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卷积识别手写数字.py
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卷积识别手写数字.py
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# -*- coding: utf-8 -*-
"""
Created on Sun Mar 10 10:57:55 2019
@author: 汤国频
"""
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
#定义初始化权重的函数
def weight_variavles(shape):
w = tf.Variable(tf.truncated_normal(shape, stddev=0.1))
return w
#定义一个初始化偏置的函数
def bias_variavles(shape):
b = tf.Variable(tf.constant(0.0, shape=shape))
return b
def model():
#1.建立数据的占位符 x [None, 784] y_true [None, 10]
with tf.variable_scope("date"):
x = tf.placeholder(tf.float32, [None, 784])
y_true = tf.placeholder(tf.int32, [None, 10])
#2.卷积层1 卷积:5*5*1,32个filter,strides= 1-激活-池化
with tf.variable_scope("conv1"):
#随机初始化权重
w_conv1 = weight_variavles([5, 5, 1, 32])
b_conv1 = bias_variavles([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]) #不能填None,不知道就填-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，步长为2，【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.卷积层2 卷积:5*5*32,64个filter,strides= 1-激活-池化
with tf.variable_scope("conv2"):
#随机初始化权重和偏置
w_conv2 = weight_variavles([5, 5, 32, 64])
b_conv2 = bias_variavles([64])
#卷积、激活、池化
#[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，步长为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("fc"):
#随机初始化权重和偏置:
w_fc = weight_variavles([7*7*64, 10])
b_fc = bias_variavles([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[NONE, 10]
y_predict = tf.matmul(x_fc_reshape, w_fc) + b_fc
return x, y_true, y_predict
def conv_fc():
#获取数据
mnist = input_data.read_data_sets('MNIST_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.计算交叉熵损失
with tf.variable_scope("soft_cross"):
#求平均交叉熵损失,tf.reduce_mean对列表求平均值
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_true, logits=y_predict)) #返回损失值的列表
#4.梯度下降求出最小损失,注意在深度学习中，或者网络层次比较复杂的情况下，学习率通常不能太高
with tf.variable_scope("optimizer"):
train_op = tf.train.AdamOptimizer(0.001).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 类型为列表1为预测正确，0为预测错误[1, 0, 1, 0......]
accuray = tf.reduce_mean(tf.cast(equal_list, tf.float32))
init_op = tf.global_variables_initializer()
saver = tf.train.Saver()
#开启会话运行
with tf.Session() as sess:
sess.run(init_op)
#循环去训练
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})
#print(sess.run(tf.argmax(y_predict[:,0], 1), feed_dict = {x: mnist_x, y_true: mnist_y}))
#print(sess.run(tf.argmax(mnist_y[0], 0)))
print("训练第%d步，准确率为：%f" % (i, sess.run(accuray,feed_dict = {x: mnist_x, y_true: mnist_y})))
saver.save(sess, "D:/Dict/a/fcc_model.ckpt")
return None
if __name__ == "__main__":
conv_fc()