TensorFlow实现随机训练与批量训练的对比分析

TensorFlow更新模型变量。它能一次操作一个数据点，也可以一次操作大量数据。一个训练例子上的操作可能导致比较“古怪”的学习过程，但使用大批量的训练会造成计算成本昂贵。到底选用哪种训练类型对机器学习算法的收敛非常关键。
为了TensorFlow计算变量梯度来让反向传播工作，我们必须度量一个或者多个样本的损失。
随机训练会一次随机抽样训练数据和目标数据对完成训练。另外一个可选项是，一次大批量训练取平均损失来进行梯度计算，批量训练大小可以一次上扩到整个数据集。这里将显示如何扩展前面的回归算法的例子——使用随机训练和批量训练。

批量训练和随机训练的不同之处在于它们的优化器方法和收敛。

登录后复制

# 随机训练和批量训练#----------------------------------
##  This python function illustrates two different training methods:#  batch and stochastic training.  
For each model, we will use#  a regression model that predicts one model variable.import matplotlib
.pyplot as pltimport numpy as npimport tensorflow as tffrom tensorflow.python.framework import opsops
.reset_default_graph()# 随机训练:# Create graphsess = tf.Session()# 声明数据x_vals = np
.random.normal(1, 0.1, 100)y_vals = np.repeat(10., 100)x_data = tf.placeholder(shape=[1], 
dtype=tf.float32)y_target = tf.placeholder(shape=[1], dtype=tf.float32)
# 声明变量 (one model parameter = A)A = tf.Variable(tf.random_normal(shape=[1]))
# 增加操作到图my_output = tf.multiply(x_data, A)# 增加L2损失函数loss = tf.square(my_output - y_target)
# 初始化变量init = tf.global_variables_initializer()sess.run(init)
# 声明优化器my_opt = tf.train.GradientDescentOptimizer(0.02)train_step = my_opt
.minimize(loss)loss_stochastic = []# 运行迭代for i in range(100):    
rand_index = np.random.choice(100)    rand_x = [x_vals[rand_index]]    
rand_y = [y_vals[rand_index]]    sess.run(train_step, feed_dict={x_data: rand_x, y_target: rand_y})    
if (i+1)%5==0:        print('Step #' + str(i+1) + ' A = ' + str(sess.run(A)))        
temp_loss = sess.run(loss, feed_dict={x_data: rand_x, y_target: rand_y})        
print('Loss = ' + str(temp_loss))        loss_stochastic.append(temp_loss)# 批量训练:
# 重置计算图ops.reset_default_graph()sess = tf.Session()# 声明批量大小
# 批量大小是指通过计算图一次传入多少训练数据batch_size = 20
# 声明模型的数据、占位符x_vals = np.random.normal(1, 0.1, 100)y_vals = np.repeat(10., 100)x_data = tf
.placeholder(shape=[None, 1], dtype=tf.float32)y_target = tf.placeholder(shape=[None, 1], dtype=tf.float32)
# 声明变量 (one model parameter = A)A = tf.Variable(tf.random_normal(shape=[1,1]))
# 增加矩阵乘法操作（矩阵乘法不满足交换律）my_output = tf.matmul(x_data, A)# 增加损失函数
# 批量训练时损失函数是每个数据点L2损失的平均值loss = tf.reduce_mean(tf.square(my_output - y_target))
# 初始化变量init = tf.global_variables_initializer()sess.run(init)# 声明优化器my_opt = tf.train
.GradientDescentOptimizer(0.02)train_step = my_opt.minimize(loss)loss_batch = []
# 运行迭代for i in range(100):    rand_index = np.random.choice(100, size=batch_size)    
rand_x = np.transpose([x_vals[rand_index]])    rand_y = np.transpose([y_vals[rand_index]])    
sess.run(train_step, feed_dict={x_data: rand_x, y_target: rand_y})    if (i+1)%5==0:        
print('Step #' + str(i+1) + ' A = ' + str(sess.run(A)))        temp_loss = sess
.run(loss, feed_dict={x_data: rand_x, y_target: rand_y})        print('Loss = ' + str(temp_loss))        
loss_batch.append(temp_loss)plt.plot(range(0, 100, 5), loss_stochastic, 'b-', 
label='Stochastic Loss')plt.plot(range(0, 100, 5), loss_batch, 'r--', 
label='Batch Loss, size=20')plt.legend(loc='upper right', prop={'size': 11})plt
.show()1.2.3.4.5.6.7.8.9.10.11.12.13.14.15.16.17.18.19.20.21.22.23.24.25.26.27.28.29.30.
31.32.33.34.35.36.37.38.39.40.41.42.43.44.45.46.47.48.49.50.51.52.53.54.55.56.57.58.59.60.
61.62.63.64.65.66.67.68.69.70.71.72.73.74.75.76.77.78.79.80.81.82.83.84.85.86.87.88.89.90.
91.92.93.94.95.96.97.98.99.100.101.102.103.104.

