code: #! /usr/bin/python
  # -*- coding: utf-8 -*-
  
  import time
  
  import numpy as np
  import tensorflow as tf
  
  import tensorlayer as tl
  from tensorlayer.layers import *
  
  tf.logging.set_verbosity(tf.logging.DEBUG)
  tl.logging.set_verbosity(tl.logging.DEBUG)
  
  sess = tf.InteractiveSession()
  
  X_train, y_train, X_test, y_test = tl.files.load_cifar10_dataset(shape=(-1, 32, 32, 3), plotable=False)
  
  
  def model(x, y_, reuse):
      W_init = tf.truncated_normal_initializer(stddev=5e-2)
      W_init2 = tf.truncated_normal_initializer(stddev=0.04)
      b_init2 = tf.constant_initializer(value=0.1)
      with tf.variable_scope("model", reuse=reuse):
          net = InputLayer(x, name='input')
          net = Conv2d(net, 64, (5, 5), (1, 1), act=tf.nn.relu, padding='SAME', W_init=W_init, name='cnn1')
          net = MaxPool2d(net, (3, 3), (2, 2), padding='SAME', name='pool1')
          net = LocalResponseNormLayer(net, depth_radius=4, bias=1.0, alpha=0.001 / 9.0, beta=0.75, name='norm1')
  
          net = Conv2d(net, 64, (5, 5), (1, 1), act=tf.nn.relu, padding='SAME', W_init=W_init, name='cnn2')
          net = LocalResponseNormLayer(net, depth_radius=4, bias=1.0, alpha=0.001 / 9.0, beta=0.75, name='norm2')
          net = MaxPool2d(net, (3, 3), (2, 2), padding='SAME', name='pool2')
  
          net = FlattenLayer(net, name='flatten')
          net = DenseLayer(net, 384, act=tf.nn.relu, W_init=W_init2, b_init=b_init2, name='d1relu')
          net = DenseLayer(net, 192, act=tf.nn.relu, W_init=W_init2, b_init=b_init2, name='d2relu')
          net = DenseLayer(net, 10, act=None, W_init=W_init2, name='output')
          y = net.outputs
  
          ce = tl.cost.cross_entropy(y, y_, name='cost')
          # L2 for the MLP, without this, the accuracy will be reduced by 15%.
          L2 = 0
          for p in tl.layers.get_variables_with_name('relu/W', True, True):
              L2 += tf.contrib.layers.l2_regularizer(0.004)(p)
          cost = ce + L2
  
          correct_prediction = tf.equal(tf.argmax(y, 1), y_)
          acc = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
  
          return net, cost, acc
  
  
  def model_batch_norm(x, y_, reuse, is_train):
      """Batch normalization should be placed before rectifier."""
      W_init = tf.truncated_normal_initializer(stddev=5e-2)
      W_init2 = tf.truncated_normal_initializer(stddev=0.04)
      b_init2 = tf.constant_initializer(value=0.1)
      with tf.variable_scope("model", reuse=reuse):
          net = InputLayer(x, name='input')
          net = Conv2d(net, 64, (5, 5), (1, 1), padding='SAME', W_init=W_init, b_init=None, name='cnn1')
          net = BatchNormLayer(net, decay=0.99, is_train=is_train, act=tf.nn.relu, name='batch1')
          net = MaxPool2d(net, (3, 3), (2, 2), padding='SAME', name='pool1')
  
          net = Conv2d(net, 64, (5, 5), (1, 1), padding='SAME', W_init=W_init, b_init=None, name='cnn2')
          net = BatchNormLayer(net, decay=0.99, is_train=is_train, act=tf.nn.relu, name='batch2')
          net = MaxPool2d(net, (3, 3), (2, 2), padding='SAME', name='pool2')
  
          net = FlattenLayer(net, name='flatten')  # output: (batch_size, 2304)
          net = DenseLayer(net, 384, act=tf.nn.relu, W_init=W_init2, b_init=b_init2, name='d1relu')
          net = DenseLayer(net, 192, act=tf.nn.relu, W_init=W_init2, b_init=b_init2, name='d2relu')
          net = DenseLayer(net, 10, act=None, W_init=W_init2, name='output')
          y = net.outputs
  
          ce = tl.cost.cross_entropy(y, y_, name='cost')
          # L2 for the MLP, without this, the accuracy will be reduced by 15%.
          L2 = 0
          for p in tl.layers.get_variables_with_name('relu/W', True, True):
              L2 += tf.contrib.layers.l2_regularizer(0.004)(p)
          cost = ce + L2
  
          correct_prediction = tf.equal(tf.argmax(y, 1), y_)
          acc = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
  
