实例21：识别图中模糊的手写数字
1. 下载数据集
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)print('输入数据：',mnist.train.images)
print('输入数据打印shape：',mnist.train.images.shape)
import pylab
im = mnist.train.images[1]
im = im.reshape(-1, 28)
pylab.imshow(im)
pylab.show()
print ('输入数据打shape:',mnist.test.images.shape)
print ('输入数据打shape:',mnist.validation.images.shape)输入数据： [[0. 0. 0. ... 0. 0. 0.] [0. 0. 0. ... 0. 0. 0.] [0. 0. 0. ... 0. 0. 0.] ... [0. 0. 0. ... 0. 0. 0.] [0. 0. 0. ... 0. 0. 0.] [0. 0. 0. ... 0. 0. 0.]] 输入数据打印shape： (55000, 784)
输入数据打shape: (10000, 784) 输入数据打shape: (5000, 784)
2. 分析图片特点
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
import pylab
tf.reset_default_graph()
# 定义占位符
x = tf.placeholder(tf.float32, [None, 784]) # MNIST数据集的维度28x28=784
y = tf.placeholder(tf.float32, [None, 10]) # 数字0-9，共10个类别Extracting MNIST_data/train-images-idx3-ubyte.gz Extracting MNIST_data/train-labels-idx1-ubyte.gz Extracting MNIST_data/t10k-images-idx3-ubyte.gz Extracting MNIST_data/t10k-labels-idx1-ubyte.gz
3. 构建模型
# 初始化模型权重
W = tf.Variable(tf.random_normal([784, 10]))
b = tf.Variable(tf.zeros([10]))
# softmax分类
pred = tf.nn.softmax(tf.matmul(x, W) + b)
# 损失函数
cost = tf.reduce_mean(-tf.reduce_sum(y*tf.log(pred), reduction_indices=1))
# 定义参数
learning_rate = 0.01
# 使用梯度下降优化器
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)4. 训练模型
training_epochs = 25
batch_size = 100
display_step = 1
saver = tf.train.Saver()
model_path = "H:/tensorflow_projects/chap5/mnist_model.ckpt"
# 启动session
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())# Initializing OP
# 启动循环开始训练
for epoch in range(training_epochs):
avg_cost = 0.
total_batch = int(mnist.train.num_examples/batch_size)
# 遍历全部数据集
for i in range(total_batch):
batch_xs, batch_ys = mnist.train.next_batch(batch_size)
# Run optimization op (backprop) and cost op (to get loss value)
_, c = sess.run([optimizer, cost], feed_dict={x: batch_xs, y: batch_ys})
# Compute average loss
avg_cost += c / total_batch
# 显示训练中的详细信息
if (epoch+1) % display_step == 0:
print ("Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(avg_cost))
print( " Finished!")
# 测试 model
correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
# 计算准确率
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print ("Accuracy:", accuracy.eval({x: mnist.test.images, y: mnist.test.labels}))
# Save model weights to disk
save_path = saver.save(sess, model_path)
print("Model saved in file: %s" % save_path)Epoch: 0001 cost= 8.528780973 Epoch: 0002 cost= 4.351987058 Epoch: 0003 cost= 3.044533993 Epoch: 0004 cost= 2.405865938 Epoch: 0005 cost= 2.023756936 Epoch: 0006 cost= 1.771609710 Epoch: 0007 cost= 1.594264874 Epoch: 0008 cost= 1.463273387 Epoch: 0009 cost= 1.362599298 Epoch: 0010 cost= 1.283132398 Epoch: 0011 cost= 1.218332462 Epoch: 0012 cost= 1.164574228 Epoch: 0013 cost= 1.118905594 Epoch: 0014 cost= 1.079640089 Epoch: 0015 cost= 1.045503370 Epoch: 0016 cost= 1.015250035 Epoch: 0017 cost= 0.988325027 Epoch: 0018 cost= 0.963962568 Epoch: 0019 cost= 0.942083137 Epoch: 0020 cost= 0.922068430 Epoch: 0021 cost= 0.903581946 Epoch: 0022 cost= 0.886608397 Epoch: 0023 cost= 0.870939313 Epoch: 0024 cost= 0.856314616 Epoch: 0025 cost= 0.842578177 Finished! Accuracy: 0.825 Model saved in file: H:/tensorflow_projects/chap5/mnist_model.ckpt
5. 测试模型
# 测试 model
correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
# 计算准确率
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print ("Accuracy:", accuracy.eval({x: mnist.test.images, y: mnist.test.labels}))6. 保存模型
# Save model weights to disk
save_path = saver.save(sess, model_path)
print("Model saved in file: %s" % save_path)saver = tf.train.Saver()7. 读取模型
#读取模型
print("Starting 2nd session...")
