minst深度学习程序不收敛
是关于tensorflow的问题。我是tensorflow的初学者。从书上抄了minst的学习程序。但是运行之后,无论学习了多少批次,成功率基本不变。
我做了许多尝试,去掉了正则化,去掉了滑动平均,还是不行。把batch_size改成了2,观察变量运算情况,输入x是正确的,但神经网络的输出y很多情况下在x不一样的情况下y的两个结果是完全一样的。进而softmax的结果也是一样的。百思不得其解,找不到造成这种情况的原因。这里把代码和运行情况都贴出来,请大神帮我找找原因。大过年的,祝大家春节快乐万事如意。
补充一下,进一步的测试表明,不是不能完成训练,而是要到700000轮以上,且最高达到65%左右就不能提高了。仔细看每一步的参数,是regularization值过大10e15以上,一点点减少,前面的训练都在训练它了。这东西我不是很明白。
import struct
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.widgets import Slider, Button
import tensorflow as tf
import time
#把MNIST的操作封装在一个类中,以后用起来方便。
class MyMinst():
def decode_idx3_ubyte(self,idx3_ubyte_file):
with open(idx3_ubyte_file, 'rb') as f:
print('解析文件:', idx3_ubyte_file)
fb_data = f.read()
offset = 0
fmt_header = '>iiii' # 以大端法读取4个 unsinged int32
magic_number, num_images, num_rows, num_cols = struct.unpack_from(fmt_header, fb_data, offset)
print('idex3 魔数:{},图片数:{}'.format(magic_number, num_images))
offset += struct.calcsize(fmt_header)
fmt_image = '>' + str(num_rows * num_cols) + 'B'
images = np.empty((num_images, num_rows*num_cols)) #做了修改
for i in range(num_images):
im = struct.unpack_from(fmt_image, fb_data, offset)
images[i] = np.array(im)#这里用一维数组表示图片,np.array(im).reshape((num_rows, num_cols))
offset += struct.calcsize(fmt_image)
return images
def decode_idx1_ubyte(self,idx1_ubyte_file):
with open(idx1_ubyte_file, 'rb') as f:
print('解析文件:', idx1_ubyte_file)
fb_data = f.read()
offset = 0
fmt_header = '>ii' # 以大端法读取两个 unsinged int32
magic_number, label_num = struct.unpack_from(fmt_header, fb_data, offset)
print('idex1 魔数:{},标签数:{}'.format(magic_number, label_num))
offset += struct.calcsize(fmt_header)
labels = np.empty(shape=[0,10],dtype=float) #神经网络需要把label变成10位float的数组
fmt_label = '>B' # 每次读取一个 byte
for i in range(label_num):
n=struct.unpack_from(fmt_label, fb_data, offset)
labels=np.append(labels,[[0,0,0,0,0,0,0,0,0,0]],axis=0)
labels[i][n]=1
offset += struct.calcsize(fmt_label)
return labels
def __init__(self):
#固定的训练文件位置
self.img=self.decode_idx3_ubyte("/home/zhangyl/Downloads/mnist/train-images.idx3-ubyte")
self.result=self.decode_idx1_ubyte("/home/zhangyl/Downloads/mnist/train-labels.idx1-ubyte")
print(self.result[0])
print(self.result[1000])
print(self.result[25000])
#固定的验证文件位置
self.validate_img=self.decode_idx3_ubyte("/home/zhangyl/Downloads/mnist/t10k-images.idx3-ubyte")
self.validate_result=self.decode_idx1_ubyte("/home/zhangyl/Downloads/mnist/t10k-labels.idx1-ubyte")
#每一批读训练数据的起始位置
self.train_read_addr=0
#每一批读训练数据的batchsize
self.train_batchsize=100
#每一批读验证数据的起始位置
self.validate_read_addr=0
#每一批读验证数据的batchsize
self.validate_batchsize=100
#定义用于返回batch数据的变量
self.train_img_batch=self.img
self.train_result_batch=self.result
self.validate_img_batch=self.validate_img
self.validate_result_batch=self.validate_result
def get_next_batch_traindata(self):
n=len(self.img) #对参数范围适当约束
if self.train_read_addr+self.train_batchsize<=n :
