tensorflow使用SEnet实现mnist
# -*- coding: utf-8 -*-
# mnist 回顾一下tensorflow用法
# 这里使用卷积神经网络，所以将784reshape成28*28的矩阵
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
def get_data():
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
return mnist
# 设置权重函数
def weight_variable(shape):
initial = tf.truncated_normal(shape, stddev = 0.1)
return tf.Variable(initial)
# 设置阈值函数
def bias_variable(shape):
initial = tf.constant(0.1, shape=shape)
return tf.Variable(initial)
# 设置卷积层
def conv2d(x,W):
return tf.nn.conv2d(x,W,strides=[1,1,1,1],padding = "SAME")
# 设置池化层
def pool(x):
return tf.nn.max_pool(x,ksize=[1,2,2,1],strides = [1,2,2,1],padding = "SAME")
def SE_block(x,ratio):
shape = x.get_shape().as_list()
channel_out = shape[3]
# print(shape)
with tf.variable_scope("squeeze_and_excitation"):
# 第一层，全局平均池化层
squeeze = tf.nn.avg_pool(x,[1,shape[1],shape[2],1],[1,shape[1],shape[2],1],padding = "SAME")
# 第二层，全连接层
w_excitation1 = weight_variable([1,1,channel_out,channel_out/ratio])
b_excitation1 = bias_variable([channel_out/ratio])
excitation1 = conv2d(squeeze,w_excitation1) + b_excitation1
excitation1_output = tf.nn.relu(excitation1)
# 第三层，全连接层
w_excitation2 = weight_variable([1, 1, channel_out / ratio, channel_out])
b_excitation2 = bias_variable([channel_out])
excitation2 = conv2d(excitation1_output, w_excitation2) + b_excitation2
excitation2_output = tf.nn.sigmoid(excitation2)
# 第四层，点乘
excitation_output = tf.reshape(excitation2_output,[-1,1,1,channel_out])
h_output = excitation_output * x
return h_output
if __name__ == "__main__":
# 设置输入
x = tf.placeholder(tf.float32,[None, 784])
# 将其变为二维的图片,-1为x的第一位数量
x_image = tf.reshape(x,[-1,28,28,1])
# 配置第一层卷积层
W_conv1 = weight_variable([5,5,1,32])
b_conv1 = bias_variable([32])
h1_conv = tf.nn.relu(conv2d(x_image,W_conv1)+b_conv1)
h1_conv = SE_block(h1_conv,4)
# 配置第一层池化层
h1_pool = pool(h1_conv)
# 配置第二层卷积层
W_conv2 = weight_variable([5, 5, 32, 64])
b_conv2 = bias_variable([64])
h2_conv = tf.nn.relu(conv2d(h1_pool, W_conv2) + b_conv2)
h2_conv = SE_block(h2_conv, 4)
# 配置第二层池化层
h2_pool = pool(h2_conv)
# 配置全连接层1
W_fc1 = weight_variable([7*7*64,1024])
b_fc1 = bias_variable([1024])
h2_pool_flat = tf.reshape(h2_pool,shape = [-1,7*7*64])
h_fc1 = tf.nn.relu(tf.matmul(h2_pool_flat,W_fc1)+b_fc1)
#配置dropout层
keep_prob = tf.placeholder("float")
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
# 配置全连接层2
W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])
y_predict = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)
# 接受y_label
y_label = tf.placeholder(tf.float32,[None,10])
mnist = get_data()
# 交叉熵
cross_entropy = -tf.reduce_sum(y_label * tf.log(y_predict))
# 梯度下降法
train_step = tf.train.AdamOptimizer(1e-3).minimize(cross_entropy)
# 求准确率
correct_prediction = tf.equal(tf.argmax(y_predict, 1), tf.argmax(y_label, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float")) # 精确度计算
sess = tf.InteractiveSession()
sess.run(tf.initialize_all_variables())
for i in range(20000):
batch = mnist.train.next_batch(50)
if i % 100 == 0: # 训练100次，验证一次
train_acc = accuracy.eval(feed_dict={x: batch[0], y_label: batch[1], keep_prob: 1.0})
print 'step %d, training accuracy %g' % (i, train_acc)
train_step.run(feed_dict={x: batch[0], y_label: batch[1], keep_prob: 0.5})
point = 0
point_temp =0
for i in xrange(10):
testSet = mnist.test.next_batch(50)
point_temp = accuracy.eval(feed_dict={x: testSet[0], y_label: testSet[1], keep_prob: 1.0})
print "test accuracy %g"%point_temp
point += point_temp
print "test accuracy:"+str(point/10)
准确率：
test accuracy 0.92
test accuracy 1
test accuracy 0.92
test accuracy 0.94
test accuracy:0.9700000047683716