TensorFlow implements convolutional neural network

1. Introduction to
Convolutional Neural Network Convolutional neural network (CNN), firstly in the 1860s, biologists studied the cat visual cortex and found that each visual neuron will only process a small area of vision. image, the receptive field. Later, in the 1980s, Japanese scientists proposed the concept of a neurocognitive machine (Neocognitron), which can also be regarded as the initial realization prototype of the convolutional neural network. In the CS231n class, I said that the convolutional neural network was not generated overnight. From this development process, we can see that this is indeed the case. The main points of convolutional neural networks are Local Connection, Weight sharing, and Down-Sampling in Pooling.
2. Construction of a simple neural network
Two convolutional layers and a fully connected layer are used here. The purpose is to explain how to define the convolutional layer and the fully connected layer in tensorflow.

#第一步，很简单，导入MNIST数据集，创建默认的Interactive Session
from tensorflow.examples.tutorials.mnist import input_data

import tensorflow as tf


mnist = input_data.read_data_sets("MNIST_data", one_hot = True)
sess = tf.InteractiveSession()
#定义权重和偏差的初始化函数，这样省得后来一遍遍定义，直接调用初始化函数就可以了。
def weight_variable(shape):
    initial = tf.truncated_normal(shape, stddev = 0.1)
    return tf.Variable(initial)

def bias_variable(shape):
    initial = tf.constant(0.1, shape = shape)
    return tf.Variable(initial)
#定义卷积层和池化层
def conv2d(x, W):
    return tf.nn.conv2d(x, W, strides = [1, 1, 1, 1], padding = 'SAME')

def max_pool_2_2(x):
    return tf.nn.max_pool(x, ksize = [1, 2, 2, 1], strides = [1, 2, 2, 1], padding = 'SAME')

'''
定义输入的placeholder，x是特征，y_是真实的label。因为卷积神经网络是会用到2D的空间信息，
所以要把784维的数据恢复成28*28的结构，使用的函数就是tf.shape的函数。
'''
x = tf.placeholder(tf.float32, [None, 784])
y_ = tf.placeholder(tf.float32, [None, 10])
x_image = tf.reshape(x, [-1, 28, 28, 1])

#第一个卷积神经
W_conv1 = weight_variable([5, 5, 1, 32])
b_conv1 = bias_variable([32])
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
h_pool1 = max_pool_2_2(h_conv1)

#第二个卷积神经
W_conv2 = weight_variable([5, 5, 32, 64])
b_conv2 = bias_variable([64])
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
h_pool2 = max_pool_2_2(h_conv2)

#定义第一个全连接层
W_fc1 = weight_variable([7 * 7 * 64, 1024])
b_fc1 = bias_variable([1024])
h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * 64])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)

keep_prob = tf.placeholder(tf.float32)
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

#最后一个输出层也要对权重和偏差进行初始化。
W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])
y_conv = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)

#定义损失函数和训练的步骤，使用Adam优化器最小化损失函数。
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y_conv), reduction_indices = [1]))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)

#计算预测的精确度。
correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

'''
对全局的变量进行初始化，迭代20000次训练,使用的minibatch为50，所以总共训练的样本数量为100万。
'''
tf.global_variables_initializer().run()
for i in range(20000):
    batch = mnist.train.next_batch(50)
    if i % 100 == 0:
        train_accuracy = accuracy.eval(feed_dict = {x: batch[0], y_: batch[1], keep_prob: 1.0})
        print("step %d, training accuracy %g"%(i, train_accuracy))
    train_step.run(feed_dict = {x: batch[0], y_: batch[1], keep_prob: 0.5})

#输出最后的准确率。
print("test accuracy %g"%accuracy.eval(feed_dict = {x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))

TensorFlow implements convolutional neural network

Guess you like