self.train_img_batch=self.img[self.train_read_addr:self.train_read_addr+self.train_batchsize]
self.train_result_batch=self.result[self.train_read_addr:self.train_read_addr+self.train_batchsize]
self.train_read_addr+=self.train_batchsize #改变起始位置
if self.train_read_addr==n :
self.train_read_addr=0
else:
self.train_img_batch=self.img[self.train_read_addr:n]
self.train_img_batch.append(self.img[0:self.train_read_addr+self.train_batchsize-n])
self.train_result_batch=self.result[self.train_read_addr:n]
self.train_result_batch.append(self.result[0:self.train_read_addr+self.train_batchsize-n])
self.train_read_addr=self.train_read_addr+self.train_batchsize-n #改变起始位置,这里没考虑batchsize大于n的情形
return self.train_img_batch,self.train_result_batch #测试一下用临时变量返回是否可行
def set_train_read_addr(self,addr):
self.train_read_addr=addr
def set_train_batchsize(self,batchsize):
self.train_batchsize=batchsize
if batchsize <1 :
self.train_batchsize=1
def set_validate_read_addr(self,addr):
self.validate_read_addr=addr
def set_validate_batchsize(self,batchsize):
self.validate_batchsize=batchsize
if batchsize<1 :
self.validate_batchsize=1
myminst=MyMinst() #minst类的实例
batch_size=2 #设置每一轮训练的Batch大小
learning_rate=0.8 #初始学习率
learning_rate_decay=0.999 #学习率的衰减
max_steps=300000 #最大训练步数
#定义存储训练轮数的变量,在使用tensorflow训练神经网络时,
#一般会将代表训练轮数的变量通过trainable参数设置为不可训练的
training_step = tf.Variable(0,trainable=False)
#定义得到隐藏层和输出层的前向传播计算方式,激活函数使用relu()
def hidden_layer(input_tensor,weights1,biases1,weights2,biases2,layer_name):
layer1=tf.nn.relu(tf.matmul(input_tensor,weights1)+biases1)
return tf.matmul(layer1,weights2)+biases2
x=tf.placeholder(tf.float32,[None,784],name="x-input")
y_=tf.placeholder(tf.float32,[None,10],name="y-output")
#生成隐藏层参数,其中weights包含784*500=39200个参数
weights1=tf.Variable(tf.truncated_normal([784,500],stddev=0.1))
biases1=tf.Variable(tf.constant(0.1,shape=[500]))
#生成输出层参数,其中weights2包含500*10=5000个参数
weights2=tf.Variable(tf.truncated_normal([500,10],stddev=0.1))
biases2=tf.Variable(tf.constant(0.1,shape=[10]))
#计算经过神经网络前后向传播后得到的y值
y=hidden_layer(x,weights1,biases1,weights2,biases2,'y')
#初始化一个滑动平均类,衰减率为0.99
#为了使模型在训练前期可以更新的更快,这里提供了num_updates参数,并设置为当前网络的训练轮数
#averages_class=tf.train.ExponentialMovingAverage(0.99,training_step)
#定义一个更新变量滑动平均值的操作需要向滑动平均类的apply()函数提供一个参数列表
#train_variables()函数返回集合图上Graph.TRAINABLE_VARIABLES中的元素。
#这个集合的元素就是所有没有指定trainable_variables=False的参数
#averages_op=averages_class.apply(tf.trainable_variables())
#再次计算经过神经网络前向传播后得到的y值,这里使用了滑动平均,但要牢记滑动平均值只是一个影子变量
#average_y=hidden_layer(x,averages_class.average(weights1),
# averages_class.average(biases1),
# averages_class.average(weights2),
# averages_class.average(biases2),
# 'average_y')
#softmax,计算交叉熵损失,L2正则,随机梯度优化器,学习率采用指数衰减
#函数原型为sparse_softmax_cross_entropy_with_logits(_sential,labels,logdits,name)
#与softmax_cross_entropy_with_logits()函数的计算方式相同,更适用于每个类别相互独立且排斥
#的情况,即每一幅图只能属于一类
#在1.0.0版本的TensorFlow中,这个函数只能通过命名参数的方式来使用,在这里logits参数是神经网
