TensorFlow计算模型--计算图

计算图的概念

TensorFlow两个重要概念:Tensor和Flow,Tensor就是张量（可以理解为多维数组）,Flow就是计算相互转化的过程。TensorFlow的计算方式类似Spark的有向无环图(DAG),在创建Session之后才开始计算（类似Action算子）。

简单示例

import tensorflow as tf a = tf.constant([1.0,2.0],name="a")b = tf.constant([3.0,4.0],name="b")result = a + bsess = tf.Session()print(sess.run(result)) # [ 4.  6.]

TensorFlow数据模型--张量

张量的概念

张量可以简单理解为多维数组。 零阶张量表示标量(scalar)，也就是一个数。一阶张量表示为向量(vector),也就是一维数组。n阶张量表示为n维数组。但张量在TensorFlow中只是对结算结果的引用，它保存的是如何得到这些数字的计算过程。

import tensorflow as tfa = tf.constant([1.0,2.0],name="a")b = tf.constant([3.0,4.0],name="b")result = a + bprint(result)# Tensor("add_1:0", shape=(2,), dtype=float32)

上面输出了三个属性:名字(name)、维度(shape)、类型(type)
张量的第一个属性名字是张量的唯一标识符，也显示出这个张量是如何计算出来的
张量的第二个属性维度是张量的维度信息，上面输出结果shape(2,)表示是一个一维数组，长度为2
张量的第三个属性类型是每个张量都会有的唯一类型，TensorFlow会对所有参与运算的张量进行类型检查，如果类型不匹配会报错。

TensorFlow运行模型--会话

创建会话的两种方式

# 创建一个会话sess = tf.Session()sess.run()sess.cloes()# 这种创建会话的方式需要显示关闭会话，释放资源

# 使用python 上下位管理器来管理这个会话with tf.Session() as sess:    sess.run()# 不需要显示调用"sess.close()"函数来关闭会话# 当上下文退出时会话关闭和资源释放也自动完成了

TensorFlow会生成一个默认的计算图，可以通过tf.Tensor.eval函数来计算一个张量的取值

import tensorflow as tfa = tf.constant([1.0,2.0],name="a")b = tf.constant([3.0,4.0],name="b")result = a + bwith tf.Session() as sess:    # 两种方式计算张量的取值    print(sess.run(result))    print(result.eval(session=sess))

神经网络参数与TenworFlow变量

变量(tf.Variable)的作用就是保存和更新神经网络中的参数

# 声明一个2 * 3 的矩阵变量，矩阵均值为0，标准差为2的随机数import tensorflow as tfweights = tf.Variable(tf.random_normal([2,3],stddev=2))# 初始化变量init = tf.global_variables_initializer()with tf.Session() as sess:    sess.run(init)    print(sess.run(weights))    # [[-0.69297457  1.13187325  2.36984086]    #  [ 1.20076609  0.77468276  2.01622796]]

TensorFlow随机数生成函数

TensorFlow常数生成函数

神经网络程序

import tensorflow as tffrom numpy.random import RandomState# 定义训练数据batch的大小batch_size = 8# 定义神经网络的参数w1 = tf.Variable(tf.random_normal([2,3],stddev=1,seed=1))w2 = tf.Variable(tf.random_normal([3,1],stddev=1,seed=1))# 在shape的一个维度上使用None可以方便使用不同的batch大小x = tf.placeholder(tf.float32,shape=(None,2),name='x-input')y_ = tf.placeholder(tf.float32,shape=(None,1),name='y-input')# 定义神经网络前向传播的过程a = tf.matmul(x,w1)y = tf.matmul(a,w2)# 定义损失函数和反响传播算法cross_entropy = -tf.reduce_mean(y_ * tf.log(tf.clip_by_value(y,1e-10,1.0)))train_step = tf.train.AdamOptimizer(0.001).minimize(cross_entropy)# 通过随机数生成一个模拟数据集rdm = RandomState(1)dataset_size = 128X = rdm.rand(dataset_size,2)# 定义规则来给出样本的标签，x1+x2<1的样例都被认为是正样本，其他为负样本，0：负样本，1：正样本Y = [[int(x1+x2<1)] for (x1,x2) in X]# 创建一个会话来运行TensorFlow程序with tf.Session() as sess:    # 初始化变量    init_op = tf.global_variables_initializer()    sess.run(init_op)        print(sess.run(w1))    print(sess.run(w2))        # 设定训练的轮数    STEPS = 5000        for i in range(STEPS):        # 每次选取batch_size 个样本进行训练        start = (i * batch_size)% dataset_size        end = min(start+batch_size,dataset_size)                # 通过选取的样本训练神经网络并更新参数        sess.run(train_step,feed_dict={x:X[start:end],y_:Y[start:end]})        if i % 1000 == 0:            total_cross_entropy = sess.run(cross_entropy,feed_dict={x:X,y_:Y})            print("After %d trainint step(s),cross entropy on all data is %g" % (i,total_cross_entropy))                print(sess.run(w1))    print(sess.run(w2))