with tf.Session() as sess:
# Initialize variables
sess.run(tf.global_variables_initializer())
# Restore model weights from previously saved model
saver.restore(sess, model_path)
# 测试 model
correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
# 计算准确率
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print ("Accuracy:", accuracy.eval({x: mnist.test.images, y: mnist.test.labels}))
output = tf.argmax(pred, 1)
batch_xs, batch_ys = mnist.train.next_batch(2)
outputval,predv = sess.run([output,pred], feed_dict={x: batch_xs})
print(outputval,predv,batch_ys)
im = batch_xs[0]
im = im.reshape(-1,28)
pylab.imshow(im)
pylab.show()
im = batch_xs[1]
im = im.reshape(-1,28)
pylab.imshow(im)
pylab.show()Accuracy: 0.825 [0 8] [[9.9999976e-01 4.6237684e-18 2.0244670e-08 4.7625484e-08 7.0704164e-18 2.7070349e-10 9.5091435e-12 6.9175507e-17 9.4598128e-08 7.1266972e-15] [5.7434350e-05 3.0411970e-02 1.3331110e-02 1.6055863e-01 1.1928177e-03 2.4296941e-02 9.0290455e-04 1.7760798e-05 7.6825178e-01 9.7868522e-04]] [[1. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]]
实例22：交叉熵实验
- softmax_cross_entropy_with_logits
- -tf.reduce_sum(labels * tf.log(tf.nn.softxmax(logits)), 1)
# -*- coding: utf-8 -*-
import tensorflow as tf
labels = [[0,0,1],[0,1,0]]
logits = [[2, 0.5,6],
[0.1,0, 3]]
logits_scaled = tf.nn.softmax(logits)
logits_scaled2 = tf.nn.softmax(logits_scaled)
result1 = tf.nn.softmax_cross_entropy_with_logits(labels=labels, logits=logits)
result2 = tf.nn.softmax_cross_entropy_with_logits(labels=labels, logits=logits_scaled)
result3 = -tf.reduce_sum(labels*tf.log(logits_scaled),1)
with tf.Session() as sess:
print ("scaled=",sess.run(logits_scaled))
print ("scaled2=",sess.run(logits_scaled2)) #经过第二次的softmax后，分布概率会有变化
print ("rel1=",sess.run(result1),"\n")#正确的方式
print ("rel2=",sess.run(result2),"\n")#如果将softmax变换完的值放进去会，就相当于算第二次softmax的loss，所以会出错
print ("rel3=",sess.run(result3))scaled= [[0.01791432 0.00399722 0.97808844] [0.04980332 0.04506391 0.90513283]] scaled2= [[0.21747023 0.21446465 0.56806517] [0.2300214 0.22893383 0.5410447 ]] rel1= [0.02215516 3.0996735 ] rel2= [0.56551915 1.4743223 ] rel3= [0.02215518 3.0996735 ]
实例23： one_hot实验
#标签总概率为1
labels = [[0.4,0.1,0.5],[0.3,0.6,0.1]]
result4 = tf.nn.softmax_cross_entropy_with_logits(labels=labels, logits=logits)
with tf.Session() as sess:
print ("rel4=",sess.run(result4),"\n") rel4= [2.1721554 2.7696736]
实例24：sparse交叉熵的使用
#sparse
labels = [2,1] #其实是0 1 2 三个类。等价 第一行 001 第二行 010
result5 = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=labels, logits=logits)
with tf.Session() as sess:
print ("rel5=",sess.run(result5),"\n")rel5= [0.02215516 3.0996735 ]
实例25：计算loss值
tf.reduce_mean(-tf.reduce_sum(labels * tf.log(logits_scaled),1) ) = tf.reduce_mean(result)
#注意！！！这个函数的返回值并不是一个数，而是一个向量，
#如果要求交叉熵loss，我们要对向量求均值，
#就是对向量再做一步tf.reduce_mean操作
loss=tf.reduce_mean(result1)
with tf.Session() as sess:
