CIFAR-10/code.py at master · Rohit3475/CIFAR-10 · GitHub

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from matplotlib import pyplot
#from scipy.misc import toimage
from keras.datasets import cifar10
import keras
from keras.models import Sequential
from keras.utils import np_utils
from keras.layers import Dense,Activation,Flatten,Dropout , BatchNormalization
from keras.layers import Conv2D,MaxPooling2D
from keras.preprocessing.image import ImageDataGenerator
from keras import regularizers
from keras.callbacks import LearningRateScheduler
import numpy as np
import os
import tarfile

def lr_schedule(epoch):
    lrate = 0.001
    if epoch > 75:
        lrate = 0.0005
    elif epoch > 100:
        lrate = 0.0003
    return lrate

(x_train,y_train),(x_test,y_test) = cifar10.load_data()
x_train=x_train.astype('float32')
x_test=x_test.astype('float32')
weight_decay = 1e-4
num_classes = 10
y_train=np_utils.to_categorical(y_train,num_classes)
y_test=np_utils.to_categorical(y_test,num_classes)
model = Sequential()
model.add(Conv2D(32,(3,3),padding='same',kernel_regularizer=regularizers.l2(weight_decay),input_shape=x_train.shape[1:]))
model.add(Activation('relu'))
model.add(BatchNormalization())
model.add(Conv2D(32,(3,3),padding='same',kernel_regularizer=regularizers.l2(weight_decay)))
model.add(Activation('relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.2))
model.add(Conv2D(64,(3,3),padding='same',kernel_regularizer=regularizers.l2(weight_decay)))
model.add(Activation('relu'))
model.add(BatchNormalization())
model.add(Conv2D(64,(3,3),padding='same',kernel_regularizer=regularizers.l2(weight_decay)))
model.add(Activation('relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.3))
model.add(Conv2D(128,(3,3),padding='same',kernel_regularizer=regularizers.l2(weight_decay)))
model.add(Activation('relu'))
model.add(BatchNormalization())
model.add(Conv2D(128,(3,3),padding='same',kernel_regularizer=regularizers.l2(weight_decay)))
model.add(Activation('relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.4))
model.add(Flatten())
model.add(Dense(num_classes,activation='softmax'))

model.summary()

datagen=ImageDataGenerator(rotation_range=15,width_shift_range=0.1,height_shift_range=0.1,horizontal_flip=True)
datagen.fit(x_train)
batch_size = 64

opt_rms = keras.optimizers.rmsprop(lr=0.001,decay=1e-6)
model.compile(loss='categorical_crossentropy', optimizer=opt_rms, metrics=['accuracy'])


model.fit_generator(datagen.flow(x_train, y_train, batch_size=batch_size),\
                    steps_per_epoch=x_train.shape[0] // batch_size,epochs=125,\
                    verbose=1,validation_data=(x_test,y_test),callbacks=[LearningRateScheduler(lr_schedule)])
#save to disk
#print("\n%s: %.2f%%" % (model.metrics_names[1], scores[1]*100))
#save to disks
model_json = model.to_json()
with open('model.json', 'w') as json_file:
    json_file.write(model_json)
model.save_weights('model.h5')


#testing
scores = model.evaluate(x_test,y_test batch_size=128, verbose=1)
print('\nTest result: %.3f loss: %.3f' % (scores[1]*100,scores[0]))