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setup_cifar.py
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setup_cifar.py
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## setup_cifar.py -- cifar data and model loading code
##
## Copyright (C) 2016, Nicholas Carlini <nicholas@carlini.com>.
##
## This program is licenced under the BSD 2-Clause licence,
## contained in the LICENCE file in this directory.
import tensorflow as tf
import numpy as np
import os
import pickle
import gzip
import pickle
import urllib.request
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras.utils import np_utils
from keras.models import load_model
def load_batch(fpath, label_key='labels'):
f = open(fpath, 'rb')
d = pickle.load(f, encoding="bytes")
for k, v in d.items():
del(d[k])
d[k.decode("utf8")] = v
f.close()
data = d["data"]
labels = d[label_key]
data = data.reshape(data.shape[0], 3, 32, 32)
final = np.zeros((data.shape[0], 32, 32, 3),dtype=np.float32)
final[:,:,:,0] = data[:,0,:,:]
final[:,:,:,1] = data[:,1,:,:]
final[:,:,:,2] = data[:,2,:,:]
final /= 255
final -= .5
labels2 = np.zeros((len(labels), 10))
labels2[np.arange(len(labels2)), labels] = 1
return final, labels
def load_batch(fpath):
f = open(fpath,"rb").read()
size = 32*32*3+1
labels = []
images = []
for i in range(10000):
arr = np.fromstring(f[i*size:(i+1)*size],dtype=np.uint8)
lab = np.identity(10)[arr[0]]
img = arr[1:].reshape((3,32,32)).transpose((1,2,0))
labels.append(lab)
images.append((img/255)-.5)
return np.array(images),np.array(labels)
class CIFAR:
def __init__(self):
train_data = []
train_labels = []
if not os.path.exists("cifar-10-batches-bin"):
urllib.request.urlretrieve("https://www.cs.toronto.edu/~kriz/cifar-10-binary.tar.gz",
"cifar-data.tar.gz")
os.popen("tar -xzf cifar-data.tar.gz").read()
for i in range(5):
r,s = load_batch("cifar-10-batches-bin/data_batch_"+str(i+1)+".bin")
train_data.extend(r)
train_labels.extend(s)
train_data = np.array(train_data,dtype=np.float32)
train_labels = np.array(train_labels)
self.test_data, self.test_labels = load_batch("cifar-10-batches-bin/test_batch.bin")
VALIDATION_SIZE = 5000
self.validation_data = train_data[:VALIDATION_SIZE, :, :, :]
self.validation_labels = train_labels[:VALIDATION_SIZE]
self.train_data = train_data[VALIDATION_SIZE:, :, :, :]
self.train_labels = train_labels[VALIDATION_SIZE:]
class CIFARModel:
def __init__(self, restore=None, session=None, use_logits=True):
self.num_channels = 3
self.image_size = 32
self.num_labels = 10
model = Sequential()
model.add(Conv2D(64, (3, 3),
input_shape=(32, 32, 3)))
model.add(Activation('relu'))
model.add(Conv2D(64, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(128, (3, 3)))
model.add(Activation('relu'))
model.add(Conv2D(128, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(256))
model.add(Activation('relu'))
model.add(Dense(256))
model.add(Activation('relu'))
model.add(Dense(10))
if not use_logits:
model.add(Activation('softmax'))
if restore:
model.load_weights(restore)
self.model = model
def predict(self, data):
return self.model(data)