print ("loss=",sess.run(loss))loss= 1.5609143
labels = [[0,0,1],[0,1,0]]
loss2 = tf.reduce_mean(-tf.reduce_sum(labels * tf.log(logits_scaled),1) )
with tf.Session() as sess:
print ("loss2=",sess.run(loss2))loss2= 1.5609144
# -*- coding: utf-8 -*-
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/")
print ('输入数据:',mnist.train.images)
print ('输入数据打shape:',mnist.train.images.shape)
import pylab
im = mnist.train.images[1]
im = im.reshape(-1,28)
pylab.imshow(im)
pylab.show()
print ('输入数据打shape:',mnist.test.images.shape)
print ('输入数据打shape:',mnist.validation.images.shape)
import tensorflow as tf #导入tensorflow库
tf.reset_default_graph()
# tf Graph Input
x = tf.placeholder(tf.float32, [None, 784]) # mnist data维度 28*28=784
y = tf.placeholder(tf.int32, [None]) # 0-9 数字=> 10 classes
# Set model weights
W = tf.Variable(tf.random_normal([784, 10]))
b = tf.Variable(tf.zeros([10]))
z= tf.matmul(x, W) + b
# 构建模型
pred = tf.nn.softmax(z) # Softmax分类
# Minimize error using cross entropy
#cost = tf.reduce_mean(-tf.reduce_sum(y*tf.log(pred), reduction_indices=1))
cost = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=z))
#参数设置
learning_rate = 0.01
# 使用梯度下降优化器
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)
training_epochs = 25
batch_size = 100
display_step = 1
# 启动session
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())# Initializing OP
# 启动循环开始训练
for epoch in range(training_epochs):
avg_cost = 0.
total_batch = int(mnist.train.num_examples/batch_size)
# 遍历全部数据集
for i in range(total_batch):
batch_xs, batch_ys = mnist.train.next_batch(batch_size)
# Run optimization op (backprop) and cost op (to get loss value)
_, c = sess.run([optimizer, cost], feed_dict={x: batch_xs,
y: batch_ys})
# Compute average loss
avg_cost += c / total_batch
# 显示训练中的详细信息
if (epoch+1) % display_step == 0:
print ("Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(avg_cost))
print( " Finished!")输入数据: [[0. 0. 0. ... 0. 0. 0.] [0. 0. 0. ... 0. 0. 0.] [0. 0. 0. ... 0. 0. 0.] ... [0. 0. 0. ... 0. 0. 0.] [0. 0. 0. ... 0. 0. 0.] [0. 0. 0. ... 0. 0. 0.]] 输入数据打shape: (55000, 784)
输入数据打shape: (10000, 784) 输入数据打shape: (5000, 784) Epoch: 0001 cost= 8.143192529 Epoch: 0002 cost= 4.322669148 Epoch: 0003 cost= 2.981214518 Epoch: 0004 cost= 2.356783852 Epoch: 0005 cost= 1.998906395 Epoch: 0006 cost= 1.765469893 Epoch: 0007 cost= 1.600443805 Epoch: 0008 cost= 1.477601487 Epoch: 0009 cost= 1.381630285 Epoch: 0010 cost= 1.305191407 Epoch: 0011 cost= 1.241832566 Epoch: 0012 cost= 1.188988984 Epoch: 0013 cost= 1.143483993 Epoch: 0014 cost= 1.104311068 Epoch: 0015 cost= 1.069696186 Epoch: 0016 cost= 1.039322816 Epoch: 0017 cost= 1.012039655 Epoch: 0018 cost= 0.987467080 Epoch: 0019 cost= 0.965332884 Epoch: 0020 cost= 0.945004881 Epoch: 0021 cost= 0.926361536 Epoch: 0022 cost= 0.909262278 Epoch: 0023 cost= 0.893383189 Epoch: 0024 cost= 0.878501318 Epoch: 0025 cost= 0.864673607 Finished!
