TensorFlow实现随机训练和批量训练的方法_Python

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。

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									# 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