训练神经网络的过程可以分为3个步骤:

1. 定义神经网络的结构和前向传播的输出结果
2. 定义损失函数以及选择反向传播优化的算法
3. 生成会话(tf.Session)并在训练数据上仿佛运行反向传播优化算法

tensorflow实现线性回归

'''A linear regression learning algorithm example using TensorFlow library.Author: Aymeric DamienProject: https://github.com/aymericdamien/TensorFlow-Examples/'''from __future__ import print_functionimport tensorflow as tfimport numpyimport matplotlib.pyplot as pltrng = numpy.random# Parameterslearning_rate = 0.01training_epochs = 1000display_step = 50# Training Datatrain_X = numpy.asarray([3.3,4.4,5.5,6.71,6.93,4.168,9.779,6.182,7.59,2.167,                         7.042,10.791,5.313,7.997,5.654,9.27,3.1])train_Y = numpy.asarray([1.7,2.76,2.09,3.19,1.694,1.573,3.366,2.596,2.53,1.221,                         2.827,3.465,1.65,2.904,2.42,2.94,1.3])n_samples = train_X.shape[0]# tf Graph InputX = tf.placeholder("float")Y = tf.placeholder("float")# Set model weightsW = tf.Variable(rng.randn(), name="weight")b = tf.Variable(rng.randn(), name="bias")# Construct a linear modelpred = tf.add(tf.multiply(X, W), b)# Mean squared errorcost = tf.reduce_sum(tf.pow(pred-Y, 2))/(2*n_samples)# Gradient descent#  Note, minimize() knows to modify W and b because Variable objects are trainable=True by defaultoptimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)# Initializing the variablesinit = tf.global_variables_initializer()# Launch the graphwith tf.Session() as sess:    sess.run(init)    # Fit all training data    for epoch in range(training_epochs):        for (x, y) in zip(train_X, train_Y):            sess.run(optimizer, feed_dict={X: x, Y: y})        # Display logs per epoch step        if (epoch+1) % display_step == 0:            c = sess.run(cost, feed_dict={X: train_X, Y:train_Y})            print("Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(c), \                "W=", sess.run(W), "b=", sess.run(b))    print("Optimization Finished!")    training_cost = sess.run(cost, feed_dict={X: train_X, Y: train_Y})    print("Training cost=", training_cost, "W=", sess.run(W), "b=", sess.run(b), '\n')    # Graphic display    plt.plot(train_X, train_Y, 'ro', label='Original data')    plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label='Fitted line')    plt.legend()    plt.show()    # Testing example, as requested (Issue #2)    test_X = numpy.asarray([6.83, 4.668, 8.9, 7.91, 5.7, 8.7, 3.1, 2.1])    test_Y = numpy.asarray([1.84, 2.273, 3.2, 2.831, 2.92, 3.24, 1.35, 1.03])    print("Testing... (Mean square loss Comparison)")    testing_cost = sess.run(        tf.reduce_sum(tf.pow(pred - Y, 2)) / (2 * test_X.shape[0]),        feed_dict={X: test_X, Y: test_Y})  # same function as cost above    print("Testing cost=", testing_cost)    print("Absolute mean square loss difference:", abs(        training_cost - testing_cost))    plt.plot(test_X, test_Y, 'bo', label='Testing data')    plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label='Fitted line')    plt.legend()    plt.show()