实例26 学习率衰减
# -*- coding: utf-8 -*-
import tensorflow as tf
global_step = tf.Variable(0, trainable=False)
initial_learning_rate = 0.1 #初始学习率
learning_rate = tf.train.exponential_decay(initial_learning_rate,
global_step,
decay_steps=10,decay_rate=0.9)
opt = tf.train.GradientDescentOptimizer(learning_rate)
add_global = global_step.assign_add(1)
with tf.Session() as sess:
tf.global_variables_initializer().run()
print(sess.run(learning_rate))
for i in range(20):
g, rate = sess.run([add_global, learning_rate])
print(g,rate)
0.1 1 0.1 2 0.09791484 3 0.09688862 4 0.095873155 5 0.094868325 6 0.09387404 7 0.092890166 8 0.09191661 9 0.09095325 10 0.089999996 11 0.08905673 12 0.088123344 13 0.08719975 14 0.08628584 15 0.0853815 16 0.084486626 17 0.08360115 18 0.08272495 19 0.08185792 20 0.08099999
实例27：·Maxout网络实现mnist分类.py
# -*- coding: utf-8 -*-
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("H:/tensorflow_projects/chap6/MNIST_data/")
print ('输入数据:',mnist.train.images)
print ('输入数据打shape:',mnist.train.images.shape)
import pylab
im = mnist.train.images[1]
im = im.reshape(-1,28)
pylab.imshow(im)
pylab.show()
print ('输入数据打shape:',mnist.test.images.shape)
print ('输入数据打shape:',mnist.validation.images.shape)
import tensorflow as tf #导入tensorflow库
def max_out(inputs, num_units, axis=None):
shape = inputs.get_shape().as_list()
if shape[0] is None:
shape[0] = -1
if axis is None: # Assume that channel is the last dimension
axis = -1
num_channels = shape[axis]
if num_channels % num_units:
raise ValueError('number of features({}) is not '
'a multiple of num_units({})'.format(num_channels, num_units))
shape[axis] = num_units
shape += [num_channels // num_units]
outputs = tf.reduce_max(tf.reshape(inputs, shape), -1, keep_dims=False)
return outputs
tf.reset_default_graph()
# tf Graph Input
x = tf.placeholder(tf.float32, [None, 784]) # mnist data维度 28*28=784
y = tf.placeholder(tf.int32, [None]) # 0-9 数字=> 10 classes
# Set model weights
W = tf.Variable(tf.random_normal([784, 100]))
b = tf.Variable(tf.zeros([100]))
z= tf.matmul(x, W) + b
#maxout = tf.reduce_max(z,axis= 1,keep_dims=True)
maxout= max_out(z, 50)
# Set model weights
W2 = tf.Variable(tf.truncated_normal([50, 10], stddev=0.1))
b2 = tf.Variable(tf.zeros([10]))
# 构建模型
#pred = tf.nn.softmax(tf.matmul(maxout, W2) + b2)
pred = tf.matmul(maxout, W2) + b2
# 构建模型
#pred = tf.nn.softmax(z) # Softmax分类
# Minimize error using cross entropy
#cost = tf.reduce_mean(-tf.reduce_sum(y*tf.log(pred), reduction_indices=1))
cost = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=pred))
#参数设置
learning_rate = 0.04
# 使用梯度下降优化器
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)
training_epochs = 200
batch_size = 100
display_step = 1
# 启动session
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())# Initializing OP
# 启动循环开始训练
for epoch in range(training_epochs):
avg_cost = 0.