#络不包括softmax层的前向传播结果,lables参数给出了训练数据的正确答案
softmax=tf.nn.softmax(y)
cross_entropy=tf.nn.sparse_softmax_cross_entropy_with_logits(logits=y+1e-10,labels=tf.argmax(y_,1))
#argmax()函数原型为argmax(input,axis,name,dimension)用于计算每一个样例的预测答案,其中
# input参数y是一个batch_size*10(batch_size行,10列)的二维数组。每一行表示一个样例前向传
# 播的结果,axis参数“1”表示选取最大值的操作只在第一个维度进行。即只在每一行选取最大值对应的下标
# 于是得到的结果是一个长度为batch_size的一维数组,这个一维数组的值就表示了每一个样例的数字识别
# 结果。
regularizer=tf.contrib.layers.l2_regularizer(0.0001)
#计算L2正则化损失函数
regularization=regularizer(weights1)+regularizer(weights2)
#计算模型的正则化损失
loss=tf.reduce_mean(cross_entropy)#+regularization
#总损失
#用指数衰减法设置学习率,这里staircase参数采用默认的False,即学习率连续衰减
learning_rate=tf.train.exponential_decay(learning_rate,training_step,
batch_size,learning_rate_decay)
#使用GradientDescentOptimizer优化算法来优化交叉熵损失和正则化损失
train_op=tf.train.GradientDescentOptimizer(learning_rate).minimize(loss,
global_step=training_step)
#在训练这个模型时,每过一遍数据既需要通过反向传播来更新神经网络中的参数,又需要
# 更新每一个参数的滑动平均值。control_dependencies()用于这样的一次性多次操作
#同样的操作也可以使用下面这行代码完成:
#train_op=tf.group(train_step,average_op)
#with tf.control_dependencies([train_step,averages_op]):
# train_op=tf.no_op(name="train")
#检查使用了滑动平均模型的神经网络前向传播结果是否正确
#equal()函数原型为equal(x,y,name),用于判断两个张量的每一维是否相等。
#如果相等返回True,否则返回False
crorent_predicition=tf.equal(tf.argmax(y,1),tf.argmax(y_,1))
#cast()函数的原型为cast(x,DstT,name),在这里用于将一个布尔型的数据转换为float32类型
#之后对得到的float32型数据求平均值,这个平均值就是模型在这一组数据上的正确率
accuracy=tf.reduce_mean(tf.cast(crorent_predicition,tf.float32))
#创建会话和开始训练过程
with tf.Session() as sess:
#在稍早的版本中一般使用initialize_all_variables()函数初始化全部变量
tf.global_variables_initializer().run()
#准备验证数据
validate_feed={x:myminst.validate_img,y_:myminst.validate_result}
#准备测试数据
test_feed= {x:myminst.img,y_:myminst.result}
for i in range(max_steps):
if i%1000==0:
#计算滑动平均模型在验证数据上的结果
#为了能得到百分数输出,需要将得到的validate_accuracy扩大100倍
validate_accuracy= sess.run(accuracy,feed_dict=validate_feed)
print("After %d trainning steps,validation accuracy using average model is %g%%" %(i,validate_accuracy*100))
#产生这一轮使用一个batch的训练数据,并进行训练
#input_data.read_data_sets()函数生成的类提供了train.next_batch()函数
#通过设置函数的batch_size参数就可以从所有的训练数据中读取一个小部分作为一个训练batch
myminst.set_train_batchsize(batch_size)
xs,ys=myminst.get_next_batch_traindata()
var_print=sess.run([x,y,y_,loss,train_op,softmax,cross_entropy,regularization,weights1],feed_dict={x:xs,y_:ys})
print("after ",i," trainning steps:")
print("x=",var_print[0][0],var_print[0][1],"y=",var_print[1],"y_=",var_print[2],"loss=",var_print[3],
"softmax=",var_print[5],"cross_entropy=",var_print[6],"regularization=",var_print[7],var_print[7])
time.sleep(0.5)
#使用测试数据集检验神经网络训练之后的正确率
#为了能得到百分数输出,需要将得到的test_accuracy扩大100倍
test_accuracy=sess.run(accuracy,feed_dict=test_feed)
print("After %d training steps,test accuracy using average model is %g%%"%(max_steps,test_accuracy*100))
下面是运行情况的一部分:
x= [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 8. 76. 202. 254.
