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helper.py
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helper.py
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# Sebastian Raschka 2016-2017
#
# Supporting code for the book
# "Introduction to Artificial Neural Networks and Deep Learning:
# A Practical Guide with Applications in Python"
#
# Source: https://github.com/rasbt/deep-learning-book
# Author: Sebastian Raschka <sebastianraschka.com>
# License: MIT
from urllib.request import urlretrieve
import tarfile
import os
import sys
import pickle
import numpy as np
import scipy.misc
from tensorflow.examples.tutorials.mnist import input_data
def download_and_extract_cifar(target_dir,
cifar_url='http://www.cs.toronto.edu/'
'~kriz/cifar-10-python.tar.gz'):
if not os.path.exists(target_dir):
os.mkdir(target_dir)
fbase = os.path.basename(cifar_url)
fpath = os.path.join(target_dir, fbase)
if not os.path.exists(fpath):
def get_progress(count, block_size, total_size):
sys.stdout.write('\rDownloading ... %s %d%%' % (fbase,
float(count * block_size) /
float(total_size) * 100.0))
sys.stdout.flush()
local_filename, headers = urlretrieve(cifar_url,
fpath,
reporthook=get_progress)
sys.stdout.write('\nDownloaded')
else:
sys.stdout.write('Found existing')
statinfo = os.stat(fpath)
file_size = statinfo.st_size / 1024**2
sys.stdout.write(' %s (%.1f Mb)\n' % (fbase, file_size))
sys.stdout.write('Extracting %s ...\n' % fbase)
sys.stdout.flush()
with tarfile.open(fpath, 'r:gz') as t:
t.extractall(target_dir)
return fpath.replace('cifar-10-python.tar.gz', 'cifar-10-batches-py')
def unpickle_cifar(fpath):
with open(fpath, 'rb') as f:
dct = pickle.load(f, encoding='bytes')
return dct
class Cifar10Loader():
def __init__(self, cifar_path, normalize=False,
channel_mean_center=False, zero_center=False):
self.cifar_path = cifar_path
self.batchnames = [os.path.join(self.cifar_path, f)
for f in os.listdir(self.cifar_path)
if f.startswith('data_batch')]
self.testname = os.path.join(self.cifar_path, 'test_batch')
self.num_train = self.count_train()
self.num_test = self.count_test()
self.normalize = normalize
self.channel_mean_center = channel_mean_center
self.zero_center = zero_center
self.train_mean = None
def _compute_train_mean(self):
cum_mean = np.zeros((1, 1, 1, 3))
for batch in self.batchnames:
dct = unpickle_cifar(batch)
dct[b'labels'] = np.array(dct[b'labels'], dtype=int)
dct[b'data'] = dct[b'data'].reshape(
dct[b'data'].shape[0], 3, 32, 32).transpose(0, 2, 3, 1)
mean = dct[b'data'].mean(axis=(0, 1, 2), keepdims=True)
cum_mean += mean
self.train_mean = cum_mean / len(self.batchnames)
return None
def load_test(self, onehot=True):
dct = unpickle_cifar(self.testname)
dct[b'labels'] = np.array(dct[b'labels'], dtype=int)
dct[b'data'] = dct[b'data'].reshape(
dct[b'data'].shape[0], 3, 32, 32).transpose(0, 2, 3, 1)
if onehot:
dct[b'labels'] = (np.arange(10) ==
dct[b'labels'][:, None]).astype(int)
if self.normalize:
dct[b'data'] = dct[b'data'].astype(np.float32)
dct[b'data'] = dct[b'data'] / 255.0
if self.channel_mean_center:
if self.train_mean is None:
self._compute_train_mean()
dct[b'data'] -= self.train_mean
if self.zero_center:
if self.normalize:
dct[b'data'] -= .5
else:
dct[b'data'] -= 127.5
return dct[b'data'], dct[b'labels']
def load_train_epoch(self, batch_size=50, onehot=True,
shuffle=False, seed=None):
rgen = np.random.RandomState(seed)
for batch in self.batchnames:
dct = unpickle_cifar(batch)
dct[b'labels'] = np.array(dct[b'labels'], dtype=int)
dct[b'data'] = dct[b'data'].reshape(
dct[b'data'].shape[0], 3, 32, 32).transpose(0, 2, 3, 1)
if onehot:
dct[b'labels'] = (np.arange(10) ==
dct[b'labels'][:, None]).astype(int)
if self.normalize:
dct[b'data'] = dct[b'data'].astype(np.float32)
dct[b'data'] = dct[b'data'] / 255.0
if self.channel_mean_center:
if self.train_mean is None:
self._compute_train_mean()
dct[b'data'] -= self.train_mean
if self.zero_center:
if self.normalize:
dct[b'data'] -= .5
else:
dct[b'data'] -= 127.5
arrays = [dct[b'data'], dct[b'labels']]
del dct
indices = np.arange(arrays[0].shape[0])
if shuffle:
rgen.shuffle(indices)
for start_idx in range(0, indices.shape[0] - batch_size + 1,
batch_size):
index_slice = indices[start_idx:start_idx + batch_size]
yield (ary[index_slice] for ary in arrays)
def count_train(self):
cnt = 0
for f in self.batchnames:
dct = unpickle_cifar(f)
cnt += len(dct[b'labels'])
return cnt
def count_test(self):
dct = unpickle_cifar(self.testname)
return len(dct[b'labels'])
def mnist_export_to_jpg(path='./'):
for s in ('test', 'train', 'valid'):
for i in range(10):
outpath = os.path.join(path, 'mnist_%s/%d' % (s, i))
if not os.path.exists(outpath):
os.makedirs(outpath)
np.random.seed(123)
mnist = input_data.read_data_sets("./", one_hot=False)
batch_x, batch_y = mnist.train.next_batch(50000)
cnt = -1
for data, label in zip(batch_x[:45000], batch_y[:45000]):
cnt += 1
outpath = os.path.join(path, 'mnist_train/%d/%d.jpg' % (label, cnt))
scipy.misc.imsave(outpath, (data*255).reshape(28, 28))
for data, label in zip(batch_x[45000:], batch_y[45000:]):
cnt += 1
outpath = os.path.join(path, 'mnist_valid/%d/%d.jpg' % (label, cnt))
scipy.misc.imsave(outpath, (data*255).reshape(28, 28))
batch_x, batch_y = mnist.test.next_batch(10000)
cnt = -1
for data, label in zip(batch_x, batch_y):
cnt += 1
outpath = os.path.join(path, 'mnist_test/%d/%d.jpg' % (label, cnt))
scipy.misc.imsave(outpath, (data*255).reshape(28, 28))