tensorflow实现逻辑回归

'''A logistic regression learning algorithm example using TensorFlow library.This example is using the MNIST database of handwritten digits(http://yann.lecun.com/exdb/mnist/)Author: Aymeric DamienProject: https://github.com/aymericdamien/TensorFlow-Examples/'''from __future__ import print_functionimport tensorflow as tf# Import MNIST datafrom tensorflow.examples.tutorials.mnist import input_datamnist = input_data.read_data_sets("/tmp/data/", one_hot=True)# Parameterslearning_rate = 0.01training_epochs = 25batch_size = 100display_step = 1# tf Graph Inputx = tf.placeholder(tf.float32, [None, 784]) # mnist data image of shape 28*28=784y = tf.placeholder(tf.float32, [None, 10]) # 0-9 digits recognition => 10 classes# Set model weightsW = tf.Variable(tf.zeros([784, 10]))b = tf.Variable(tf.zeros([10]))# Construct modelpred = tf.nn.softmax(tf.matmul(x, W) + b) # Softmax# Minimize error using cross entropycost = tf.reduce_mean(-tf.reduce_sum(y*tf.log(pred), reduction_indices=1))# Gradient Descentoptimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)# Initializing the variablesinit = tf.global_variables_initializer()# Launch the graphwith tf.Session() as sess:    sess.run(init)    # Training cycle    for epoch in range(training_epochs):        avg_cost = 0.        total_batch = int(mnist.train.num_examples/batch_size)        # Loop over all batches        for i in range(total_batch):            batch_xs, batch_ys = mnist.train.next_batch(batch_size)            # Run optimization op (backprop) and cost op (to get loss value)            _, c = sess.run([optimizer, cost], feed_dict={x: batch_xs,                                                          y: batch_ys})            # Compute average loss            avg_cost += c / total_batch        # Display logs per epoch step        if (epoch+1) % display_step == 0:            print("Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(avg_cost))    print("Optimization Finished!")    # Test model    correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))    # Calculate accuracy    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))    print("Accuracy:", accuracy.eval({x: mnist.test.images, y: mnist.test.labels}))

tensorflow实现K-近邻

'''A nearest neighbor learning algorithm example using TensorFlow library.This example is using the MNIST database of handwritten digits(http://yann.lecun.com/exdb/mnist/)Author: Aymeric DamienProject: https://github.com/aymericdamien/TensorFlow-Examples/'''from __future__ import print_functionimport numpy as npimport tensorflow as tf# Import MNIST datafrom tensorflow.examples.tutorials.mnist import input_datamnist = input_data.read_data_sets("/tmp/data/", one_hot=True)# In this example, we limit mnist dataXtr, Ytr = mnist.train.next_batch(5000) #5000 for training (nn candidates)Xte, Yte = mnist.test.next_batch(200) #200 for testing# tf Graph Inputxtr = tf.placeholder("float", [None, 784])xte = tf.placeholder("float", [784])# Nearest Neighbor calculation using L1 Distance# Calculate L1 Distancedistance = tf.reduce_sum(tf.abs(tf.add(xtr, tf.negative(xte))), reduction_indices=1)# Prediction: Get min distance index (Nearest neighbor)pred = tf.arg_min(distance, 0)accuracy = 0.# Initializing the variablesinit = tf.global_variables_initializer()# Launch the graphwith tf.Session() as sess:    sess.run(init)    # loop over test data    for i in range(len(Xte)):        # Get nearest neighbor        nn_index = sess.run(pred, feed_dict={xtr: Xtr, xte: Xte[i, :]})        # Get nearest neighbor class label and compare it to its true label        print("Test", i, "Prediction:", np.argmax(Ytr[nn_index]),"True Class:", np.argmax(Yte[i]))        # Calculate accuracy        if np.argmax(Ytr[nn_index]) == np.argmax(Yte[i]):            accuracy += 1./len(Xte)    print("Done!")    print("Accuracy:", accuracy)

笔记来自<< TensorFlow:实战Google深度学习框架 >>