255. 163. 37. 2. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 13. 182. 253. 253. 253.
253. 253. 253. 23. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 15. 179. 253. 253. 212. 91.
218. 253. 253. 179. 109. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 105. 253. 253. 160. 35. 156.
253. 253. 253. 253. 250. 113. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 19. 212. 253. 253. 88. 121. 253.
233. 128. 91. 245. 253. 248. 114. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 104. 253. 253. 110. 2. 142. 253.
90. 0. 0. 26. 199. 253. 248. 63. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 1. 173. 253. 253. 29. 0. 84. 228.
39. 0. 0. 0. 72. 251. 253. 215. 29. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 36. 253. 253. 203. 13. 0. 0. 0.
0. 0. 0. 0. 0. 82. 253. 253. 170. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 36. 253. 253. 164. 0. 0. 0. 0.
0. 0. 0. 0. 0. 11. 198. 253. 184. 6. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 36. 253. 253. 82. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 138. 253. 253. 35. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 128. 253. 253. 47. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 48. 253. 253. 35. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 154. 253. 253. 47. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 48. 253. 253. 35. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 102. 253. 253. 99. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 48. 253. 253. 35. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 36. 253. 253. 164. 0. 0. 0. 0.
0. 0. 0. 0. 0. 16. 208. 253. 211. 17. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 32. 244. 253. 175. 4. 0. 0. 0.
0. 0. 0. 0. 0. 44. 253. 253. 156. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 171. 253. 253. 29. 0. 0. 0.
0. 0. 0. 0. 30. 217. 253. 188. 19. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 171. 253. 253. 59. 0. 0. 0.
0. 0. 0. 60. 217. 253. 253. 70. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 78. 253. 253. 231. 48. 0. 0.
0. 26. 128. 249. 253. 244. 94. 15. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 8. 151. 253. 253. 234. 101. 121.
219. 229. 253. 253. 201. 80. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 38. 232. 253. 253. 253. 253.
253. 253. 253. 201. 66. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 232. 253. 253. 95. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 3. 86. 46.
0. 0. 0. 0. 0. 0. 91. 246. 252. 232. 57. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 103. 252. 187.
13. 0. 0. 0. 0. 22. 219. 252. 252. 175. 0. 0. 0. 0.
0. 0. 0. 0. 0. 10. 0. 0. 0. 0. 8. 181. 252. 246.
30. 0. 0. 0. 0. 65. 252. 237. 197. 64. 0. 0. 0. 0.
0. 0. 0. 0. 0. 87. 0. 0. 0. 13. 172. 252. 252. 104.
0. 0. 0. 0. 5. 184. 252. 67. 103. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 8. 172. 252. 248. 145. 14.
0. 0. 0. 0. 109. 252. 183. 137. 64. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 5. 224. 252. 248. 134. 0. 0.
0. 0. 0. 53. 238. 252. 245. 86. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 12. 174. 252. 223. 88. 0. 0. 0.
0. 0. 0. 209. 252. 252. 179. 9. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 11. 171. 252. 246. 61. 0. 0. 0. 0.
0. 0. 83. 241. 252. 211. 14. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 129. 252. 252. 249. 220. 220. 215. 111. 192.
220. 221. 243. 252. 252. 149. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 144. 253. 253. 253. 253. 253. 253. 253. 253.
253. 255. 253. 226. 153. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 44. 77. 77. 77. 77. 77. 77. 77. 77.
153. 253. 235. 32. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 74.
214. 240. 114. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 24. 221.
243. 57. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 8. 180. 252.
119. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 136. 252. 153.
7. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 3. 136. 251. 226. 34.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 123. 252. 246. 39. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 165. 252. 127. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 165. 175. 3. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] y= [[ 0.58273095 0.50121385 -0.74845004 0.35842288 -0.13741069 -0.5839622
0.2642774 0.5101677 -0.29416046 0.5471707 ]
[ 0.58273095 0.50121385 -0.74845004 0.35842288 -0.13741069 -0.5839622
0.2642774 0.5101677 -0.29416046 0.5471707 ]] y_= [[1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
[0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]] loss= 2.2801425 softmax= [[0.14659645 0.13512042 0.03872566 0.11714067 0.07134604 0.04564939
0.10661562 0.13633572 0.06099501 0.14147504]
[0.14659645 0.13512042 0.03872566 0.11714067 0.07134604 0.04564939
0.10661562 0.13633572 0.06099501 0.14147504]] cross_entropy= [1.9200717 2.6402135] regularization= 50459690000000.0 50459690000000.0
after 45 trainning steps:
x= [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 25. 214. 225. 90. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 7. 145. 212. 253. 253. 60. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 106. 253. 253. 246. 188. 23. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 45.
164. 254. 253. 223. 108. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 24. 236.
253. 252. 124. 28. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 100. 217. 253.
218. 116. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 158. 175. 225. 253.
92. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 24. 217. 241. 248. 114.
2. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 21. 201. 253. 253. 114. 3.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 107. 253. 253. 213. 19. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 170. 254. 254. 169. 0. 0.
0. 0. 0. 2. 13. 100. 133. 89. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 18. 210. 253. 253. 100. 0. 0.
0. 19. 76. 116. 253. 253. 253. 176. 4. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 41. 222. 253. 208. 18. 0. 0.
93. 209. 232. 217. 224. 253. 253. 241. 31. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 157. 253. 253. 229. 32. 0. 154.
250. 246. 36. 0. 49. 253. 253. 168. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 128. 253. 253. 253. 195. 125. 247.
166. 69. 0. 0. 37. 236. 253. 168. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 37. 253. 253. 253. 253. 253. 135.
32. 0. 7. 130. 73. 202. 253. 133. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 7. 185. 253. 253. 253. 253. 64.
0. 10. 210. 253. 253. 253. 153. 9. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 66. 253. 253. 253. 253. 238.
218. 221. 253. 253. 235. 156. 37. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 5. 111. 228. 253. 253. 253.
253. 254. 253. 168. 19. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 9. 110. 178. 253.
253. 249. 63. 5. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
121. 121. 240. 253. 218. 121. 121. 44. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 17. 107. 184. 240.
253. 252. 252. 252. 252. 252. 252. 219. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 75. 122. 230. 252. 252. 252.
253. 252. 252. 252. 252. 252. 252. 239. 56. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 77. 129. 213. 244. 252. 252. 252. 252. 252.
253. 252. 252. 209. 252. 252. 252. 225. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 240. 252. 252. 252. 252. 252. 252. 213. 185.
53. 53. 53. 89. 252. 252. 252. 120. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 240. 232. 198. 93. 164. 108. 66. 28. 0.
0. 0. 0. 81. 252. 252. 222. 24. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 76. 50. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 171. 252. 243. 108. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 144. 238. 252. 115. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
7. 70. 241. 248. 133. 28. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
121. 252. 252. 172. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 64.
255. 253. 209. 21. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 13. 246.
253. 207. 21. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 10. 172. 252.
209. 92. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 13. 168. 252. 252.
92. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 43. 208. 252. 241. 53.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 15. 166. 252. 204. 62. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 13. 166. 243. 191. 29. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 10. 168. 231. 177. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 6. 172. 241. 50. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 177. 202. 19. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] y= [[ 0.8592988 0.3954708 -0.77875614 0.26675048 0.19804694 -0.61968666
0.18084174 0.4034736 -0.34189415 0.43645462]
[ 0.8592988 0.3954708 -0.77875614 0.26675048 0.19804694 -0.61968666
0.18084174 0.4034736 -0.34189415 0.43645462]] y_= [[0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]
[0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]] loss= 2.2191708 softmax= [[0.19166051 0.12052987 0.0372507 0.10597225 0.09893605 0.04367344
0.09724841 0.12149832 0.05765821 0.12557226]
[0.19166051 0.12052987 0.0372507 0.10597225 0.09893605 0.04367344
0.09724841 0.12149832 0.05765821 0.12557226]] cross_entropy= [2.3304868 2.1078548] regularization= 50459690000000.0 50459690000000.0
after 46 trainning steps:
x= [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 196. 99. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 5. 49. 0. 0. 0.
