cifar10.py

#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Thu Nov 16 13:23:44 2017

@author: abhijeet
"""

# Imports
import numpy as np
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout
from keras.layers import Flatten
from keras.constraints import maxnorm
from keras.optimizers import SGD
from keras.layers.convolutional import Conv2D
from keras.layers.convolutional import MaxPooling2D
from keras.utils import np_utils 
from keras.datasets import cifar10
from keras import backend as K
from matplotlib import pyplot
from PIL import Image
from sklearn.model_selection import train_test_split

# load data
(X_train, y_train), (X_test, y_test) = cifar10.load_data()
X_train, X_valid, y_train, y_valid = train_test_split(X_train, y_train, test_size=0.1)

# create a grid of 3x3 images
for i in range(0, 9):
    pyplot.subplot(330 + 1 + i)
    pyplot.imshow(Image.fromarray(np.rollaxis(X_train[i],0,3),'RGB'))
    
## show the plot
pyplot.show()

K.set_image_dim_ordering('th')
seed = 7
np.random.seed(seed)

X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
X_valid = X_valid.astype('float32')

X_train = X_train / 255.0
X_test = X_test / 255.0
X_valid = X_valid / 255.0

y_train = np_utils.to_categorical(y_train)
y_test = np_utils.to_categorical(y_test)
y_valid = np_utils.to_categorical(y_valid)
num_classes = y_test.shape[1]
print(X_train.shape)

#Create the model now
model = Sequential()
model.add(Conv2D(32,(3,3), input_shape = (3,32,32), padding = 'same', activation = 'relu'))
model.add(Dropout(0.2))
model.add(Conv2D(32,(3,3), padding = 'same', activation = 'relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Conv2D(64,(3,3), padding = 'same', activation = 'relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Flatten())
model.add(Dropout(0.2))
model.add(Dense(512,activation='relu',kernel_constraint=maxnorm(3)))
model.add(Dropout(0.2))
model.add(Dense(num_classes, activation='softmax'))

epochs = 25 
lrate = 0.01
decay = lrate/epochs
sgd = SGD(lr=lrate, momentum=0.9, decay=decay, nesterov=False)

model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])

print(model.summary())

model.fit(X_train, y_train, validation_data=(X_valid, y_valid), epochs=epochs, batch_size=32)
scores = model.evaluate(X_test,y_test,verbose=0)
print("Accuracy: %.2f%%" % (scores[1]*100))