total_batch = int(mnist.train.num_examples/batch_size)
# 遍历全部数据集
for i in range(total_batch):
batch_xs, batch_ys = mnist.train.next_batch(batch_size)
# Run optimization op (backprop) and cost op (to get loss value)
_, c = sess.run([optimizer, cost], feed_dict={x: batch_xs,
y: batch_ys})
# Compute average loss
avg_cost += c / total_batch
# 显示训练中的详细信息
if (epoch+1) % display_step == 0:
print ("Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(avg_cost))
print( " Finished!")Extracting H:/tensorflow_projects/chap6/MNIST_data/train-images-idx3-ubyte.gz Extracting H:/tensorflow_projects/chap6/MNIST_data/train-labels-idx1-ubyte.gz Extracting H:/tensorflow_projects/chap6/MNIST_data/t10k-images-idx3-ubyte.gz Extracting H:/tensorflow_projects/chap6/MNIST_data/t10k-labels-idx1-ubyte.gz 输入数据: [[0. 0. 0. ... 0. 0. 0.] [0. 0. 0. ... 0. 0. 0.] [0. 0. 0. ... 0. 0. 0.] ... [0. 0. 0. ... 0. 0. 0.] [0. 0. 0. ... 0. 0. 0.] [0. 0. 0. ... 0. 0. 0.]] 输入数据打shape: (55000, 784)
输入数据打shape: (10000, 784) 输入数据打shape: (5000, 784)Epoch: 0001 cost= 1.669748494 Epoch: 0002 cost= 0.819802765 Epoch: 0003 cost= 0.668256996 Epoch: 0004 cost= 0.599882030 Epoch: 0005 cost= 0.551539327 Epoch: 0006 cost= 0.519114701 Epoch: 0007 cost= 0.501650673 Epoch: 0008 cost= 0.480439953 Epoch: 0009 cost= 0.465431287 Epoch: 0010 cost= 0.454214447 Epoch: 0011 cost= 0.442614048 Epoch: 0012 cost= 0.429748516 Epoch: 0013 cost= 0.419512733 Epoch: 0014 cost= 0.412809217 Epoch: 0015 cost= 0.403128482 Epoch: 0016 cost= 0.395945490 Epoch: 0017 cost= 0.387481769 Epoch: 0018 cost= 0.382592868 Epoch: 0019 cost= 0.376352434 Epoch: 0020 cost= 0.371442565 Epoch: 0021 cost= 0.366640467 Epoch: 0022 cost= 0.360618622 Epoch: 0023 cost= 0.357322852 Epoch: 0024 cost= 0.353282172 Epoch: 0025 cost= 0.348204653 Epoch: 0026 cost= 0.344141857 Epoch: 0027 cost= 0.340688343 Epoch: 0028 cost= 0.336875352 Epoch: 0029 cost= 0.332229141 Epoch: 0030 cost= 0.329368933 Epoch: 0031 cost= 0.324990445 Epoch: 0032 cost= 0.323535117 Epoch: 0033 cost= 0.319696042 Epoch: 0034 cost= 0.316543529 Epoch: 0035 cost= 0.314367712 Epoch: 0036 cost= 0.309627955 Epoch: 0037 cost= 0.308954497 Epoch: 0038 cost= 0.305743327 Epoch: 0039 cost= 0.303948994 Epoch: 0040 cost= 0.300707549 Epoch: 0041 cost= 0.298111228 Epoch: 0042 cost= 0.295571287 Epoch: 0043 cost= 0.293599232 Epoch: 0044 cost= 0.292371846 Epoch: 