TensorFlow实现随机训练和批量训练的方法

• Z时代
• 2024-01-10
• 分类：综合

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。

# 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 plt
import numpy as np
import tensorflow as tf
from tensorflow.python.framework import ops
ops.reset_default_graph()
# 随机训练:
# Create graph
sess = 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()

输出：

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 = 58.766

Step #10 A = [[ 3.74766445]]

Loss = 38.4875

Step #15 A = [[ 4.88928795]]

Loss = 27.5632

Step #20 A = [[ 5.82038736]]

Loss = 17.9523

Step #25 A = [[ 6.58999157]]

Loss = 13.3245

Step #30 A = [[ 7.20851326]]

Loss = 8.68099

Step #35 A = [[ 7.71694899]]

Loss = 4.60659

Step #40 A = [[ 8.1296711]]

Loss = 4.70107

Step #45 A = [[ 8.47107315]]

Loss = 3.28318

Step #50 A = [[ 8.74283409]]

Loss = 1.99057

Step #55 A = [[ 8.98811722]]

Loss = 2.66906

Step #60 A = [[ 9.18062305]]

Loss = 3.26207

Step #65 A = [[ 9.31655025]]

Loss = 2.55459

Step #70 A = [[ 9.43130589]]

Loss = 1.95839

Step #75 A = [[ 9.55670166]]

Loss = 1.46504

Step #80 A = [[ 9.6354847]]

Loss = 1.49021

Step #85 A = [[ 9.73470974]]

Loss = 1.53289

Step #90 A = [[ 9.77956581]]

Loss = 1.52173

Step #95 A = [[ 9.83666706]]

Loss = 0.819207

Step #100 A = [[ 9.85569191]]

Loss = 1.2197

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