pros
这个教程后半段，使用了卷积网络，卷积网，池化，密连接层，将测试精度提升到了99.2%。




6.tensorflow运行机制
以底层api方式解释tensorflow运行