文章目录
设计流程：
1、准备数据
2、卷积、激活、池化（两层）
3、全连接层
4、计算准确率
代码实现：
# @XST1520203418
# 要天天开心呀
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
from tensorflow.contrib.slim.python.slim.nets.inception_v3 import inception_v3_base
FLAGS = tf.app.flags.FLAGS
tf.app.flags.DEFINE_integer("is_train", 1, "指定程序是预测还是训练")
def full_connected():
# 获取真实的数据
mnist = input_data.read_data_sets("D:/ProgramData/机器学习/数据/MNIST/", one_hot=True)
# 1、建立数据的占位符 x [None, 784] y_true [None, 10]
with tf.variable_scope("data"):
x = tf.placeholder(tf.float32, [None, 784])
y_true = tf.placeholder(tf.int32, [None, 10])
# 2、建立一个全连接层的神经网络 w [784, 10] b [10]
with tf.variable_scope("fc_model"):
# 随机初始化权重和偏置
weight = tf.Variable(tf.random_normal([784, 10], mean=0.0, stddev=1.0), name="w")
bias = tf.Variable(tf.constant(0.0, shape=[10]))
# 预测None个样本的输出结果matrix [None, 784]* [784, 10] + [10] = [None, 10]
y_predict = tf.matmul(x, weight) + bias
# 3、求出所有样本的损失，然后求平均值
with tf.variable_scope("soft_cross"):
# 求平均交叉熵损失
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_true, logits=y_predict))
# 4、梯度下降求出损失
with tf.variable_scope("optimizer"):
train_op = tf.train.GradientDescentOptimizer(0.1).minimize(loss)
# 5、计算准确率
with tf.variable_scope("acc"):
equal_list = tf.equal(tf.argmax(y_true, 1), tf.argmax(y_predict, 1))
# equal_list None个样本 [1, 0, 1, 0, 1, 1,..........]
accuracy = tf.reduce_mean(tf.cast(equal_list, tf.float32))
# 收集变量 单个数字值收集
tf.summary.scalar("losses", loss)
tf.summary.scalar("acc", accuracy)
# 高纬度变量收集
tf.summary.histogram("weightes", weight)
tf.summary.histogram("biases", bias)
# 定义一个初始化变量的op
init_op = tf.global_variables_initializer()
# 定义一个合并变量de op
merged = tf.summary.merge_all()
# 创建一个saver
saver = tf.train.Saver()
# 开启会话去训练
with tf.Session() as sess:
# 初始化变量
sess.run(init_op)
# 建立events文件，然后写入
filewriter = tf.summary.FileWriter("./summary/test01/", graph=sess.graph)
if FLAGS.is_train == 1:
# 迭代步数去训练，更新参数预测
for i in range(2000):
# 取出真实存在的特征值和目标值
mnist_x, mnist_y = mnist.train.next_batch(50)
# 运行train_op训练
sess.run(train_op, feed_dict={x: mnist_x, y_true: mnist_y})
# 写入每步训练的值
summary = sess.run(merged, feed_dict={x: mnist_x, y_true: mnist_y})
filewriter.add_summary(summary, i)
print("训练第%d步,准确率为:%f" % (i, sess.run(accuracy, feed_dict={x: mnist_x, y_true: mnist_y})))
# 保存模型
saver.save(sess, "./tmp/ckpt/fc_model")
else:
# 加载模型
saver.restore(sess, "./tmp/ckpt/fc_model")
# 如果是0，做出预测
for i in range(100):
# 每次测试一张图片 [0,0,0,0,0,1,0,0,0,0]
x_test, y_test = mnist.test.next_batch(1)
print("第%d张图片，手写数字图片目标是:%d, 预测结果是:%d" % (
i,
tf.argmax(y_test, 1).eval(),
tf.argmax(sess.run(y_predict, feed_dict={x: x_test, y_true: y_test}), 1).eval()
))
return None
if __name__ == "__main__":
full_connected()
自定义卷积模型实现：
# @XST1520203418
# 要天天开心呀
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
from tensorflow.contrib.slim.python.slim.nets.inception_v3 import inception_v3_base
FLAGS = tf.app.flags.FLAGS
tf.app.flags.DEFINE_integer("is_train", 1, "指定程序是预测还是训练")
# 定义一个初始化权重的函数
def weight_variables(shape):
w = tf.Variable(tf.random_normal(shape=shape, mean=0.0, stddev=1.0))
return w
# 定义一个初始化偏置的函数
def bias_variables(shape):
b = tf.Variable(tf.constant(0.0, shape=shape))
return b
def model():
"""
自定义的卷积模型
:return:
"""
# 1、准备数据的占位符 x [None, 784] y_true [None, 10]
with tf.variable_scope("data"):
x = tf.placeholder(tf.float32, [None, 784])
y_true = tf.placeholder(tf.int32, [None, 10])
# 2、一卷积层 卷积: 5*5*1，32个，strides=1 激活: tf.nn.relu 池化
with tf.variable_scope("conv1"):
# 随机初始化权重, 偏置[32]
w_conv1 = weight_variables([5, 5, 1, 32])
b_conv1 = bias_variables([32])
# 对x进行形状的改变[None, 784] [None, 28, 28, 1]
x_reshape = tf.reshape(x, [-1, 28, 28, 1])
# [None, 28, 28, 1]-----> [None, 28, 28, 32]
x_relu1 = tf.nn.relu(tf.nn.conv2d(x_reshape, w_conv1, strides=[1, 1, 1, 1], padding="SAME") + b_conv1)
# 池化 2*2 ,strides2 [None, 28, 28, 32]---->[None, 14, 14, 32]
x_pool1 = tf.nn.max_pool(x_relu1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="SAME")
# 3、二卷积层卷积: 5*5*32，64个filter，strides=1 激活: tf.nn.relu 池化：
with tf.variable_scope("conv2"):
# 随机初始化权重, 权重：[5, 5, 32, 64] 偏置[64]
w_conv2 = weight_variables([5, 5, 32, 64])
b_conv2 = bias_variables([64])
# 卷积，激活，池化计算
# [None, 14, 14, 32]-----> [None, 14, 14, 64]
x_relu2 = tf.nn.relu(tf.nn.conv2d(x_pool1, w_conv2, strides=[1, 1, 1, 1], padding="SAME") + b_conv2)
# 池化 2*2, strides 2, [None, 14, 14, 64]---->[None, 7, 7, 64]
x_pool2 = tf.nn.max_pool(x_relu2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="SAME")
# 4、全连接层 [None, 7, 7, 64]--->[None, 7*7*64]*[7*7*64, 10]+ [10] =[None, 10]
with tf.variable_scope("conv2"):
# 随机初始化权重和偏置
w_fc = weight_variables([7 * 7 * 64, 10])
b_fc = bias_variables([10])
# 修改形状 [None, 7, 7, 64] --->None, 7*7*64]
x_fc_reshape = tf.reshape(x_pool2, [-1, 7 * 7 * 64])
# 进行矩阵运算得出每个样本的10个结果
y_predict = tf.matmul(x_fc_reshape, w_fc) + b_fc
return x, y_true, y_predict
def conv_fc():
# 获取真实的数据
mnist = input_data.read_data_sets("./data/mnist/input_data/", one_hot=True)
# 定义模型，得出输出
x, y_true, y_predict = model()
# 进行交叉熵损失计算
# 3、求出所有样本的损失，然后求平均值
with tf.variable_scope("soft_cross"):
# 求平均交叉熵损失# 求平均交叉熵损失
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_true, logits=y_predict))
# 4、梯度下降求出损失
with tf.variable_scope("optimizer"):
train_op = tf.train.GradientDescentOptimizer(0.0001).minimize(loss)
# 5、计算准确率
with tf.variable_scope("acc"):
equal_list = tf.equal(tf.argmax(y_true, 1), tf.argmax(y_predict, 1))
# equal_list None个样本 [1, 0, 1, 0, 1, 1,..........]
accuracy = tf.reduce_mean(tf.cast(equal_list, tf.float32))
# 定义一个初始化变量的op
init_op = tf.global_variables_initializer()
# 开启回话运行
with tf.Session() as sess:
sess.run(init_op)
# 循环去训练
for i in range(1000):
# 取出真实存在的特征值和目标值
mnist_x, mnist_y = mnist.train.next_batch(50)
# 运行train_op训练
sess.run(train_op, feed_dict={x: mnist_x, y_true: mnist_y})
print("训练第%d步,准确率为:%f" % (i, sess.run(accuracy, feed_dict={x: mnist_x, y_true: mnist_y})))
return None
if __name__ == "__main__":
conv_fc()