使用tensorflow搭建一个神经网络，实现一个分类问题

工欲善其事必先利其器，首先，我们来说说关于环境搭建的问题。

安装的方法有一万种，但是我还是推荐下面这种安装方法，简单方便，不会出现很多莫名其妙的问题。

Anaconda + Jupyter + tensorflow

安装的具体流程见下面的视频链接：

https://www.youtube.com/watch?v=G2GqLWOERjQ (需要科学上网)

数据集

数据集采用的比利时这个国家的交通标志。从 https://btsd.ethz.ch/shareddata/ 可以获得数据， BelgiumTSC_Training (171.3MBytes)和 BelgiumTSC_Testing (76.5MBytes)分别代表我们的训练数据和测试数据。

数据集的说明

Trainging文件夹中有62个文件夹，每一个文件夹中若干张图片，文件夹中图片就是我们的属性，标签是文件夹的名字。

我们的训练目标就是，给定一张图片，判断这张图片属于哪一个文件夹（分类问题）。

上干货，代码！

-加载数据，并创建训练集的属性和标签

def load_data(data_dir):
    # Get all subdirectories of data_dir. Each represents a label.
    directories = [d for d in os.listdir(data_dir) 
                   if os.path.isdir(os.path.join(data_dir, d))]
#     print(directories)
    # Loop through the label directories and collect the data in
    # two lists, labels and images.
    labels = []
    images = []
    for d in directories:
        label_dir = os.path.join(data_dir, d)
        file_names = [os.path.join(label_dir, f) 
                      for f in os.listdir(label_dir) 
                      if f.endswith(".ppm")]
        for f in file_names:
            images.append(data.imread(f))
            labels.append(int(d))
    return images, labels

ROOT_PATH = "E:/machineLearning/tensorflow/data/"  #这里需要根据自己数据存放的路径进行修改
train_data_dir = os.path.join(ROOT_PATH, "BelgiumTSC_Training/Training")
test_data_dir = os.path.join(ROOT_PATH, "BelgiumTSC_Testing/Testing")

images, labels = load_data(train_data_dir)

images_array = np.array(images)
labels_array = np.array(labels)

# Print the `images` dimensions
print(images_array.ndim)

# Print the number of `images`'s elements
print(images_array.size)

# Print the first instance of `images`
# print(images_array[0])

# Print the `labels` dimensions
print(labels_array.ndim)

# Print the number of `labels`'s elements
print(labels_array.size)

# Count the number of labels
print(len(set(labels_array)))

-特征抽取

缩放图像：

# Resize images
images32 = [transform.resize(image, (28, 28)) for image in images]
images32 = np.array(images32)
print(images32[0])

将彩色图像灰度化

for i in range(len(traffic_signs)):
    plt.subplot(1, 4, i+1)
    plt.axis('off')
    plt.imshow(images32[traffic_signs[i]], cmap="gray")
    plt.subplots_adjust(wspace=0.5)
    
plt.show()

print(images32.shape)

-使用Tensorflow训练一个神经网络

import tensorflow as tf
x = tf.placeholder(dtype = tf.float32, shape = [None, 28, 28])
y = tf.placeholder(dtype = tf.int32, shape = [None])
images_flat = tf.contrib.layers.flatten(x)
logits = tf.contrib.layers.fully_connected(images_flat, 62, tf.nn.relu)
loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(labels = y, logits = logits))
train_op = tf.train.AdamOptimizer(learning_rate=0.001).minimize(loss)
correct_pred = tf.argmax(logits, 1)
accuracy = 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)

运行神经网络

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: images32, y: labels})
        if i % 10 == 0:
            print("Loss: ", loss)
        print('DONE WITH EPOCH')

执行神经网络

# Pick 10 random images
sample_indexes = random.sample(range(len(images32)), 10)
sample_images = [images32[i] for i in sample_indexes]
sample_labels = [labels[i] for i in sample_indexes]

# Run the "predicted_labels" op.
predicted = sess.run([correct_pred], feed_dict={x: sample_images})[0]
                        
# Print the real and predicted labels
print(sample_labels)
print(predicted)

-展示预测结果

# 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])

plt.show()

-预测测试集

# Load the test data
test_images, test_labels = load_data(test_data_dir)

# Transform the images to 28 by 28 pixels
test_images28 = [transform.resize(image, (28, 28)) for image in test_images]

# Convert to grayscale
from skimage.color import rgb2gray
test_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 matches 
match_count = sum([int(y == y_) for y, y_ in zip(test_labels, predicted)])

# Calculate the accuracy
accuracy = match_count / len(test_labels)

# Print the accuracy
print("Accuracy: {:.3f}".format(accuracy))

-关闭session

sess.close()

预测的准确率大概是57.8%