安装TensorFlow

TensorFlow官方网站

本文主要是在linux环境下，使用原生pip安装（简单不易出错）。同时只考虑CPU版， GPU版在考虑性能时参考官网设置。

安装命令

pip install tensorflow

几个小概念

You maight think of TensorFlow Core programs as consisting of two discrete sections:

  • Building the computational graph
  • Running the computational graph

computational graph is a series of TensorFlow operations arranged into a graph of nodes.

tf.placeholder: is a promise to provide a value later.
tf.Variable allow us to add trainable parameters to graph.

入门案例

import tensorflow as tf
import numpy as np

def test_tf_is_installed():
    node1 = tf.constant(3.0, dtype=tf.float32)
    node2 = tf.constant(4.0)
    print(node1,node2) 
    
def using_tf_train():
    """
    TensorFlow provides optimizers that slowly change each variable in order to 
    minimize the loss function. The simplest optimizer is gradient_descent. It 
    modifies each variable according to the magnitude of the derivative of loss 
    w.r.t. that variable. In general, computing symbolic derivatives manually is 
    tedious and error-prone. Consequently, TensorFlow can automatically produce 
    derivatives given only a description of the model using the function tf.gradients.
    For simplicity, optimizers typically do this for you.
    """

    # Model parameters
    W = tf.Variable([.3], dtype=tf.float32)
    b = tf.Variable([-.3], dtype=tf.float32)
    
    # Model input and output
    x = tf.placeholder(tf.float32)
    linear_model = W * x + b
    y = tf.placeholder(tf.float32)
    
    # loss
    loss = tf.reduce_sum(tf.square(linear_model - y))
    
    # optimizer
    optimizer = tf.train.GradientDescentOptimizer(0.01)
    train = optimizer.minimize(loss)
    
    # train data
    x_train = [1,2,3,4]
    y_train = [0,-1,-2,-3]
    
    # train loop
    init = tf.global_variables_initializer()
    sess = tf.Session()
    sess.run(init)
    for i in range(1000):
        sess.run(train, {x:x_train, y:y_train})
        
        
    # evluate training accuracy
    curr_W, curr_b, curr_loss = sess.run([W,b,loss],{x:x_train, y:y_train})
    print("W:%s b:%s loss: %s"%(curr_W, curr_b, curr_loss))
    
    
def using_tf_estimator():
    
    feature_columns = [tf.feature_column.numeric_column("x",shape=[1])]
    
    estimator = tf.estimator.LinearRegressor(feature_columns=feature_columns)
    
    x_train = np.array([1.,2.,3.,4.])
    y_train = np.array([0.,-1.,-2.,-3.])
    x_eval = np.array([2.,5.,8.,1.])
    y_eval = np.array([-1.01,-4.1,-7,0.])
    
    input_fn = tf.estimator.inputs.numpy_input_fn(
            {"x":x_train},y_train,batch_size=4,  num_epochs=None, shuffle=True)
    train_input_fn = tf.estimator.inputs.numpy_input_fn(
            {"x":x_train}, y_train, batch_size=4, num_epochs=1000, shuffle=False)
    eval_input_fn = tf.estimator.inputs.numpy_input_fn(
            {"x":x_eval}, y_eval, batch_size=4, num_epochs=1000, shuffle=False)
    
    estimator.train(input_fn=input_fn, steps=1000)
    train_metric = estimator.evaluate(input_fn=train_input_fn)
    eval_metric = estimator.evaluate(input_fn=eval_input_fn)
    
    print("===============begin train==============\n")
    print("train metrics: %r" % train_metric)
    
    print("===============begin eval===============\n")
    print("eval metrics: %r" % eval_metric)    
    
    
    
def iris_CNN():
    # load dataset
    iris = tf.contrib.learn.datasets.load_dataset('iris')
    x_train,x_test,y_train,y_test = train_test_split(
            iris.data, iris.target, test_size=0.2,random_state=42)
    
    feature_columns = infer_real_valued_columns_from_input(
            x_train)
    classifier = tf.contrib.learn.DNNClassifier(
            feature_columns=feature_columns, hidden_units=[10,20,10], n_classes=3)
    
    classifier.fit(x_train,y_train,steps=200)
    predictions = list(classifier.predict(x_test,as_iterable=True))
    score = accuracy_score(y_test,predictions)
    
    print('Accuracy: {0:f}'.format(score))    

if __name__=='__main__':
    test_tf_is_installed()
    using_tf_train()
    using_tf_estimator()
    iris_CNN()

有机器学习基础的话，应该很容易看懂。这里简单说明一下：

  • if __name__=='__main__'这个语句相当于C语言的main函数。
  • test_tf_is_installed() 测试TensorFlow环境是否安装成功。
  • using_tf_train(), using_tf_estimator() 测试最基本的线性回归算法的tf实现形式。
  • iris_CNN()卷积网络实现iris分类问题。