0045 cost= 0.290433042 Epoch: 0046 cost= 0.286466155 Epoch: 0047 cost= 0.284913121 Epoch: 0048 cost= 0.282463599 Epoch: 0049 cost= 0.282443535 Epoch: 0050 cost= 0.278840295 Epoch: 0051 cost= 0.277910688 Epoch: 0052 cost= 0.275044623 Epoch: 0053 cost= 0.274304534 Epoch: 0054 cost= 0.271387891 Epoch: 0055 cost= 0.270530891 Epoch: 0056 cost= 0.269293524 Epoch: 0057 cost= 0.267875358 Epoch: 0058 cost= 0.265286128 Epoch: 0059 cost= 0.263074537 Epoch: 0060 cost= 0.261540208 Epoch: 0061 cost= 0.261259574 Epoch: 0062 cost= 0.259737343 Epoch: 0063 cost= 0.258162930 Epoch: 0064 cost= 0.256089119 Epoch: 0065 cost= 0.254655639 Epoch: 0066 cost= 0.253505012 Epoch: 0067 cost= 0.252484518 Epoch: 0068 cost= 0.249667299 Epoch: 0069 cost= 0.249462925 Epoch: 0070 cost= 0.249046204 Epoch: 0071 cost= 0.247562397 Epoch: 0072 cost= 0.245829041 Epoch: 0073 cost= 0.244501937 Epoch: 0074 cost= 0.243986385 Epoch: 0075 cost= 0.242621479 Epoch: 0076 cost= 0.241314949 Epoch: 0077 cost= 0.238647706 Epoch: 0078 cost= 0.238957213 Epoch: 0079 cost= 0.237347329 Epoch: 0080 cost= 0.234964659 Epoch: 0081 cost= 0.236123101 Epoch: 0082 cost= 0.233973439 Epoch: 0083 cost= 0.232953551 Epoch: 0084 cost= 0.232046905 Epoch: 0085 cost= 0.229982579 Epoch: 0086 cost= 0.229070544 Epoch: 0087 cost= 0.228393014 Epoch: 0088 cost= 0.227479590 Epoch: 0089 cost= 0.227268234 Epoch: 0090 cost= 0.225049027 Epoch: 0091 cost= 0.224516309 Epoch: 0092 cost= 0.223888728 Epoch: 0093 cost= 0.223191615 Epoch: 0094 cost= 0.221796969 Epoch: 0095 cost= 0.221250222 Epoch: 0096 cost= 0.220323073 Epoch: 0097 cost= 0.218742449 Epoch: 0098 cost= 0.218513060 Epoch: 0099 cost= 0.217564493 Epoch: 0100 cost= 0.215474659 Epoch: 0101 cost= 0.214555269 Epoch: 0102 cost= 0.213661779 Epoch: 0103 cost= 0.214191178 Epoch: 0104 cost= 0.213189474 Epoch: 0105 cost= 0.212041208 Epoch: 0106 cost= 0.211847621 Epoch: 0107 cost= 0.210278228 Epoch: 0108 cost= 0.208721001 Epoch: 0109 cost= 0.209450811 Epoch: 0110 cost= 0.207888889 Epoch: 0111 cost= 0.206186019 Epoch: 0112 cost= 0.205807320 Epoch: 0113 cost= 0.205915253 Epoch: 0114 cost= 0.204875258 Epoch: 0115 cost= 0.204274523 Epoch: 0116 cost= 0.204331738 Epoch: 0117 cost= 0.201808658 Epoch: 0118 cost= 0.201525647 Epoch: 0119 cost= 0.199703673 Epoch: 0120 cost= 0.200700889 Epoch: 0121 cost= 0.199350320 Epoch: 0122 cost= 0.198106946 Epoch: 