TensorFlow实战之：Quick Start

https://segmentfault.com/a/1190000003984727

QuickStart

tensorflow-googles-latest-machine

Installation

因为众所周知的原因，在国内搭建Tensorflow的环境又经历了一些波折。笔者习惯用Docker作为复杂依赖项目的开发环境，Google提供的安装方式有如下几个。

Binary Installation

TensorFlow的Python的API是2.7，最简单的方式就是在MAC或者Unix上使用pip命令导入。

• Linux

# For CPU-only version$ pip install https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.5.0-cp27-none-linux_x86_64.whl# For GPU-enabled version (only install this version if you have the CUDA sdk installed)$ pip install https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.5.0-cp27-none-linux_x86_64.whl

• Mac OS X

# Only CPU-version is available at the moment.$ pip install https://storage.googleapis.com/tensorflow/mac/tensorflow-0.5.0-py2-none-any.whl

Docker

笔者就是用的这个方式，不过可能比较笨吧，之前用的代理下载DockerHub的镜像都没问题，但是死活下不了TensorFlow，只能默默开了个VPS下了镜像然后打包成tar再拖到本地导入，笔者打包的tar文件在这里。导入到本地的Docker镜像库中只需要：

docker load < /tmp/tensorflow.tar

具体的Docker的安装过程可以参考笔者的其他文章，镜像下载导入好了之后直接：

docker run -it b.gcr.io/tensorflow/tensorflow

这个默认的镜像比较小，只包含了一些必要的运行条件，Google还提供了一个更完整的镜像b.gcr.io/tensorflow/tensorflow-full。

如果需要重新编译的话：

• tensorflow/tensorflow

$ docker build -t $USER/tensorflow -f Dockerfile.lite .

• tensorflow/tensorflow-full

同样需要依赖于tensor flow的基础镜像，所以还是得翻墙

$ git clone https://github.com/tensorflow/tensorflow$ docker build -t $USER/tensorflow-full -f Dockerfile.cpu .

• [](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/tools/docker#tensorflowtensorflow-gpu)tensorflow/tensorflow-gpu

这个需要的稍微复杂一点：

$ cp -a /usr/local/cuda .$ docker build -t $USER/tensorflow-gpu-base -f Dockerfile.gpu_base .# Flatten the image$ export TC=$(docker create $USER/tensorflow-gpu-base)$ docker export $TC | docker import - $USER/tensorflow-gpu-flat$ docker rm $TC$ export TC=$(docker create $USER/tensorflow-gpu-flat /bin/bash)$ docker commit --change='CMD ["/bin/bash"]'  --change='ENV CUDA_PATH /usr/local/cuda' --change='ENV LD_LIBRARY_PATH /usr/local/cuda/lib64' --change='WORKDIR /root' $TC $USER/tensorflow-full-gpu$ docker rm $TC

VirtualEnv

这也是Google官方推荐的一种构建方式，virtualenv是Python领域的一种环境管理，首先是安装所有必备工具：

# On Linux:$ sudo apt-get install python-pip python-dev python-virtualenv# On Mac:$ sudo easy_install pip  # If pip is not already installed$ sudo pip install --upgrade virtualenv

其次是创建一个新的工作区间：

$ virtualenv --system-site-packages ~/tensorflow$ cd ~/tensorflow

接下来是启用这个工作区间：

$ source bin/activate  # If using bash$ source bin/activate.csh  # If using csh(tensorflow)$  # Your prompt should change

然后在该工作区间中，安装TensorFlow：

(tensorflow)$ pip install --upgrade <$url_to_binary.whl>

最后直接运行程序：

(tensorflow)$ python tensorflow/models/image/mnist/convolutional.py# When you are done using TensorFlow:(tensorflow)$ deactivate  # Deactivate the virtualenv$  # Your prompt should change back

Example

有人在Github上开源了一波TensorFlow的示范教程，大概是这里TensorFlow-Tutorials。

Multiply

import tensorflow as tfa = tf.placeholder("float") # Create a symbolic variable 'a'b = tf.placeholder("float") # Create a symbolic variable 'b'y = tf.mul(a, b) # multiply the symbolic variablessess = tf.Session() # create a session to evaluate the symbolic expressionsprint "%f should equal 2.0" % sess.run(y, feed_dict={a: 1, b: 2}) # eval expressions with parameters for a and bprint "%f should equal 9.0" % sess.run(y, feed_dict={a: 3, b: 3})

Linear Regression

import tensorflow as tfimport numpy as nptrX = np.linspace(-1, 1, 101)trY = 2 * trX + np.random.randn(*trX.shape) * 0.33 # create a y value which is approximately linear but with some random noiseX = tf.placeholder("float") # create symbolic variablesY = tf.placeholder("float")def model(X, w):    return tf.mul(X, w) # lr is just X*w so this model line is pretty simplew = tf.Variable(0.0, name="weights") # create a shared variable (like theano.shared) for the weight matrixy_model = model(X, w)cost = (tf.pow(Y-y_model, 2)) # use sqr error for cost functiontrain_op = tf.train.GradientDescentOptimizer(0.01).minimize(cost) # construct an optimizer to minimize cost and fit line to my datasess = tf.Session()init = tf.initialize_all_variables() # you need to initialize variables (in this case just variable W)sess.run(init)for i in range(100):    for (x, y) in zip(trX, trY):         sess.run(train_op, feed_dict={X: x, Y: y})print(sess.run(w))  # something around 2

Logistic Regression

import tensorflow as tfimport numpy as npimport input_datadef init_weights(shape):    return tf.Variable(tf.random_normal(shape, stddev=0.01))def model(X, w):    return tf.matmul(X, w) # notice we use the same model as linear regression, this is because there is a baked in cost function which performs softmax and cross entropymnist = input_data.read_data_sets("MNIST_data/", one_hot=True)trX, trY, teX, teY = mnist.train.images, mnist.train.labels, mnist.test.images, mnist.test.labelsX = tf.placeholder("float", [None, 784]) # create symbolic variablesY = tf.placeholder("float", [None, 10])w = init_weights([784, 10]) # like in linear regression, we need a shared variable weight matrix for logistic regressionpy_x = model(X, w)cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(py_x, Y)) # compute mean cross entropy (softmax is applied internally)train_op = tf.train.GradientDescentOptimizer(0.05).minimize(cost) # construct optimizerpredict_op = tf.argmax(py_x, 1) # at predict time, evaluate the argmax of the logistic regressionsess = tf.Session()init = tf.initialize_all_variables()sess.run(init)for i in range(100):    for start, end in zip(range(0, len(trX), 128), range(128, len(trX), 128)):        sess.run(train_op, feed_dict={X: trX[start:end], Y: trY[start:end]})    print i, np.mean(np.argmax(teY, axis=1) ==                     sess.run(predict_op, feed_dict={X: teX, Y: teY}))