Last active
October 23, 2016 23:54
-
-
Save hakanu/1cc91000548978e0245a901e565040d1 to your computer and use it in GitHub Desktop.
Convolutional neural network based on cifar data. Base code: https://github.com/tflearn/tflearn/blob/master/examples/images/convnet_cifar10.py
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
# -*- coding: utf-8 -*- | |
""" Convolutional network applied to CIFAR-10 dataset classification task. | |
References: | |
Learning Multiple Layers of Features from Tiny Images, A. Krizhevsky, 2009. | |
Links: | |
[CIFAR-10 Dataset](https://www.cs.toronto.edu/~kriz/cifar.html) | |
""" | |
from __future__ import division, print_function, absolute_import | |
import tflearn | |
from tflearn.data_utils import shuffle, to_categorical | |
from tflearn.layers.core import input_data, dropout, fully_connected | |
from tflearn.layers.conv import conv_2d, max_pool_2d | |
from tflearn.layers.estimator import regression | |
from tflearn.data_preprocessing import ImagePreprocessing | |
from tflearn.data_augmentation import ImageAugmentation | |
# Data loading and preprocessing | |
from tflearn.datasets import cifar10 | |
(X, Y), (X_test, Y_test) = cifar10.load_data() | |
X, Y = shuffle(X, Y) | |
Y = to_categorical(Y, 10) | |
Y_test = to_categorical(Y_test, 10) | |
# Real-time data preprocessing | |
img_prep = ImagePreprocessing() | |
img_prep.add_featurewise_zero_center() | |
img_prep.add_featurewise_stdnorm() | |
# Real-time data augmentation | |
img_aug = ImageAugmentation() | |
img_aug.add_random_flip_leftright() | |
img_aug.add_random_rotation(max_angle=25.) | |
# Convolutional network building | |
network = input_data(shape=[None, 32, 32, 3], | |
data_preprocessing=img_prep, | |
data_augmentation=img_aug) | |
network = conv_2d(network, 32, 3, activation='relu') | |
network = max_pool_2d(network, 2) | |
network = conv_2d(network, 64, 3, activation='relu') | |
network = conv_2d(network, 64, 3, activation='relu') | |
network = max_pool_2d(network, 2) | |
network = fully_connected(network, 512, activation='relu') | |
network = dropout(network, 0.5) | |
network = fully_connected(network, 10, activation='softmax') | |
network = regression(network, optimizer='adam', | |
loss='categorical_crossentropy', | |
learning_rate=0.01) | |
# Train using classifier | |
model = tflearn.DNN(network, tensorboard_verbose=0) | |
model.fit(X, Y, n_epoch=2, shuffle=False, validation_set=(X_test, Y_test), | |
show_metric=True, batch_size=50, run_id='cifar10_cnn') |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment