Tensor Flow 是一个采用数据流图（data flow graphs），用于数值计算的开源软件库。节点（Nodes）在图中表示数学操作，图中的线（edges）则表示在节点间相互联系的多维数据数组，即张量（tensor）。

这是谷歌开源的一个强大的做深度学习的软件库，提供了C++ 和 Python 接口，下面给出用Tensor Flow 建立CNN 网络做笑脸识别的一个简单用例。

我们用到的数据库是GENKI4K，这个数据库有4000张图像，首先做人脸检测与剪切，将图像resize到 64×64 的大小，然后用一个 CNN 网络做识别。

网络的基本结构如下：

input -> conv 1 -> pool 1 -> conv 2 -> pool 2 -> conv 3 -> pool 3 -> fc 1 -> out

input -> 64×64

conv 1 -> filter size: 5×5, output: 60×60

pool 1 -> filter size: 2×2, output: 30×30

conv 2 -> filter size: 7×7, output: 24×24

pool 2 -> filter size: 2×2, output: 12×12

conv 3 -> filter size: 5×5, output: 8×8

pool 3 -> filter size: 2×2, output: 4×4

fc 1 -> hidden nodes: 100, output: 1×100

out -> 1×2

import string, os, sysimport numpy as npimport matplotlib.pyplot as pltimport scipy.ioimport randomimport tensorflow as tf# set the folder pathdir_name = 'GENKI4K/Feature_Data'# set the file pathfiles = os.listdir(dir_name)for f in files:    print (dir_name + os.sep + f)file_path = dir_name + os.sep + files[10]# get the datadic_mat = scipy.io.loadmat(file_path)data_mat = dic_mat['Face_64']file_path2 = dir_name + os.sep + files[15]dic_label = scipy.io.loadmat(file_path2)label_mat = dic_label['Label']file_path3 = dir_name + os.sep+files[16]# get the labellabel = label_mat.ravel()label_y = np.zeros((4000, 2))label_y[:, 0] = labellabel_y[:, 1] = 1-labelT_ind=random.sample(range(0, 4000), 4000)# Parameterslearning_rate = 0.001batch_size = 40batch_num=4000/batch_sizetrain_epoch=100# Network Parametersn_input = 4096 # data input (img shape: 64*64)n_classes = 2 # total classes (smile & non-smile)dropout = 0.5 # Dropout, probability to keep units# tf Graph inputx = tf.placeholder(tf.float32, [None, n_input])y = tf.placeholder(tf.float32, [None, n_classes])keep_prob = tf.placeholder(tf.float32) #dropout (keep probability)# Create some wrappers for simplicitydef conv2d(x, W, b, strides=1):    # Conv2D wrapper, with bias and relu activation    x = tf.nn.conv2d(x, W, strides=[1, strides, strides, 1], padding='VALID')    x = tf.nn.bias_add(x, b)    return tf.nn.relu(x)def maxpool2d(x, k=2):    # MaxPool2D wrapper    return tf.nn.max_pool(x, ksize=[1, k, k, 1], strides=[1, k, k, 1],                          padding='VALID')# Create modeldef conv_net(x, weights, biases, dropout):    # Reshape input picture    x = tf.reshape(x, shape=[-1, 64, 64, 1])    # Convolution Layer    conv1 = conv2d(x, weights['wc1'], biases['bc1'])    # Max Pooling (down-sampling)    conv1 = maxpool2d(conv1, k=2)    # Convolution Layer    conv2 = conv2d(conv1, weights['wc2'], biases['bc2'])    # Max Pooling (down-sampling)    conv2 = maxpool2d(conv2, k=2)    # Convolution Layer    conv3 = conv2d(conv2, weights['wc3'], biases['bc3'])    # Max Pooling (down-sampling)    conv3 = maxpool2d(conv3, k=2)    # Fully connected layer    # Reshape conv2 output to fit fully connected layer input    fc1 = tf.reshape(conv3, [-1, weights['wd1'].get_shape().as_list()[0]])    fc1 = tf.add(tf.matmul(fc1, weights['wd1']), biases['bd1'])    fc1 = tf.nn.relu(fc1)    # Apply Dropout    # fc1 = tf.nn.dropout(fc1, dropout)    # Output, class prediction    out = tf.add(tf.matmul(fc1, weights['out']), biases['out'])    return out# Store layers weight & biasweights = {    # 5x5 conv, 1 input, 16 outputs    'wc1': tf.Variable(tf.random_normal([5, 5, 1, 16])),    # 7x7 conv, 16 inputs, 8 outputs    'wc2': tf.Variable(tf.random_normal([7, 7, 16, 8])),    # 5x5 conv, 8 inputs, 16 outputs    'wc3': tf.Variable(tf.random_normal([5, 5, 8, 16])),    # fully connected, 7*7*64 inputs, 1024 outputs    'wd1': tf.Variable(tf.random_normal([4*4*16, 100])),    # 1024 inputs, 10 outputs (class prediction)    'out': tf.Variable(tf.random_normal([100, n_classes]))}biases = {    'bc1': tf.Variable(tf.random_normal([16])),    'bc2': tf.Variable(tf.random_normal([8])),    'bc3': tf.Variable(tf.random_normal([16])),    'bd1': tf.Variable(tf.random_normal([100])),    'out': tf.Variable(tf.random_normal([n_classes]))}# Construct modelpred = conv_net(x, weights, biases, keep_prob)# Define loss and optimizercost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y))optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)# Evaluate modelcorrect_pred = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))# Initializing the variablesinit = tf.initialize_all_variables()with tf.Session() as sess:    sess.run(init)    for epoch in range(0, train_epoch):        for batch in range (0, batch_num):            arr_3 = T_ind[batch * batch_size:(batch + 1) * batch_size]            batch_x = data_mat[arr_3, :]            batch_y = label_y[arr_3, :]            # Run optimization op (backprop)            sess.run(optimizer, feed_dict={x: batch_x, y: batch_y,                                           keep_prob: dropout})        # Calculate loss and accuracy        loss, acc = sess.run([cost, accuracy], feed_dict={x: data_mat,                                                              y: label_y,                                                              keep_prob: 1.})        print("Epoch: " + str(epoch) + ", Loss= " + \                  "{:.3f}".format(loss) + ", Training Accuracy= " + \                  "{:.3f}".format(acc))

100个训练周期的结果：