          return net, cost, acc
  
  
  def distort_fn(x, is_train=False):
      """
      The images are processed as follows:
      .. They are cropped to 24 x 24 pixels, centrally for evaluation or randomly for training.
      .. They are approximately whitened to make the model insensitive to dynamic range.
      For training, we additionally apply a series of random distortions to
      artificially increase the data set size:
      .. Randomly flip the image from left to right.
      .. Randomly distort the image brightness.
      """
      # print('begin',x.shape, np.min(x), np.max(x))
      x = tl.prepro.crop(x, 24, 24, is_random=is_train)
      # print('after crop',x.shape, np.min(x), np.max(x))
      if is_train:
          # x = tl.prepro.zoom(x, zoom_range=(0.9, 1.0), is_random=True)
          # print('after zoom', x.shape, np.min(x), np.max(x))
          x = tl.prepro.flip_axis(x, axis=1, is_random=True)
          # print('after flip',x.shape, np.min(x), np.max(x))
          x = tl.prepro.brightness(x, gamma=0.1, gain=1, is_random=True)
          # print('after brightness',x.shape, np.min(x), np.max(x))
          # tmp = np.max(x)
          # x += np.random.uniform(-20, 20)
          # x /= tmp
      # normalize the image
      x = (x - np.mean(x)) / max(np.std(x), 1e-5)  # avoid values divided by 0
      # print('after norm', x.shape, np.min(x), np.max(x), np.mean(x))
      return x
  
  
  x = tf.placeholder(dtype=tf.float32, shape=[None, 24, 24, 3], name='x')
  y_ = tf.placeholder(dtype=tf.int64, shape=[None], name='y_')
  
  # using local response normalization
  # network, cost, _ = model(x, y_, False)
  # _, cost_test, acc = model(x, y_, True)
  # you may want to try batch normalization
  network, cost, _ = model_batch_norm(x, y_, False, is_train=True)
  _, cost_test, acc = model_batch_norm(x, y_, True, is_train=False)
  
  # train
  n_epoch = 50000
  learning_rate = 0.0001
  print_freq = 1
  batch_size = 128
  
  train_params = network.all_params
  train_op = tf.train.AdamOptimizer(learning_rate, beta1=0.9, beta2=0.999, epsilon=1e-08,
                                    use_locking=False).minimize(cost, var_list=train_params)
  
  sess.run(tf.global_variables_initializer())
  
  network.print_params(False)
  network.print_layers()
  
  print('   learning_rate: %f' % learning_rate)
  print('   batch_size: %d' % batch_size)
  
  for epoch in range(n_epoch):
      start_time = time.time()
      for X_train_a, y_train_a in tl.iterate.minibatches(X_train, y_train, batch_size, shuffle=True):
          X_train_a = tl.prepro.threading_data(X_train_a, fn=distort_fn, is_train=True)  # data augmentation for training
          sess.run(train_op, feed_dict={x: X_train_a, y_: y_train_a})
  
      if epoch + 1 == 1 or (epoch + 1) % print_freq == 0:
          print("Epoch %d of %d took %fs" % (epoch + 1, n_epoch, time.time() - start_time))
          # train_loss, train_acc, n_batch = 0, 0, 0
          # for X_train_a, y_train_a in tl.iterate.minibatches(
          #                         X_train, y_train, batch_size, shuffle=True):
          #     X_train_a = tl.prepro.threading_data(X_train_a, fn=distort_fn, is_train=False)  # central crop
          #     err, ac = sess.run([cost_test, acc], feed_dict={x: X_train_a, y_: y_train_a})
          #     train_loss += err; train_acc += ac; n_batch += 1
          # print("   train loss: %f" % (train_loss/ n_batch))
          # print("   train acc: %f" % (train_acc/ n_batch))
          test_loss, test_acc, n_batch = 0, 0, 0
          for X_test_a, y_test_a in tl.iterate.minibatches(X_test, y_test, batch_size, shuffle=False):
              X_test_a = tl.prepro.threading_data(X_test_a, fn=distort_fn, is_train=False)  # central crop
              err, ac = sess.run([cost_test, acc], feed_dict={x: X_test_a, y_: y_test_a})
              test_loss += err
              test_acc += ac
              n_batch += 1
          print("   test loss: %f" % (test_loss / n_batch))
          print("   test acc: %f" % (test_acc / n_batch))
  
apis: tensorflow.placeholder tensorflow.constant_initializer tensorflow.truncated_normal_initializer tensorflow.global_variables_initializer tensorflow.logging.set_verbosity tensorflow.train.AdamOptimizer tensorflow.variable_scope tensorflow.InteractiveSession tensorflow.cast tensorflow.contrib.layers.l2_regularizer tensorflow.argmax numpy.std numpy.mean