0123 cost= 0.198094789 Epoch: 0124 cost= 0.196696438 Epoch: 0125 cost= 0.196361274 Epoch: 0126 cost= 0.196492676 Epoch: 0127 cost= 0.194797525 Epoch: 0128 cost= 0.194349858 Epoch: 0129 cost= 0.193110045 Epoch: 0130 cost= 0.192708968 Epoch: 0131 cost= 0.192399970 Epoch: 0132 cost= 0.190516700 Epoch: 0133 cost= 0.190331284 Epoch: 0134 cost= 0.190980941 Epoch: 0135 cost= 0.189532741 Epoch: 0136 cost= 0.188812766 Epoch: 0137 cost= 0.187239818 Epoch: 0138 cost= 0.187442517 Epoch: 0139 cost= 0.186436391 Epoch: 0140 cost= 0.185879297 Epoch: 0141 cost= 0.184914501 Epoch: 0142 cost= 0.185321765 Epoch: 0143 cost= 0.183773249 Epoch: 0144 cost= 0.183931502 Epoch: 0145 cost= 0.183287879 Epoch: 0146 cost= 0.182621817 Epoch: 0147 cost= 0.181577222 Epoch: 0148 cost= 0.180124871 Epoch: 0149 cost= 0.181275859 Epoch: 0150 cost= 0.180238542 Epoch: 0151 cost= 0.178712672 Epoch: 0152 cost= 0.178188846 Epoch: 0153 cost= 0.177580589 Epoch: 0154 cost= 0.177027715 Epoch: 0155 cost= 0.177836312 Epoch: 0156 cost= 0.176792373 Epoch: 0157 cost= 0.175756311 Epoch: 0158 cost= 0.174947099 Epoch: 0159 cost= 0.174266882 Epoch: 0160 cost= 0.174342527 Epoch: 0161 cost= 0.172602550 Epoch: 0162 cost= 0.172811079 Epoch: 0163 cost= 0.172335094 Epoch: 0164 cost= 0.171968882 Epoch: 0165 cost= 0.171027398 Epoch: 0166 cost= 0.169943000 Epoch: 0167 cost= 0.170124644 Epoch: 0168 cost= 0.168496490 Epoch: 0169 cost= 0.169623626 Epoch: 0170 cost= 0.168593532 Epoch: 0171 cost= 0.167650817 Epoch: 0172 cost= 0.167899388 Epoch: 0173 cost= 0.166965650 Epoch: 0174 cost= 0.166645279 Epoch: 0175 cost= 0.166120962 Epoch: 0176 cost= 0.165155771 Epoch: 0177 cost= 0.165017686 Epoch: 0178 cost= 0.163808241 Epoch: 0179 cost= 0.163797412 Epoch: 0180 cost= 0.162719157 Epoch: 0181 cost= 0.163193959 Epoch: 0182 cost= 0.161633140 Epoch: 0183 cost= 0.162454181 Epoch: 0184 cost= 0.161832177 Epoch: 0185 cost= 0.161416251 Epoch: 0186 cost= 0.159936835 Epoch: 0187 cost= 0.160258861 Epoch: 0188 cost= 0.159245104 Epoch: 0189 cost= 0.158908117 Epoch: 0190 cost= 0.157777246 Epoch: 0191 cost= 0.157958048 Epoch: 0192 cost= 0.157402902 Epoch: 0193 cost= 0.157361584 Epoch: 0194 cost= 0.156321988 Epoch: 0195 cost= 0.156084833 Epoch: 0196 cost= 0.155017134 Epoch: 0197 cost= 0.155896032 Epoch: 0198 cost= 0.154472644 Epoch: 0199 cost= 0.154645715 Epoch: 0200 cost= 0.153077820 Finished!
