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#! /usr/bin/python
# -*- coding: utf8 -*-
""" tl.prepro for data augmentation """
import tensorflow as tf
import tensorlayer as tl
from tensorlayer.layers import *
import numpy as np
import time, os, io
from PIL import Image
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):
tl.layers.set_name_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 = Conv2dLayer(net, act=tf.nn.relu, shape=[5, 5, 3, 64],
# strides=[1, 1, 1, 1], padding='SAME', # 64 features for each 5x5x3 patch
# W_init=W_init, name ='cnn1') # output: (batch_size, 24, 24, 64)
net = MaxPool2d(net, (3, 3), (2, 2), padding='SAME',name='pool1')
# net = PoolLayer(net, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1],
# padding='SAME', pool = tf.nn.max_pool, name ='pool1',)# output: (batch_size, 12, 12, 64)
net = LocalResponseNormLayer(net, depth_radius=4, bias=1.0,
alpha=0.001 / 9.0, beta=0.75, name='norm1')
# net.outputs = tf.nn.lrn(net.outputs, 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 = Conv2dLayer(net, act=tf.nn.relu, shape=[5, 5, 64, 64],
# strides=[1, 1, 1, 1], padding='SAME', # 64 features for each 5x5 patch
# W_init=W_init, name ='cnn2') # output: (batch_size, 12, 12, 64)
net = LocalResponseNormLayer(net, depth_radius=4, bias=1.0,
alpha=0.001 / 9.0, beta=0.75, name='norm2')
# net.outputs = tf.nn.lrn(net.outputs, 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 = PoolLayer(net, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1],
# padding='SAME', pool = tf.nn.max_pool, name ='pool2') # output: (batch_size, 6, 6, 64)
net = FlattenLayer(net, name='flatten') # output: (batch_size, 2304)
net = DenseLayer(net, n_units=384, act=tf.nn.relu,
W_init=W_init2, b_init=b_init2, name='d1relu') # output: (batch_size, 384)
net = DenseLayer(net, n_units=192, act=tf.nn.relu,
W_init=W_init2, b_init=b_init2, name='d2relu') # output: (batch_size, 192)
net = DenseLayer(net, n_units=10, act=tf.identity,
W_init=tf.truncated_normal_initializer(stddev=1/192.0),
name='output') # output: (batch_size, 10)
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):
tl.layers.set_name_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 = Conv2dLayer(net, act=tf.identity, shape=[5, 5, 3, 64],
# strides=[1, 1, 1, 1], padding='SAME', # 64 features for each 5x5x3 patch
# W_init=W_init, b_init=None, name='cnn1') # output: (batch_size, 24, 24, 64)
net = BatchNormLayer(net, is_train, act=tf.nn.relu, name='batch1')
net = MaxPool2d(net, (3, 3), (2, 2), padding='SAME',name='pool1')
# net = PoolLayer(net, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1],
# padding='SAME', pool=tf.nn.max_pool, name='pool1',) # output: (batch_size, 12, 12, 64)
net = Conv2d(net, 64, (5, 5), (1, 1), padding='SAME',
W_init=W_init, b_init=None, name='cnn2')
# net = Conv2dLayer(net, act=tf.identity, shape=[5, 5, 64, 64],
# strides=[1, 1, 1, 1], padding='SAME', # 64 features for each 5x5 patch
# W_init=W_init, b_init=None, name ='cnn2') # output: (batch_size, 12, 12, 64)
net = BatchNormLayer(net, is_train, act=tf.nn.relu, name='batch2')
net = MaxPool2d(net, (3, 3), (2, 2), padding='SAME',name='pool2')
# net = PoolLayer(net, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1],
# padding='SAME', pool = tf.nn.max_pool, name ='pool2') # output: (batch_size, 6, 6, 64)
net = FlattenLayer(net, name='flatten') # output: (batch_size, 2304)
net = DenseLayer(net, n_units=384, act=tf.nn.relu,
W_init=W_init2, b_init=b_init2, name='d1relu') # output: (batch_size, 384)
net = DenseLayer(net, n_units=192, act = tf.nn.relu,
W_init=W_init2, b_init=b_init2, name='d2relu') # output: (batch_size, 192)
net = DenseLayer(net, n_units=10, act = tf.identity,
W_init=tf.truncated_normal_initializer(stddev=1/192.0),
name='output') # output: (batch_size, 10)
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):
"""
Description
-----------
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(tf.float32, shape=[None, 24, 24, 3], name='x')
y_ = tf.placeholder(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)
tl.layers.initialize_global_variables(sess)
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))
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