tensorflow实战一（分类交通标志）

此代码编写依据 DataCamp 发表的一篇针对 TensorFlow 初学者的教程。此教程从向量和张量的基本概念说起，一步步实现了一个分类交通标志图像的神经网络。

1.加载并预处理数据

数据下载地址：http://btsd.ethz.ch/shareddata/。下载 BelgiumTSC_Training 和 BelgiumTSC_Testing。
数据下载完毕后，提取文件夹，整理好数据存放地址。我的Training和Testing数据存放地址如下：
/home/zhangxueying/images/TrafficSigns/Training
/home/zhangxueying/images/TrafficSigns/Testing
开始导入数据：（并将图片数据信息统一为28x28的灰度图片）

#load dataimport os                    #python中的os模块用于处理文件和目录import skimage               #python中的skimage模块用于图像处理import numpy as np           #python中的numpy模块用于科学计算from skimage import data,transformfrom skimage.color import rgb2gray     #rgb2gray将图片转化为灰度#here data_directory="/home/zhangxueying/image/TrafficSigns/Training"def load_data(data_directory):    directories=[d for d in os.listdir(data_directory) if os.path.isdir(os.path.join(data_directory,d))]    #d is every classification file    labels=[]    images=[]    for d in directories:        #每一类的路径        label_directory=os.path.join(data_directory,d)        file_names=[os.path.join(label_directory,f) for f in os.listdir(label_directory) if f.endswith(".ppm")]        #file_names is every photo which is end with ".ppm"        for f in file_names:            images.append(skimage.data.imread(f))   #read image            labels.append(int(d))                   #read label    return images,labels#images and labels are listROOT_PATH="/home/zhangxueying/image"train_data_directory=os.path.join(ROOT_PATH,"TrafficSigns/Training")test_data_directory=os.path.join(ROOT_PATH,"TrafficSigns/Testing")images,labels=load_data(train_data_directory)# Rescale the images in the `images` arrayimages28 = [transform.resize(image, (28, 28)) for image in images]# Convert `images28` to an arrayimages28 = np.array(images28)# Convert `images28` to grayscaleimages28 = rgb2gray(images28)

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2.可视化图像

(1)加载和预处理完图像信息后，需要可视化下图像，来判断上述工作的正确性

import matplotlib.pyplot as plt      #python中强大的画图模块from load import*                    #导入和预处理代码写于load.py中，需要用到其中加载和处理后的images28traffic_signs = [300, 2250, 3650, 4000]      #随机选取for i in range(len(traffic_signs)):     #i from 0 to 3    plt.subplot(1, 4, i + 1)    plt.axis('off')    plt.imshow(images28[traffic_signs[i]], cmap="gray")    #你确实必须指定颜色图(即 cmap)，并将其设置为 gray 以给出灰度图像的图表。    # 这是因为 imshow() 默认使用一种类似热力图的颜色图。    plt.subplots_adjust(wspace=0.5)       #调整各个图之间的间距# Show the plotplt.show()

(2)绘制所有 62 个类的整体情况

# Import the `pyplot` module as `plt`import matplotlib.pyplot as pltfrom load import*# Get the unique labelsunique_labels = set(labels)# Initialize the figureplt.figure(figsize=(15, 15))# Set a counteri = 1# For each unique label,for label in unique_labels:    # You pick the first image for each label    image = images28[labels.index(label)]    # Define 64 subplots    plt.subplot(8, 8, i)    # Don't include axes    plt.axis('off')    # Add a title to each subplot    plt.title("Label {0} ({1})".format(label, labels.count(label)))    # Add 1 to the counter    i += 1    # And you plot this first image    plt.imshow(image)# Show the plotplt.show()

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3.训练神经网络
这里使用了全连接神经网络

# Import `tensorflow`import tensorflow as tffrom load import*# Initialize placeholdersx = tf.placeholder(dtype=tf.float32, shape=[None, 28, 28])y = tf.placeholder(tf.int32, [None])#然后构建你的网络。首先使用 flatten() 函数展平输入，# 其会给你一个形状为 [None, 784] 的数组，而不是 [None, 28, 28]——这是你的灰度图像的形状。# Flatten the input dataimages_flat = tf.contrib.layers.flatten(x)# Fully connected layer构建一个全连接层，其可以生成大小为 [None, 62] 的 logits。logits 是运行在早期层未缩放的输出上的函数，其使用相对比例来了解单位是否是线性的。logits = tf.contrib.layers.fully_connected(images_flat, 62, tf.nn.relu)# Define a loss function#定义损失函数了。sparse_softmax_cross_entropy_with_logits()，其可以计算 logits 和标签之间的稀疏 softmax 交叉熵。回归(regression)被用于预测连续值，而分类(classification)则被用于预测离散值或数据点的类别。你可以使用 reduce_mean() 来包裹这个函数，它可以计算一个张量的维度上各个元素的均值。loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y,                                                                     logits=logits))# Define an optimizertrain_op = tf.train.AdamOptimizer(learning_rate=0.001).minimize(loss)# Convert logits to label indexescorrect_pred = tf.argmax(logits, 1)# Define an accuracy metricaccuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))'''print("images_flat: ", images_flat)print("logits: ", logits)print("loss: ", loss)print("predicted_labels: ", correct_pred)'''tf.set_random_seed(1234)sess = tf.Session()sess.run(tf.global_variables_initializer())for i in range(201):    print('EPOCH', i)    _, accuracy_val = sess.run([train_op, accuracy], feed_dict={x: images28, y: labels})    if i % 10 == 0:        print("Loss: ", loss)    print('DONE WITH EPOCH')

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4.评估神经网络
(1)随机选择几张图来大致判断分类的准确性

# Import `matplotlib`import matplotlib.pyplot as pltimport random       #随机from nnet import*  # Pick 10 random imagessample_indexes = random.sample(range(len(images28)), 10) sample_images = [images28[i] for i in sample_indexes]sample_labels = [labels[i] for i in sample_indexes]# Run the "correct_pred" operationpredicted = sess.run([correct_pred], feed_dict={x: sample_images})[0]# Print the real and predicted labelsprint(sample_labels)print(predicted)# Display the predictions and the ground truth visually.fig = plt.figure(figsize=(10, 10))for i in range(len(sample_images)):    truth = sample_labels[i]    prediction = predicted[i]    plt.subplot(5, 2, 1 + i)    plt.axis('off')    color = 'green' if truth == prediction else 'red'    plt.text(40, 10, "Truth:        {0}\nPrediction: {1}".format(truth, prediction),             fontsize=12, color=color)    plt.imshow(sample_images[i], cmap="gray")plt.show()

分析图像可知，预测正确的图像有7个，错误的有3个。准确率大致为70%

(2)将建好的模型用测试集数据，来计算准确率

# Import `skimage`from skimage import transformfrom nnet import*# Load the test datatest_images, test_labels = load_data(test_data_directory)# Transform the images to 28 by 28 pixelstest_images28 = [transform.resize(image, (28, 28)) for image in test_images]# Convert to grayscalefrom skimage.color import rgb2graytest_images28 = rgb2gray(np.array(test_images28))# Run predictions against the full test set.predicted = sess.run([correct_pred], feed_dict={x: test_images28})[0]# Calculate correct matchesmatch_count = sum([int(y == y_) for y, y_ in zip(test_labels, predicted)])# Calculate the accuracyaccuracy = match_count / len(test_labels)# Print the accuracyprint("Accuracy: {:.3f}".format(accuracy))

此分类问题比较简单，数据量也较少。