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# -*- coding: utf-8 -*- | |
""" Deep Residual Network. | |
Applying a Deep Residual Network to CIFAR-10 Dataset classification task. | |
References: | |
- K. He, X. Zhang, S. Ren, and J. Sun. Deep Residual Learning for Image | |
Recognition, 2015. | |
- Learning Multiple Layers of Features from Tiny Images, A. Krizhevsky, 2009. | |
Links: | |
- [Deep Residual Network](http://arxiv.org/pdf/1512.03385.pdf) | |
- [CIFAR-10 Dataset](https://www.cs.toronto.edu/~kriz/cifar.html) | |
""" | |
from __future__ import division, print_function, absolute_import | |
import tflearn | |
# Residual blocks | |
# 32 layers: n=5, 56 layers: n=9, 110 layers: n=18 | |
n = 5 | |
# Data loading | |
from tflearn.datasets import cifar10 | |
(X, Y), (testX, testY) = cifar10.load_data() | |
Y = tflearn.data_utils.to_categorical(Y) | |
testY = tflearn.data_utils.to_categorical(testY) | |
# Real-time data preprocessing | |
img_prep = tflearn.ImagePreprocessing() | |
img_prep.add_featurewise_zero_center(per_channel=True) | |
# Real-time data augmentation | |
img_aug = tflearn.ImageAugmentation() | |
img_aug.add_random_flip_leftright() | |
img_aug.add_random_crop([32, 32], padding=4) | |
# Building Residual Network | |
net = tflearn.input_data(shape=[None, 32, 32, 3], | |
data_preprocessing=img_prep, | |
data_augmentation=img_aug) | |
net = tflearn.conv_2d(net, 16, 3, regularizer='L2', weight_decay=0.0001) | |
net = tflearn.residual_block(net, n, 16) | |
net = tflearn.residual_block(net, 1, 32, downsample=True) | |
net = tflearn.residual_block(net, n-1, 32) | |
net = tflearn.residual_block(net, 1, 64, downsample=True) | |
net = tflearn.residual_block(net, n-1, 64) | |
net = tflearn.batch_normalization(net) | |
net = tflearn.activation(net, 'relu') | |
net = tflearn.global_avg_pool(net) | |
# Regression | |
net = tflearn.fully_connected(net, 10, activation='softmax') | |
mom = tflearn.Momentum(0.1, lr_decay=0.1, decay_step=32000, staircase=True) | |
net = tflearn.regression(net, optimizer=mom, | |
loss='categorical_crossentropy') | |
# Training | |
model = tflearn.DNN(net, checkpoint_path='model_resnet_cifar10', | |
max_checkpoints=10, tensorboard_verbose=0, | |
clip_gradients=0.) | |
model.fit(X, Y, n_epoch=200, validation_set=(testX, testY), | |
snapshot_epoch=False, snapshot_step=500, | |
show_metric=True, batch_size=128, shuffle=True, | |
run_id='resnet_cifar10') |