# coding: utf-8
import tensorflow as tf
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
print(tf.__version__)
print(tf.test.is_gpu_available())
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
tf.reset_default_graph() #清除default graph和不断增加的节点
x = tf.placeholder(tf.float32, [None, 784]) # mnist 中每张图片共有28*28=784个像素点
y = tf.placeholder(tf.float32, [None, 10]) # 0-9 一共10个数字=> 10 个类别
norm = tf.random_normal([100]) #生成100个随机数
with tf.Session() as sess:
norm_data=norm.eval()
print(norm_data[:10]) #打印前10个随机数
import matplotlib.pyplot as plt
plt.hist(norm_data)
plt.show()
W = tf.Variable(tf.random_normal([784, 10]))
b = tf.Variable(tf.zeros([10]))
forward=tf.matmul(x, W) + b # 前向输出
tf.summary.histogram('forward',forward)#将前向输出值以直方图显示
pred = tf.nn.softmax(forward) # Softmax分类
train_epochs = 30
batch_size = 100
total_batch= int(mnist.train.num_examples/batch_size)
display_step = 1
learning_rate=0.01
loss_function = tf.reduce_mean(-tf.reduce_sum(y*tf.log(pred), reduction_indices=1)) # 交叉熵
tf.summary.scalar('loss', loss_function)#将损失以标量显示
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss_function) #梯度下降
# 检查预测类别tf.argmax(pred, 1)与实际类别tf.argmax(y, 1)的匹配情况
correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
# 准确率
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) # 将布尔值转化为浮点数,并计算平均值
tf.summary.scalar('accuracy', accuracy)#将准确率以标量显示
sess = tf.Session() #声明会话
init = tf.global_variables_initializer() # 变量初始化
sess.run(init)
merged_summary_op = tf.summary.merge_all()#合并所有summary
writer = tf.summary.FileWriter('log/mnist_single_neuron', sess.graph) #创建写入符
# 开始训练
for epoch in range(train_epochs ):
for batch in range(total_batch):
xs, ys = mnist.train.next_batch(batch_size)# 读取批次数据
sess.run(optimizer,feed_dict={x: xs,y: ys}) # 执行批次训练
#生成summary
summary_str = sess.run(merged_summary_op,feed_dict={x: xs,y: ys})
writer.add_summary(summary_str, epoch)#将summary 写入文件
#total_batch个批次训练完成后,使用验证数据计算误差与准确率
loss,acc = sess.run([loss_function,accuracy],
feed_dict={x: mnist.validation.images, y: mnist.validation.labels})
# 打印训练过程中的详细信息
if (epoch+1) % display_step == 0:
print("Train Epoch:", '%02d' % (epoch+1), "Loss=", "{:.9f}".format(loss)," Accuracy=","{:.4f}".format(acc))
print("Train Finished!")
print("Test Accuracy:", sess.run(accuracy,
feed_dict={x: mnist.test.images, y: mnist.test.labels}))
prediction_result=sess.run(tf.argmax(pred,1), # 由于pred预测结果是one-hot编码格式,所以需要转换为0~9数字
feed_dict={x: mnist.test.images })
prediction_result[0:10] #查看预测结果中的前10项
import matplotlib.pyplot as plt
import numpy as np
def plot_images_labels_prediction(images,labels,
prediction,idx,num=10):
fig = plt.gcf()
fig.set_size_inches(10, 12)
if num>25: num=25
for i in range(0, num):
ax=plt.subplot(5,5, 1+i)
ax.imshow(np.reshape(images[idx],(28, 28)),
cmap='binary')
title= "label=" +str(np.argmax(labels[idx]))
if len(prediction)>0:
title+=",predict="+str(prediction[idx])
ax.set_title(title,fontsize=10)
ax.set_xticks([]);ax.set_yticks([])
idx+=1
plt.show()
plot_images_labels_prediction(mnist.test.images,
mnist.test.labels,
prediction_result,0)
# 预测结果
plot_images_labels_prediction(mnist.test.images,
mnist.test.labels,
prediction_result,10,25)