0. 0. 0. 34. 244. 98. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 89. 135. 0. 0. 0.
0. 0. 0. 40. 253. 98. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 171. 150. 0. 0. 0.
0. 0. 0. 40. 253. 98. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 254. 233. 0. 0. 0.
0. 0. 0. 77. 253. 98. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 255. 136. 0. 0. 0.
0. 0. 0. 77. 254. 99. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 254. 135. 0. 0. 0.
0. 0. 0. 123. 253. 98. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 254. 135. 0. 0. 0.
0. 0. 0. 136. 253. 98. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 16. 254. 135. 0. 0. 0.
0. 0. 0. 136. 237. 8. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 98. 254. 135. 0. 0. 38.
99. 98. 98. 219. 155. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 196. 255. 208. 186. 254. 254.
255. 254. 254. 254. 254. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 105. 254. 253. 239. 180. 135.
39. 39. 39. 237. 170. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 137. 92. 24. 0. 0.
0. 0. 0. 234. 155. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 13. 237. 155. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 79. 253. 155. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 31. 242. 155. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 61. 248. 155. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 234. 155. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 234. 155. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 196. 155. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 50. 236. 255. 124. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
53. 231. 253. 253. 107. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 9.
193. 253. 253. 230. 4. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 7. 156.
253. 253. 149. 36. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 24. 253.
253. 190. 8. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 3. 175. 253.
253. 72. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 123. 253. 253.
138. 3. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 10. 244. 253. 230.
34. 0. 9. 24. 23. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 181. 253. 249. 123.
0. 69. 195. 253. 249. 146. 15. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 21. 231. 253. 202. 0.
70. 236. 253. 253. 253. 253. 170. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 22. 139. 253. 213. 26. 13.
200. 253. 253. 183. 252. 253. 220. 22. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 72. 253. 253. 129. 0. 86.
253. 253. 129. 4. 105. 253. 253. 70. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 72. 253. 253. 77. 22. 245.
253. 183. 4. 0. 2. 105. 253. 70. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 132. 253. 253. 11. 24. 253.
253. 116. 0. 0. 1. 150. 253. 70. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 189. 253. 241. 10. 24. 253.
253. 59. 0. 0. 82. 253. 212. 30. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 189. 253. 147. 0. 24. 253.
253. 150. 30. 44. 208. 212. 31. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 189. 253. 174. 3. 7. 185.
253. 253. 227. 247. 184. 30. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 150. 253. 253. 145. 95. 234.
253. 253. 253. 126. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 72. 253. 253. 253. 253. 253.
253. 253. 169. 14. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 5. 114. 240. 253. 253. 234.
135. 44. 3. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] y= [[ 0.7093834 0.30119324 -0.80789334 0.1838598 0.12065991 -0.6538477
0.49587095 0.6995347 -0.38699397 0.33823296]
[ 0.7093834 0.30119324 -0.80789334 0.1838598 0.12065991 -0.6538477
0.49587095 0.6995347 -0.38699397 0.33823296]] y_= [[0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]
[0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]] loss= 2.2107558 softmax= [[0.16371341 0.10884525 0.03590371 0.09679484 0.09086671 0.04188326
0.1322382 0.16210894 0.05469323 0.11295244]
[0.16371341 0.10884525 0.03590371 0.09679484 0.09086671 0.04188326
0.1322382 0.16210894 0.05469323 0.11295244]] cross_entropy= [2.3983614 2.0231504] regularization= 50459690000000.0 50459690000000.0
after 47 trainning steps:
x= [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
11. 139. 212. 253. 159. 86. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 34. 89.