输出：

登录后复制

Step #5 A = [ 1.47604525]Loss = [ 72.55678558]Step #10 A = [ 3.01128507]Loss = [ 48.22986221]Step 
#15 A = [ 4.27042341]Loss = [ 28.97912598]Step #20 A = [ 5.2984333]Loss = [ 16.44779968]Step 
#25 A = [ 6.17473984]Loss = [ 16.373312]Step #30 A = [ 6.89866304]Loss = [ 11.71054649]Step 
#35 A = [ 7.39849901]Loss = [ 6.42773056]Step #40 A = [ 7.84618378]Loss = [ 5.92940331]Step 
#45 A = [ 8.15709782]Loss = [ 0.2142024]Step #50 A = [ 8.54818344]Loss = [ 7.11651039]Step 
#55 A = [ 8.82354641]Loss = [ 1.47823763]Step #60 A = [ 9.07896614]Loss = [ 3.08244276]Step 
#65 A = [ 9.24868107]Loss = [ 0.01143846]Step #70 A = [ 9.36772251]Loss = [ 2.10078788]Step 
#75 A = [ 9.49171734]Loss = [ 3.90913701]Step #80 A = [ 9.6622715]Loss = [ 4.80727625]Step 
#85 A = [ 9.73786926]Loss = [ 0.39915398]Step #90 A = [ 9.81853104]Loss = [ 0.14876099]Step 
#95 A = [ 9.90371323]Loss = [ 0.01657014]Step #100 A = [ 9.86669159]Loss = [ 0.444787]Step 
#5 A = [[ 2.34371352]]Loss = 58.766Step #10 A = [[ 3.74766445]]Loss = 38.4875Step 
#15 A = [[ 4.88928795]]Loss = 27.5632Step #20 A = [[ 5.82038736]]Loss = 17.9523Step 
#25 A = [[ 6.58999157]]Loss = 13.3245Step #30 A = [[ 7.20851326]]Loss = 8.68099Step 
#35 A = [[ 7.71694899]]Loss = 4.60659Step #40 A = [[ 8.1296711]]Loss = 4.70107Step 
#45 A = [[ 8.47107315]]Loss = 3.28318Step #50 A = [[ 8.74283409]]Loss = 1.99057Step 
#55 A = [[ 8.98811722]]Loss = 2.66906Step #60 A = [[ 9.18062305]]Loss = 3.26207Step 
#65 A = [[ 9.31655025]]Loss = 2.55459Step #70 A = [[ 9.43130589]]Loss = 1.95839Step 
#75 A = [[ 9.55670166]]Loss = 1.46504Step #80 A = [[ 9.6354847]]Loss = 1.49021Step 
#85 A = [[ 9.73470974]]Loss = 1.53289Step #90 A = [[ 9.77956581]]Loss = 1.52173Step 
#95 A = [[ 9.83666706]]Loss = 0.819207Step #100 A = [[ 9.85569191]]Loss = 1.21971.2.3.4.5.6.7.8.9
.10.11.12.13.14.15.16.17.18.19.20.21.22.23.24.25.26.27.28.29.30.31.32.33.34.35.36.37.38.39.40.41.
42.43.44.45.46.47.48.49.50.51.52.53.54.55.56.57.58.59.60.61.62.63.64.65.66.67.68.69.70.71.72.73.74.
75.76.77.78.79.80.

免责声明：本文系网络转载或改编，未找到原创作者，版权归原作者所有。如涉及版权，请联系删