『TensorFlow』读书笔记_简单卷积神经网络

# Author : Hellcat
# Time   : 2017/12/7

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

mnist = input_data.read_data_sets(‘../../../Mnist_data‘,one_hot=True)
sess = tf.InteractiveSession()

def weight_variable(shape):
    initial = tf.truncated_normal(shape,stddev=0.1)
    return tf.Variable(initial)

def bias_variable(shape):
    # 偏置项使用极小值初始化，防止Relu出现死亡节点（dead neuron）
    initial = tf.constant(0.1, shape=shape)
    return tf.Variable(initial)

def conv2d(x, W):
    return tf.nn.conv2d(x, W, strides=[1,1,1,1], padding=‘SAME‘)

def max_pool_2x2(x):
    # 2x2池化，步长为2
    return tf.nn.max_pool(x, ksize=[1,2,2,1], strides=[1,2,2,1], padding=‘SAME‘)

x = tf.placeholder(tf.float32, [None, 784])
y_ = tf.placeholder(tf.float32, [None, 10])
x_image = tf.reshape(x, [-1, 28, 28, 1])

# 5x5滤波器，1通道，32特征图
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_2x2(h_conv1)

# 5x5滤波器，32通道，64特征图
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_2x2(h_conv2)

# 28x28，经过2次步长为2的最大池化(SAME)，大小变为28/2/2，即7x7
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)

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

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)

# axis=1,按行来计算
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y_conv),axis=1))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)

correct_prediction = tf.equal(tf.argmax(y_conv,axis=1), tf.argmax(y_,axis=1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

tf.global_variables_initializer().run()
for i in range(20000):
    batch = mnist.train.next_batch(50)
    train_step.run(feed_dict={x:batch[0],y_:batch[1],keep_prob:0.5})
    if i % 100 == 0:
        train_accuracy = accuracy.eval(feed_dict={x:batch[0],y_:batch[1],keep_prob:1.0})
        print(‘step {0} traning accuracy {1:.3f}‘.format(i,train_accuracy))

print(‘test accuracy {}‘.format(accuracy.eval(
    feed_dict={x:mnist.test.images,y_:mnist.test.labels,keep_prob:1.0})))