203. 253. 252. 252. 252. 252. 74. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 49. 184. 234. 252.
252. 184. 110. 100. 208. 252. 199. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 95. 233. 252. 252. 176.
56. 0. 0. 0. 17. 234. 249. 75. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 220. 253. 178. 54. 4.
0. 0. 0. 0. 43. 240. 243. 50. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 221. 255. 180. 55. 5.
0. 0. 0. 7. 160. 253. 168. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 116. 253. 252. 252. 67.
0. 0. 0. 91. 252. 231. 42. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 32. 190. 252. 252. 185.
38. 0. 119. 234. 252. 54. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 15. 177. 252. 252.
179. 155. 236. 227. 119. 4. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 26. 221. 252.
252. 253. 252. 130. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 32. 229.
253. 255. 144. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 66. 236.
252. 253. 92. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 66. 234. 252.
252. 253. 92. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 19. 236. 252. 252.
252. 253. 92. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 53. 181. 252. 168. 43.
232. 253. 92. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 179. 255. 218. 32. 93.
253. 252. 84. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 81. 244. 239. 33. 0. 114.
252. 209. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 207. 252. 237. 70. 153. 240.
252. 32. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 207. 252. 253. 252. 252. 252.
210. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 61. 242. 253. 252. 168. 96.
12. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
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0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
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0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
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0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
68. 254. 255. 254. 107. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 11. 176.
230. 253. 253. 253. 212. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 28. 197. 253.
253. 253. 253. 253. 229. 107. 14. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 194. 253. 253.
253. 253. 253. 253. 253. 253. 53. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 69. 241. 253. 253.
253. 253. 241. 186. 253. 253. 195. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 10. 161. 253. 253. 253.
246. 40. 57. 231. 253. 253. 195. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 140. 253. 253. 253. 253.
154. 0. 25. 253. 253. 253. 195. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 213. 253. 253. 253. 135.
8. 0. 3. 128. 253. 253. 195. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 77. 238. 253. 253. 253. 7.
0. 0. 0. 116. 253. 253. 195. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 11. 165. 253. 253. 231. 70. 1.
0. 0. 0. 78. 237. 253. 195. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 33. 253. 253. 253. 182. 0. 0.
0. 0. 0. 0. 200. 253. 195. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 98. 253. 253. 253. 24. 0. 0.
0. 0. 0. 0. 42. 253. 195. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 197. 253. 253. 253. 24. 0. 0.
0. 0. 0. 0. 163. 253. 195. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 197. 253. 253. 189. 13. 0. 0.
0. 0. 0. 53. 227. 253. 121. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 197. 253. 253. 114. 0. 0. 0.
0. 0. 21. 227. 253. 231. 27. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 197. 253. 253. 114. 0. 0. 0.
5. 131. 143. 253. 231. 59. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 197. 253. 253. 236. 73. 58. 217.
223. 253. 253. 253. 174. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 197. 253. 253. 253. 253. 253. 253.
253. 253. 253. 253. 48. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 149. 253. 253. 253. 253. 253. 253.
253. 253. 182. 15. 3. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 12. 168. 253. 253. 253. 253. 253.
248. 89. 23. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
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0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] y= [[ 0.5813921 0.21609789 -0.8359629 0.10818548 0.44052082 -0.6865921
0.78338754 0.5727978 -0.4297532 0.24992661]
[ 0.5813921 0.21609789 -0.8359629 0.10818548 0.44052082 -0.6865921
0.78338754 0.5727978 -0.4297532 0.24992661]] y_= [[0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]
[1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]] loss= 2.452383 softmax= [[0.14272858 0.09905256 0.03459087 0.08892009 0.1239742 0.04016358
0.1746773 0.14150718 0.05192496 0.10246069]
[0.14272858 0.09905256 0.03459087 0.08892009 0.1239742 0.04016358
0.1746773 0.14150718 0.05192496 0.10246069]] cross_entropy= [2.9579558 1.9468105] regularization= 50459690000000.0 50459690000000.0
已终止